Documents published in Scipedia

• Towards Computational Efficient Fully Coupled Aeroelastic Simulations of Turbomachinery Blades with TRACE and CalculiX

  M. Freimuth, C. Berthold, F. Herbst

  coupled2023.

  
  

  Abstract

  In the industrial design of turbomachinery blades their aeroelastic behaviour is most commonly investigated by methods, that use linearization or rely on unidirectional coupling. To circumvent the limitations of those methods a new fully coupled simulation in the time domain with the structural solver CalculiX and the turbomachinery CFD solver TRACE is currently developed and presented in this paper. The coupling library preCICE is chosen to couple the mentioned elaborated single physics solvers due to its focus on high performance computing applications. Within this work the preCICE adapter for CalculiX has recently been enabled to work with a special CalculiX method, that allows to investigate dedicated eigenforms of the blades or other structural objects. It is furthermore shown that the use of this CalculiX method can lead to a massive speedup for use cases, where the structural dynamics response can be well described by a piece-wise linear approximation within each increment. On the other hand a completely new preCICE adapter for TRACE has been developed and is introduced here. The preCICE-coupled system of CalculiX and TRACE is successfully validated against a TRACE-internal coupling approach by investigating a simple testcase with a NACA profile blade that shows good agreement.

  Abstract
  In the industrial design of turbomachinery blades their aeroelastic behaviour is most commonly investigated by methods, that use linearization or rely on unidirectional coupling. [...]

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• Implementing a Comprehensive Hydromechanical Model for Sedimentary Basins by Coupling a 3D Mechanical Code to a Classic Basin Fluid Flow Code with the PreCICE Library

  J. Gratien, D. Colombo, S. Chaisemartin, A. Pasteau, B. Vialey

  coupled2023.

  
  

  Abstract

  In this paper, we present a coupling strategy for the implementation of a comprehensive hydromechanical model for sedimentary basins [1]. The preCICE framework [2] has been used to efficiently implement an iterative coupling scheme between ArcTem, a sedimentary basin fluid flow code, and Code Aster [20], a finite element general purpose mechanical code. We discuss several issues related to different domain partitions used by the parallel codes, to the time evolutivity of the 3D meshes and to the different time stepping used by each code. We have used the preCICE framework communication system to efficiently exchange data between the codes and to interpolate fields between usually non-matching meshes. The iterative coupling scheme is managed by means of the functionalities provided by preCICE. We validate our coupled code on a real sedimentary basin study [19]. We evaluate the flexibility of the preCICE framework to efficiently implement advanced iterative coupling algorithms. We compare the performance of our solution to a standard approach based on a centralized supervisor controlling the coupling flow and using files to exchange data between the codes.

  Abstract
  In this paper, we present a coupling strategy for the implementation of a comprehensive hydromechanical model for sedimentary basins [1]. The preCICE framework [2] has been [...]

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• Ensemble Kalman Inversion for reduced Multi-scale Model via Deep-learning

  H. Bansal, Y. Hong, K. Veroy

  coupled2023.

  
  

  Abstract

  In the context of nonlinear multi-scale problems, the inverse estimation of macroscopic distribution of some microscopic parameters based on macroscopic measurements poses significant challenges. These challenges arise from (1) the high computational cost to solve the complex forward problem, and (2) the need for derivatives of the complex multi-scale forward model, which combines macro-scale and micro-scale simulations, both of which are typically nonlinear. To address these challenges, we propose a novel approach that combines ensemble Kalman inversion for derivative-free inverse estimation and a physics-informed deep learningbased model order reduction (DL-MOR) to accelerate the micro-scale simulation. We evaluate the performance of our method using a non-linear hyper-elastic model. The results demonstrate the effectiveness of DL-MOR in significantly speeding up the micro-scale simulation and enabling relatively accurate estimation of the microscopic parameter using only macro-scale boundary measurements.

  Abstract
  In the context of nonlinear multi-scale problems, the inverse estimation of macroscopic distribution of some microscopic parameters based on macroscopic measurements poses [...]

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• Non-Intrusive Model Order Reduction for Sintering Applications

  R. Dhopeshawar, H. Bansal, K. Veroy, H. Shi, D. Giuntini

  coupled2023.

  
  

  Abstract

  We are interested in thermo-mechanical problems arising in the context of a practically relevant manufacturing process called sintering. These models can be defined using a non-linear material model, namely the Skorohod-Olevsky Viscous Sintering (SOVS) constitutive model. This SOVS model is used to predict macroscopic sintering behavior, such as shrinkage and density evolution. Also, it relies on material properties such as temperature-dependent viscosity and surface tension. However, high-fidelity simulations of coupled, macroscopic, thermo-mechanical models are computationally intensive. Furthermore, developing reducedorder models addressing the non-linearities is challenging due to the history dependence and presence of internal variables. Performing parametric studies, optimization, real-time control, or parameter estimation for such problems, thus, becomes infeasible. In order to accelerate sintering simulations for such multi-query scenarios, a surrogate model is vital. Here, we present a non-intrusive reduced-order modelling framework based on proper orthogonal decomposition and Gaussian process regression. Furthermore, we discuss the performance of such a surrogate model using different metrics for the two-parameter Arrhenius-type viscosity function

  Abstract
  We are interested in thermo-mechanical problems arising in the context of a practically relevant manufacturing process called sintering. These models can be defined using [...]

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• Component-based model order reduction procedure for large scales Thermo-Hydro-Mechanical systems

  A. Iollo, G. Sambataro, T. Taddei

  coupled2023.

  
  

  Abstract

  In this work we develop a component-based model order reduction (CB-pMOR) procedure for a class of problems in nonlinear mechanics with internal variables. The work is motivated by applications to thermo-hydro-mechanical (THM) systems for radioactive waste disposal. The THM system is coupled, time-dependent, and highly nonlinear; furthermore, the solution to the problem depends on several parameters, which might be related to the geometric configuration (e.g. the number of repositories, their distance or their size) or the material properties of the medium. We investigate the effectiveness of the proposed method in terms of accuracy and computational costs for a two-dimensional THM system in the case of overlapping partitions

  Abstract
  In this work we develop a component-based model order reduction (CB-pMOR) procedure for a class of problems in nonlinear mechanics with internal variables. The work is motivated [...]

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• A Comparison Of Direct Solvers In FROSch Applied To Chemo-Mechanics

  A. Heinlein, B. Keifer, S. Prüger, O. Rheinbach, F. Röver

  coupled2023.

  
  

  Abstract

  Sparse direct linear solvers are at the computational core of domain decomposition preconditioners and therefore have a strong impact on their performance. In this paper, we consider the Fast and Robust Overlapping Schwarz (FROSch) solver framework of the Trilinos software library, which contains a parallel implementations of the GDSW domain decomposition preconditioner. We compare three different sparse direct solvers used to solve the subdomain problems in FROSch. The preconditioner is applied to different model problems; linear elasticity and more complex fully-coupled deformation diffusion-boundary value problems from chemomechanics. We employ FROSch in fully algebraic mode, and therefore, we do not expect numerical scalability. Strong scalability is studied from 64 to 4 096 cores, where good scaling results are obtained up to 1 728 cores. The increasing size of the coarse problem increases the solution time for all sparse direct solvers.

  Abstract
  Sparse direct linear solvers are at the computational core of domain decomposition preconditioners and therefore have a strong impact on their performance. In this paper, [...]

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• Coupled Spatio-Temporal Dynamics and Nonlocality in Advanced Mathematical Models for the Analysis of Complex Neurodegenerative Disease Pathologies

  S. Pal, R. Melik

  coupled2023.

  
  

  Abstract

  One in six of the world’s population has to deal with neurodegenerative disorders, and while medical devices exist to detect, prevent, and treat such disorders, some fundamentals of the progression of associated diseases remain ambiguous. In this contribution, we focus on Alzheimer’s disease (AD), where amyloid-beta (Aβ) and tau proteins are among the main contributors to the development or propagation of AD. The Aβ proteins clump together to form plaques and disrupt cell functions. Moreover, the abnormal chemical change in the brain helps to build sticky tau tangles that block the neuron’s transport system. Astrocytes generally maintain a healthy balance in the brain by clearing the Aβ toxic plaques. Even so, over-activated astrocytes release chemokines and cytokines and also react to pro-inflammatory cytokines, further increasing the production of Aβ. We have provided details of a novel coupled mathematical model that can capture astrocytes’ dual behaviour, emphasizing the importance of spatio-temporal coupling and nonlocality. We have demonstrated that the disease propagation depends on memory effects, that is the disease’s earlier status, which involves non-Markovian processes. We have explained how to integrate brain connectome data in the network model and to study this effect, as well as the dual role of astrocytes as a coupled phenomenon. Depending on toxic loads in the brain, we have also discussed details of the analysis of the neuronal damage in the brain. We have explained how the memory effect can slow down the propagation of toxic proteins in the brain, decreasing the rate of neuronal damage. Representative numerical examples have been given, and special attention has been paid to nonequilibrium considerations and stochastic modelling frameworks in the study of neurodegenerative diseases.

  Abstract
  One in six of the world’s population has to deal with neurodegenerative disorders, and while medical devices exist to detect, prevent, and treat such disorders, some [...]

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• Development of an FSI Environment for the Aerodynamic Performance Assessment of Flapping Wings

  R. Poletti, M. Barucca, L. Koloszar, M. Mendez, J. Degroote

  coupled2023.

  
  

  Abstract

  Insects and birds take advantage of their flexible wings to modulate the aerodynamic forces and increase their flight efficiency. A deep understanding of the aeroelastic benefits could be valuable to design Flapping Wing Micro Air Vehicles (FWMAVs) that exploit nature’s full potential. This work presents an open-source, high-fidelity, Fluid-Structure Interaction solver (FSI) to simulate flapping and deforming wings. The proposed approach uses the code CoCoNuT to couple the Computational Structural Mechanics (CSM) software Kratos Multiphysics with the Computational Fluid Dynamics (CFD) software OpenFOAM. The coupling code relies on the Interface Quasi-Newton with Inverse Jacobian method (IQNI). The CSM evaluates the wing deformation using a classic Finite Element Method with shell elements, while the CFD solver uses the deformable overset method. In the CFD solver, the deformation of the wing is interpolated onto the grid’s boundaries to accurately simulate wings with large motion and deformation. The FSI solver is tested in the case of an airfoil in heave motion and validated with experimental data. The results demonstrate the strong influence of wing deformation on its aerodynamic performance

  Abstract
  Insects and birds take advantage of their flexible wings to modulate the aerodynamic forces and increase their flight efficiency. A deep understanding of the aeroelastic benefits [...]

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• A numerical approach for the characterization of the free-fall dynamics of thin disks

  A. Lolli, G. Corsi

  coupled2023.

  
  

  Abstract

  This study focuses on investigating numerically the dynamics of thin disks freely falling inside viscous and incompressible fluids. We solve the model by using the finite element method and the main falling modes are identified using the Reynolds number and dimensionless inertia moment. The results are mapped in a phase diagram for comparison with existing literature and validation of the simulations. The effect of introducing a hole in the disk geometry is investigated, analyzing variations in trajectories and Strouhal numbers. Furthermore, falling styles are quantified by examining the fluctuations of the orientation of the axis of the disks over time. Additionally, a quantitative analysis of the solid body velocities for the fluttering and tumbling modes highlights the significant differences between them. The study successfully characterizes the main falling modes through qualitative and quantitative analyses.

  Abstract
  This study focuses on investigating numerically the dynamics of thin disks freely falling inside viscous and incompressible fluids. We solve the model by using the finite [...]

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• Efficient numerical methods for simulating cardiac electrophysiology with cellular resolution

  F. Chegini, A. Foehly, N. Huynh, L. Pavarino, M. Potse, S. Scacchi, M. Weiser

  coupled2023.

  
  

  Abstract

  The cardiac extracellular-membrane-intracellular (EMI) model enables the precise geometrical representation and resolution of aggregates of individual myocytes. As a result, it not only yields more accurate simulations of cardiac excitation compared to homogenized models but also presents the challenge of solving much larger problems. In this paper, we introduce recent advancements in three key areas: (i) the creation of artificial, yet realistic grids, (ii) efficient higher-order time stepping achieved by combining low-overhead spatial adaptivity on the algebraic level with progressive spectral deferred correction methods, and (iii) substructuring domain decomposition preconditioners tailored to address the complexities of heterogeneous problem structures. The efficiency gains of these proposed methods are demonstrated through numerical results on cardiac meshes of different sizes.

  Abstract
  The cardiac extracellular-membrane-intracellular (EMI) model enables the precise geometrical representation and resolution of aggregates of individual myocytes. As a result, [...]

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