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Large rotation isogeometric shell model for alternating stiff/soft curved laminates including warping and interlayer thickness change Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-12 Leonardo Leonetti, Domenico Magisano, Giovanni Garcea
The mechanics of laminates made up of elastic alternating stiff/soft layers is dominated by bending and membrane actions in the stiff layers, while transverse shear deformations concentrate in the soft interlayers producing significant zigzag warping effects. Additionally, curved geometry and large deformations can induce an interlayer thickness strain affecting the overall response of the laminate
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A physics-informed GAN framework based on model-free data-driven computational mechanics Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-12 Kerem Ciftci, Klaus Hackl
Model-free data-driven computational mechanics, first proposed by Kirchdoerfer and Ortiz, replace phenomenological models with numerical simulations based on sample datasets in strain–stress space. In this study, we integrate this paradigm within physics-informed generative adversarial networks (GANs). We enhance the conventional physics-informed neural network framework by implementing the principles
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Gradient-annihilated PINNs for solving Riemann problems: Application to relativistic hydrodynamics Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-12 Antonio Ferrer-Sánchez, José D. Martín-Guerrero, Roberto Ruiz de Austri-Bazan, Alejandro Torres-Forné, José A. Font
We present a novel methodology based on Physics-Informed Neural Networks (PINNs) for solving systems of partial differential equations admitting discontinuous solutions. Our method, called Gradient-Annihilated PINNs (GA-PINNs), introduces a modified loss function that forces the model to partially ignore high-gradients in the physical variables, achieved by introducing a suitable weighting function
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Bayesian reinforcement learning reliability analysis Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-12 Tong Zhou, Tong Guo, Chao Dang, Michael Beer
A Bayesian reinforcement learning reliability method that combines Bayesian inference for the failure probability estimation and reinforcement learning-guided sequential experimental design is proposed. The reliability-oriented sequential experimental design is framed as a finite-horizon Markov decision process (MDP), with the associated utility function defined by a measure of epistemic uncertainty
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A robust optimization framework for design of robotic system with kinematic and dynamic criteria Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-12 Shuoshuo Shen, Dequan Zhang, Xu Han, Chao Jiang, Qing Li
Industrial robot, as one class of digitalized intelligent equipment, plays a significant role in enhancing production efficiency and quality through implementing desired kinematic precision and reliable performance for modern high-tech industries. This study proposes a robust optimization framework to account for the kinematic and dynamic uncertainties in industrial robotic systems. The design objective
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Topology optimization for rigid and compliant hybrid mechanisms Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-11 Shuhao Xia, Tao Gong, Bicheng Chen, Xianmin Zhang, Nianfeng Wang
This paper proposes a topology optimization method integrating the variable trajectory constraints to design rigid and compliant hybrid mechanisms. The variable trajectory constraints are distributed in the design domain and are uniformly modeled by nonlinear spring model. Each of the variable trajectory constraints has an active or inactive state and different states map various mechanical properties
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Numerical modeling of ferroelectric materials in the presence of flexoelectricity Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-11 Prince Henry Serrao, Sergey Kozinov
Higher-order two-way electromechanical coupling between strain gradients and electric field, known as flexoelectricity, has a strong influence on the micro- and nanoelectromechanical devices characterization as it is highly pronounced on smaller scales. Flexoelectricity in dielectrics and piezoelectrics has been well analyzed recently, however, its influence on the behavior of ferroelectric materials
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Peridynamic analysis of thermal behaviour of PCM composites for heat storage Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-10 Petr Nikolaev, Andrey P. Jivkov, Marius Fifre, Majid Sedighi
One possibility to utilize excess energy from electricity generation or other industrial processes is to use thermal energy storage systems based on phase change materials (PCM). These systems can accumulate and release significant amounts of heat energy during the phase transitions. The volume and properties of PCM undergo rapid changes during the transitions, creating strong physical non-linearities
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Prediction of microstructural-dependent mechanical properties, progressive damage, and stress distribution from X-ray computed tomography scans using a deep learning workflow Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-10 Mohammad Rezasefat, Haoyang Li, James D. Hogan
Creating computationally efficient models that link processing methods, material structures, and properties is essential for the development of new materials. Translating microstructural details to macro-level mechanical properties often proves to be an arduous challenge. This paper introduces a novel deep learning-based framework to predict 3D material stress fields, mechanical behavior, and progressive
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An efficient and easy-to-implement recovery-based a posteriori error estimator for isogeometric analysis of the Stokes equation Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-07 Abdullah Abdulhaque, Trond Kvamsdal, Kjetil André Johannessen, Mukesh Kumar, Arne Morten Kvarving
In this article, we present an recovery-based a posteriori error estimator for adaptive isogeometric analysis (IGA) of the Stokes equation, which is straightforward to implement. The increased regularity of splines in IGA versus Lagrange polynomials used in the classical finite element method plays a significant role for the success of the presented recovery technique. We consider LR B-splines (Johannessen
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Multi-scale identification of composite using modified constitutive relation error: Formulation and numerical study Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-07 Shaojuan Huang, Pierre Feissel, Pierre Villon
This paper investigates a new multi-scale approach, in which only one scale measurement is used to simultaneously identify the micro heterogeneous properties of composite at the measurement level and the macro homogeneous ones at the specimen level. Since the lack of information outside the measurement zone prevents identifying properties in this area, many multi-scale identification methods require
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Modeling the dynamic fracture of concrete — A robust, efficient, and accurate mesoscale description Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-07 Christoph Grunwald, Werner Riedel, Martin Sauer, Alexander Stolz, Stefan Hiermaier
Dynamic loading of concrete often leads to excessive cracking and fragmentation. Since these processes are massively influenced by the underlying heterogeneity of the material, discrete modeling of the lower-scale features strongly improves the simulation results. At the same time, the computational effort is greatly increased. We therefore propose here a mesomechanical simulation approach which is
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Enhancing nonlinear solvers for the Navier–Stokes equations with continuous (noisy) data assimilation Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-06 Bosco García-Archilla, Xuejian Li, Julia Novo, Leo G. Rebholz
We consider nonlinear solvers for the incompressible, steady (or at a fixed time step for unsteady) Navier–Stokes equations in the setting where partial measurement data of the solution is available. The measurement data is incorporated/assimilated into the solution through a nudging term addition to the Picard iteration that penalized the difference between the coarse mesh interpolants of the true
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The subdivision-based IGA-EIEQ numerical scheme for the Navier–Stokes equations coupled with Cahn–Hilliard phase-field model of two-phase incompressible flow on complex curved surfaces Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-05 Qing Pan, Yunqing Huang, Timon Rabczuk, Xiaofeng Yang
We develop an accurate and robust numerical scheme for solving the incompressible hydrodynamically coupled Cahn–Hilliard system of the two-phase fluid flow system on complex surfaces. Our algorithm leverages a number of efficient techniques, including the subdivision-based isogeometric analysis (IGA) method for spatial discretization, the explicit Invariant Energy Quadratization (EIEQ) method for linearizing
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A Phase-Field Discrete Element Method to study chemo-mechanical coupling in granular materials Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-05 Alexandre Sac-Morane, Manolis Veveakis, Hadrien Rattez
This paper presents an extension of the discrete element method using a phase-field formulation to incorporate grain shape and its evolution. The introduction of a phase variable enables an effective representation of grain geometry and facilitates the application of physical laws, such as chemo-mechanical couplings, for modeling shape changes. These physical laws are solved numerically using the finite
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Arbitrary polygon-based CSFEM-PFCZM for quasi-brittle fracture of concrete Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-05 Yu-jie Huang, Zhi-shan Zheng, Feng Yao, Chen Zeng, Hui Zhang, Sundararajan Natarajan, Shi-lang Xu
In recent years, engineering and research communities have shown a growing interest in polygon elements due to their adaptability to complex geometries. However, their applicability for investigating the quasi-brittle damage and fracture of concrete structures is still an open question. This work thus develops a numerical framework to integrate the phase-field regularized cohesive zone model (PFCZM)
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A nested non-invasive stochastic isogeometric method for the deformation of porous functionally graded material plates with high-dimensional material uncertainties Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-05 Junli Guo, Yahui Zhang
The stochastic isogeometric analysis (SIGA) based on random geometric methods plays a crucial role in structural random analysis. However, existing intrusive SIGA methods suffer from limitations of low applicability or efficiency. On the other hand, current material uncertainties are mostly limited to low dimensions and often ignore spatial correlations. Therefore, we propose an efficient and adaptive
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Learning mesh motion techniques with application to fluid–structure interaction Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-02 Johannes Haubner, Ottar Hellan, Marius Zeinhofer, Miroslav Kuchta
Mesh degeneration is a bottleneck for fluid–structure interaction (FSI) simulations and for shape optimization via the method of mappings. In both cases, an appropriate mesh motion technique is required. The choice is typically based on heuristics, e.g., the solution operators of partial differential equations (PDE), such as the Laplace or biharmonic equation. Especially the latter, which shows good
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Isogeometric collocation for solving the biharmonic equation over planar multi-patch domains Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-02 Mario Kapl, Aljaž Kosmač, Vito Vitrih
We present an isogeometric collocation method for solving the biharmonic equation over planar bilinearly parameterized multi-patch domains. The developed approach is based on the use of the globally -smooth isogeometric spline space (Kapl and Vitrih, 2021) to approximate the solution of the considered partial differential equation, and proposes as collocation points two different choices, namely on
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A nonlocal hybrid model for elasto-plastic fracture of rock-like materials Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-02 Haitao Yu, Xiaokun Hu, Antonio Bobet, Xiao Yan
An unsolved challenge for successful simulations of rock-like materials is how to describe the entire fracture process, including the transition from elasto-plastic to strain-softening behavior and crack propagation. A novel nonlocal hybrid model (NHM) based on the Hoek-Brown criterion is proposed to characterize the combined elasto-plastic and fracture behaviors of rock-like materials. The proposed
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Hutchinson Trace Estimation for high-dimensional and high-order Physics-Informed Neural Networks Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-01 Zheyuan Hu, Zekun Shi, George Em Karniadakis, Kenji Kawaguchi
Physics-Informed Neural Networks (PINNs) have proven effective in solving partial differential equations (PDEs), especially when some data are available by seamlessly blending data and physics. However, extending PINNs to high-dimensional and even high-order PDEs encounters significant challenges due to the computational cost associated with automatic differentiation in the residual loss function calculation
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A simple and efficient hybrid discretization approach to alleviate membrane locking in isogeometric thin shells Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-01 Roger A. Sauer, Zhihui Zou, Thomas J.R. Hughes
This work presents a new hybrid discretization approach to alleviate membrane locking in isogeometric finite element formulations for Kirchhoff–Love shells. The approach is simple, and requires no additional dofs and no static condensation. It does not increase the bandwidth of the tangent matrix and is effective for both linear and nonlinear problems. It combines isogeometric surface discretizations
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Self-consistency Reinforced minimal Gated Recurrent Unit for surrogate modeling of history-dependent non-linear problems: Application to history-dependent homogenized response of heterogeneous materials Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-01 Ling Wu, Ludovic Noels
Multi-scale simulations can be accelerated by substituting the meso-scale problem resolution by a surrogate trained from off-line simulations. In the context of history-dependent materials, Recurrent Neural Networks (RNN) have widely been considered to act as such a surrogate, since their hidden variables allow for a memory effect.
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Denoising diffusion-based synthetic generation of three-dimensional (3D) anisotropic microstructures from two-dimensional (2D) micrographs Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-01 Kang-Hyun Lee, Gun Jin Yun
Integrated computational materials engineering (ICME) has significantly enhanced the systemic analysis of the relationship between microstructure and material properties, paving the way for developing high-performance materials. However, analyzing microstructure-sensitive material behavior remains challenging due to the scarcity of three-dimensional (3D) microstructure datasets. Moreover, this challenge
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Coupled peridynamic model for geometrically nonlinear deformation and fracture analysis of thin shell structures Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-01 Guojun Zheng, Bolin Zhang, Yang Xia, Guozhe Shen
In this work, a coupling shell model that integrates peridynamics and classical continuum mechanics (CCM) to analyze the fracture process of thin shell structures under geometrically nonlinear deformations is developed. First, the updated Lagrangian formulation is used to derive the formulations of virtual strain energy density under each geometrically nonlinear incremental step, and then the constitutive
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Efficient and exquisite concurrent optimization of hierarchical structures with non-uniform eccentric body centered cubic lattice Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-01 Zhendong Yang, Changdong Zhang, Wenhe Liao, Tingting Liu, Hao Yang
To obtain lightweight structures with desirable mechanical performances and to enlarge the design space, this work presents an innovative concurrent optimization methodology for the precise inverse design of micro lattices and the modeling of hierarchical structures. The inverse design of micro lattices with continuously changing anisotropic properties is achieved by offsetting the center nodes of
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The kinematic-constraint-inspired non-ordinary state-based peridynamics with fractional viscoelastic-viscoplastic constitutive model to simulating time-dependent deformation and failure of rocks Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-01 Da-Lang Tian, Xiao-Ping Zhou
An in-depth understanding of the time-dependent deformation and failure behavior of rocks is essential for the long-term stability of surrounding rock mass around underground engineering. In this paper, the kinematic-constraint-inspired non-ordinary state-based peridynamic (KCNOSBPD) formulation is proposed to incorporate time dependence by adopting a fractional viscoelastic-viscoplastic (FVEVP) constitutive
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Reformulation for stress topology optimization of continuum structures by floating projection Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-01 Xiaodong Huang, Weibai Li, Khodamorad Nabaki, Xiaolei Yan
Stress topology optimization of continuum structures is an important topic for structural design and has been widely investigated. However, stress topology optimization itself is ill-conditioned and the resulting optimized designs highly depend on the used parameters, mesh size, and so on. In this paper, stress minimization and stress-constrained topology optimization problems are reformulated by introducing
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A stabilization-free Virtual Element Method based on divergence-free projections Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-29 Stefano Berrone, Andrea Borio, Francesca Marcon
In this paper, we propose and analyze a Stabilization Free Virtual Element Method (SFVEM), that allows the definition of bilinear forms that do not require an arbitrary stabilization term, thanks to the exploitation of higher-order polynomial projections on divergence free vectors of polynomials. The method is introduced in the lowest order formulation for the Poisson problem. We provide a sufficient
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Accelerated finite volume schemes for dynamic convection-dominant power-law fluid flows Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-28 Felipe A. Díaz, Roberto C. Cabrales, Ernesto Castillo, Nelson O. Moraga
This work evaluates the benefits of using the Anderson acceleration method in a finite volume context to solve convective-dominant power-law non-Newtonian fluid flows. We use a pressure-correction fractional step approach adapted to the finite volume method as a segregation strategy with the Picard linearization scheme. We evaluate the accuracy and performance of the Anderson method when we use TVD
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Lagrangian operator inference enhanced with structure-preserving machine learning for nonintrusive model reduction of mechanical systems Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-28 Harsh Sharma, David A. Najera-Flores, Michael D. Todd, Boris Kramer
Complex mechanical systems often exhibit strongly nonlinear behavior due to the presence of nonlinearities in the energy dissipation mechanisms, material constitutive relationships, or geometric/connectivity mechanics. Numerical modeling of these systems leads to nonlinear full-order models that possess an underlying Lagrangian structure. This work proposes a Lagrangian operator inference method enhanced
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A comparison of Algebraic Multigrid Bidomain solvers on hybrid CPU–GPU architectures Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-27 Edoardo Centofanti, Simone Scacchi
The numerical simulation of cardiac electrophysiology is a highly challenging problem in scientific computing. The Bidomain system is the most complete mathematical model of cardiac bioelectrical activity. It consists of an elliptic and a parabolic partial differential equation (PDE), of reaction–diffusion type, describing the spread of electrical excitation in the cardiac tissue. The two PDEs are
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Machine learning aided uncertainty quantification for engineering structures involving material-geometric randomness and data imperfection Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-26 Qihan Wang, Di Wu, Guoyin Li, Zhenyu Liu, Jingzhong Tong, Xiaojun Chen, Wei Gao
In real-world engineering, uncertainty is ubiquitous within material properties, structural geometry, load conditions, and the like. These uncertainties have substantial impacts on the estimation of structural performance. Furthermore, information or datasets in real life commonly contain imperfections, e.g., noise, outliers, or missing data. To quantify these impacts induced by uncertainties on structural
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An extended isogeometric boundary element formulation for three-dimensional linear elastic fracture mechanics Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-26 Matheus Rocha, Jon Trevelyan, Edson Denner Leonel
This paper presents a novel extended isogeometric boundary element formulation (XIGABEM) for three-dimensional linear elastic fracture mechanics. The formulation utilises the Dual BEM to accommodate coincident geometries for opposing crack surfaces, and inherits the well-known advantages of the NURBS basis as other isogeometric implementations. The originality herein involves the extension of the above-mentioned
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Topology optimization method for continuous fiber reinforced composites with different moduli in tension and compression Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-26 Zheng Qiu, Quhao Li, Shutian Liu
Continuous fiber-reinforced composite (CFRC) materials may exhibit different moduli in tension and compression. This nonlinear behavior significantly impacts the optimal design of CFRC structures. However, current topology optimization methods for CFRCs primarily rely on the assumption of uniform moduli, leading to inaccurate analyzes and suboptimal results. In this paper, a novel concurrent topology
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Solving the complete pseudo-impulsive radiation and diffraction problem using a spectral element method Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-26 Jens Visbech, Allan P. Engsig-Karup, Harry B. Bingham
This paper presents a novel, efficient, high-order accurate, and stable spectral element-based model for computing the complete three-dimensional linear radiation and diffraction problem for floating offshore structures. We present a solution to a pseudo-impulsive formulation in the time domain, where the frequency-dependent quantities, such as added mass, radiation damping, and wave excitation force
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High-order space–time parallel computing of the Navier–Stokes equations Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-26 Meiyuan Zhen, Xuan Liu, Xuejun Ding, Jinsheng Cai
Space–time parallel methods can leverage modern parallel computing architectures to further accelerate numerical simulations where parallelizing only in space limits concurrency. In this paper, we develop a high-order space–time parallel computing method for solving the Navier–Stokes equations. The method is based on revisionist integral deferred correction (RIDC) algorithm proposed by Christlieb,
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A DEIM Tucker tensor cross algorithm and its application to dynamical low-rank approximation Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-26 Behzad Ghahremani, Hessam Babaee
We introduce a Tucker tensor cross approximation method that constructs a low-rank representation of a -dimensional tensor by sparsely sampling its fibers. These fibers are selected using the discrete empirical interpolation method (DEIM). Our proposed algorithm is referred to as DEIM fiber sampling (). For a rank- approximation of an tensor, requires access to only tensor entries, a requirement that
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InVAErt networks: A data-driven framework for model synthesis and identifiability analysis Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-26 Guoxiang Grayson Tong, Carlos A. Sing Long, Daniele E. Schiavazzi
Use of generative models and deep learning for physics-based systems is currently dominated by the task of emulation. However, the remarkable flexibility offered by data-driven architectures would suggest to extend this representation to other aspects of system analysis including model inversion and identifiability. We introduce InVAErt (pronounced ) networks, a comprehensive framework for data-driven
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Active Kriging-based conjugate first-order reliability method for highly efficient structural reliability analysis using resample strategy Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-24 Changqi Luo, Shun-Peng Zhu, Behrooz Keshtegar, Wojciech Macek, Ricardo Branco, Debiao Meng
Efficient structural reliability analysis method is crucial to solving reliability analysis of complex structural problems. High-computational cost and low-failure probability problems greatly limit the efficiency in structural reliability analysis problems, causing the safety and reliability of the structure to be questioned. In this work, a highly efficient structural reliability analysis method
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Generative models for the deformation of industrial shapes with linear geometric constraints: Model order and parameter space reductions Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-24 Guglielmo Padula, Francesco Romor, Giovanni Stabile, Gianluigi Rozza
Real-world applications of computational fluid dynamics often involve the evaluation of quantities of interest for several distinct geometries that define the computational domain or are embedded inside it. For example, design optimization studies require the realization of response surfaces from the parameters that determine the geometrical deformations to relevant outputs to be optimized. In this
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Reconstructing relaxed configurations in elastic bodies: Mathematical formulations and numerical methods for cardiac modeling Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-24 N.A. Barnafi, F. Regazzoni, D. Riccobelli
Modeling the behavior of biological tissues and organs often necessitates the knowledge of their shape in the absence of external loads. However, when their geometry is acquired through imaging techniques, bodies are typically subject to mechanical deformation due to the presence of external forces, and the load-free configuration needs to be reconstructed. This paper addresses this crucial and frequently
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Computational homogenization of higher-order electro-mechanical materials with built-in generalized periodicity conditions Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-22 J. Barceló-Mercader, D. Codony, A. Mocci, I. Arias
We present a formulation for high-order generalized periodicity conditions in the context of a high-order electromechanical theory including flexoelectricity, strain gradient elasticity and gradient dielectricity, with the goal of studying periodic architected metamaterials. Such theory results in fourth-order governing partial differential equations, and the periodicity conditions involve continuity
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Towards a sharper phase-field method: A hybrid diffuse–semisharp approach for microstructure evolution problems Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-22 Jędrzej Dobrzański, Stanisław Stupkiewicz
A new approach is developed for computational modelling of microstructure evolution problems. The approach combines the phase-field method with the recently-developed laminated element technique (LET) which is a simple and efficient method to model weak discontinuities using non-conforming finite-element meshes. The essence of LET is in treating the elements that are cut by an interface as simple laminates
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A new paradigm for multi-fidelity continuation using parallel model refinement Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-21 Johann Gross, Vasudev Gupta, Christian Berthold, Malte Krack
Numerical path continuation is commonly applied to determine how limit states of dynamical systems evolve with a free parameter. For high-fidelity models of complicated nonlinear systems, the sequential nature inherent to continuation can become an unsurmountable obstacle. In the present work, we propose a new paradigm for obtaining the targeted solution curves in a parallelized way. The point of departure
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Multi-scale topological design of asymmetric porous sandwich structures with unidentical face sheets and composite core Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-20 Zhe Ding, Zhimiao Zou, Lei Zhang, Xiaobai Li, Yan Zhang
Compared with conventional symmetric sandwich structure with identical face sheets and single-material core, asymmetric porous sandwich structures (APSSs), which are composed of unidentical face sheets and composite core, usually take better advantage of all materials and provide superior bending stiffness. However, current studies regarding the APSSs are mainly analytical- and experimental-based methods
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Invariant data-driven subgrid stress modeling on anisotropic grids for large eddy simulation Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-20 Aviral Prakash, Kenneth E. Jansen, John A. Evans
We present a new approach for constructing data-driven subgrid stress models for large eddy simulation of turbulent flows using anisotropic grids. The key to our approach is a Galilean, rotationally, reflectionally and unit invariant model form that also embeds filter anisotropy in such a way that an important subgrid stress identity is satisfied. We use this model form to train a data-driven subgrid
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A monolithic fluid–structure interaction approach using mixed LSFEM with high-order time integration Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-19 Solveigh Averweg, Alexander Schwarz, Carina Schwarz, Jörg Schröder
This contribution deals with the solution of a new monolithically coupled fluid–structure interaction approach using mixed least-squares (LS) stress–velocity (SV) formulations with implicit time discretization schemes and adaptive time stepping. The variational approach for the fluid is based on the incompressible Navier–Stokes equations in Arbitrary-Lagrangian–Eulerian (ALE) description to consider
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A bubble VEM-fully discrete polytopal scheme for mixed-dimensional poromechanics with frictional contact at matrix–fracture interfaces Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-19 Jérôme Droniou, Guillaume Enchéry, Isabelle Faille, Ali Haidar, Roland Masson
This article addresses the discretisation of fractured/faulted poromechanical models using 3D polyhedral meshes in order to cope with the geometrical complexity of faulted geological models. A new polytopal scheme is proposed for contact-mechanics, based on a mixed formulation combining a fully discrete space and suitable reconstruction operators for the displacement field together with a face-wise
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Incorporating sufficient physical information into artificial neural networks: A guaranteed improvement via physics-based Rao-Blackwellization Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-19 Gian-Luca Geuken, Jörn Mosler, Patrick Kurzeja
The concept of Rao-Blackwellization is employed to improve predictions of artificial neural networks by physical information. The error norm and the proof of improvement are transferred from the original statistical concept to a deterministic one, using sufficient information on physics-based conditions. The proposed strategy is applied to material modeling and illustrated by examples of the identification
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Machine Learning approaches for the design of biomechanically compatible bone tissue engineering scaffolds Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-19 Silvia Ibrahimi, Luca D’Andrea, Dario Gastaldi, Massimo W. Rivolta, Pasquale Vena
Triply-Periodic Minimal Surfaces (TPMS) analytical formulation does not provide a direct correlation between the input parameters (analytical) and the mechanical and morphological properties of the structure. In this work, we created a dataset with more than one thousand TPMS scaffolds for the training of Machine Learning (ML) models able to find such correlation. Finite Element Modeling and image
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Physics-Based Self-Learning Spiking Neural Network enhanced time-integration scheme for computing viscoplastic structural finite element response Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-19 Saurabh Balkrishna Tandale, Marcus Stoffel
The present study introduces a new physics-based self-learning spiking neural framework to compute geometrically and physically nonlinear structural response. While the so-called traditional or second-generation deep neural networks are used in many applications in the class of physics-informed neural networks, we propose a hybrid model that consists of third-generation Leaky-Integrated and Fire (LIF)
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Rate-dependent phase field fracture simulation in polymers with adaptive mixed isogeometric approach Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-17 Pengmin Hu, Hao Zhen, Huashi Yang, Chuang Xu, Chunying Dong
The fracture of polymers involves viscous dissipation and finite deformation, which poses difficulties for theoretical and numerical analysis. The phase field model (PFM) is a promising tool for fracture simulation, but the near incompressibility nature of polymers presents challenges. This study proposes a fourth-order PFM to deal with the rate-dependent fracture of nearly incompressible polymers
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Concurrent multiscale simulations of nonlinear random materials using probabilistic learning Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-16 Peiyi Chen, Johann Guilleminot, Christian Soize
This work is concerned with the construction of statistical surrogates for concurrent multiscale modeling in structures comprising nonlinear random materials. The development of surrogates approximating a homogenization operator is a fairly classical topic that has been addressed through various methods, including polynomial- and deep-learning-based models. Such approaches, and their extensions to
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Construction and application of an algebraic dual basis and the Fine-Scale Greens’ Function for computing projections and reconstructing unresolved scales Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-15 Suyash Shrestha, Joey Dekker, Marc Gerritsma, Steven Hulshoff, Ido Akkerman
In this paper, we build on the work of Hughes and Sangalli (2007) dealing with the explicit computation of the Fine-Scale Greens’ function. The original approach chooses a set of functionals associated with a projector to compute the Fine-Scale Greens’ function. The construction of these functionals, however, does not generalise to arbitrary projections, higher dimensions, or Spectral Element methods
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Intelligent calibration method for microscopic parameters of soil‒rock mixtures based on measured landslide accumulation morphology Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-15 Chunhui Ma, Lei Chen, Kai Yang, Jie Yang, Ying Tu, Lin Cheng
The discrete element method (DEM) has been widely used in landslide research. However, selecting microscopic parameters is complicated due to the diversity and complexity of landslide material composition, induced mechanism and geological conditions. This study proposed an intelligent calibration method of microscopic parameters based on the measured landslide accumulation morphology. Post-sliding
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Corrigendum to “Multi-Fidelity Cost-Aware Bayesian Optimization” [Computer Methods in Applied Mechanics and Engineering 407 (2023) 115937] Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-15 Zahra Zanjani Foumani, Mehdi Shishehbor, Amin Yousefpour, Ramin Bostanabad
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Topology Optimization of Adaptive Structures: New Limits of Material Economy Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-15 Gennaro Senatore, Yafeng Wang
Adaptive structures can counteract the effect of external loads and other environmental actions through active manipulation of the internal force flow (i.e., the load path) and geometry (i.e., form or shape). Previous studies have shown that adaptation enables significant mass and energy saving compared to conventional structures that resist the effect of loading solely through material strength and
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Incremental Neural Controlled Differential Equations for modeling of path-dependent material behavior Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-02-14 Yangzi He, Shabnam J. Semnani
Data-driven surrogate modeling has emerged as a promising approach for reducing computational expenses of multi-scale simulations. Recurrent Neural Network (RNN) is a common choice for modeling of path-dependent behavior. However, previous studies have shown that RNNs fail to make predictions that are consistent with perturbation in the input strain, leading to potential oscillations and lack of convergence