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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
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Unsupervised learning of history-dependent constitutive material laws with thermodynamically-consistent neural networks in the modified Constitutive Relation Error framework Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-04 Antoine Benady, Emmanuel Baranger, Ludovic Chamoin
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Fully nonlinear inverse poroelasticity: Stress-free configuration recovery Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-04 Nicolás A. Barnafi, Argyrios Petras, Luca Gerardo-Giorda
Typical pipelines for model geometry generation in computational biomedicine stem from images, which are usually considered to be at rest and in absence of external stimuli, despite the object being either in mechanical equilibrium under several well-known forces or in a transient state. We refer to the stress-free geometry computation as the inverse elasticity problem (IEP), and in this work we extend
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Finite element analysis of the Oseen eigenvalue problem Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-04 Felipe Lepe, Gonzalo Rivera, Jesus Vellojin
We propose and analyze a finite element method for the Oseen eigenvalue problem. This problem is an extension of the Stokes eigenvalue problem, where the presence of the convective term leads to a non-symmetric problem and hence, to complex eigenvalues and eigenfunctions. With the aid of the compact operators theory, we prove that for inf-sup stable finite elements the convergence holds and hence,
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Data-Physics Driven Three-Scale Approach for Ultra-Fast Resin Transfer Molding (UF-RTM) Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-04 Junhe Cui, Andrea La Spina, Jacob Fish
We introduce an efficient computational framework designed for simulating the mold filling process in ultra-fast resin transfer molding (UF-RTM), a term used to describe the application of ultra-low viscosity resins (50–200 at room temperature) under ultra-high pressure (∼200 bars)—a combination of pressure and viscosity not yet explored in practical applications. The primary innovation of this framework
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An objective FE-formulation for Cosserat rods based on the spherical Bézier interpolation Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-03 Leopoldo Greco, Alessandro Cammarata, Domenico Castello, Massimo Cuomo
A generalization of the spherical linear interpolation (or ) for the finite rotations to the case of more than two control variables on SO(3) is introduced to design an objective FE-formulation for the non-linear space Cosserat rod model. The interpolation uses the De Casteljau’s algorithm.
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A computational approach to identify the material parameters of the relaxed micromorphic model Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-03 Mohammad Sarhil, Lisa Scheunemann, Peter Lewintan, Jörg Schröder, Patrizio Neff
We determine the material parameters in the relaxed micromorphic generalized continuum model for a given periodic microstructure in this work. This is achieved through a least squares fitting of the total energy of the relaxed micromorphic homogeneous continuum to the total energy of the fully-resolved heterogeneous microstructure, governed by classical linear elasticity. We avoid establishing exact
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I-FENN with Temporal Convolutional Networks: Expediting the load-history analysis of non-local gradient damage propagation Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-03 Panos Pantidis, Habiba Eldababy, Diab Abueidda, Mostafa E. Mobasher
In this paper, we demonstrate for the first time how the Integrated Finite Element Neural Network (I-FENN) framework, previously proposed by the authors , can efficiently simulate the entire loading history of non-local gradient damage propagation. To achieve this goal, we first adopt a Temporal Convolutional Network (TCN) as the neural network of choice to capture the history-dependent evolution of
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Machine learning enhanced exploration of bubble dynamics beneath a horizontal wall Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-03 Xiangyu Zhang, Jiasheng Huang, K.M. Liew
A better understanding of the dynamic behavior of a single bubble rising and impacting on a horizontal substrate is crucial, yet it has traditionally been constrained by the necessity for demanding experiments and extensive time-consuming simulations. This work develops a machine learning-enhanced model to determine characteristic quantities for the dynamic behavior of rising bubbles beneath the horizontal
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A multi-material topology optimization approach to hybrid material structures with gradient lattices Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-02 Yedan Li, Wenke Qiu, Zhen Liu, Yuhan Liu, Liang Xia
In this paper, we present a novel approach for designing hybrid material structures that incorporate solid, void, and spatially-varying lattices. This approach extends the recursively defined power-law type multi-material interpolation model by incorporating additional gradient material phases. Unlike conventional models, the new extended model considers the secondary ``solid'' material phase as a
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An a posteriori error analysis based on equilibrated stresses for finite element approximations of frictional contact Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-02 Ilaria Fontana, Daniele A. Di Pietro
We consider the unilateral contact problem between an elastic body and a rigid foundation in a description that includes both Tresca and Coulomb friction conditions. For this problem, we present an a posteriori error analysis based on an equilibrated stress reconstruction in the Arnold–Falk–Winther space that includes a guaranteed upper bound distinguishing the different components of the error. This
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A local ROM for Rayleigh–Bénard bifurcation problems Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-02 Jesús Cortés, Henar Herrero, Francisco Pla
This work presents a local reduced-order method for computing bifurcation diagrams in 2D Rayleigh–Bénard convection problems. The proposed method is based on Proper Orthogonal Decomposition, and employs a reduced-order study of the regularity of solutions to detect new solution branches within the bifurcation diagram. The locality of the method is achieved through k-means clustering, and the selection
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A new stabilized time-spectral finite element solver for fast simulation of blood flow Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-02 Mahdi Esmaily, Dongjie Jia
The increasing application of cardiorespiratory simulations for diagnosis and surgical planning necessitates the development of computational methods significantly faster than the current technology. To achieve this objective, we leverage the time-periodic nature of these flows by discretizing equations in the frequency domain instead of the time domain. This approach markedly reduces the size of the
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Rate-optimal higher-order adaptive conforming FEM for biharmonic eigenvalue problems on polygonal domains Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-02 Carsten Carstensen, Benedikt Gräßle
The a posteriori error analysis of the classical Argyris finite element methods dates back to 1996, while the optimal convergence rates of associated adaptive finite element schemes are established only very recently in 2021. It took a long time to realize the necessity of an extension of the classical finite element spaces to make them hierarchical. This paper establishes the novel adaptive schemes
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An indirect training approach for implicit constitutive modelling using recurrent neural networks and the virtual fields method Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-01 Rúben Lourenço, Petia Georgieva, Elias Cueto, A. Andrade-Campos
Accurate material description is crucial to achieve high-quality results in computational analysis software. Phenomenological constitutive laws generalize the material behaviour observed in simple mechanical tests. The resulting empirical expressions contain parameters that need to be calibrated through an inverse optimization process. Advancements in Digital Image Correlation (DIC) techniques have
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Echocardiogram-based ventricular isogeometric cardiac analysis using multi-patch fitted NURBS Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-01 Robin Willems, Lex Verberne, Olaf van der Sluis, Clemens V. Verhoosel
Monitoring the cardiac function often relies on non-invasive vital measurements, electrocardiograms, and low-resolution echocardiograms. The monitoring data can be augmented with numerical modeling results to support treatment-risk assessment. Often, medical images are not suitable for high-fidelity modeling due to their spatial sparsity and low resolution. In this work, we present a workflow that
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An IGA-FEA model for flexoelectricity-induced healing of microcracks in cortical bone Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-04-01 Carina Witt, Tobias Kaiser, Andreas Menzel
The remodelling process in bones is closely related to electromechanical phenomena. In addition to streaming potentials and piezoelectricity, flexoelectricity has been found to serve as an initiator for remodelling in cortical bone. Since flexoelectricity is coupled to strain gradients, the effect is size-dependent and, accordingly, most relevant on small scales. This means that particularly microcracks
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A novel quasi-smooth tetrahedral numerical manifold method and its application in topology optimization based on parameterized level-set method Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-30 Shanyao Deng, Pan Wang, Weibin Wen, Jun Liang
In this paper, a novel quasi-smooth tetrahedral numerical manifold method (NMM) and its two-dimensional (2D) counterpart are proposed. A new topology optimization method is established by combining the quasi-smooth manifold element (QSME) with the parameterized level set method (PLSM). The QSME introduces an innovative displacement function characterized by high accuracy and high-order continuity,
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A data-driven force-thermal coupling load identification method considering multi-source uncertainties of structural characteristics and measuring noises Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-28 Lei Wang, Haoyu Zhang, Yue Wang, Di Wu
The problem of load identification denotes identifying loads based on the measurement of structural responses, which is the inverse problem in structural dynamics. With advancements in aeronautical technology, the working environment of aircraft becomes even more complex. To accurately monitor and forecast the load environment where the structure works can provide guidance for the structural design
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Computational study promoting engineering biomaterial pre-design to well adapt pores distribution on bone/scaffold assembly section Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-28 Abdelkader Boucetta, Salah Ramtani, Diego A. Garzón-Alvarado, Jolanda Spadavecchia
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A T-splines-oriented isogeometric topology optimization for plate and shell structures with arbitrary geometries using Bézier extraction Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-28 Xiao Zhang, Mi Xiao, Liang Gao, Jie Gao
Recently, the non-uniform rational B-splines (NURBS) have been considerably employed in modeling plate and shell structures, or developing the related size, shape or topology optimization methods for their design. However, the NURBS with the tensor product feature strongly hinders the effectiveness of the optimization on complex structures. The primary intention of the current research is to propose
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Multi-strategy improved artificial rabbit optimization algorithm based on fusion centroid and elite guidance mechanisms Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-28 Hefan Huang, Rui Wu, Haisong Huang, Jianan Wei, Zhenggong Han, Long Wen, Yage Yuan
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Isosurface extraction for piecewise-linear reconstruction of complex interfaces on arbitrary grids Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-27 Joaquín López
In the field of volume of fluid type schemes, there is still great room for improvement in the accuracy of the methods used to reconstruct interfaces, especially when highly complex interfaces are involved. Isosurface extraction is exploited in a series of piecewise-linear interface reconstruction methods to simulate complex interface dynamic problems on arbitrary grids, and high-order surface patches
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On the determination of the quasi-static evolution of brittle plane cracks via stationarity principle Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-27 Gabriele Cricrì
The crack front evolution in brittle solids is commonly modelled by defining some crack increment criterion, which can be derived from considerations on the stress singularity, from the definition of a dissipative potential, from the introduction of phenomenological concepts such as the crack mobility, and so on. In this work, we faced the problem with no allowance for any crack increment criterion
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Solver-free reduced order homogenization for nonlinear periodic heterogeneous media Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-27 Andrew Beel, Jacob Fish
Reduced-order homogenization (ROH) and related methods are important computational tools for simulating the material behavior of composites. These methods generally sacrifice accuracy in exchange for superior computational efficiency, relative to methods such as classical computational homogenization (CCH). In this study, building on the recently developed solver-free CCH, we propose a fine-scale solver-free
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Multi-scale design of composite material structures for maximizing fundamental natural frequency Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-27 Sunghan Lee, Min Kyu Oh, Cheolwoong Kim, Mingook Jung, Jeonghoon Yoo
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Multiharmonic multiscale modelling in 3-D nonlinear magnetoquasistatics: Composite material made of insulated particles Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-26 Janne Ruuskanen, Antoine Marteau, Innocent Niyonzima, Alexandre Halbach, Joonas Vesa, Gérard Meunier, Timo Tarhasaari, Paavo Rasilo
The use of the classical finite element method (FEM) to solve problems with magnetic composites leads to huge linear systems that are impossible to solve. Instead, homogenization and multiscale methods are often used with the composite material replaced by a homogeneous material with the homogenized constitutive law obtained by solving cell-problems representing the mesoscale material structure. For
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A fast and accurate domain decomposition nonlinear manifold reduced order model Comput. Methods Appl. Mech. Eng. (IF 7.2) Pub Date : 2024-03-26 Alejandro N. Diaz, Youngsoo Choi, Matthias Heinkenschloss
This paper integrates nonlinear-manifold reduced order models (NM-ROMs) with domain decomposition (DD). NM-ROMs approximate the full order model (FOM) state in a nonlinear-manifold by training a shallow, sparse autoencoder using FOM snapshot data. These NM-ROMs can be advantageous over linear-subspace ROMs (LS-ROMs) for problems with slowly decaying Kolmogorov -width. However, the number of NM-ROM