Documents published in Scipedia

• Investigation of higher harmonic Lamb waves for facilitating delamination characterization

  A. Gangwar, D. Joglekar

  eccomas2022.

  
  

  Abstract

  We advance this field by systematically exploring nonlinear interactions of A0 Lamb mode signals with delamination defects of various sizes and interlaminar locations, to facilitate the characterization of delaminations. The interrogation signal, in this regard, is a modulated sinusoid whose frequency varies in steps of 20 kHz between 40 kHz and 100 kHz. Commercial FEM software is used for modelling the contact at delamination interfaces and for simulating the Lamb wave propagation through the waveguide with delamination defect. It is demonstrated that the intermittently acting contact pressure between the two surfaces of delamination acts as a source of nonlinearity, resulting in generation of higher order harmonics of interrogation frequency. A metric for measuring nonlinearity, the nonlinearity index (NI), is used to quantify the strength of wave-damage interactions over a range of interrogation frequencies. The NI index is observed to vary with both the interlaminar location as well as the width of the delamination. The maximum value of NI is further influenced by the frequency of excitation signal. To infer the effect of delamination parameters on the NI, a concept of a concept of contact energy intensity is introduced, which is largely dependent on the size and the interlaminar position of the delamination. The nonlinearity index patterns are explained by combining the intensity of the contact energy with the phase difference between waves traveling through the sub-laminations and the flexural rigidity of the two sub-laminates at the delamination location. The inferences provided can potentially be used for determining the interlaminar location and width of delamination employing higher harmonic Lamb wave signals generated by the breathing delamination.

  Abstract
  We advance this field by systematically exploring nonlinear interactions of A0 Lamb mode signals with delamination defects of various sizes and interlaminar locations, to [...]

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• Forming effects on high-cycle fatigue in an aluminum sheet structure using the Ottosen-Stenström-Ristinmaa model

  S. Tveit, A. Reyes, E. Erduran

  eccomas2022.

  
  

  Abstract

  In this paper a multi-stage numerical analysis is presented with the aim to investigate effects of forming on the high-cycle fatigue performance of a deep-drawn aluminum sheet structure for use in a floating photovoltaic system. Forming simulations of a subsection of the full geometry are performed in a realistic two-step drawing-springback cycle. A simplified global analysis of service load response is performed to obtain displacements at the submodel boundary, that are used to generate boundary conditions for a local service load analysis. The local analysis is then performed on three different models of the subsection: (I) excluding forming effects, (II) including effects of thinning, and (III) including effects of thinning and residual stresses. The critical location with respect to the fatigue limit criterion in the Ottosen-Stenström-Ristinmaa high-cycle fatigue model was identified for case (I), and this location was used to compare the different models to assess effects of forming on high-cycle fatigue performance. Furthermore, the dynamic friction coefficient , and the isotropickinematic mixing coefficient were varied in order to investigate their respective effects.

  Abstract
  In this paper a multi-stage numerical analysis is presented with the aim to investigate effects of forming on the high-cycle fatigue performance of a deep-drawn aluminum sheet [...]

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• Using artificial intelligence techniques for the accurate estimation of the ultimate pure bending of steel circular tubes

  M. Ben Seghier, V. Plevris, G. Solorzano

  eccomas2022.

  
  

  Abstract

  In this paper, the potential of building more accurate and robust models for the prediction of the ultimate pure bending capacity of steel circular tubes using artificial intelligence techniques is investigated. Therefore, a database consisting of 104 tests for fabricated and cold-formed steel circular tubes are collected from the open literature and used to train and validate the proposed data-driven approaches which include the Random Forest methodology in two variants: the original version in which the control parameters are manually updated, and an enhanced RF-PSO variant, where Particle Swarm Optimization is used for optimizing these parameters. The data set has four input parameters, namely the tube thickness, tube diameter, yield strength of steel and steel elasticity modulus, while the ultimate pure bending capacity is considered as the target output variable. The obtained results are compared to the real test values through various statistical indicators such as the root mean square error and the coefficient of determination. The results indicate that the proposed enhanced model can provide an accurate solution for modelling the complex behavior of steel circular tubes under pure bending conditions.

  Abstract
  In this paper, the potential of building more accurate and robust models for the prediction of the ultimate pure bending capacity of steel circular tubes using artificial [...]

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• Multi-physical modelling and analysis of lubricated transmissions using a coupled finite element approach

  S. Neeckx, B. Blockmans, W. Rottiers, W. Desmet

  eccomas2022.

  
  

  Abstract

  Lubrication is vital to improve performance, efficiency and durability in transmissions; it serves to minimize wear, noise, vibration and friction. The proper functioning of lubricated transmissions relies on interactions amongst a wide range of physical phenomena (contact dynamics, fluid-structure interaction and heat transfer) operating at different spatial and temporal scales. A lubricated transmission model is proposed in this work to obtain accurate information regarding all physical domains and the coupling thereof to quantify performance, efficiency and durability. The model consists of a thermo-elastically coupled flexible multibody model for the gear pair and a Thermo-Elasto-Hydrodynamic Lubrication (TEHL) model for the lubricant, both based on first principle modelling to ensure high fidelity.

  Abstract
  Lubrication is vital to improve performance, efficiency and durability in transmissions; it serves to minimize wear, noise, vibration and friction. The proper functioning [...]

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• Advanced deep learning comparisons for non-invasive tunnel lining assessment from ground penetrating radar profiles

  M. Rosso, G. Marasco, L. Tanzi, S. Aiello, A. Aloisio, R. Cucuzza, B. Chiaia, G. Cirrincione, G. Marano

  eccomas2022.

  
  

  Abstract

  Innovative, automated, and non-invasive techniques have been developed by scientific community to indirectly assess structural conditions and support the decision-making process for a worthwhile maintenance schedule. Nowadays, machine learning tools are in the spotlight because of their outstanding capabilities to deal with data coming from even heterogeneous sources and their ability to extract information from the structural systems, providing highly effective, reliable, and efficient damage classification tools. In the current study, a supervised multi-level damage classification strategy has been developed regarding Ground Penetrating Radar (GPR) profiles for the assessment of tunnel lining conditions. In previous research, the authors firstly considered a convolutional neural network (CNN), adopting the quite popular ResNet-50, initialized through transfer learning. In the present work, further enhancements have been attempted by adopting two configurations of the newest state-of-art advanced neural architectures: the neural transformers. The foremost is the original Vision Transformer (ViT), whose core is an encoder entirely based on the innovative self-attention mechanism and does not rely on convolution at all. The second is an improvement of ViT which merges convolution and self-attention, the Compact Convolution Transformer (CCT). In conclusion, a critical discussion of the different pros and cons of adopting the above-mentioned different architectures is finally provided, highlighting the actual powerfulness of these technologies in the future civil engineering paradigm nevertheless.

  Abstract
  Innovative, automated, and non-invasive techniques have been developed by scientific community to indirectly assess structural conditions and support the decision-making process [...]

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• Thermal and structural modelling of thermoset composite repairs towards optimization of the cure cycle for minimum distortion

  T. Koenis, N. van Hoorn, M. Moghadasi

  eccomas2022.

  
  

  Abstract

  During manufacturing of composite materials residual stresses can result in distortion of the final part. This distortion is even more critical when building on existing parts, such as for a repair, as the added material has to conform to the original structure. To predict this distortion due to curing of thermoset carbon-matrix composite repairs, a numerical modelling method is employed. The temperature cycle applied for the cure of thermoset composites can significantly influence the amount of residual stress and resulting deformation after manufacturing. Therefore, a method is devised to parametrise and subsequently tune this temperature cycle for minimum distortion after manufacturing. Numerical tests with the optimised temperature cycle resulted in a 36% reduction in process induced strain for a repair of a flat laminate plate. The same methodology is applied to a wing box consisting of two composite skins connected by two C-spars where a scarf repair is applied in the skin. The repair patch is locally heated by a heating blanket to cure the repair patch. A subsequent thermalmechanical model is used to investigate the amount of residual stress and strain after cure and the influence of underlying structural elements on the repair. The developed framework can support the patch fabrication with accurate design and analysis for repairs with minimum distortion. Which in turn will result in development of cost-effective composite repairs.

  Abstract
  During manufacturing of composite materials residual stresses can result in distortion of the final part. This distortion is even more critical when building on existing parts, [...]

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• A machine learning based approach to predict the stress intensity factors in 2D linear elastic fracture mechanics

  P. Zhang

  eccomas2022.

  
  

  Abstract

  Within the framework of linear elastic fracture mechanics, the stress intensity factors (SIFs) are the mostly applied crack-tip characterizing parameters. To obtain the SIFs, approximate formulae are widely used because exact analytical solutions are available only for very simple geometrical and loading configurations [1]. However, even approximate solutions for SIFs are also rather limited to very simple geometrical and loading conditions. In this work, an accurate and efficient SIF prediction model based on Physics-informed neural network (PINN) [4] is developed, where we incorporate the equilibrium equations and constitutive relations into the PINN. In order to capture the singular behavior of the stress and displacement fields around the crack tip, we extend the standard PINN structure by adding two more trainable parameters

  Abstract
  Within the framework of linear elastic fracture mechanics, the stress intensity factors (SIFs) are the mostly applied crack-tip characterizing parameters. To obtain the SIFs, [...]

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• Dynamic characterization of offshore wind turbines supported on a jacket using Artificial Neural Networks

  R. Quevedo-Reina, G. Álamo, L. Padrón, J. Aznárez

  eccomas2022.

  
  

  Abstract

  The dynamic characterization of an offshore wind turbine (OWT) and its foundation is an important task within the design stage of the support structure. The system fundamental frequency should not coincide with that of the loads that affect it, otherwise resonance phenomena can conclude with the collapse of the structure or its deterioration due to fatigue. Assuming a linear behaviour, the system fundamental frequency can be computed by solving the eingenvalue problem after defining the stiffness and mass global matrices. This procedure can become computationally expensive if soil-structure interaction (SSI) phenomena is taken into account. For this reason, a surrogate model based on Artificial Neural Networks (ANN) is proposed for estimating the fundamental frequency of the wind turbine assembly, jacket support structure and pile foundation considering SSI effects. A dataset is generated to train the ANN. This synthetic data collects the characteristics of the OWT-jacket-foundation and its fundamental frequency, which is obtained by a finite element substructuring model. The SSI is reproduced through impedance functions computed through a previously developed continuum model. Comparing the predictions of the ANN with the results obtained by the structural model, it is observed that this type of regression allows to reproduce in a sufficiently precise way the dependence of the fundamental frequency with respect to the variables that define the system. Thus the use of Machine Learning techniques, such as ANNs, makes it possible to take into account the system behaviour obtained by rigorous models in large-scale calculations that it would be unfeasible otherwise.

  Abstract
  The dynamic characterization of an offshore wind turbine (OWT) and its foundation is an important task within the design stage of the support structure. The system fundamental [...]

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• Postbuckling failure mechanism of square aluminum plates under shear loading

  C. Kalfountzos, G. Bikakis, E. Theotokoglou

  eccomas2022.

  
  

  Abstract

  Buckling is very important for structural design, especially when the structure is thin-walled. Slender plates are widely used as structural members in civil, naval and aerospace engineering and they are able to carry considerable additional loads in the postbuckling range. Engineers take advantage of this postbuckling reserve and design structures that are allowed to buckle and operate below their ultimate loads for further weight and cost reduction. Consequently, both buckling and postbuckling behavior of slender plates are of critical importance during the design of lightweight thin-walled engineering structures.

  Abstract
  Buckling is very important for structural design, especially when the structure is thin-walled. Slender plates are widely used as structural members in civil, naval and aerospace [...]

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• New predictive models for ballistic limit of spacecraft honeycomb-core sandwich panels subjected to hypervelocity impact

  R. Carriere, A. Cherniaev

  eccomas2022.

  
  

  Abstract

  Parameters of the honeycomb core (such as cell size and foil thickness), as well as the material of the core, influence the ballistic performance of honeycomb-core sandwich panels in cases of hypervelocity impact (HVI) by orbital debris. Two predictive models capable of accounting for this influence have been developed in this study: one utilized a conventional approach based on a dedicated ballistic limit equation, while the other employed an artificial neural network trained to predict the outcomes of HVI on HCSP. BLE fitting and ANN training were conducted using a database composed of 46 numerical experiments, performed with a validated numerical model and ten physical tests derived from the literature. The new ballistic limit equation is based on the Whipple shield BLE, in which the standoff distance between the facesheets was replaced by a function of the honeycomb cell size, foil thickness, and yield strength of the HC material. The corresponding fit factors were determined by minimizing the sum of squared errors between the BLE predictions and the results of HVI tests listed in the database. The BLE was then tested against a new set of simulation data and demonstrated an excellent predictive accuracy, with the discrepancy ranging from 1.13% to 5.58% only. The artificial neural network was developed using MATLAB's Deep Learning Toolbox framework and was trained utilizing the same HCSP HVI database as was employed for the BLE fitting. The ANN demonstrated a very good predictive accuracy, when tested against a set of simulation data not previously used in the training of the network, with the discrepancy ranging from 0.67% to 7.27%. Both of the developed predictive models (the BLE and the ANN) are recommended for use in the design of orbital debris shielding for spacecraft, involving honeycomb-core sandwich panels.

  Abstract
  Parameters of the honeycomb core (such as cell size and foil thickness), as well as the material of the core, influence the ballistic performance of honeycomb-core sandwich [...]

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