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Data-driven discovery of interpretable Lagrangian of stochastically excited dynamical systems Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-05-09 Tapas Tripura, Satyam Panda, Budhaditya Hazra, Souvik Chakraborty
Exploring the intersection of deterministic and stochastic dynamics, this paper delves into Lagrangian discovery for conservative and non-conservative systems under stochastic excitation. Traditional Lagrangian frameworks, adept at capturing deterministic behavior, are extended to incorporate stochastic excitation. The study critically evaluates recent computational methodologies for learning Lagrangians
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Joint-mode diffusion analysis of spectral/hp continuous Galerkin methods: Towards superior dissipation estimates for implicit LES Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-05-09 R.C. Moura, L.D. Fernandes, A.F.C. da Silva, S.J. Sherwin
We present a new linear eigensolution analysis technique that provides superior estimates of dissipation distribution in wavenumber space for the continuous Galerkin (CG) method. The technique builds upon traditional dispersion–diffusion analyses that have been applied to spectral/hp element methods, but in particular is an improvement upon the non-modal eigenanalysis approach proposed by Fernandez
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Gradient preserving Operator Inference: Data-driven reduced-order models for equations with gradient structure Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-05-08 Yuwei Geng, Jasdeep Singh, Lili Ju, Boris Kramer, Zhu Wang
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Automated linear solver selection for simulation of multiphysics processes in porous media Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-05-08 Yury Zabegaev, Eirik Keilegavlen, Einar Iversen, Inga Berre
Porous media processes involve various physical phenomena such as mechanical deformation, transport, and fluid flow. Accurate simulations must capture the strong couplings between these phenomena. Choosing an efficient solver for a multiphysics problem usually entails the decoupling into subproblems related to separate physical phenomena. Then, the suitable solvers for each subproblem and the iteration
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A local multi-layer approach to modelling interactions between shallow water flows and obstructions Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-05-07 James Mckenna, Vassilis Glenis, Chris Kilsby
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A convex cone programming based implicit material point method Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-05-04 Xi-Wen ZHOU, Yin-Fu JIN, Kai-Yuan HE, Zhen-Yu YIN
For conventional Material Point Method (MPM), both explicit-based and implicit-based MPM have shortcomings: explicit MPM has high requirements on time steps, and implicit MPM has high requirements on convergence. To circumvent these limitations, this paper innovatively proposes a convex cone programming-based implicit MPM (CP-MPM) algorithm, which ensures excellent convergence of solving complex problems
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Data-driven hierarchical multiscale FDEM for simulating rock meso-macro mechanical behavior Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-05-03 Ruifeng Zhao, Zhijun Wu, Xiangyu Xu, Zhiyang Wang
This study presents a data-driven based hierarchical multiscale combined finite-discrete element method (DHM-FDEM) for accurately reproducing rock macro-scale mechanical behavior while ensuring acceptable computational costs. To construct the DHM-FDEM scheme, firstly, upscale finite elements assembly (UFEA) and upscale crack elements assembly (UCEA) are constructed, incorporating meso-scale finite
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Neural-Integrated Meshfree (NIM) Method: A differentiable programming-based hybrid solver for computational mechanics Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-05-03 Honghui Du, QiZhi He
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Bayesian conditional diffusion models for versatile spatiotemporal turbulence generation Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-05-03 Han Gao, Xu Han, Xiantao Fan, Luning Sun, Li-Ping Liu, Lian Duan, Jian-Xun Wang
Turbulent flows, characterized by their chaotic and stochastic nature, have historically presented formidable challenges to predictive computational modeling. Traditional eddy-resolved numerical simulations often require vast computational resources, making them impractical or infeasible for numerous engineering applications. As an alternative, deep learning-based surrogate models have emerged, offering
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A novel decoupled approach combining invertible cross-entropy method with Gaussian process modeling for reliability-based design and topology optimization Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-05-03 Thu Van Huynh, Sawekchai Tangaramvong, Bach Do, Wei Gao
Design optimization considering the presence of uncertainties in parameters poses an extremely challenging problem. The source of difficulties comes with reliability-based formulations, where addressing the probabilistic problem exhausts the large computing efforts for failure estimations of the structure violating limit-state functions (LSFs). This paper proposes a novel decoupled approach for effectively
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A robust finite strain isogeometric solid-beam element Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-05-02 Abdullah Shafqat, Oliver Weeger, Bai-Xiang Xu
In this work, an efficient and robust isogeometric three-dimensional solid-beam finite element is developed for large deformations and finite rotations with merely displacements as degrees of freedom. The finite strain theory and hyperelastic constitutive models are considered and B-Spline and NURBS are employed for the finite element discretization. Similar to finite elements based on Lagrange polynomials
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Prismatic-element SBPML coupled with SBFEM for 3D infinite transient wave problems Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-05-01 Guoliang Zhang, Mi Zhao, Junqi Zhang, Xiuli Du
In this paper, an enhanced prismatic-element scaled boundary perfectly matched layer (SBPML) is developed, which is a novel time-domain artificial boundary method for 3D infinite wave problems. The SBPML permits the utilization of an artificial boundary with general geometry and can consider planar physical surfaces and interfaces extending to infinity. Moreover, this enhancement enables the seamless
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A variational data assimilation approach for sparse velocity reference data in coarse RANS simulations through a corrective forcing term Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-30 Oliver Brenner, Justin Plogmann, Pasha Piroozmand, Patrick Jenny
The Reynolds-averaged Navier–Stokes (RANS) equations provide a computationally efficient method for solving fluid flow problems in engineering applications. However, the use of closure models to represent turbulence effects can reduce their accuracy. To address this issue, recent research has explored data-driven techniques such as data assimilation and machine learning.
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Modeling the large deformation failure behavior of unsaturated porous media with a two-phase fully-coupled smoothed particle finite element method Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-29 Ming Liu, Wenxiong Huang, Wei-Hai Yuan, Wei Zhang
In this paper, a computational framework based on the Smoothed Particle Finite Element Method is developed to study the coupled seepage-deformation process in unsaturated porous media. Governing equations are derived from the balance laws of solid and fluid phases considering partial saturation effects in porous media. Moreover, an hourglass control method is implemented to avoid the rank-deficiency
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Scalable computation of energy functions for nonlinear balanced truncation Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-29 Boris Kramer, Serkan Gugercin, Jeff Borggaard, Linus Balicki
Nonlinear balanced truncation is a model order reduction technique that reduces the dimension of nonlinear systems in a manner that accounts for either open- or closed-loop observability and controllability aspects of the system. A computational challenges that has so far prevented its deployment on large-scale systems is that the energy functions required for characterization of controllability and
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Pressure-stabilized fixed-stress iterative solutions of compositional poromechanics Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-29 Ryan M. Aronson, Nicola Castelletto, François P. Hamon, Joshua A. White, Hamdi A. Tchelepi
We consider the numerical behavior of the fixed-stress splitting method for coupled poromechanics as undrained regimes are approached. We explain that pressure stability is related to the splitting error of the scheme, not the fact that the discrete saddle point matrix never appears in the fixed-stress approach. This observation reconciles previous results regarding the pressure stability of the splitting
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Nonlinear topology optimization of flexoelectric soft dielectrics at large deformation Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-29 Xing Chen, Song Yao, Julien Yvonnet
We propose a novel nonlinear topology optimization framework tailored for flexoelectric soft dielectrics undergoing large deformation. A numerical method based on Isogeometric analysis (IGA) is introduced to nonlinear soft dielectrics at finite strain, ensuring the -continuity for flexoelectric problems. We outline the process of consistent linearizations and IGA discretizations. Additionally, we introduce
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A microstructure-based graph neural network for accelerating multiscale simulations Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-29 J. Storm, I.B.C.M. Rocha, F.P. van der Meer
Simulating the mechanical response of advanced materials can be done more accurately using concurrent multiscale models than with single-scale simulations. However, the computational costs stand in the way of the practical application of this approach. The costs originate from microscale Finite Element (FE) models that must be solved at every macroscopic integration point. A plethora of surrogate modeling
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Weak-form latent space dynamics identification Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-29 April Tran, Xiaolong He, Daniel A. Messenger, Youngsoo Choi, David M. Bortz
Recent work in data-driven modeling has demonstrated that a weak formulation of model equations enhances the noise robustness of a wide range of computational methods. In this paper, we demonstrate the power of the weak form to enhance the LaSDI (Latent Space Dynamics Identification) algorithm, a recently developed data-driven reduced order modeling technique.
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Adaptive Deep Fourier Residual method via overlapping domain decomposition Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-29 Jamie M. Taylor, Manuela Bastidas, Victor M. Calo, David Pardo
The Deep Fourier Residual (DFR) method is a specific type of variational physics-informed neural network (VPINN). It provides a robust neural network-based solution to partial differential equations (PDEs). The DFR strategy is based on minimizing the dual norm of the weak residual of a PDE, which is equivalent to minimizing the energy norm of the error. To compute the dual norm of the weak residual
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QuadWire: An extended one dimensional model for efficient mechanical simulations of bead-based additive manufacturing processes Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-27 Laurane Preumont, Rafaël Viano, Daniel Weisz-Patrault, Pierre Margerit, Grégoire Allaire
This paper presents the basis of a new mechanical model named dedicated to efficient simulations of bead-based additive manufacturing processes in which elongated beads undergoing significant cooling and eigenstrain are assembled to form 3D parts. The key contribution is to use a multi-particular approach containing 4 particles per material point to develop an extended 1D model capable of capturing
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Multi-failure mode reliability analysis method based on intelligent directional search with constraint feedback Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-27 Yue Zhang, Shaojun Feng, Hao Yang, Peng Hao, Bo Wang
Searching for the most probable point (MPP) by traditional methods cannot avoid the demand for accurate gradients of limit state functions (LSFs) essentially. It is difficult to calculate the accurate gradient of unknown high nonlinear LSFs. In this paper, a multi-failure mode reliability analysis method based on intelligent directional search with constraint feedback (IDS) is proposed, focusing on
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An FFT-based adaptive polarization method for infinitely contrasted media with guaranteed convergence Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-26 Karam Sab, Jérémy Bleyer, Sébastien Brisard, Martin Dolbeau
We propose an FFT-based iterative algorithm for solving the Lippmann–Schwinger equation in the context of periodic homogenization of infinitely contrasted linear elastic composites. Our work initially reformulates the Moulinec–Suquet, Eyre–Milton and Monchiet–Bonnet schemes using a residual formulation. Subsequently, we introduce an enhanced scheme, termed Adaptive Eyre–Milton (AEM), as a natural extension
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Dynamically configured physics-informed neural network in topology optimization applications Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-26 Jichao Yin, Ziming Wen, Shuhao Li, Yaya Zhang, Hu Wang
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Robust topology optimization for transient dynamic response minimization Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-25 Shubham Saurabh, Abhinav Gupta, Rajib Chowdhury, Pakeeruraju Podugu
This paper presents a novel framework for topology optimization (TO) of structures subjected to uncertain transient loading. Random transient uncertain but bounded loading is modeled in the form of an ellipsoid convex model. A transformation matrix is defined with uncertainty in input parameters based on orientation and lengths of semi-major and semi-minor axes of the ellipsoid. Latin hypercube sampling
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A Partition of Unity construction of the stabilization function in Nitsche’s method for variational problems Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-25 Pablo Jiménez Recio, Marc Alexander Schweitzer
In this paper we develop a partition-of-unity construction of the stabilization function required in Nitsche’s method, which can be seen as a generalization of the element-wise construction that is widely used in finite element methods. This allows for the use of Nitsche’s method within the Partition of Unity Method with a stabilization function that is not simply a constant over the whole boundary
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Robust and efficient implementation of finite strain generalized continuum models for material failure: Analytical, numerical, and automatic differentiation with hyper-dual numbers Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-25 Alexander Dummer, Matthias Neuner, Peter Gamnitzer, Günter Hofstetter
Generalized continuum models for representing nonlinear material behavior including material failure in the finite strain regime are commonly formulated based on scalar elastic and dissipation potential functions. The evolution of stresses and internal variables, i.e., the material state, is governed by partial derivatives of the potential functions with respect to deformation and stress measures.
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AK-Gibbs: An active learning Kriging model based on Gibbs importance sampling algorithm for small failure probabilities Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-25 Wei Zhang, Ziyi Zhao, Huanwei Xu, Xiaoyu Li, Zhonglai Wang
In engineering practices, it is a time-consuming procedure to estimate the small failure probability of highly nonlinear and dimensional limit state functions and Kriging-based methods are more effective representatives. However, it is an important challenge to construct the candidate importance sample pool for Kriging-based small failure probability analysis methods with multiple input random variables
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Anti-derivatives approximator for enhancing physics-informed neural networks Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-24 Jeongsu Lee
This study presents a novel strategy for constructing an approximator for arbitrary univariate functions. The proposed approximation utilizes the anti-derivatives of a Fourier series expansion for the presumed piecewise function, resulting in a remarkable feature that enables the simultaneous approximation of an arbitrary function and its anti-derivatives. These anti-derivatives can be employed to
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Enhanced fully resolved CFD-DEM-PBFM simulation of non-spherical particle–fluid interactions during hydraulic collection Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-24 Yefeng Yang, Yin Wang
The interactions between non-spherical particles and fluids are commonplace in both nature and engineering applications, such as deep-sea nodules hydraulic collection. However, accurately simulating granular particles with non-spherical shapes and gaining a deep understanding of the intricate mechanisms involved in fluid–particle interactions still pose significant challenges. In this study, the superquadric
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Recurrent neural network plasticity models: Unveiling their common core through multi-task learning Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-24 Julian N. Heidenreich, Dirk Mohr
Recurrent neural network models are known to be particularly suitable for data-driven constitutive modeling due to their built-in memory variables. The main challenge preventing their widespread application to engineering materials lies in their excessive need of data for training. Here, we postulate that RNN models of elasto-plastic solids feature a large common core that is shared by all materials
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Shape optimization of embedded solids using implicit Vertex-Morphing Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-23 Manuel Meßmer, Reza Najian Asl, Stefan Kollmannsberger, Roland Wüchner, Kai-Uwe Bletzinger
One of the biggest challenges in optimizing the shape of complex solids is the requirement to maintain a reasonable mesh quality not only at the boundary but also for the bulk discretization of the interior. Thus, additional regularization and, in many cases, re-meshing of the structure during the iterative process is unavoidable with a Lagrangian description. By tracking the shape update using an
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Phase field modeling of hyperelastic material interfaces –Theory, implementation and application to phase transformations Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-23 Hendrik Wilbuer, Patrick Kurzeja, Jörn Mosler
Interface mechanics can significantly govern the evolution of multiple phases on smaller scales, e.g., determining the properties of TWIP- and TRIP-steels, geopolymers or Li-ion batteries. The present contribution is specifically centered around the influence of interface elasticity on mechanically induced phase transformations. A geometrically exact finite element framework is developed for this purpose
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RiemannONets: Interpretable neural operators for Riemann problems Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-22 Ahmad Peyvan, Vivek Oommen, Ameya D. Jagtap, George Em Karniadakis
Developing the proper representations for simulating high-speed flows with strong shock waves, rarefactions, and contact discontinuities has been a long-standing question in numerical analysis. Herein, we employ neural operators to solve Riemann problems encountered in compressible flows for extreme pressure jumps (up to pressure ratio). In particular, we first consider the DeepONet that we train in
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In silico model of colon electromechanics for manometry prediction after laser tissue soldering Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-22 René Thierry Djoumessi, Pietro Lenarda, Alessio Gizzi, Simone Giusti, Pietro Alduini, Marco Paggi
The present study introduces an advanced multi-physics and multi-scale modeling approach to investigate in silico colon motility. We introduce a generalized electromechanical framework, integrating cellular electrophysiology and smooth muscle contractility, thus advancing a first-of-its-kind computational model of colon motility after intraluminal laser tissue soldering. The proposed theoretical framework
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Active learning-assisted multi-fidelity surrogate modeling based on geometric transformation Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-18 Chunlong Hai, Weiqi Qian, Wenzheng Wang, Liquan Mei
Multi-fidelity data are common in various scientific and engineering fields. High-fidelity data, often more accurate, come with greater expense, such as precision experimental testing or high-resolution simulation. Conversely, low-fidelity data are less accurate but more cost-effective. Multi-fidelity surrogate modeling, which integrates multi-fidelity data to build a model, is widely used for its
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A computational framework for large strain electromechanics of electro-visco-hyperelastic beams Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-17 Nasser Firouzi, Timon Rabczuk, Javier Bonet, Krzysztof Kamil Żur
In this paper, a new framework for large strain of electro-active viscoelastic polymeric beams is developed. The kinematical quantities of beam are derived, and then the constitutive equations of electromechanical beam are developed. To expand the formulation to viscoelastic regime, a generalization of quasi-linear viscoelasticity theory for electo-mechanical deformation is developed and called electro-mechanical
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Solving the discretised multiphase flow equations with interface capturing on structured grids using machine learning libraries Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-17 Boyang Chen, Claire E. Heaney, Jefferson L.M.A. Gomes, Omar K. Matar, Christopher C. Pain
This paper solves the discretised multiphase flow equations using tools and methods from machine-learning libraries. The idea comes from the observation that convolutional layers can be used to express a discretisation as a neural network whose weights are determined by the numerical method, rather than by training, and hence, we refer to this approach as Neural Networks for PDEs (NN4PDEs). To solve
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Convex model-based regularization method for force reconstruction Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-16 Qinghe Shi, Bochao Lin, Chen Yang, Kejun Hu, Wenqin Han, Zhenxian Luo
In the process of reconstructing structural forces, the influence of measurement errors and inherent model inaccuracies cannot be ignored. These errors exhibit a degree of correlation, and the presence of such correlation inevitably affects the quantification of uncertainties in force reconstruction. Objectively, the inherent ill-posed nature of structural inverse problems makes it difficult to obtain
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A conservative discontinuous-Galerkin-in-time (DGiT) multirate time integration framework for interface-coupled problems with applications to solid–solid interaction and air–sea models Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-16 Jeffrey M. Connors, Justin Owen, Paul Kuberry, Pavel Bochev
In this paper we extend the DGiT multirate framework, developed in Connors and Sockwell (2022) for scalar transmission problems, to a solid–solid interaction (SSI) problem involving two coupled elastic solids and a coupled air–sea model with the rotating, thermal shallow water equations. In so doing we aim to demonstrate the broad applicability of the mathematical theory and governing principles established
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Extreme sparsification of physics-augmented neural networks for interpretable model discovery in mechanics Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-16 Jan Niklas Fuhg, Reese Edward Jones, Nikolaos Bouklas
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GO-MELT: GPU-optimized multilevel execution of LPBF thermal simulations Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-15 Joseph P. Leonor, Gregory J. Wagner
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Multi-scale time-stepping of Partial Differential Equations with transformers Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-13 AmirPouya Hemmasian, Amir Barati Farimani
Developing fast surrogates for Partial Differential Equations (PDEs) will accelerate design and optimization in almost all scientific and engineering applications. Neural networks have been receiving ever-increasing attention and demonstrated remarkable success in computational modeling of PDEs, however; their prediction accuracy is not at the level of full deployment. In this work, we utilize the
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Isogeometric form finding of membrane shells by optimised Airy stress function Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-12 Claudia Chianese, Luciano Rosati, Francesco Marmo
A two-stage form-finding procedure, based on Isogeometric Analysis (IgA), is proposed to determine the configuration of shells having a prescribed planar footprint so as to carry applied loads in a state of purely membrane stresses. The boundary-value problem of a membrane shell is described by Pucher’s equation in terms of Airy stress function, external loads and shell mid-plane elevation. Within
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Nonlinear elasticity with the Shifted Boundary Method Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-11 Nabil M. Atallah, Guglielmo Scovazzi
We propose a new unfitted/immersed computational framework for nonlinear solid mechanics, which bypasses the complexities associated with the generation of CAD representations and subsequent body-fitted meshing. This approach allows to speed up the cycle of design and analysis in complex geometry and requires relatively simple computer graphics representations of the surface geometries to be simulated
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Maximum bound principle and non-negativity preserving ETD schemes for a phase field model of prostate cancer growth with treatment Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-11 Qiumei Huang, Zhonghua Qiao, Huiting Yang
Prostate cancer (PCa) is a significant global health concern that affects the male population. In this study, we present a numerical approach to simulate the growth of PCa tumors and their response to drug therapy. The approach is based on a previously developed model, which consists of a coupled system comprising one phase field equation and two reaction–diffusion equations. To solve this system,
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Eulerian formulation of the tensor-based morphology equations for strain-based blood damage modeling Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-11 Nico Dirkes, Fabian Key, Marek Behr
The development of blood-handling medical devices, such as ventricular assist devices, requires the analysis of their biocompatibility. Among other aspects, this includes , i.e., red blood cell damage. For this purpose, computational fluid dynamics (CFD) methods are employed to predict blood flow in prototypes. The most basic hemolysis models directly estimate red blood cell damage from fluid stress
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Accelerated computational micromechanics for solute transport in porous media Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-11 Mina Karimi, Kaushik Bhattacharya
Reactive transport in permeable porous media is relevant for a variety of applications, but poses a significant challenge due to the range of length and time scales. Multiscale methods that aim to link microstructure with the macroscopic response of geo-materials have been developed, but require the repeated solution of the small-scale problem and provide the motivation for this work. We present an
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Gappy AE: A nonlinear approach for Gappy data reconstruction using auto-encoder Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-10 Youngkyu Kim, Youngsoo Choi, Byounghyun Yoo
We introduce a novel data reconstruction algorithm known as Gappy auto-encoder (Gappy AE) to address the limitations associated with Gappy proper orthogonal decomposition (Gappy POD), a widely used method for data reconstruction when dealing with sparse measurements or missing data. Gappy POD has inherent constraints in accurately representing solutions characterized by slowly decaying Kolmogorov N-widths
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Analysis of ‘Investigating an extended multiphase flow model that includes specific interfacial area’, Computer Methods in Applied Mechanics and Engineering, 418:116594, 2024 Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-09 William G. Gray, Cass T. Miller
Comments are provided on the recent paper by Ebadi et al. (2024) which demonstrates that the formulated model that was solved contains misconceptions or errors that render the work unsuitable for describing the evolution of interfacial areas in two-fluid porous medium systems. The need for kinematic equations is described and components of a theoretically consistent approach are summarized.
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Stress-hybrid virtual element method on six-noded triangular meshes for compressible and nearly-incompressible linear elasticity Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-09 Alvin Chen, Joseph E. Bishop, N. Sukumar
In this paper, we present a first-order Stress-Hybrid Virtual Element Method (SH-VEM) on six-noded triangular meshes for linear plane elasticity. We adopt the Hellinger–Reissner variational principle to construct a weak equilibrium condition and a stress based projection operator. In each element, the stress projection operator is expressed in terms of the nodal displacements, which leads to a displacement
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A hybrid virtual element formulation for 2D elasticity problems Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-09 F.S. Liguori, A. Madeo, S. Marfia, E. Sacco
In this paper, a hybrid variational framework for the Virtual Element Method (VEM) is proposed and a family of polygonal elements for plane elasticity is developed. Under specific assumptions, it is proved that the minimization of Total Potential Energy and the projection operation typical of enhanced VEM can be deduced from the stationary condition of the Hellinger–Reissner mixed functional. Since
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A fast cosine transformation accelerated method for predicting effective thermal conductivity Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-08 Changqing Ye, Shubin Fu, Eric T. Chung
Predicting effective thermal conductivity by solving a Partial Differential Equation (PDE) defined on a high-resolution Representative Volume Element (RVE) is a computationally intensive task. In this paper, we tackle the task by proposing an efficient and implementation-friendly computational method that can fully leverage the computing power offered by hardware accelerators, namely, graphical processing
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Model order reduction of time-domain vibro-acoustic finite element simulations with poroelastic materials Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-08 Yinshan Cai, Sjoerd van Ophem, Wim Desmet, Elke Deckers
This paper presents a stability-preserving model reduction approach for a vibro-acoustic finite element model including poroelastic materials. Most of the research on these systems in the past was conducted in the frequency domain and there were less focus on the stability properties. However, with the increasing of interest in time-domain auralization and virtual sensing, stability-preserving model
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Sparse learning model with embedded RIP conditions for turbulence super-resolution reconstruction Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-08 Qinyi Huang, Wei Zhu, Feng Ma, Qiang Liu, Jun Wen, Lei Chen
In practical engineering scenarios, constraints arising from sensor placement, quantity, and the limitations of current testing technologies often lead to turbulence data characterized by low resolution and irregular structures. Turbulence super-resolution reconstruction is crucial for extracting finer details from irregularly structured, low-resolution measurement data, thereby facilitating comprehensive
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A multivariate level set method for concurrent optimization of graded lattice structures with multiple microstructure prototypes Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-08 Zhengtao Shu, Liang Gao, Hao Li
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Modeling the failure process of rock masses using a 3D nodal-based continuous-discontinuous deformation analysis method Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-06 Yang Xia, Yongtao Yang
A three-dimensional nodal-based continuous-discontinuous deformation analysis method (3D-NCDDAM) is developed in this study for modeling the failure process of rock masses. In the 3D-NCDDAM, four-node tetrahedral elements which can be automatally generated are used to discretize the problem domain. To reduce computational cost, and effectively model the failure process of rock masses at concerned regions
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A non-uniform rational B-splines (NURBS) based optimization method for fiber path design Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-06 Xuyu Zhang, Yi Min Xie, Cong Wang, He Li, Shiwei Zhou
This work presents a systematic optimization method utilizing non-uniform rational B-splines (NURBS) to represent fibers in composites and design their paths. Beyond mean compliance, the objective function incorporates repulsive energy to prevent fiber knots and intersections. Utilizing NURBS control points as design variables reduces the number of design variables and expands the solution space, as
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Adaptive scaled boundary finite element method for two/three-dimensional structural topology optimization based on dynamic responses Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-05 Rut Su, Xiaoran Zhang, Sawekchai Tangaramvong, Chongmin Song
This paper presents an efficient solution for designing the topology of structures that can withstand dynamic loads. The method, called image/stereolithography (STL)-based adaptive scaled boundary finite element (SBFE), is a novel approach to topology optimization (TO) that is particularly effective for designing two/three-dimensional structures. The SBFE-based TO algorithm adopts a bi-evolutionary
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CGKOA: An enhanced Kepler optimization algorithm for multi-domain optimization problems Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-05 Gang Hu, Changsheng Gong, Xiuxiu Li, Zhiqi Xu