Scipedia: Documents published in 2024

Exploring Plasticity Material Models in Level-Set Topology Optimization

Jesús Sánchez Pinedo — Tue, 29 Oct 2024 10:34:37 +0100

This work investigates various elastoplastic material models in topology optimiza tion. A novel topology optimization framework using the level-set method and COMSOL Multi physics is proposed to handle the complexities introduced by nonlinear material behaviors. This method broadens the range of applicable plasticity models and streamlines nonlinear analysis in topology optimization.

Aero-Structural Design of a Medium-Altitude Medium-Endurance Fixed Wing UAV

Jesús Sánchez Pinedo — Tue, 29 Oct 2024 10:34:19 +0100

The UAV market is currently very populated, driving the manufacturers to design more efficient solutions to obtain a competitive edge. A cost-effective approach is to improve ex isting products using new technologies and design tools. This work addresses the desire of a UAV manufacturer to develop a growth version of an existing Medium-Altitude Medium-Endurance (MAME) Unmanned Aerial Vehicle (UAV). To that end, the aerostructural optimization of the wing is performed using coupled high-fidelity Computational Fluid Dynamics (CFD) and Computational Structural Dynamics (CSD). Gradient-based optimization fed with derivatives of functions of interest computed using the adjoint method are used for computational efficiency. The coupled problem is posed in the aerostructural optimization framework, targeting for max imum aircraft range, being the solution a result of the concurrent discipline analyses. The set of design variables include wing twist distribution, using the free-form deformation approach, material thicknesses and carbon fibre orientations. The optimized wing geometry exhibits a gain of 5% in aircraft range, with 2% better aerodynamic efficiency (L/D) and 63% wing weight reduction. The impact of multilayer composite manufacturing constraints, namely adjacency of ply angles in neighbouring regions and the orthogonality between ply angles, was found not to be significant. The studies identified weaknesses of the baseline wing and provided meaningful engineering insights for the next generation MAME UAV design

Sorry,I have some other ideas for now, so I won't submit it just yet

Lihong Huang — Tue, 29 Oct 2024 03:36:03 +0100

The challenge of financing dependency in Catalonia by advancing rents, maintaining home ownership

David Igual — Sun, 27 Oct 2024 20:19:54 +0100

The strong short-term demographic impact resulting from increases in the number of elderly people and life expectancy poses an unprecedented scenario in terms of financing the expenses required by older people with dependency. In these last years of life, there is a very significant increase in expenses, which cannot be met with recurring pension income. This fact, together with the lack of individual foresight and the limitations of the administrations to provide this coverage, raise the need to look for new alternatives. The proposal proposed in this work consists of an innovation that can help to give sustainability to the system, since a high percentage of citizens have a home they own that is likely to provide the necessary liquidity through the advance of rents to meet the payment of dependency expenses

Finite Element Formulations for Axisymmetric Solids: A New Approach Based on the Petrov-Galerkin Method

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 12:39:38 +0200

The sensitivity of conventional finite element formulations to distorted meshes
is a well-known problem. In this work, it is investigated whether an 8-node Petrov-Galerkin
f
inite element formulation can reduce this mesh-sensitive behavior when simulating linear-elastic
axisymmetric problems. The numerical investigations show that the Petrov-Galerkin formulation
exhibits significantly better behavior than the Bubnov-Galerkin formulation in certain cases.
However, the results also indicate that this is not generally the case.

High-order Flux Reconstruction schemes for turbulent combustion

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 12:39:19 +0200

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A Multi-layered Integral Approach to the de-icing process

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 12:38:59 +0200

The modification of the shallow water icing model to handle de-icing phenomenon is the main focus of this study. As stated in the original model [1], the runback water is modeled utilizing a lubrication assumption for the water film velocity profile. A constant film temperature Tf(t,x) is then calculated under the thin-film hypothesis. Unlike the simplified icing model, the temperature field within the ice layer Tice(t,x,z) is no longer assumed to be constant. Instead, a temperature profile is utilized, enabling the generation of a static film on the wall when a heat conduction source term from a resistance is present [2]. A Temperature profile Ts(t,x,z) is also used in the static film layer if the model predicts the occurrence of this state. In the energy equation for both the solid ice and liquid portion of the static water film, transverse transfers are not considered, a 1D heat equation is then resolved. An integral approach and proper boundary conditions are used to close the problem. The validity of the integral method deteriorates as the thickness over which vertical integration is performed increases. To avoid this problem, a multi-layer approach is proposed. The thickness of the ice block is then divided into three layers of identical thickness. The purpose of this study is to offer a straightforward and robust method suitable for conducting industrial test cases. The model will first be introduced, followed by a description of the numerical approach. Subsequently, validation test cases will be conducted. Realistic de-icing scenarios will then be designed to evaluate the model [3]. Additionally, non uniform roughness effects will be examined.

Calibration of Heston Stochastic Local Volatility Model by Numerical Solution of Nonlinear Fokker-Planck Equation

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 12:38:39 +0200

The Fokker-Planck partial differential equation is used to compute the probability density function of the Heston stochastic local volatility model. The solution of the Fokker Planck equation is required for the calibration of the leverage function, which plays an important role in the Heston stochastic local volatility model. The current study describes a numerical method for solving the nonlinear Fokker-Planck partial differential equation. The solution is demonstrated to converge to the one generated from the implied volatility surface by comparing call option prices.

Immersed boundary methods for high-order discretization of the compressible Reynolds Averaged Navier-Stokes equations

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 12:38:20 +0200

The Immersed Boundary Method (IBM) presents clear advantages for CFD simu lation of compressible ows around complex geometries. In contrast to the standard body- tted approach, in which meshes are designed to conform to geometries, the IBM treats solid obsta cles via local modi cation of the governing equations. Popular modi cations rest on adding volumetric penalization terms to those mesh cells that are covered by immersed bodies [1] or on imposing special boundary conditions on mesh faces surrounding them [2]. In the context of the nodal Discontinuous Galerkin Spectral Element Method (DGSEM), one can also apply subcell based limiting strategies to further discretize the immersed mesh cells employing a compatible low-order method [3]. In this paper, we present a comparison between these three techniques in a high-order setting to solve compressible ows around 2D geometries using the RANS equations with the Spalart-Allmaras one-equation turbulence model. Our results show that introducing wall model-based terms is necessary for IBM formulations to yield correct RANS ow elds, and suggest that subcell-based limiting in the context of IBM can be advantageous in terms of convergence while maintaining solution accuracy.

Local Timestep Super-Time-Stepping Integration Methods Applied to Heat and Mass Transfer in Anisotropic Porous Media

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 12:33:01 +0200

In fire safety problems, the simulation of the thermal degradation of anisotropic porous materials is complex due to the large amplitudes of time and space scales. Degraded areas where temperature increases and gas transport occurs might be very localised, sadly reducing the overall stability of numerical solvers and increasing the computation time. This can be mitigated thanks to Super-Time-Stepping methods, which are based on the use of a multi-step explicit scheme. In this study, these methods are first presented and their advantage to handle diffusion-advection problems such as the thermal degradation of anisotropic porous materials are emphasized on a simple use case. Then, a possible acceleration of the computation by using local timestep Super-Time-Stepping methods, particularly adapted to the heterogeneous problems previously mentioned, is discussed.

A Multigrid preconditioner for Discontinuous Galerkin Methods Applied to Numerical Weather Prediction

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 12:24:58 +0200

High fidelity fluid simulations have important applications in science and engineer ing, examples include numerical weather prediction and simulation aided design. Discontinuous Galerkin (DG) methods are promising high order discretizations for simulating unsteady com pressible fluid flow in three dimensions. Systems arising from such discretizations are often stiff and require implicit time integration. This motivates the study of fast, parallel, low-memory solvers for the resulting algebraic equation systems. For (low order) finite volume (FV) discretizations, multigrid (MG) methods have been suc cessfully applied to steady and unsteady fluid flows. But for high order DG methods applied to f low problems, such solvers are currently lacking. The lack of efficient solvers suitable for contemporary computer architectures inhibits wider adoption of DG methods. This motivates our research to construct a Jacobian-free precondi tioner for high order DG discretizations. The preconditioner is based on a multigrid method constructed for a low order finite volume discretization defined on a subgrid of the DG mesh. Numerical experiments on atmospheric flow problems show the benefit of this approach.

Finite Element Simulation of a Rainfall Induced Shallow Landslide in an Experimental Hillslope with a Multiphase Porous Media Model

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 12:18:51 +0200

This study presents the results of a FEM numerical simulation of a large scale physical model of a slope subjected to rainfall infiltration. The slope failure is modelled as a coupled variably saturated thermo-hydro-mechanical problem, using the Pastor-Zienkiewicz generalised plasticity model to obtain the soil’s mechanical response. Small strain and quasi static loading conditions are assumed, and plane strain conditions are adopted in the slope stability analysis. The hydraulic and mechanical parameters are calibrated based on the available experimental data. The numerical results are compared with the experimental data of the mechanical and the hydraulic responses up to failure.

Impact of saturated granular masses against rigid obstacles: the role of fluid compressibility and front inclination

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 12:18:32 +0200

In recent years, the impact of saturated granular flows against rigid obstacles has been studied by using different numerical approaches. The very low compressibility of water causes numerical instabilities when impact problems are simulated. In this work, a sensitivity analysis has been done by using a Material Point Method code to assess the influence of fluid compressibility and front inclination on numerical results. When the mass front is inclined, fluid bulk modulus does not significantly affect the solution and can be reduced to speed up the computations and reduce spurious numerical oscillations.

Studies on Effect of Interaction of Coaxial Jets on Thrust of Air-breathing Rocket

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 12:14:35 +0200

It is anticipated that the rocket-based combined cycle engine, which incorporates a rocket engine and an airbreathing engine, will offer enhanced propulsive capabilities compared to conventional rocket engines. The exhaust nozzle is a coaxial nozzle comprising a convergent divergent nozzle in the center and a convergent nozzle around it. This represents an unprecedented configuration for a rocket engine nozzle. The objective of this study is to numerically analyze the flow field near the nozzle exit and to elucidate the impact of jet interference on thrust to facilitate the detailed design of rockets. In this study, an airbreathing sounding rocket, currently under research and development at JAXA, is employed as the analysis target. The resulting calculation yielded the flow field data around the nozzle. When the central jet is over-expanded, the velocity and pressure distributions at the nozzle outlet undergo alterations due to the mutual effect of one jet pulling in the other jet. The combined thrust of the two nozzles activated simultaneously was found to be lower than the sum of the individual thrusts of the two cases in which only one of the nozzles was activated. Conversely, the thrust remains constant when the central jet is under-expanded.

Data-driven Permeability Prediction of 3D Fibrous Microstructures

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 12:04:35 +0200

For the manufacturing process simulation of fiber-reinforced polymer composites, f low simulations have to be performed at multiple spatial scales which govern the flow through the fiber structures. Repetitive multiscale flow simulations are computationally expensive and time-consuming. In order to speed up the multiscale simulation workflow, fast machine learning surrogate models or emulators could be used to replace one or more of the flow simulations. In this work, feature-based emulators and geometry-based emulators are developed using neural networks for predicting the permeability of 3D fibrous microstructures based on a reference dataset (doi:10.5281/zenodo.10047095). The best model achieved a mean relative error of 8.33% on the test set with a significantly faster inference time compared to a conventional simulator.

Machine learning model for correlating microstructural features and macroscopic properties of heterogeneous composites

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 12:04:03 +0200

This study explores the use of machine learning (ML) models in predicting the macroscopic properties of heterogeneous composites. Traditional micromechanics parameters have limitations, thus ML models with and without feature engineering are utilized. For artificial neural network (ANN) models with feature engineering, microstructural descriptors from SEM images of nickel-based superalloys are used to predict hardness. 10 descriptors are selected to reduce the computational cost of the deep neural network (DNN) with the support of the shallow neural network (SNN), and accuracy is enhanced by incorporating two additional descriptors. The result surpasses existing physics-based models. Models without feature engineering employ a convolutional neural network (CNN) to predict the effective thermal conductivity of thermal insulation composite materials. The CNN model demonstrates accurate predictions for novel microstructures. ML models can achieve more efficient predictions than traditional methods, indicating their potential in advancing materials science. In summary, harnessing artificial intelligence to capture the scattering characteristics of heterogeneous materials enables both DNN and CNN models to achieve more efficient predictions compared to traditional methods. This highlights the potential of machine learning in advancing materials science and expediting the development of materials with desired properties.

Quasi Gasdynamic Heterogeneous Model for Modeling a Mixture of Compressible Fluids

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 11:41:45 +0200

Quasi-gasdynamic type regularization is presented for a heterogeneous model of a two-fluid mixture of compressible fluids. This model allows to describe the flows of stiffened gases. The reduced four-equation model for dynamics of the heterogeneous compressible two fluid mixture with equations of state of a stiffened gas is considered. A further reduced form of this model with the excluded volume concentrations and a quadratic equation for the common pressure of the components can be called a quasi-homogeneous form. A finite difference algorithm is used, built with the finite volume method. By solving one and two dimensional test problems it is shown that the presented algorithm is a stable and reliable way to model fluid mixtures with strong shock waves.

Deep Reinforcement Learning implementation with Physics-Informed Neural Network for Heat Conduction Control

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 11:41:25 +0200

As modern systems become more complex, their control strategy can no longer solely rely on measurement data gathered by instrumentation. Instead, it must also incorporate information derived from mathematical models. The complexity of system models can result in excessively long computation times, making the control process impractical. As a solution, surrogate models are implemented to provide estimates within an acceptable timeframe for decision-making purposes. The surrogate model can be a Physics-Informed Neural Network that is used to obtain the system state on the next time step; such information can be used with a Deep Reinforcement Learning algorithm to train a control strategy based on simulations, replacing the need for running direct numerical simulations. On this work, we explore a Deep Q-Learning strategy on 1D heat conduction problem in which temperature distribution feeds a control system to activate a heat source, aiming to obtain a constant, previously defined temperature value. The main goal is to stabilize the bar temperature at the middle point of it without recurring to numerical simulations.

A second-order reproducing kernel for SPH method to enhance accuracy and consistency

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 11:36:16 +0200

The application of smoothed particle hydrodynamics (SPH) encounters challenges related to consistency, stability, and accuracy. Inconsistencies in SPH arise from non-uniform particle distribution and a lack of neighboring particles at the boundary, leading to numerical instability and inaccurate particle approximations. Various methods have been proposed to address these issues. One such framework is the corrected SPH, designed to ensure consistency of the method. In this work, performance of some correction procedures are analysed through gradient calculations of a function. The root mean square error of the gradient approximation is analysed to justify the method’s convergence and accuracy

A 3D Particle Model For Reinforced Concrete Fracture Aanalysis

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 11:35:56 +0200

A numerical model for the analysis of reinforced concrete structures must incorporate tools capable of representing the formation and propagation of cracks, their effect on the overall behavior of the structure, and the interaction between reinforcement and concrete. Detailed rigid particle models (PM) that take directly into consideration the physical mechanisms and the influence of the material aggregate structure have gained relevance and have shown to be able to predict, evaluate and understand cracking phenomena in concrete. The 3D particle models correlate well with experimental results from concrete specimens, particularly in terms of elastic response, peak values, fracture process and fracture location. This paper presents the 3D explicit formulation of steel reinforcement bars using discrete elements with cylindrical geometry. The incorporation of steel elements allows the particle model to be applied to the analysis of fracture in reinforced concrete structures. The rigid elements of cylindrical geometry interact with the concrete, modeled by spherical particles, through a contact interface. The model is validated in three-point beam bending tests, without transverse steel reinforcement. The numerical results obtained show that the proposed model correctly simulates the actual behavior, representing the fracture evolution process and the load displacement relationship for different steel ratios.

Modeling small scale processes in Antarctic sea ice

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 11:31:19 +0200

The Antarctic sea ice, which undergoes annual freezing and melting, plays a signif icant role in the global climate cycle. Adverse environmental conditions in the Southern Ocean influence the extent and amount of ice in the Marginal Ice Zones (MIZ), the BioGeo- Chemical (BGC) cycles, and their interconnected relationships. The ’Pancake’ floes are a composition of porous sea ice matrix with interstitial brine, nutrients, and biological com- munities inside the pores. To realistically model these multi-phasic and multi-component coupled processes, the extended Theory of Porous Media (eTPM) is used to develop mod- els capable of simulating the different seasonal variations. All critical variables like salinity, ice volume fraction, and tem perature, among others, are considered and have their equations of state. The phase transition phenomenon is approached through a micro-macro linking scheme. A Phase- field solidification model coupled with salinity is used to model the micro-scale freezing processes and up-scaled to the macro scale eTPM model. This allows for modeling the salt trapped inside the pores. For the biological part, a BGC flux model for sea ice is also set up to simulate the algal species present in the sea ice matrix. Processes like photosynthesis are dependent on temperature and salinity, and are derived through an ODE-PDE coupling with the eTPM model. Academic sim ulations and results are presented as validation for the mathematical model. These high-fidelity models will eventually lead to their incorporation into large-scale global climate models.

Lightweight AMG preconditioners for CFD simulations on symmetric domains

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 11:27:49 +0200

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Performance analysis of parallel-in-time techniques in modern supercomputers

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 11:27:27 +0200

This paper aims to improve the e ciency of large-scale turbulent simulations by improving the arithmetic intensity of the operations. This is done by applying a parallel-in time ensemble averaging technique so that multiple ow states are run simultaneouslly in the same device. This transforms sparse matrix-vector products into sparse matrix-matrix products, improving the arithmetic intensity. The performance of these operations as well as the speed-ups generated in the operation itself, in the whole iteration and an estimation in the whole simulation is presented, so that for cases in which the averaging interval is signi cantlly longer than the transition interval, remarkable speed-ups in the whole iteration are obtained.

A Hybrid Hexahedral Solid-shell Element with Self-equilibrated Stresses for the Geometrically Nonlinear Static Analysis of Composite Laminated Structures

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 11:17:20 +0200

Hybrid nite elements with self-equilibrated assumed stresses have proven to pro vide several advantages for analysing shell structures. They guarantee high performance when using coarse meshes and accurately represent the stress eld. Additionally, they do not require assumptions about the displacement eld within the element domain, and the integration is ef ciently performed only along their contours. This work exploits those advantages to develop a solid-shell nite element for the geometrically nonlinear static analysis of composite laminated structures. In particular, an eight-node nite element, which has 24 displacement variables and 18 stress parameters, is developed. The displacement eld is described only by translations, eliminating the need for complex nite rotation treatments in large displacement problems. A Total Lagrangian formulation is used with the Green-Lagrange strain tensor and the second Piola-Kirchho stress tensor. Thickness locking is cured using an assumed natural strain formu lation for the transversal normal stress, and the assumed stress eld eliminates shear locking. Then, for the analysis of linear-elastic problems, no domain integration is needed, and all the element operators are obtained by line integrals. The resulting formulation is e cient and allows for easy implementation. Computed numerical results show the accuracy and robustness of the presented element when used for both the linear elastic static and geometrically nonlinear elastic static analysis of composite laminated shell structures.

Hierarchic Large Rotation Shell Model with Warping: Isogeometric Formulation and Modeling of Alternating Stiff/Soft Laminates

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 11:17:02 +0200

This paper presents a hierarchic large rotation Kirchho-Love shell model with warping. Two unknowns are introduced for each through-the-thickness function warping, rep resenting its amplitudes in two directions tangent to the shell surface. NURBS are used to approximate reference surface displacement and warping amplitudes in the weak form. The transverse shear strains depend only on the warping parameters linearly and are free from lock ing. A patch-wise reduced integration avoids membrane locking and improves e ciency. Focus is given to composites made up of multiple sti layers coupled with soft interlayers. The alternat ing layup with high sti ness ratios induces a signi cant sectional warping with transverse shear strains concentrated in the soft layers. Two warping models are investigated: WI) all sti layers maintain the same director orthogonal to the deformed surface with independent transverse shear deformations of the soft layers; WZ) a single zigzag function linking these deformations. The numerical tests con rm the great accuracy of the hierarchic shell model in reproducing the solid solution with a small number of discrete parameters, provided that the correct warping model is chosen. WI is reliable for all alternating layups. WZ reduces the unknowns to ve per surface point, regardless of the number of layers, and is accurate for uniform soft layers

Recent advances in the postbuckling analysis of composite laminated structures

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 11:16:44 +0200

The current demand for lightweight structures in a wide range of engineering appli cations leads to using thin-walled composite laminated structures whose behaviour is governed by buckling and postbuckling phenomena. Such a demand is pushing the borders of computational mechanics to enhance methods and algorithms for studying those structures' geometrically non linear responses. This work presents some of the authors' developments in analysing lightweight composite laminated structures. The literature survey introduces a family of nite elements known as MISS elements, where MISS stands for mixed isostatic self-equilibrated stresses. The description of those elements, which are derived from the Hellinger-Reissner functional, is followed by a discussion on their advantages concerning displacement-based elements when studying com posite laminated thin-walled structures. Subsequently, a framework for the postbuckling analysis of composite structures with the MISS-4C element that uses the Koiter multi-modal approach is presented.

Post-buckling behaviour and delamination growth in defected variable angle tow composite laminates

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 11:16:26 +0200

Due to their specific strength and stiffness properties, composite materials are largely used in lightweight structural applications in aerospace, automotive and mechanical engineering. Understanding how these materials fail under service loads is a challenging aspect of designing advanced composite structures. In fact, the failure of composite laminated structures is often governed by complex interactions of multiple interlaminar failure and damage mechanisms. Among them, delamination is one of the damage modes requiring large attention due to the low interlaminar resistance between the different layers comprised in a composite laminate. In addition, this phenomenon may be triggered by defects introduced in the construction phase or by the presence of connections leading to stress concentrations. When coupled with buckling phenomena, delamination inevitably decreases the load-carrying capacity of lightweight composite structures. Variable Angle Tow (VAT) laminates have been proven to improve the buckling and post-buckling response of those structures significantly. However, little is known about the geometrically nonlinear behaviour of VAT composite laminates with delaminations. This work applies the cohesive finite element method to model delamination growth in VAT composite laminates containing initial defects under compressive loading conditions. Numerical simulations investigate the effects of the fibre angle variation on the geometrically nonlinear static response of VAT composite laminates compared to that of their classical straight fibre counterparts.

Numerical and analytical evaluation of resin flow during PCB pressing cycles

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 11:10:20 +0200

This paper presents a comprehensive sequence of analytical methods for evaluating printed circuit board (PCB) pressing processes, augmented by numerical finite element (FE) simulations. The study considers the properties of the prepreg material, the structure of the PCB copper layer, and various pressing process parameters, all of which can be adjusted according to specific requirements. Our results are validated through experiments and compared with the established Squeeze Flow Model. The pro posed methodology identifies potential weaknesses in the design of the copper layer, material selection, and pressing parameters prior to production. Additionally, the implementation of this methodology in an interactive user interface allows for rapid and efficient results, facilitating timely decision-making and process optimization

Computational analysis of non-proportional biaxial shear reverse experiments superimposed by different cyclic loads

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 11:09:58 +0200

This paper deals with anisotropic ductile damage and fracture behavior under low positive stress triaxialities. Novel tension–shear biaxial low–cycle experiments with different numbers of loading cycles (up to twenty) have been performed using a cruciform biaxially loaded specimen. During the experiment, a tensile preload is first imposed on the horizontal axis until it reaches 3kN; then, it is kept constant while different shear cyclic loading sequences are superimposed on the vertical axis until failure. All cyclic loadings are driven to a large strain range to investigate ductile damage, and the same amplitude is maintained for each repeated reverse loading cycle within a single loading pattern. In addition, numerical simulations are performed with an anisotropic stress-state-dependent plastic-damage continuum model, also considering the Bauschinger effect. The experimental and numerical analysis of the evolution of the first principal total strain and damage strains highlights the influence of the cyclic loading history on the material behavior. Moreover, fracture surfaces are examined by scanning electron microscopy to analyze the different mechanical performances at the micro-level

Adaptive Refinement Techniques using Multiresolution Wave Propagation Simulations Guided by Inherent Convergence Indicators

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 11:03:34 +0200

The multiresolution finite wavelet domain method has been meticulously studied in wave propagation simulations. The multiresolution procedure always starts with the coarse solution, and then finer solutions can be superimposed on the coarse solution, until convergence is achieved. Based on remarkable observations on the multiple resolution components of the method, a residual-based convergence indicator that reveals convergence at the coarse solution is developed. This convergence metric is rapidly applicable and straightforward and can also divulge the spatial and temporal ranges/domains that the already obtained solution needs to be enhanced. In that way, an automatic adaptive refinement technique is proposed for the local enrichment of the solution, only in the specific grid points and time-steps that it is needed. A numerical case study regarding wave propagation in an inhomogeneous rod manifests the effectiveness and accuracy of the proposed automatic refinement methodology, as also the performance of the suggested convergence indicator.

Seismic Performance Assessment of Bouçã Arch Dam. Non-Linear Analysis Considering Joint Movements and Concrete Damage Under Tension and Compression

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 11:03:08 +0200

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Simultaneous, Dynamical Analysis of Structural Ropes and Membranes on all Level-sets

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 11:02:45 +0200

Thelevel sets of scalar functions may imply the geometries of individual ropes and membranes. All level sets within an interval, considered in some bulk domain, define infinitely many geometries at once. A mechanical model is proposed which enables the simultaneous, dynamic analysis of all such geometries. For the solution of the governing equations, a tailored numerical method coined Bulk Trace FEM is employed for the spatial discretization, using higher-order background meshes in the bulk domains. The HHT-α method is used for the temporal discretization. Numerical results are presented that demonstrate the potential of the proposed mechanical model and numerical method

Micro-scale analysis of rough surfaces using non-conforming meshes

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 10:56:17 +0200

This work presents a finite element model of the contact between a flat rigid surface and a rough deformable elastoplastic body, enabling the micro-scale analysis of the contact conditions. To assure the computational efficiency a non-conforming mesh is adopted to describe the deformable body. The results show that the model can capture the main effects of the bulk material constitutive behaviour, which is known to have a significant impact on the problem, as well on local changes on the friction conditions.

Offline and Online Performance Contrast of Optimal Control

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 10:50:25 +0200

Civil structures are quite vulnerable to extreme dynamic loads as well as to nat ural disasters. The aforementioned problem is well-known and interestingly, unavoidable as it is almost impossible to know when that type of loads or disasters are going to hit the existing structures. Due to such unpredictability, many are interested to adopt the online or real-time control and monitoring strategy instead of conventional approach. However, still offline moni toring and vibration control strategies are useful to understand the overall performance of the investigated dynamical problem as it might not be an option to go for online due to feasibility or other constrains. In order to hold a debate, herein, the controlled performance of a multi degree-of-freedom system has investigated both adopting offline and online approaches. The linear-quadratic regulator (LQR) algorithm has been employed as the control law and it is as sumed that controller will behave as an active control system. In order to understand the effect of the optimal control, the displacements and velocities at different degree-of-freedom’s level have considered and compared. The outcome suggests that both approaches have advantages and disadvantages such as offline approach is quite useful to understand at the design phase of the project, while, online approach might be very effected after the construction.

Determination of Neutral Axis and Effect of the Variation of Cross Sectional Area on the Mechanical Properties of Smart Meta-Structure

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 10:47:57 +0200

In recent years, manufacturing has paved the way to enhance structural properties using 3D printed structures by constructing complex shapes. The properties of such structures depend on the arrangement of the internal lattices. Honeycomb is one such simple lattice struc ture that is widely used by researchers as it exhibits a high strength-to-weight ratio. However, the elastic properties of the lattice structures are intrinsic functions of the material properties and the geometric shape. Hence, it is impossible to modulate the elastic properties once constructed. Recent studies have shown that the active modulation of the elastic properties can be achieved by incorporating smart materials over the substrate layers of the lattice. The analytical expressions have been developed for honeycomb/ auxetic honeycomb lattice considering the Euler-Bernoulli bi-layer beam to determine its elastic properties. The expression is well valid for lattices where the thickness of the smart material is relatively less compared to the substrate thickness. How ever, it does not produce consistent results as the thickness of the smart material increases due to the shift of the position of the neutral axis, which was earlier assumed to be at the geomet ric centre of the substrate beam. This paper presents a modified formulation that considers the change in the position of the neutral axis as the thickness of the smart material patches varies. This modification allows the use of the analytical expression for beams with higher thickness ratios and can be used to understand the impact of forces in shear deformation. In addition, the variation in the elastic properties has also been investigated for different cross-sectional shapes such as I-section, T-section, and rectangular cross-section. The formulation presented here is generic, and the concept can be used in various futuristic multi-functional structural systems and devices across different length scales

ψ − flow: A Novel Physics-Constrained Architecture to Enforce Incompressibility and Boundary Conditions for Fast and Accurate Flow Predictions

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 10:42:53 +0200

Deeplearning models have demonstrated remarkable capabilities at producing fast predictions of complex flow fields. However, incorporating known physics is essential to ensure that physical solutions can generalize to flow regimes not used for training. In this study, a formulation that, by construction, enforces flow incompressibility and respects the invariance of physical laws across different unit systems is introduced. We demonstrate that this approach can achieve performance improvements of up to 100 times compared to purely data-driven methods, all while maintaining fidelity to other crucial physical quantities. Moreover, we show that for canonical flow test cases, such a physics-constrained model can yield accurate results even with training datasets as small as a few hundred points and neural networks containing only a handful of neurons. It is also shown, however, that physics-constrained machine learning models are not silver bullets out of the box, and require careful consideration in their application and integration with other constraints. Specifically, this study addresses how a problem that is mathematically simple may not necessarily be straightforward in machine learning terms, and discusses ongoing efforts to bridge this gap. We conclude by discussing the place of physics-constrained machine learning models within a landscape primarily dominated by physics-informed approaches, in particular in the context of real-world problems where data and computational resources are often limited

On the Usage of PANS for the CFD Simulation of Wind Turbines

Jesús Sánchez Pinedo — Wed, 23 Oct 2024 10:37:59 +0200

With the rapid evolution of o↵shore wind energy, engineering tools are crucial to catalyze technological developments and increase their maturity, therefore leading to lower costs. Complex turbine-turbine interactions require a good knowledge of the physics of the flow on, around and down/upstream of each turbine, which can be provided using high-fidelity CFD simulations. Turbulence models play a critical role on this matter and an adequate balance between accuracy and computational e ort is necessary. While RANS approaches are quite e cient, LES should provide the most accurate result. Yet, even nowadays, LES blade-resolved simulations are still computationally prohibitive for industrial purposes. A middle-ground exists in SRS formulations, such as hybrid ones as DDES, or bridging ones such as PANS. In the present work emphasis is placed on PANS, since numerical and modelling errors can be studied and quantified independently, as opposite to other SRS approaches. Using as a benchmark the UNAFLOWwindturbine, it is found that traditional RANS and DDES turbulence formulations are able to predict integral forces, but partially fail in capturing wake mixing. Nevertheless, PANS, while enabling the user to select the ratio of turbulent quantities modelled, is not able to properly capture the integral forces due to premature separation in the blades. Several causes are discussed, including insu cient mesh refinement in the near-wall region and lack of turbulent content of the numerical inlet, preventing laminar to turbulent flow transition. Future work should focus on inlet synthetic turbulence generation, in line with existent literature, in order to improve the shortcomings faced in properly resolving the near-wall flow.

ENERGÍA NUCLEAR: UNA ALTERNATIVA VÁLIDA PARA LAS INVERSIONES SOSTENIBLES - FULL PAPER

Marta Carrasquer — Wed, 23 Oct 2024 09:14:05 +0200

En medio de la crisis climática y la demanda de soluciones sostenibles, la energía nuclear ha resurgido como una opción clave para reducir las emisiones de carbono sin comprometer la generación energética. No obstante, enfrenta desafíos como la percepción pública, los riesgos de accidentes y la gestión de residuos radiactivos. A pesar de no emitir dióxido de carbono durante su operación, su papel en la transición hacia una economía de bajas emisiones depende de superar tanto barreras técnicas como prejuicios sociales. Este estudio explora cómo la energía nuclear puede contribuir de forma efectiva y sostenible en la lucha contra el cambio climático. Y a la vez, investiga la viabilidad de la energía nuclear dentro del ámbito de las inversiones sostenibles, detallando un universo de inversión en tecnologías nucleares e instrumentos financieros que apoyan la transición hacia un futuro energético descarbonizado.

The Influence of Bilingualism in Altering the Cortical Executive Functions of Adolescents

Aashka Shah — Mon, 14 Oct 2024 04:21:04 +0200

195 countries, 7,000+ officially known languages, thousands of hidden dialects. When we think of languages, words that come to mind are often related to communication. Especially with knowing multiple languages, we think of amplifying relations with interpersonal communication. Yet, a problem exists with the lack of knowledge about bilingualism’s advantage besides these perceptions in globalized societies. This issue impacts monolinguals because of decreasing language education neglecting bilingualism’s increase of cognitive skills. Limited research on the benefits of bilingualism among adolescents exacerbates this problem, as existing studies often focus on infants and older adults, considering these age groups depict clear benefits without confounding variables. However, adolescents have the greatest adaptability for language acquisition, asserting suitability in measuring bilingualism before brain development hinders learning abilities in adulthood (Smith, 2018). The purpose of this study is to underscore the beneficial advantage bilingualism—specifically among adolescents—provides unconsciously daily, ultimately aiding to promote language education.

Multicolor Parallel Fourth-Order Implicit Finite Difference for Solving the 2D Poisson Equation

R. Itza Balam — Wed, 09 Oct 2024 05:43:06 +0200

The Poisson equation is central in numerous physics and engineering applications, such as computational fluid dynamics and acoustic wave propagation, where efficient and accurate solutions are essential. This study focuses on the numerical solution of the 2D Poisson equation with Dirichlet boundary conditions using a fourth-order compact Implicit Finite Difference scheme. Finite difference methods, particularly high-order schemes, are advantageous for solving the Poisson equation due to their efficiency and suitability for structured grids. To address the computational demands of large-scale problems, we incorporate domain decomposition and the Multicolor Successive Over Relaxation method, facilitating parallel computation. Through numerical experiments, we demonstrate that our approach significantly enhances both accuracy and computational efficiency when compared to traditional second-order methods.

L’impacte i el desenvolupament ordenat de l’economia col.laborativa com a un fenòmen econòmicament determinant.

Genèric Congrés2025 — Tue, 08 Oct 2024 09:38:03 +0200

Eix 3: La musculatura del sistema empresarial català

L’impacte i el desenvolupament ordenat de l’economia col.laborativa com a un fenòmen econòmicament determinant.

Abstract

L’economia col.laborativa, redefinida com a economia de plataformes és un fenomen emergent que utilitza la tecnologia i l’entorn digital per proporcionar serveis atractius i/o productes desintermediant i disrumpint l’statu quo de moltes indústries, inclús creant-ne de noves.

Cada any s’inverteixen milers de milions d’euros en aquesta nova economia arreu del món fent créixer el fenomen i expandint-lo de forma exponencial. Catalunya és seu d'algunes d’aquestes empreses, tant startups de producció pròpia com scaleups d’origen internacional donant pas així a un nou sector d’activitat econòmica lligat a les noves tecnologies digitals.

La velocitat amb què el sector tecnològic i digital en l’àmbit de l’economia de plataformes evoluciona els seus models de negoci i la seva implementació per part dels usuaris planteja un gran repte tant de seguiment i comprensió com de predicció i prevenció a temps dels seu impacte. Hem estat testimonis de com els perjudicis que el desconeixement, el destemps o la manca d’adequació a la regulació existent poden afectar a un sector enguany tan determinant. Amb l’objectiu d’evitar aquests desajustos i de permetre un desenvolupament de l’economia de plataformes sostenible, racional i beneficiós per Catalunya i els seus ciutadans i ciutadanes, és imperiós conèixer el sector, les seves necessitats, els seus actors principals i les tendències que cal tenir en compte.

Multiscale data-driven modeling of the thermomechanical behavior of granular media with thermal expansion effects

Eugenio Oñate — Fri, 04 Oct 2024 14:07:15 +0200

A multiscale data-driven (MSDD) methodology is proposed for simulating the thermomechanical behavior of granular materials subjected to thermal expansion. The macroscale is handled using a continuous model based on the Finite Volume Method (FVM), while the microscale response is captured at Representative Volume Elements (RVEs) with the Discrete Element Method (DEM). To significantly reduce the computational cost of the analyses, the microscale DEM computations are not performed online, �.�., simultaneously with the macroscale FVM ones, as generally done in standard multiscale approaches. Instead, they are performed in advance to create a comprehensive database of RVE solutions under different initial conditions and thermal strains. This dataset is then used to train an Artificial Neural Network (ANN), which serves as a surrogate model for the macroscale solver. The MSDD approach is validated against pure DEM solutions of problems with distinct thermal conditions. Remarkably, we demonstrate that with only three input parameters, namely porosity, fabric, and thermal strain, the surrogate model can predict the microstructure evolution, as well as the updated conductivity and Cauchy stress tensors of the granular assembly. This allows for a generally accurate simulation of transient thermomechanical analyses at a drastically lower computational cost than the pure DEM approach.

A critical review of how eXtended Reality (XR) has addressed key factors influencing Safety on Construction Projects (fSCPs)

Eugenio Oñate — Tue, 01 Oct 2024 14:13:56 +0200

The construction industry is known for its high accident rates. One hundred key factors affecting construction safety (fSCPs)—associated with general aspects of organisational management, materials and equipment, the construction site, and human aspects related to the worker and work team—have been identified. EXtended Reality (XR), which encompasses Virtual Reality (VR), Augmented Reality (AR), and Mixed Reality (MR) technologies, is being used in construction safety management. XR offers multiple advantages, given its characteristics of visualisation, immersion, and interaction with digital models and real objects. XR’s potential safety uses are widely recognised, and several studies have tested these technologies for different applications. However, most such efforts have been focused on identifying functionalities, applications, and technological aspects of XR in general, rather than studying whether those uses affect the factors relevant to security management. This study examined the literature on XR experiences that have addressed fSCPs and analysed how these developments have been related to the construction sector’s methodologies and technologies, such as building information modelling (BIM).

A continuum–discrete multiscale methodology using machine learning for thermal analysis of granular media

Eugenio Oñate — Tue, 01 Oct 2024 13:58:07 +0200

This work presents a data-driven continuum–discrete multiscale methodology to simulate heat transfer through granular materials. The two scales are hierarchically coupled, where the effective thermal conductivity tensor required by the continuous method at the macroscale is obtained from offline microscale analyses. A set of granular media samples is created through the Discrete Element Method (DEM) to relate microstructure properties with thermal conductivity. The protocol for generating these Representative Volume Elements (RVEs) and homogenizing the microscale response is presented and validated by assessing the representativeness of the granular assemblies. The study found that two local properties, the porosity and the fabric of the material, are sufficient to accurately estimate a representative thermal conductivity tensor. The created dimensionless database of microscale results is used for training a surrogate model based on machine learning. In this way, effective thermal conductivity tensors that accurately reflect the local microstructure can be efficiently predicted from the surrogate model by taking the microstructural properties as inputs. The proposed multiscale methodology enables us to solve heat problems in granular media using a continuum approach with accuracy comparable to a pure discrete computational method but at significantly reduced computational cost.

A modified Johnson–Cook model for the plastic behavior of metals in ultrasonic vibration-assisted upsetting processes

Eugenio Oñate — Tue, 01 Oct 2024 13:47:55 +0200

In this work, we present a constitutive description of the plastic behavior of the metals and alloys under Ultrasonic Vibration (UV). When UV is imposed, it induces a softening effect on the plastic deformation of metals and alloys, referred to as the acoustoplastic phenomenon. The volumetric softening mechanism of the acoustoplasticity must consider stress superposition and acoustic softening. Taking the Johnson–Cook (JC) constitutive model as the reference for metal thermo-plasticity, the flow stress reduction due to UV is introduced by three types of softening terms: subtractive, multiplicative, and coupled. Those different configurations will be quantified via physical and phenomenological assumptions. For the study and validation of the proposed constitutive models, we focus on a well-known material, 6063 aluminum alloy. The ultrasonic vibration is modeled by an assisted upsetting process of an aluminum billet and compared with experiments. The impact of variations of the strain rate, ultrasonic amplitude, and frequency on the softening behavior of the models are analyzed and compared with available experimental results. We find out that the subtractive and coupled models agreed well with the experimental results showing the errors ranging from 2.4–4.8% and 4.4%–8%, respectively. However, the predictive ability of the multiplicative model exhibits large discrepancies at the low stage of strains following the acceptable predictions at high stains. By understanding the impact of the UV on the acoustoplastic deformation of the metals and alloys we can establish a reliable theoretical framework for prospective numerical studies of the UV-assisted manufacturing processes which is the goal of the current research presented here.

Hydromechanical embedded finite element for conductive and impermeable strong discontinuities in porous media

Eugenio Oñate — Mon, 30 Sep 2024 16:22:14 +0200

The pore pressure inside oil and gas reservoirs compartmentalized by sealing faults increases during injection processes. The rise in the pore pressure can induce fault reactivation, leading to hydraulic issues such as fluid leakage from the reservoir to other layers and seismicity. Therefore, it is essential to accurately model the mechanisms involved in this problem, primarily related to the presence of a strong discontinuity, a fault, inside the domain. Several numerical approaches can be used to represent the presence of discontinuities. The embedded finite element method (EFEM) has recently gained attention because it does not require the mesh to conform with the discontinuities, thus circumventing the typical mesh generation challenges of modeling faulted domains. The current EFEM formulations cannot properly model a hydromechanical problem such as fault reactivation, due to simplifications in their derivation. Hence, this work proposes a new fully coupled hydromechanical EFEM formulation based on the Strong Discontinuity Approach that can represent discontinuities acting as preferential flow paths or barriers for the fluid flow. The formulation is applied to a fault reactivation problem, showing the main reactivation mechanisms. This paper also discusses the presence of spurious oscillations along the discontinuities and their relations with the mesh discretization.

The P-DNS method for turbulent fluid flows: An overview

Eugenio Oñate — Mon, 30 Sep 2024 15:58:37 +0200

An overview of the Pseudo-Direct Numerical Simulation (P-DNS) method is presented. This is a multi-scale method aiming at numerically solving the unknown fields at two different scales, namely coarse and fine. The P-DNS method is built around four key ideas. The first one is that of numerically solving both scales, which facilitates obtaining solutions to problems of both concurrent multi-scale and hierarchical multi-scale types. The second key idea is that of computing off-line the fine solution via Direct Numerical Simulation in simplified domains, termed representative volume elements (RVEs), while the third idea is that of storing the basic (physics-informed) results obtained from this solution in a problem-independent unique dimensionless database. This database may be subsequently used for solving different problems at the coarse level, i.e. by using coarse meshes in the corresponding problem domains, via a surrogate model. In this sense P-DNS resembles Reduced Order Methods, which require a previous off-line evaluation of the modes to be used in the solution, sharing with them the benefit of solving the reduced problem, more precisely the coarse scale, in P-DNS terms, in a very efficient way. The fourth and last key idea of P-DNS is based on the fact that most of the high-frequency modes of a turbulent flow are convected by the fluid velocity of the low-frequency modes. Taking this into account the P-DNS technique is implemented in such a way that the fine instabilities are convected by the velocity field of the coarse solution. Finally, although the P-DNS method has been used to solve different computational mechanics problems, such as convection-diffusion and convection-reaction/absorption problems, the scope of this overview will be limited to its application to turbulent incompressible fluid flows, including both single phase and particle-laden flows.

Microstructure evolution modeling of Ti6Al4V alloy during cutting using the Particle Finite Element Method and homogeneous field distributions

Eugenio Oñate — Mon, 30 Sep 2024 15:30:03 +0200

This paper aims to explore the evolution of microstructural parameters induced during the cutting of Ti6Al4V alloy (TC4). The microstructure characteristics of the workpiece material is directly tied to its mechanical response during machining. During TC4 cutting, microstructure evolution is observed as a result of severe plastic deformations. The characterization of this phenomenon has significant interest from both academia and industry. In this work we present a developed modeling technique which combines the Particle Finite Element Method (PFEM) with incremental homogeneous field distributions. First, the PFEM is extended to perform a thermo-mechanical analysis capable of capturing the material responses of TC4 during orthogonal cutting. To generate serrated chips, an appropriate strain softening-based constitutive plasticity model i.e., TANH (Hyperbolic TANgent) is utilized. The PFEM’s validity is checked through comparison with available experimental results in terms of chip shapes and cutting forces. Second, the evolution of microstructural parameters such as dislocation density, vacancy concentration, dynamic recrystallization (DRx) grain size, and hardness is incrementally developed and incorporated into the PFEM using internal state variables as homogeneous field distributions. The Johnson–Mehl–Avrami–Kolmogorov (JMAK) model and Hall–Petch equation are applied for predicting grain size and hardness, respectively. The parameters of the corresponding models are modified for TC4 to accurately capture their alterations. Lastly, the predicted results of the microstructure evolution in serrated chips and machined surfaces, including average grain size and hardness, are compared with experiments, demonstrating good agreement. This implies that the PFEM combined with microscale schemes can reliably simulate the machining process of the TC4.

Modeling of ultrasonic vibration-assisted micromachining using the particle finite element method

Eugenio Oñate — Mon, 30 Sep 2024 15:23:20 +0200

When metals and alloys are exposed to ultrasonic vibrations (UV), a softening behavior occurs, caused by the phenomenon of acousto-plasticity. To obtain accurate results in a deformation analysis, this phenomenon must be included in the formulation of the constitutive material model. In this work, an acoustic-plastic model is proposed to capture the effects of ultrasonic vibrations during machining. The desired effect is to modify the chip morphology to reduce the magnitude of the cutting forces and thus reduce the energy consumption of the process. The study focuses on the modeling of ultrasonic vibration-assisted micromachining (VAMM). The particle finite element method is used and extended to perform a thermo-mechanical analysis capable of capturing the responses of conventional micromachining (CMM) and VAMM operations of 32 HRC stainless steel. The cutting speed and UV parameters, including amplitude and frequency, are integrated into the Johnson–Cook constitutive model to account for the effects of acoustic softening on the machining characteristics. The results show that the influence of UV on microcutting leads to thinner chips and lower cutting force. In the VAMM operations, an average reduction in cutting forces of 20% is achieved at five different cutting speeds. In addition, the contact length between the tool and chip decreases at different cutting speeds from 29% to a maximum of 44%. Furthermore, the thermal analysis results show that there is a negligible temperature change during the CMM and VAMM simulations, indicating that the study of the machining process can focus exclusively on its mechanical aspects when performed at the microscale. The predicted average chip thickness and effective shear angle of the workpiece material are in strong agreement with the experimental results, emphasizing the importance of considering acoustic softening in VAMM studies.

Fast explicit time integration schemes for parabolic problems in mechanics

Eugenio Oñate — Fri, 27 Sep 2024 14:34:51 +0200

We present a family of fast explicit time integration schemes of first, second and third order accuracy for parabolic problems in mechanics solved via standard numerical methods that have considerable higher computational efficiency versus existing explicit methods of the same order. The derivation of the new explicit schemes is inspired on the finite increment calculus (FIC) procedure used for obtaining stabilized numerical schemes in fluid and solid mechanics. The new (so-called) explicit FIC-Time (EFT) schemes allow considerable larger time steps than the standard first order forward Euler (FE) scheme and the second and third order Adams–Bashforth schemes. The comparison with Runge–Kutta schemes also favors the FIC-Time schemes in terms of the limit time step size (for second order schemes) and the total number of matrix-vector multiplications per time step (for second and third order schemes). The new first order explicit schemes have a faster convergence to steady-state than the FE scheme. The accuracy and efficiency of the new EFT schemes are verified in examples of application to the transient heat conduction equation using the finite element method.

Efficient discrete element modeling of heat generation and transfer in granular flows: Validation and application

Eugenio Oñate — Fri, 27 Sep 2024 14:20:38 +0200

This work presents an efficient Discrete Element Method (DEM) framework for the simulation of the thermal behavior of granular media. The main focus is on long-lasting granular flows, involving heat transfer and generation due to mechanical energy dissipation. The proposed approach uses efficient strategies to reduce the computational cost of the analyses and, therefore, to enable its application to problems of practical relevance. For instance, the contact area is adjusted to compensate for the artificial material softening that is typically considered in DEM to increase the time step size. After extended validation, the methodology is applied to the simulation of different setups of an experimental rotating drum. The numerical simulations presented good agreement with the experimental results and allowed a detailed analysis of the mechanisms and patterns of heat generation, which could not be extrapolated from the experimental campaign.

A review of the constitutive modelling of metals and alloys in machining process

Eugenio Oñate — Fri, 27 Sep 2024 14:04:27 +0200

Conventional machining still represents a predominant manufacturing process for the production of metal parts. During the last few decades, extensive research has been conducted to develop predictive models to capture complex material response during the machining process. Understanding the plastic behavior of the metals and alloys during machining operations has a great significance for researchers and engineers in both academia and industry. This paper thoroughly reviews the constitutive material models that have been employed thus far in the conventional machining studies. The aim of the paper is to present all significant constitutive models focusing the discussion on the most frequently used. First, we introduce the phenomenological models that depend on the deformation variables including strain, strain rate and temperature. Several extended versions proposed in the literature of these types of models will be reviewed. The techniques to identify the material constant parameters will also be discussed. Second, the proposed physical-based models, a kind of model that relies on the evolution of internal state variables, including dislocation density and grain size, will be addressed. Following that, novel data-driven based constitutive models are briefly debated to highlight their capabilities in order to be exploited in machining analysis. Finally, a concise overview and perspectives for future research efforts are outlined.

Fast prediction of rain erosion in wind turbine blades using a data-based computational tool

Eugenio Oñate — Fri, 27 Sep 2024 13:50:11 +0200

Wind turbines (WTs) face a high risk of failure due to environmental factors like erosion, particularly in high-precipitation areas and offshore scenarios. In this paper we introduce a novel computational tool for the fast prediction of rain erosion damage on WT blades that is useful in operation and maintenance decision making tasks. The approach is as follows: Pseudo-Direct Numerical Simulation (P-DNS) simulations of the droplet-laden flow around the blade section profile are employed to build a high-fidelity data set of impact statistics for potential operating conditions. Using this database as training data, a machine learning-based surrogate model provides the feature of the impact pattern over the 2-D section for given wind and rain conditions. With this information, a fatigue-based model estimates the remaining lifetime and erosion damage for both homogeneous and coating-substrate blade materials. This prediction is done by quantifying the accumulated droplet impact energy and evaluating operative conditions over time periods for which the weather at the installation site is known. In this work, we describe the modules that compose the prediction method, namely the database creation, the training of the surrogate model and their coupling to build the prediction tool. Then, the method is applied to predict the remaining lifetime and erosion damage to the blade sections of a reference WT. To evaluate the reliability of the tool, several site locations (offshore, coastal, and inland), the coating material and the coating thickness of the blade are investigated. In few minutes we are able to estimate erosion after many years of operation. The results are in good agreement with field observations, showing the promise of the new rain erosion prediction approach.

Multi-scale methods: applications to fluid mechanics problems

Eugenio Oñate — Fri, 27 Sep 2024 13:34:16 +0200

Multi-scale methods have emerged to facilitate its numerical solution when several simulta-
neous scales exist in the same problem, and where all of them exert a significant influence on the solution.
The difference in scales may be in the physical properties of the materials, for example density, modulus
of elasticity, viscosity or diffusion coefficients, etc. or there may also be a difference in scales in the
solution to the problem itself, such as a great difference in the magnitude of the waves or the instabilities
involved. Multi-scale methods emerged primarily for the study of composite solids, but have later been
generalized to very different applications. In this talk we will apply them to fluid mechanics problems.
We will develop two very different types of applications. On the one hand, turbulence problems, where
the multi-scale exists due to the great difference in the magnitude of the instabilities existing within a
turbulent fluid. In a second application, we will see the problem of fluids that contain particles within
them, where their homogeneous study is essential due to the large number of particles that have to be
studied.

Virtual reality for the creation of stories and scenarios for construction safety: social distancing in the COVID-19 pandemic context

Eugenio Oñate — Fri, 27 Sep 2024 13:04:47 +0200

The construction site is a complex and dynamic place. Workers are susceptible to certain risks due to the variability of their worksites, the tasks they perform, and the equipment they use. These aspects make the architecture, engineering, construction, and operation industry (AECO) have high accident rates. These typical processes are added to the growing use of new technologies in the workplace (e.g., drones, robots) that must coexist with human workers, not altering their routines and preserving a safe environment. A key aspect of occupational risk prevention (ORP) is worker training. Traditional training methods are not satisfactory. Given these deficiencies, virtual reality has shown advantages and benefits for training, allowing the development of immersive training experiences that promise to generate more meaningful learning for students. The construction sector needs to reactivate their construction sites after periods of confinement due to the direct implications on the progress of projects and, indeed, the industry's productivity (with the repercussions on costs, time and legal aspects). In this respect, training workers in covid protection measures and designing and analysing changing construction site scenarios to reduce the spread of viruses is crucial to ensure workers' health. This research shows the application and agile development of a training experience for social distancing at construction sites to prevent COVID-19 transmission, based on virtual reality and building information modelling and using serious games as a teaching strategy. Analyses are shown to exemplify the application and potential of the tools.

Development of an individual 3D particle reconstruction method for discrete mechanical modeling: Interpolation by Fourier composition

Eugenio Oñate — Fri, 27 Sep 2024 12:53:38 +0200

Reconstruction methods for granular materials are an essential step in achieving an accurate geometrical basis in the use of particle methods such as the well-known discrete element method. In this study, a novel continuous analytical method will be proposed to achieve individual 3D reconstructions of granular materials. For this purpose, the 2D Fourier Descriptor theory is used and a generalization of it to the 3D case is obtained, which is quite different from the spherical harmonics theory. Some limitations existing in the original model will be addressed by developing and calibrating a generalized approach that results in a total of four main models. Particles from a study sample are reconstructed, and the models are evaluated based on four generalized error metrics, as well as the presence of some specific limitations detected in the models. Finally, the models are compared using a decision-making method in different decisional scenarios.

THE APPLICATION OF HUMAN VISUAL ATTENTION IN MACHINE VISION

Mohammad Al-Azawi — Thu, 26 Sep 2024 22:17:04 +0200

Machine vision is still a challenging topic and attracts many researchers to research in this field. The main difference between machine vision and human vision is that, machine can see images as a set of pixels, while human can used cognitive capabilities in identifying the contents of the image. Attention is one of the important properties of Human Vision System, with which the human can focus only on part of the scene at a time; scenes with more abrupt features shall attract human attention more than other regions. In this paper, we shall simulate the human attention and discuss its application in machine vision and how it will improve the result of the retrieval process and image identification and understanding. Artificial intelligence shall be used in the proposed algorithm to cluster the salient points that were obtained either from eye trackers or from saliency extraction techniques.

Robustez y componente gigante de la red aeroportuaria de la República Mexicana mediante un análisis en Teoría de Grafos

Omar Martínez Rosas — Wed, 25 Sep 2024 06:48:14 +0200

La robustez de un grafo es un parámetro que cuantifica la vulnerabilidad de una red bajo ataque en alguno de sus nodos. La robustez se define como la fracción de la componente gigante después de que se eliminan ciertos nodos ya sea de forma aleatoria o bien de forma específica. Usando estas dos últimas definiciones y un script de programación en Python y NetworkX analizamos la dinámica de la componente conectada principal de la red de tráfico aéreo de la República Mexicana cuando se realizan ataques aleatorios y dirigidos en sus nodos. Asimismo, y a manera de comparación, calcularemos la fracción crítica de nodos eliminados en función de la densidad de la red para diferentes redes aleatorias usando el modelo de Gilbert y la red de tráfico aéreo de la República Mexicana con el fin de decidir si ésta última es o no es vulnerable a dichos ataques. Los resultados obtenidos muestran el grado de vulnerabilidad de la red de tráfico aéreo en la República Mexicana.

Research on similarity law of nonlinear shock response of ship plate frame structure under underwater explosion

Jesús Sánchez Pinedo — Tue, 17 Sep 2024 09:45:57 +0200

The anti-explosion ability of ship grillage structure is an important index to evaluate the vitality of ships. Its model test is a low-cost and effective method to evaluate the vitality of ships and guide the design of ship anti impact structures. In view of the nonlinear and nonstationary process of underwater explosion damage to ship grillage, this paper breaks through the nonlinear effect of transient explosion impact that is not considered in the traditional scale model design, focuses on the one-dimensional nonlinear impact response of ship grillage structure, and carries out the characterization study of the similarity between model experiments and real ships. Considering that the vertical motion of the prototype and the model grillage structure in the model test obey the random walking model, the vertical impact response of the deck grillage is characterized as one-dimensional nonlinear non-stationary Brownian motion, which is described by Hurst index. Based on the classical similarity law, the similarity transformation relationship between the range R and the mean square deviation S is derived, and the Hurst index of the model and the prototype meets the equal relationship; Take a section of grillage structure on a real ship and conduct prototype, 1/2, 1/3, 1/4 and 1/5 one-dimensional nonlinear explosion impact scale simulation tests respectively. The numerical response results show obvious nonlinear characteristics, and the Hurst index of displacement, velocity and acceleration response of the model within the pulse width range is less than 5% compared with the prototype. According to the scale invariance of fractional Brownian motion, the similarity conversion relationship of multiple parameters (displacement, velocity, acceleration and mean square response) is obtained. With the mean square response as the characteristic parameter, the response value of the prototype is converted through this relationship, and compared with the model simulation results, the multi parameter response error under each scale ratio is less than 20%. It provides theoretical and technical support for conducting similar experiments on nonlinear response of underwater explosion shock of ships

Energía nuclear: una alternativa válida para las inversiones sostenibles

Marta Carrasquer — Mon, 16 Sep 2024 10:33:36 +0200

Ante el creciente desafío de mitigar el cambio climático y la necesidad de una transición hacia una economía de bajas emisiones de carbono, la energía nuclear resurge como una opción viable para satisfacer la demanda energética global. Este estudio evalúa la viabilidad de la energía nuclear como inversión sostenible. Aunque la energía nuclear ha sido históricamente polémica debido a los riesgos asociados y la gestión de residuos radiactivos, su capacidad para generar electricidad con bajas emisiones de gases de efecto invernadero y de forma estable, la posiciona como un componente crucial en la transición energética.

El estudio comienza abordando los orígenes de la energía nuclear, desmitificando algunas creencias populares sobre su seguridad y destacando la evolución de la tecnología nuclear a lo largo del tiempo. Se exploran aspectos fundamentales como la construcción y desmantelamiento de plantas nucleares, así como la gestión de residuos radiactivos, un tema esencial en su aceptación pública.

Todo, absolutamente todo en la naturaleza es radiactivo. ¿Eras consciente de ello? Cualquier objeto de la vida cotidiana contiene isótopos radiactivos que emiten tipos y niveles diferentes de radiación. Porque la radiación es un fenómeno natural sin el cual, la vida en la Tierra no sería posible.

Este estudio no aborda aspectos técnicos sobre el funcionamiento de la energía nuclear, sino que se enfoca en su viabilidad como una opción de inversión sostenible.

Se profundiza en el papel de la energía nuclear en la transición energética, analizando cómo puede contribuir a la reducción de emisiones y garantizar un suministro energético confiable. También se revisa la situación actual de los países líderes en tecnología nuclear, junto con su inclusión en la Taxonomía de la UE y su impacto en los Objetivos de Desarrollo Sostenibles (ODS).

Este análisis examina como acceder desde los mercados de capitales en la contribución de la energía nuclear. Utilizando datos de proveedores externos de referencia en métricas sostenibles, la conformidad de compañías cotizadas con los requisitos clave de estándares internacionales y otros aspectos relevantes. Los resultados sugieren que, bajo condiciones específicas, la energía nuclear puede ser considerada sostenible, siempre que se cumplan rigurosos requisitos.

En definitiva, el objetivo del estudio evalúa cómo la energía nuclear puede integrarse en carteras de inversión sostenible y cómo la evolución del mercado del uranio y las tecnologías nucleares ofrecen perspectivas para los inversores, destacando su potencial para contribuir a los objetivos del Acuerdo de París y su relevancia en la diversificación de la matriz energética. La postura adoptada en este estudio es favorable para que los mercados de capitales potencien la financiación de proyectos nucleares, siempre que se cumplan los más altos estándares de seguridad y sostenibilidad.

A mathematical model of the formation of urine

Fernanda Isabel Domínguez Pérez — Thu, 12 Sep 2024 20:55:09 +0200

In this work, a mathematical model of renal hemodynamics involved in urine production is studied. The model is formed by three sub-models: one corresponds to the glomerular filtration stage, which gives input to the blood that is processed in the kidney; another one is the corresponding to the renal autoregulation process which defines the blood flow that must enter the kidney, to maintain its correct functioning, even in the face of blood pressure variations; and the last sub-model corresponding to the reabsorption and secretion stages, the former corresponds to the process by which the kidneys recover useful substances from the glomerular filtrate and return them to the blood and in the latter the kidneys eliminate unwanted substances from the blood into the peritubular capillaries.

Bayesian Estimation of the Basic Reproductive Number in a Chikungunya Outbreak in Acapulco, Guerrero

Cruz Vargas-De-León — Tue, 10 Sep 2024 01:42:04 +0200

Chikungunya is a vector-borne viral disease caused by the mosquitoes Aedes aegypti and Aedes albopictus. There is no specific medical treatment or available vaccine. This work presents a host-vector model that considers two age structures: chronological age and asymptomatic infection age. This model accounts for variability in the relapse period and susceptibility to the chikungunya virus. From this system of integro-differential equations, a particular case is derived in the form of ordinary differential equations (ODEs). The objective of this study is to estimate the basic reproductive number R0 of the ODE model for a chikungunya outbreak that occurred in 2015 in Acapulco, Mexico. A Bayesian approach using Hamiltonian Monte Carlo methods is applied to estimate the parameters of the ODE model and subsequently estimate R0. We estimate that R0 is 2.61, with a 95% credibility interval of (1.66, 3.80), which is consistent with other reports in the literature.

MIGRANTES CLIMÁTICOS EN BOLIVIA: UNA EVALUACIÓN SOBRE LAS POLÍTICAS PÚBLICAS DE PROTECCIÓN A LA MADRE TIERRA

Alex Cabello Ayzama — Sat, 31 Aug 2024 22:17:03 +0200

El presente documento presenta un análisis sobre la crisis climática en Bolivia, evaluando las políticas públicas existentes y su impacto en los migrantes climáticos. Mediante una revisión de literatura detallada, se identificaron desafíos comunes y buenas prácticas en la respuesta política. La investigación se centró en la revisión de la Política Plurinacional de Cambio Climático y casos específicos de migración interna, como el caso del Lago Poopó, para comprender mejor los efectos del cambio climático en las comunidades bolivianas. Se concluye que, aunque Bolivia ha reconocido la importancia del cambio climático a nivel constitucional, existe una brecha significativa entre la teoría y la práctica en la protección de los derechos de los afectados por este fenómeno. Se destaca la necesidad de una participación más activa del Estado y medidas concretas para abordar la crisis climática y proteger a las comunidades vulnerables.

Acute or a Cute Robot? The Effect of Angularity on Medical Service Robot Perception

Elizabeth Yadgarov — Mon, 05 Aug 2024 20:26:03 +0200

Despite the importance of medical care, one in three Americans avoid doctor visits (Cleveland Clinic Medical Professional, 2021). Considering our modernizing world, applying robotics to healthcare, particularly with Medical Service Robots (MSRs), yields the potential to improve patient engagement and overall care (“Medical robots,” n.d.). Recognizing the impact of design on user perceptions, the purpose of this study was to manipulate the angularity and curvature of MSR designs to reduce perceived threat and increase warmth and trust among patients. Participants (N=230) were randomly assigned to one of three conditions: curved MSR design, angular design, or a no-image control group. After viewing the stimulus, participants answered Likert-type scales that measured their perception of them. ANOVA tests revealed that perceived trust was unaffected by angularity, while perceived warmth approached significance (p = .06). Significant effects were found for preference (p = .02) and perceived threat (p = .04), with the curved MSR design being most preferred and the angular version perceived as most threatening. Additionally, gender was found to have a significant main effect for preference (p < .01), warmth (p = .04), and trust (p = .04) as males rated the robots higher than females. These findings can be used to create MSR designs that are most positively perceived, thus optimizing their ability to improve patient care.

Innovation in Epilepsy Management in South Asia: Design and Development of a Wearable Seizure Detection Device

Ayaan Waqar — Fri, 02 Aug 2024 09:35:03 +0200

This study introduces the Epilet Band, an innovative wristwatch-like device employing machine learning algorithms to detect Convulsive Epileptic Seizures (CES) in real-time. Distinguished by its use of Federated Machine Learning (FML), it ensures maximum data privacy and minimal data transfer. The Epilet band uses the DAP (detection, avoidance and prevention) method and incorporates a carefully selected array of sensors– accelerometer, gyroscope, temperature sensor, light intensity sensor and a digital microphone– all integrated into the Arduino Nano 33 BLE Sense. This study developed a seizure generator that replicates the movement patterns observed during epileptic seizures. Utilizing the Edge Impulse training platform, a machine learning model is trained to recognize these seizures, continually refining its accuracy through retraining and remodeling processes. The Epilet Band works by differentiating epileptic seizures from day-to-day activities by detecting muscle movement patterns produced by the two actions. Moreover, the Epilet Band actively detects ambient temperature, humidity, noise levels and light intensity (for example flashing lights over a period of 5 seconds) and alerts the user’s caretaker in case these conditions align with those which trigger epileptic seizures, with a message that an attack could be triggered. Research findings demonstrate the efficacy of the proposed Epilet band in detecting uncontrollable convulsive seizures timely. The machine learning model allows for improved accuracy of the detection algorithm as the number of trials and sample size increase with time. Being alerted of a potential seizures or triggers affords caretakers the opportunity to act fast to reduce fatalities or unfortunate accidents caused by a sudden onset of seizure. This exhaustive study underscores the innovation and scientific diligence captured by the Epilet Band, illustrating a future where epilepsy management is significantly empowered through technology trained by existing data, offering new horizons for individuals afflicted with this condition.

La necessitat d’una gestió sostenible del tipus de canvi a les empreses

Genèric Congrés2025 — Wed, 31 Jul 2024 07:49:02 +0200

El cuadro comparativo entre constitucionalismo multinivel, transconstitucionalismo e interconstitucionalismo: un equilibrio de conceptos y bases explicativas

Alex Cabello Ayzama — Tue, 30 Jul 2024 16:37:02 +0200

El presente artículo busca revisar las principales construcciones teóricas sobre constitucionalismo y los conceptos de constitucionalismo trans-, inter- y multinivel. La problematización central del artículo es cómo describir los intentos teóricos de explicar los fenómenos de trans-, inter- o multiconstitucionalismo. Es una investigación que tiene el postulado descriptivo inicial de varias matrices teóricas, con breves incursiones analíticas sobre estas mismas construcciones, y finaliza con la organización del listado de clasificaciones teóricas. El material utilizado en la redacción del texto es material bibliográfico sobre teoría constitucional, derecho constitucional y, de manera complementaria, teoría política y derecho internacional. Los resultados del trabajo son clasificaciones y ordenamiento de los catálogos teóricos que presentan los elementos de similitud y disimilitud entre los conceptos en mención.

Una nova estratègia d’internacionalització per al segon quart del segle XXI

Diego Guri — Mon, 29 Jul 2024 11:45:03 +0200

Entre els trets més significatius que caracteritzen l’economia catalana està, sens dubte, la seva obertura exterior. Milers d’empreses -la majoria pimes- han incorporat, poc o molt, la presència en els mercats internacionals com a element indefugible de la seva estratègia empresarial. Administracions i organitzacions empresarials, en paral·lel, han estructurat programes i serveis de suport tendents a reforçar aquest procés, generant-se un ecosistema d’internacionalització amb una multiplicitat d’actors que porten a la pràctica la seva visió, les seves receptes, no sempre del tot alineades, amb l’objectiu que l’empresa les tradueixi en el seu compte de resultats.

En un escenari com l’actual, on un conjunt de megatendències estan incidint de ple en el procés d’internacionalització, tendències que no es poden afrontar de manera lineal, cal una visió holística, sistèmica, amb noves maneres de plantejar l’estratègia.

Poden les empreses seguir actuant com quan s’anunciava l’adveniment d’un món pla, de la fi de la història de Fukuyama, o han de fer front a les conseqüències d’un món multipolar en crisi permanent?

No es pot parlar de digitalització, d’intel·ligència artificial, ni d’innovació sense considerar els mercats exteriors, que són motors i destinataris d’aquests processos.

Avui, més que mai, els valors socials i ambientals estan qüestionant els fonaments del nostre sistema econòmic i les regulacions associades estan afegint complexitat a la internacionalització. Una empresa no pot obviar la sostenibilitat si vol ser capdavantera.

Si donem resposta a aquestes i d’altres variables, no serà inevitable que el sistema productiu català es plantegi una nova aproximació per afrontar la internacionalització del segon quart del segle XXI?

Es proposaran els elements clau que haurien de caracteritzar una nova estratègia guanyadora de internacionalització del teixit productiu català en aquest nou entorn.

The Role of Proteins in Glial Cell Autophagy and Apoptosis in the Dentate Gyrus

William Ahmuty — Tue, 23 Jul 2024 15:32:03 +0200

The dentate gyrus is a unique part of the brain because it is known for housing neurogenesis in the adult brain, a process which normally stops early in development. This makes the dentate gyrus an area of great interest, especially in combating neurodegenerative diseases. This area, as well as the entire nervous system, is composed of both neurons, which send and receive signals, and glial cells, which are responsible for supporting neurons. Glial cells including astrocytes, oligodendrocytes, and ependymal cells are present in both the central nervous system and peripheral nervous system, while Schwann Cells are present only in the peripheral nervous system, and microglia are present only in the central nervous system. Apoptosis and autophagy are both processes which degrade and recycle materials. Autophagy degrades materials inside the cell, such as organelles and proteins, and uses lysosomes to carry out the process. Apoptosis consists of degrading old or damaged cells, and is known as programmed cell death. Many proteins affect these processes including Beclin-1, UC3, p62, and the BCL-2 family of proteins. Some studies have used machine learning algorithms in conjunction with simulation and statistical software to study these proteins and the processes they are involved in. Several areas of research remain unfilled, especially in the area of glial cells and the proteins involved in autophagy and apoptosis, as not many machine learning studies have examined this.

The role of Beclin1 in astrocyte autophagy in the dentate gyrus

William Ahmuty — Mon, 22 Jul 2024 20:33:03 +0200

Spatiotemporal Distribution of Phytoplankton in Long Island’s Coastal Waters Revealed by Remote Sensing

Cynthia Zhang — Mon, 15 Jul 2024 20:54:06 +0200

Phytoplankton play a major role in marine ecosystem health. They form the base of aquatic food webs, but under conditions of nutrient loading and high stratification, they can develop into harmful algal blooms that produce toxins harmful to humans and wildlife. Ongoing phytoplankton blooms have been observed in Long Island’s (LI) coastal waters for the past half-century, but there is a lack of a comprehensive view of phytoplankton spatiotemporal distribution and their driving factors due to the analysis of specific sampling sites, species, and years. Thus, this study obtained 20 years of chlorophyll-a, climate, and nutrient remote sensing and in situ data from the ERDDAP data server and the CTDEEP Long Island Sound Water Monitoring Program to establish phytoplankton phenology using the threshold criterion and cumulative sum of anomalies methods and to investigate regional differences, influencing factors, and interannual trends using correlation and linear trend analysis. The phenology of summer-autumn blooms in Long Island Sound (LIS) was associated with high sea surface temperatures (r = -0.46, p < 0.01). In contrast, winter-spring blooms were most strongly correlated with low salinity (r = -0.52, p < 0.01), indicating P-rich Connecticut river discharges as the dominant nutrient source. However, phytoplankton in LI’s southern shores lack access to river outlets, so phytoplankton production was driven by deep winter mixings from low SST that replenish surface water nutrient levels (r = -0.71, p < 0.05). Finally, our results showed strong decreasing interannual trends of autumn chlorophyll-a levels from 2003-2022 (r < -0.66, p < 0.05), potentially due to heightened N-limitation. Hence, the effects of declining phytoplankton productivity on LI’s fisheries and marine ecosystems should be further investigated.

Thermo-mechanical analysis of laminated composites shells exposed to fire

Julio García-Espinosa — Sat, 13 Jul 2024 10:28:03 +0200

This paper describes the research performed within the scope of H2020 project FIBRESHIP in the development and validation of a thermo-mechanical model to assess the ﬁre performance of composite structures. A one-dimensional thermal model with pyrolisis is used to obtain the temperature prole across the thickness and later introduced in the thermomechanical model with a quadrilateral shell element approach. The composite constitutive model employed is the socalled Serial/Parallel Rule of Mixtures (SPROM) which has been modied to introduce the eﬀect of the thermal deformation. A set of experimental tests are then used to validate the correctness of the numerical method proposed. The experimental data used to validate the thermal model is the classic Henderson experimental test. The thermomechanical coupling is validated against an original vertical furnace test of a FRP ships bulkhead following on the 2010 FTP Code standards. These validations demonstrate the correctness and accuracy of the proposed decoupled thermomechanical formulation.

El Pacte Nacional per a la Indústria i la reconversió turística

Genèric Congrés2025 — Fri, 12 Jul 2024 13:54:04 +0200

Cooperative financing funds

Eloi Serrano — Fri, 12 Jul 2024 12:00:07 +0200

Cooperative financing channels is an issue that has been an issue for the sector since the creation of the first cooperative enterprises. In the same way that capitalist companies need financing to consolidate, improve and promote their activities, cooperatives face the same need.
This study analyzes the financing needs of cooperative enterprises based on a survey of one hundred Catalan cooperatives. In addition to analyzing the results obtained, these are also compared with the results of the latest survey on access to finance prepared by the European Central Bank (ECB, 2023).

The Role of Emotional Intelligence in High School Students' Ethical Decision-Making

Derek Z Qu — Wed, 10 Jul 2024 03:09:06 +0200

Navigating the complex landscape of adolescence in the digital age presents unique challenges, with social media, technological divides, and academic pressures intensifying the already demanding expectations placed on teenagers. This study explores the role of Emotional Intelligence (EI) in ethical decision-making among high school students. Emotional Intelligence, encompassing abilities such as self-awareness, empathy, and emotional regulation, plays a crucial role in understanding and managing emotions, which is essential for ethical reasoning.

A survey was conducted among 68 students from an ethnically and academically diverse suburban high school, assessing their EI levels and ethical decision-making capabilities through a series of dilemmas. The results indicate a significant correlation between higher EI scores and better ethical decision-making abilities. Respondents with higher EI were more adept at navigating moral dilemmas, suggesting that EI serves as a valuable predictor of ethical behavior.

Additionally, a secondary survey compared students' theoretical ethical choices with their anticipated real-life actions, highlighting the influence of peer pressure and social norms on decision-making. The findings underscore the importance of fostering EI skills in educational settings to better equip students for the ethical challenges they will face.

This study contributes to the growing body of literature on the intersection of EI and ethics, emphasizing the need for holistic educational approaches that prioritize emotional and ethical development. Future research should expand to diverse populations and employ longitudinal methods to further elucidate the causal relationships between EI and ethical decision-making.

How AI is a factor of transversal innovation in Catalonia

Joan L. Mas-Albaigès — Thu, 04 Jul 2024 11:31:44 +0200

It can be stated that Artificial Intelligence (AI) is the digital technology that has achieved one of the most important impact and social penetration level, following an exponential evolution in recent years. Despite this explosion, AI has an almost century-old history, dating back to the work of scientists and technologists who laid its foundations from the second half of the last century. AI also has a long history in Catalonia that begins, among others, with the figure of Professor Emeritus Ramon López de Mántaras and the creation of the AI Research Institute of the CSIC (IIIA-CSIC). This article takes a look at AI in Catalonia, starting with the main agents that make up the AI research and innovation ecosystem in our country. The current state of AI in Catalonia is presented based on the sectoral report published by ACCIO in May '24, and then the CATALONIA.AI strategy promoted by the Government of Catalonia and deployed from 2020 is described. The axis of business innovation that is promoted by this strategy and that materializes in the Center for Innovation in Data Technologies and Artificial Intelligence (CIDAI) is detailed, describing both qualitatively and quantitatively the most significant activities that it promotes. The article also addresses the transformative potential of AI in business from two points of view. The first one addresses the business applications of analytical AI in conjunction with other technologies in the context of Industry 4.0. The second one focuses on Generative AI, illustrating the basic principles of the foundational models and developing the multisectoral and multimodal opportunities and the impact that this variant of AI will have on the different productive and business sectors. Next, the work analyses the recurring barriers to a multisectoral implementation of AI, which, among others, refer to issues such as data availability or lack of qualified talent. In parallel to these strictly technological issues, the article also delves into the main consequences of the AI regulations that Europe has approved and how companies with products or services based on AI systems will have to adapt to them. Finally, the article gives an insight into the impact of AI on sustainability and possible ways to solve it.

A new approach to combine physics-based and data-driven models using a localised trustworthiness metric

Jesús Sánchez Pinedo — Wed, 03 Jul 2024 09:54:17 +0200

Artificial Neural Networks (ANNs) can solve many (un)supervised learning tasks by virtue of the universal approximation theorem. In the context of on-line process control for manufacturing processes, ANNs are an ideal approach for e.g., on-line monitoring or prediction tasks. However, since they are trained on experimental input-output pairs, the governing physical relations are only implicitly included. This, for instance, can cause inaccuracies when extrapolating to out-of-sample data-points [1]. On the other hand, the numerical approximation of the governing physical laws via numerical methods holds strong potential for the accurate simulation of physical phenomena that occur during manufacturing processes. However, the corresponding computational effort is an impediment that arises with the need for numerous simulations [2]. This makes the application of such numerical schemes computationally intractable within an on-line monitoring context. As such, it is clear that ANNs and numerical simulation models have strong potential, but are fundamentally different models. However, their combination serves as a potentially efficient and accurate aggregated predictor, the so called grey-box model. Such grey-box model is based on highly efficient machine learning algorithms, the black-box member, and backed by validated data with respect to physics generated by the numerical model, the white-box member. A grey-box model capable of defining a trustworthy prediction, including a measurement of uncertainty on the estimator, remains challenging.

Enhancing predictive modeling in reactor building dose distribution: A neural network-aided approach

Jesús Sánchez Pinedo — Wed, 03 Jul 2024 09:53:56 +0200

Ensuring the safety of nuclear reactor decommissioning workers requires accurate, real-time predictions of radiation dose rates within reactor buildings. However, due to the complexity of these structures, such predictions are computationally intensive and time consuming. In this paper, we propose constructing a surrogate model using deep learning to predict radiation dose rates based on simulation results in a space containing a square pillar and a radiation source. The accuracy of the surrogate model's predictions was verified and visualized. Additionally, by applying the principle of superposition, we demonstrated that the distribution of radiation dose rates in spaces with a pillar and multiple radiation sources can be obtained by summing the surrogate model results for each radiation source. We also examined the application of the surrogate model to predicting radiation dose rates in spaces containing multiple square pillars and multiple radiation sources. This approach shows the potential for surrogate models to accurately and efficiently predict radiation dose rates in reactor buildings with complex structures and multiple radiation sources in real time.

Fuzzy statistics-aided inference in experimental design

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:45:07 +0200

Conducting research based on active influence on the examined object or process requires distinguishing an explained quantity, measured quantitatively, the possible changes of which will be considered as influencing it through a group of quantities considered as explanatory quantities. This approach implicitly postulates the existence of a cause-and-effect relationship between the explanatory quantities and the explained quantity. In practice, especially industrial practice, explanatory quantities are often called controlled factors. Knowledge of possible cause-and-effect relationships can be graded, from the most comfortable situation of the existence of appropriate binding equations and their exact solutions, through the existence of binding equations but without knowing the exact solutions, to the absence of such equations. While in the first case, experimental research serves to refine the results originally calculated for idealized models, in the second case, it is a necessary stage of identifying the parameters of the postulated model, and in the third case, it is a necessary stage of collecting data for which the simplest possible forecasting model will be constructed

Accurate solution of linear operator approximations using green's functions by a multi-level neural network approach

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:44:40 +0200

Lately, the approximation of operators for partial differential equations using deep learning has been extensively investigated. However, these deep learning approaches have limitations in terms of accuracy. In this work, we present a multi-level approach to accurately approximate linear operators using physics-informed Green operator networks. This method allows for the iterative reduction of the approximation errors through a sequence of operators, each targeting errors of increasing complexity at progressively smaller scales. Numerical examples for the one-dimensional Poisson problem will be presented to demonstrate the effectivenessof the proposed multi-level approach.

Symbolic regression via neural networks

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:44:17 +0200

Mathematical models allow researchers to understand, analyze, and predict the behavior of systems of physical, biological, and technological interest, and are required for many techniques from dynamical systems and control theory to be used. Unfortunately, it is often impossible to derive mathematical models from first principles, and in such cases system identification is a powerful tool which can be used to deduce models from observed data. Many existing system identification techniques require pre-specification of a dictionary of possible terms in a mathematical model, limiting their ability to give models with the nonlinearities which can arise for biological and other complex systems. We present a methodology which overcomes this limitation by dynamically generating the terms in a model with the necessary complexity and nonlinearity to accurately describe a system’s dynamics. This uses a multilayered, operation-based symbolic regression approach, with the capacity to learn combinations and compositions of operations by training artificial neural networks. Our approach provides a powerful alternative to genetic programming strategies for symbolic regression, and can exploit many of the attractive features of artificial neural networks such as a straightforward learning strategy.

Structural optimization through generative adversarial networks

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:43:58 +0200

The finite element method (FEM) is a well known approach to solve partial differential equations. It has important applications in structural engineering, such as in topology optimization (TO). TO involves, at each iteration, the solution of structural problems via FEM, which can add up to a high computational cost. Therefore, a line of research to accelerate TO emerged over the years focusing on machine learning (ML) approaches. Particularly, Artificial Neural Networks (ANNs) have been proposed to significantly speed-up the process by eliminating the iterative algorithm, which is intrinsic to TO. Since ANN is a supervised ML method, first a dataset is generated, containing finite element analysis (FEA) inputs, volume fraction, postprocessing, and final topologies. Then, with the Wasserstein Generative Adversarial Networks (WGANs) is trained on this dataset to map fields of physical quantities, such as the von Mises stress, to the final optimized structure. The final designs obtained via ML are quantitatively analyzed according to the metrics.

Data-driven modeling of complex mechanical components for integration in system-level simulations

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:43:40 +0200

This contribution presents a data-driven approach featuring a physics-inspired neural network structure for modeling complex components in mecha(tro)nic systems. In the present approach, gated recurrent units (GRUs) are employed to approximate the ordinary differential equations (ODEs) describing the system’s states over time, followed by a deep feedforward neural network (FFNN) mapping these states to a target variable. The networks are shown to predict a latent space capable of modeling the underlying dynamics, without the need for measuring the full state vector and only relying on input-output measurements. Subsequently it is shown that a nonlinear coordinate transformation exists between the latent space of the network and the states obtained from the reference ODE integration (simulation). To have a verification of the network’s performance, it is applied to simulation-based data of an academic example for which the states and equations are known beforehand. Furthermore, the methodology is also applied to real measurement data from an INSTRON testing system capturing shock damper and bushing dynamic behaviour. Lastly, it is demonstrated that an ODE expression can be extracted from the trained network. This feature allows seamless integration of these networks into variable time-step, system-level simulation software.

Data-efficient one-step mechanical design of composites using generative AI

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:43:17 +0200

The distribution of material phases is crucial to determine the composite's mechanical properties. While the entire structure-mechanics relationship of highly ordered material distributions can be studied with a finite number of cases, this relationship is challenging to reveal for complex irregular distributions, preventing the design of such material structures from meeting specific mechanical requirements. The noticeable developments of artificial intelligence algorithms in material design enable the discovery of hidden structure-mechanics correlations, which is essential for designing composites of complex structures. It is intriguing how these tools can assist composite design. Here, we focus on the rapid generation of complex irregular composite structures and the stress distribution in loading. We find that generative AI, enabled through fine-tuned Low-Rank Adaptation models, can be trained with a few inputs to generate synthetic composite structures and the corresponding von Mises stress distribution. The results show that this technique is convenient in generating massive composite designs with useful mechanical information that dictates stiffness, fracture, and robustness of the material with one model, and such must be done by several different experimental or simulation tests. This research offers valuable insights for improving composite design to expand the design space and automatic screening of composite designs for improved mechanical functions.

A dynamic weighted loss function for enhancing the performance of neural networks

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:42:56 +0200

In machine learning process, hyper parameters are chosen in a way to decrease the prediction error and improve the convergence. However, the optimized hyper parameters have a limit in terms of enhancing the performance of the neural networks. In this work, the datasets used for the numerical experiments arise from the resolution of partial differential equations (PDE) defined on a spatial domain. We propose a DYNAmic WEIghted Loss (DYNAWEIL) function-based approach for neural networks that are used to learn these PDE’s solutions. This a two-step process: first we train for a few numbers of epochs in a classical way then the dynamic weighted loss function replaces the classical loss function by leveraging the information from past training error histories. To validate this method, we carry out numerical experiments with different neural networks on datasets arising on two different physics: Goldstein equation [1] and radiative transfer equation [2]. Thus, in order to demonstrate the relevance of this approach, we provide a comparison among a neural network model using a classical loss function, with and without hyper parameters optimization, and a dynamic weighted loss function for both versions.

Prediction of unsteady heat transfer of temperature field on a circuit board using sub-voxels input data structure

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:42:32 +0200

In this study, we propose a sub-voxel learning method based on a Neural Operator and predict the thermal temperature field on a circuit board in unsteady heat conduction. CAE analysis reduces the cost of experiments using prototypes in the design phase. However, the computational cost is high because the number of trials increases due to changes in analysis conditions. Predictions made by machine learning are less accurate than those made by CAE analysis, but they can significantly reduce computational costs. In general, machine learning is difficult to extrapolate. Thus, the concept of a Neural Operator has attracted attention as one machine learning method incorporating physical laws. In this study, we propose a sub-voxel learning method based on the Neural Operator, inspired by the forward Euler method, and predict the thermal temperature field on a circuit board during unsteady heat conduction. The input data is the analysis data of the current cycle step, and the output data is the data of the next cycle step. Dummy temperatures were set according to the heat generation of each IC in the input data of the 0th cycle step, and pseudo-difference values were generated to extract features. The prediction accuracy of the next cycle step was compared with and without dummy temperatures

Graph neural networks for accelerating the discrete element simulation of granular ﬂow

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:42:13 +0200

Granular flow is a phenomenon widely presented in both the natural and engineering fields. Here granular materials could be either solid particles, e.g. rocks, soil, and grains, or liquid particles, e.g. mud and fresh concrete mortar. Soil landslides, particle transport, and grain accumulation have been edge-cutting hot research topics. Discrete Element Method (DEM) has been regarded as one of the most important methods to simulate granular flows and to investigate discontinuous and large deformation problems. The basic principle of DEM was to view the simulated object as consisting of discrete particles, to define specific constitutive relationships for the particles, and to study the macroscopic properties of the simulated object from a microscopic perspective based on the interactions between particles. However, DEM simulations usually consume very high computational cost for particle contact searching and detection. To accelerate the computational process of discrete element simulation, the Graph Neural Network (GNN) based deep learning model was proposed in this paper. In GNNs, graph nodes and graph edges represent the particles and their interactions. The training and testing datasets were generated using an open-source software named YADE, while the neural network model was constructed using PyTorch and Deep Graph Library (DGL). Replacing the direct calculation of particle collisions in DEM with the trained neural network model, the state of the particles at the next moment could be predicted based on the current state of the particles. It significantly increased computational speed. The proposed technique was applied in various examples, such as drum rotation and hopper stacking, and its accuracy had been verified. This study established a solid foundation and provided robust support for further research and applications of granular flow simulation based on GNN

Prediction of plural crack propagation using discovered PDE

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:41:56 +0200

This study presents a prediction of plural crack propagation using the discovered partial differential equations. 80% of structures fracture due to fatigue failure. Therefore, the evaluation of fatigue cracks is essential. Numerical analysis is costly, and machine-learning surrogate models have been proposed. Hence, the crack propagation path and remaining life are predicted using machine learning. A dataset is obtained from the results of a crack propagation analysis using a s-version FEM combined with an automatic mesh generation technique. The input parameters are the coordinates of the four crack tips, and the output parameters are the crack propagation vector and the number of cycles of 0.25 mm. Also, physics-informed neural networks (PINNs) have been widely studied in recent years. Thus, we took inspiration from PINNs and added a regularization term of PDE discovered by AI Feynman to the loss. As a result, the loss of a validation dataset for training constrained by PDE was reduced by about 77% compared to the unconstrained loss. The error in crack length decreased from -0.50% to 0.17%.

Deep convolutional architectures for uncertainty quantification and forecast in inundation problems

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:41:34 +0200

This article provides a summary of our latest research, where we investigate the application of data-driven deep learning methods to simulate the dynamics of physical systems that are governed by partial differential equations (PDEs). The main challenge is the long-term temporal extrapolation for fluid dynamics problems that exhibit steep gradients and discontinuities. We make use of deep learning techniques, specifically designed for time-series predictions like LSTM, TCN, and Attention mechanism, as well as CNN. These methods are employed to model the dynamics of systems primarily influenced by advection. We propose a combination of a Convolutional Autoencoder (CAE) model for data compression and a novel CNN-based for forecasts. These models take a series of high-fidelity vector solutions and predict the solutions for the following time steps using auto-regression. To reduce complexity and computational demands during both online and offline stages, we implement deep auto-encoder networks. These techniques are used to compress the high-fidelity snapshots before feeding them into the forecasting models. Our models are evaluated on numerical benchmarks, such as the 1D Burgers’ equation and Stoker’s dam-break problem, to assess their long-term predictive accuracy, even in scenarios that extrapolate beyond the training domain. The model that demonstrates the highest accuracy is subsequently used to simulate a hypothetical dam break in a river with real 2D bathymetry. Due to space constraints, only a selection of results is showcased, with additional findings available in our work [1] and the newer ones will also be presented in the talk. Our findings indicate that the proposed CNN future-step predictor offers significantly accurate forecasts in the considered spatiotemporal problems.

CNN-based surrogate model and temperature prediction method using superposition principle

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:41:14 +0200

A CNN-based surrogate model is being developed to accelerate CFD calculations. In order to use this surrogate model for design development, it is necessary to improve generalizability. One solution to this problem is to use the principle of superposition. For the multiple heating elements that make up the model, their temperatures are predicted by heating them individually. We devised a method to predict the temperature of the entire model by adding up these individually predicted temperature distributions. Radiation and convection phenomena, for which the superposition principle does not hold, were also considered.

Investigation of CNN-based multigrid-bidirectional networks

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:40:56 +0200

We are developing a high-speed simulation technology for physics simulations using deep learning. This technology aims to accelerate simulation time by a factor of several hundred to a thousand, significantly enhancing product performance and quality by increasing development efficiency and optimization. Currently, we are focusing on a multi-grid convolutional neural network (CNN) based architecture designed for models incorporating a combination of coarse and dense grids, addressing the challenge of model scaling and high resolution. Our previous work, reported by the Society for Computational Engineering and Science in June 2023, demonstrated the propagation of physical information from a coarse grid to a dense grid. Building on this foundation, we have now developed a technique that facilitates the propagation of physical information among multiple grids with varying resolutions. We applied this novel method to a basic temperature distribution prediction model for circuit boards and verified its high accuracy in predicting temperature distribution.

Investigation of geotribological behaviour in the soil-pile interface considering clay based on numerical and laboratory investigations

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:26:23 +0200

The contact behavior between soil and structures is an important aspect in many geotechnical applications. One example is the contact between pile and soil during pile installation which especially for open-ended pipes can lead to soil plug formation. Within the present research, contact behavior between clayey soil and pile is investigated by means of numerical and laboratory experiments focusing on the contact behavior within tubular piles. First, the contact between kaolin clay and steel is experimentally investigated with respect to overburden pressure in the so-called Geo-Tribometer developed at HSU. The results of the experimental investigations show some unexpected results leading to the assumption that the contact failure surface inside the soil specimen changes with differing overburden pressure. Additional numerical simulations are carried out for better understanding of contact stress development. Second, further laboratory investigations using soil-filled tubular piles show that adhesion-like effects significantly influence the contact behavior between soil plug and internal surface of the tube. For estimation of the adhesion values, numerical simulations by means of finite element analyses are carried out showing that as expected with increasing soil’s overburden pressure adhesion effect increases. The results are finally discussed with respect to transferability from small scale in numerical and laboratory investigations toward prototype scale.

Space-time analysis for the container problem

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:26:08 +0200

The container problem describes the behaviour of elastic porous media in a rectangular container, which is completely saturated by an ideal incompressible liquid. By time the liquid extrudes on the surface of the container while the stress resulting from a given top load acts on the shrinking elastic solid due to its compression. The analysis bases on a space time potential that contains a linear elastic term, a darcy flow term, and a boundary load term. The variation of the potential results in a space-time principle. Its minimum preserves approximately equilibrium over space and time.

Three-field model for wave propagation in porous media based on mixture theories

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:25:51 +0200

Simulations of wave propagation in porous media are important to the understanding of various phenomena, such as seismic effects and non-destructive testing. The derivation and implementation of finite element analysis for a fully dynamic three-field deformable porous media model based on the de la Cruz and Spanos (dCS) theory [1] is presented. The dCS theory accounts for the fluid viscous dissipation mechanism and nonreciprocal solid-fluid interactions, which are neglected in Biot theory [2]. While the Biot theory is based on experimental data, the dCS theory is derived from mixture theories associated with the volume fraction concept and representing the connection between micro and macro pore scales. dCS results presented build upon recent FE model for quasi-static analysis [3]. Here, for the fully dynamic case incorporating both fluid and solid inertia, the accuracy and robustness of the FEA model is verified by wave propagation examples in one and two dimensions. Time integration scheme utilized and the changes in convergence rates according to how strongly coupled is the system will be discussed. The required element approximation order for all variables to ensure numerical stability will be demonstrated. The presented model is compared with the results from Biot theory, allowing one to observe the differences between the two theories and their relevance. The solutions in the time and frequency domain are also discussed, where the analysis of the correspondent eigenproblem leads to important information regarding wave velocity and attenuation.

Development of high-strength wooden pallets utilizing local timber from Ehime Prefecture

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:25:28 +0200

Wooden pallets account for a large percentage of pallets indispensable for logistics. From the viewpoint of strength and rigidity, pallets made of foreign timber (e.g., American pine) are the mainstream, and pallets made of domestic timber, especially cypress and cedar, which are inexpensive in terms of log price, are rarely distributed. The objective of this research is to develop a domestic wood pallet with high strength and rigidity comparable to that of American pine. Structural analysis using the finite element method was conducted to calculate stresses and strains under bending and compressive loads. The analytical results were also verified by JIS flat pallet bending and compression tests.

Determination of orthotropic elastic modulus of wood by indentation with none-axisymmetric indenter and FEM simulation

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:25:07 +0200

The purpose of this study is to estimate the material constants of woods as an anisotropic material, by indentation tests using an ellipsoid indenter. Firstly, uniaxial compression tests are performed to investigate anisotropic modulus. Secondary, indentation tests are conducted to get load-displacement curve by rotating the non-axisymmetric indenter along the indentation axis. Finally, the orthotropic elastic parameters are estimated by comparing the indentation test results and finite element analysis results. The modulus along longitudinal direction, which is given by the present indentation method, are in good agreement with the measured modulus by the compression test. Therefore, it is confirmed that the longitudinal modulus can be measured by using the present method with the non-axisymmetric indenter

Constitutive modelling of wood-based materials

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 14:24:51 +0200

Wood, unlike steel and concrete, is an anisotropic material. Because of its inherent characteristics, the mechanical behaviour of wood depends on the grain direction and the load type. Appropriate material models are the fundamental basis of reliable simulations. The constitutive models incorporated in existing general design software packages are often limited, making the software unsuitable for accurately predicting the mechanical behaviour and failure modes of wood-based materials. In this paper, a comprehensive constitutive model, composed of sub-models for describing the elastic properties, strength criterion, post-peak softening for quasi-brittle failure modes, plastic flow and hardening rule for yielding failure modes, and densification perpendicular to grain, was introduced. Modelling considerations on the effects of temperature, moisture content, and loading time were discussed. Advanced and practical modelling methods and key considerations for wood-based products were introduced, aiming to support practicing engineers and researchers to become better acquainted with modelling and analysing timber structures.

Assumption bulging frequency of the real scale tank by micro-tremor measurement and the eigenvalue analysis

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 13:28:28 +0200

Damage to tanks has been reported every time an earthquake with a seismic intensity of 6 or higher occurs, such as the 2011 Tohoku Pacific Coast Earthquake (Great East Japan Earthquake), the 2016 Kumamoto Earthquake, and the 2022 Fukushima Prefecture Earthquake.[1-5] . The damage assessment revealed that there are two main types of damage that can occur inside the tanks. The first case is damage to the roof and upper sidewalls of the tank, and the second case is damage to the sidewalls and corners, mainly at the bottom of the tanks. The first is caused by the sloshing phenomenon (liquid level motion due to resonance between the natural frequency of the liquid content and the dominant frequency of seismic waves) due to longer-period seismic motion. On the other hand, the second type is caused by the bulging phenomenon[6,7] (coupled vibration between tank wall and liquid content), and is mainly caused by vibration of the tank structure due to short-period seismic motion

Power dissipation modelling in rolling contact

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 13:28:09 +0200

This paper is concerned with the modeling of power dissipation due to friction and its relation with wear estimation in wheel–rail contact. In contact models, wear is usually described in terms of the wear depth function. This function modifies the gap between the contacting bodies as well as the shape of the surfaces of the wheel and rail in contact. In this paper, besides the wear depth function, the dissipated energy, rather than the contact stress, is taken into account to evaluate the wear impact on rail or wheel surfaces. The dissipated energy allows us to more precisely evaluate the wear debris amount as well as the depth of wear and its distribution along the contact interface. A two-dimensional rolling contact problem with frictional heat flow is considered. The elasto-plastic deformation of the rail is assumed. This contact problem is governed by a coupled system of mechanical and thermal equations in terms of generalized stresses, displacement and temperature. The finite element method is used to discretize this problem. The generalized Newton method is applied to numerically solve this mechanical subproblem. The dissipated power is evaluated based on the resultant force and slip at a reference point. Numerical results including the distribution of slip velocity, power factor and wear rate are provided and discussed.

How does friction affect sliding contact mechanics?

Jesús Sánchez Pinedo — Mon, 01 Jul 2024 13:27:50 +0200

Dealing with semi-infinite solids, interfacial friction is usually neglected, as normal and tangential displacements fields are independent on each other (unless material dissimilarity occurs). However, contacts involving a sufficiently thin layer, do not stick to such a simplified assumption, as thickness related normal/tangential coupling occurs, and surface frictional shear stresses do matter. It is the case, for instance, of classical rotary seals in boundary lubrication regimes, where rough frictional contacts between thin polymeric sealing lips and rotating shafts occur. Also, functional coatings to control the interface (adhesive, frictional, chemical, etc.) behavior, may be very thin and compliant, and usually experience frictional sliding during operation. All the same, these examples indicates that conditions exists where elastic coupling between in plane and out of plane displacements cannot be neglected and must be considered, instead. Here, we present our results on the rough contact mechanics of elastic and viscoelastic thin layers. We assume sliding conditions and friction at the interface, and we investigate the contact problem in the framework of linear (visco)elasticity, by relying on the Green’s functions approach. We show that, due to the friction and coupling, the presence of interfacial friction may lead to a significant increase of the contact area (up to 10%), compared to the frictionless case, which may affect specific functional response of the interface, such as electrical and thermal conductivity. Since the normal gap distribution is also affected by coupling and friction, the leak rate at the interface turns out significantly altered too. Coupling and friction also affect the contact pressure, which presents a certain degree of asymmetry leading (even for purely elastic materials) to an additional interlocking contribution to the tangential force opposing the relative motion at the interface. Therefore, the overall macroscale friction cannot be predicted by summing-up the local friction contributions occurring at microscale as commonly expected; it should instead include an additional coupled-induced term. The surface stress tensor is also affected, as due to friction and coupling very high tensile stresses are localized at the contact trailing edge, which are likely to induce material failure. In conclusion, we show that the common practice to neglect in-plane interactions in contact mechanics may lead to misleading tribological predictions.