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A NURBS-based level set method for the manufacturing-oriented thermal buckling optimization of curvilinear fiber composite panels with cut-outs Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-30 Haoqing Ding, Ruqi Sun, Haocheng Tian, Yutao Hu, Xin Zhang, Bin Xu
Laminate composite panels with arbitrary cut-outs in a thermal environment may suffer buckling failure because of thermal stress. To address this issue, a manufacturing-oriented thermal-buckling optimization model is proposed for the design of curvilinear fiber paths. Furthermore, instead of using the traditional finite element method (FEM) with high computational costs, a cut non-uniform rational
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A semi-implicit exactly fully well-balanced relaxation scheme for the Shallow Water Linearized Moment Equations Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-30 C. Caballero-Cárdenas, I. Gómez-Bueno, A. Del Grosso, J. Koellermeier, T. Morales de Luna
When dealing with shallow water simulations, the velocity profile is often assumed to be constant along the vertical axis. However, since in many applications this is not the case, modeling errors can be significant. Hence, in this work, we deal with the Shallow Water Linearized Moment Equations (SWLME), in which the velocity is no longer constant in the vertical direction, where a polynomial expansion
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Energy-based physics-informed neural network for frictionless contact problems under large deformation Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-30 Jinshuai Bai, Zhongya Lin, Yizheng Wang, Jiancong Wen, Yinghua Liu, Timon Rabczuk, YuanTong Gu, Xi-Qiao Feng
Numerical methods for contact mechanics are of great importance in engineering applications, enabling the prediction and analysis of complex surface interactions under various conditions. In this work, we propose an energy-based physics-informed neural network (PINN) framework for solving frictionless contact problems under large deformation. Inspired by microscopic Lennard-Jones potential, a surface
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Multiphysics simulation of crystal growth with moving boundaries in FEniCS Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-30 Arved Wintzer, Bilen Emek Abali, Kaspars Dadzis
Crystal growth processes and the Czochralski process in particular involves various physical phenomena such as heat transfer, phase change or liquid flows and requires a coupled multiphysical model for realistic numerical simulations. In this work, a new and extendable model is developed using the open-source software FEniCS. Basic equations for electromagnetic induction, heat conduction and radiation
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Interval Isogeometric Analysis for coping with geometric uncertainty Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-30 Nataly A. Manque, Jan Liedmann, Franz-Joseph Barthold, Marcos A. Valdebenito, Matthias G.R. Faes
Geometric uncertainty poses a significant challenge in many engineering sub-disciplines ranging from structural design to manufacturing processes, often attributed to the underlying manufacturing technology and operating conditions. When combined with geometric complexity, this phenomenon can result in substantial disparities between numerical predictions and the actual behavior of mechanical systems
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A coupled FEM-VEM approach for crack tracking in quasi-brittle materials Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-30 Antonino Spada, Marianna Puccia, Elio Sacco, Giuseppe Giambanco
The numerical simulation of crack propagation in quasi-brittle materials has historically been mainly faced by means of consolidated approaches in the framework of the finite element method (FEM). However, the very recently developed virtual element method (VEM) is a new promising technique whose strong point is the possibility to model polygonal meshes, characterized by any number of edges. This paper
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Generative reduced basis method Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-30 Ngoc Cuong Nguyen
We present a generative reduced basis (RB) approach for the rapid and reliable solution of parametrized linear partial differential equations. Central to this approach is the construction of generative RB spaces that provide rapidly convergent approximations of the solution manifold. We propose a generative snapshot method to generate significantly larger sets of snapshots from a small initial set
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Godunov loss functions for modelling of hyperbolic conservation laws Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-29 Rami Cassia, Rich Kerswell
Machine learning techniques are being used as an alternative to traditional numerical discretization methods for solving hyperbolic partial differential equations (PDEs) relevant to fluid flow. Whilst numerical methods are higher fidelity, they are computationally expensive. Machine learning methods on the other hand are lower fidelity but can provide significant speed-ups. The emergence of physics-informed
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Empirically corrected cluster cubature (E3C) Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-29 Stephan Wulfinghoff
In computational homogenization, the microscopic problem is regularly solved via Galerkin-projection methods to speed up the computation. By evaluating the involved integrals by hyper-reduction techniques, a very high efficiency can be achieved. Here, a novel hyper-reduction method is proposed and applied to magnetostatics. The method combines the ideas of microstructural clustering with the empirical
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MS-IUFFNO: Multi-scale implicit U-net enhanced factorized fourier neural operator for solving geometric PDEs Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-29 Shengjun Liu, Hanchao Liu, Ting Zhang, Xinru Liu
Geometric partial differential equations (geometric PDEs) are defined on manifolds in Riemannian space, specifically tailored for modeling the temporal evolution of surfaces in natural sciences and engineering. For varying initial surfaces (initial conditions), traditional numerical methods require re-solving the equation even for the same geometric PDE, which significantly hinders the efficiency of
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A snapshot-free reduced-order peridynamic model for accelerating fracture analysis of composites Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-28 Han Dong, Hongjiang Wang, Jiahao Zhong, Chaohui Huang, Weizhe Wang, Yingzheng Liu
A reduced-order peridynamic (PD) model is developed to accelerate fracture simulations of composite materials. This reduced-order PD model is constructed based on a set of projection basis functions extracted from the flexibility matrix corresponding to the initial configuration, rather than from snapshots. Thus, this approach eliminates dependence on datasets with prior knowledge, resulting in superior
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Neural network solvers for parametrized elasticity problems that conserve linear and angular momentum Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-27 Wietse M. Boon, Nicola R. Franco, Alessio Fumagalli
We consider a mixed formulation of parametrized elasticity problems in terms of stress, displacement, and rotation. The latter two variables act as Lagrange multipliers to enforce the conservation of linear and angular momentum. The resulting system is computationally demanding to solve directly, especially if various instances of the model parameters need to be investigated. We therefore propose a
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A Least-Squares-Based Neural Network (LS-Net) for Solving Linear Parametric PDEs Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-27 Shima Baharlouei, Jamie M. Taylor, Carlos Uriarte, David Pardo
Developing efficient methods for solving parametric partial differential equations is crucial for addressing inverse problems. This work introduces a Least-Squares-based Neural Network (LS-Net) method for solving linear parametric PDEs. It utilizes a separated representation form for the parametric PDE solution via a deep neural network and a least-squares solver. In this approach, the output of the
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Unified Eulerian method for fluid-immersed self- and multi-body solid contact Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-27 Teo Lara, Ken Kamrin
We introduce a general simulation approach to model fluid-submerged solid contact of highly deformable objects within the Eulerian Incompressible Reference Map Technique (RMT) for fluid-solid interaction. Our approach allows solid bodies to undergo finite deformations, contact, and, importantly, self-contact while immersed in a fluid satisfying the Navier–Stokes equations. All solid boundaries are
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A generalized theory for physics-augmented neural networks in finite strain thermo-electro-mechanics Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-27 R. Ortigosa, J. Martínez-Frutos, A. Pérez-Escolar, I. Castañar, N. Ellmer, A.J. Gil
This manuscript introduces a novel neural network-based computational framework for constitutive modeling of thermo-electro-mechanically coupled materials at finite strains, with four key innovations: (i) It supports calibration of neural network models with various input forms, such as Ψnn(F,E0,θ), enn(F,D0,η), Υnn(F,E0,η), or Γnn(F,D0,θ), with F representing the deformation gradient tensor, E0 and
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A non-intrusive nonlinear structural ROM for partitioned two-way fluid–structure interaction computations Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-27 Riccardo Pellegrini, Zhaoyuan Wang, Frederick Stern, Matteo Diez
This paper introduces a nonlinear structural reduced order model (ROM) specifically developed for fluid–structure interaction (FSI) simulations involving high impact loads and large deflections, such as those arising in water slamming of flexible structures. The model is based on a nonlinear modal expansion trained offline using prestressed eigenfrequency analyses performed by nonlinear full-order
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Simultaneous and meshfree topology optimization with physics-informed Gaussian processes Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-27 Amin Yousefpour, Shirin Hosseinmardi, Carlos Mora, Ramin Bostanabad
Topology optimization (TO) provides a principled mathematical approach for optimizing the performance of a structure by designing its material spatial distribution in a pre-defined domain and subject to a set of constraints. The majority of existing TO approaches have (1) a nested nature, and (2) leverage numerical solvers for design evaluations during the optimization and hence rely on discretizing
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Frequency-adaptive multi-scale deep neural networks Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-26 Jizu Huang, Rukang You, Tao Zhou
Multi-scale deep neural networks (MscaleDNNs) with downing-scaling mapping have demonstrated superiority over traditional DNNs in approximating target functions characterized by high frequency features. However, the performance of MscaleDNNs heavily depends on the parameters in the downing-scaling mapping, which limits their broader application. In this work, we establish a fitting error bound to explain
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A large language model and denoising diffusion framework for targeted design of microstructures with commands in natural language Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-25 Nikita Kartashov, Nikolaos N. Vlassis
Microstructure plays a critical role in determining the macroscopic properties of materials, with applications spanning alloy design, MEMS devices, and tissue engineering, among many others. Computational frameworks have been developed to capture the complex relationship between microstructure and material behavior. However, despite these advancements, the steep learning curve associated with domain-specific
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Computational homogenization of flexoelectric composites within the consistent couple stress theory Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-23 Yan Shang, Ming Sun, Song Cen, Chen-Feng Li
The evaluation of flexoelectric composites with architected microstructures requires a reasonable estimation of their effective properties. To accomplish this, a computational homogenization scheme for flexoelectric composites based on the consistent couple stress theory is proposed in this work, where the extended Hill's lemma is strictly established and accordingly, different types of admissible
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Towards the simulation of metal deposition with the Particle Finite Element Method and a phase transformation model Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-23 Markus Schewe, Isabelle Noll, Thorsten Bartel, Andreas Menzel
The present paper establishes a simulation framework for modelling the deposition and solidification of steel melt in Directed Energy Deposition with a Laser Beam (DED-LB) by using the Particle Finite Element Method (PFEM). Unlike traditional finite element methods, the remeshing framework makes it possible to resolve the interaction between molten metal and substrate upon deposition, solidification
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A deep generative multiscale topology optimization framework considering manufacturing defects and parametrical uncertainties Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-22 Yichen Wu, Lei Wang, Zeshang Li, Lianmei Wu, Yaru Liu
The increasing demand for load-carrying multiscale structures with ultimate lightness requires corresponding development in topology optimization methods. However, current multiscale topology optimization methods are hindered by the contradiction between the freedom of design space and the dimensionality of the design variables. Moreover, the unstable additive manufacturing process and working conditions
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Causality enforcing parametric heat transfer solvers for evolving geometries in advanced manufacturing Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-22 Akshay J. Thomas, Ilias Bilionis, Eduardo Barocio, R. Byron Pipes
We introduce a new method for solving parametric heat transfer partial differential equations on evolving geometries in advanced manufacturing applications. Physics-informed neural networks (PINNs) are a popular framework for integrating experimental data with known physical laws specified via partial differential equations (PDEs). Despite their increasing popularity, applying PINNs to manufacturing
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Differentiable finite element method with Galerkin discretization for fast and accurate inverse analysis of multidimensional heterogeneous engineering structures Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-22 Xi Wang, Zhen-Yu Yin, Wei Wu, He-Hua Zhu
Physics-informed neural networks (PINNs) are well-regarded for their capabilities in inverse analysis. However, efficient convergence is hard to achieve due to the necessity of simultaneously handling physics constraints, data constraints, blackbox weights, and blackbox biases. Consequently, PINNs are highly challenged in the inverse analysis of unknown boundary loadings and heterogeneous material
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A theoretically-consistent parallel enrichment strategy for Bayesian active learning reliability analysis Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-22 Tong Zhou, Tong Guo, Xujia Zhu, Masaru Kitahara, Jize Zhang
Although parallel active learning reliability analysis is promising and has been widely studied, there remains an open question regarding how to achieve better theoretical consistency and avoid reliance on empirical practices heavily. A new parallel Bayesian active learning reliability method is developed in this study. First, in Bayesian failure probability estimation, a metric called integrated probability
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An univariate method for multi-material topology optimization and its application to engineering structures with unstructured meshes Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-22 Haitao Liao, Wenhao Yuan, Shigang Ai, Xujin Yuan
Multi-material topology optimization as a research hotspot has been widely investigated and all the reported multi-material interpolation models add m or m-1 design variables/level set equations to handle m levels or phases and the number of design variables is proportional to the number of material type. The current single variable interpolation model as an attractive alternative selection often leads
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Data-driven multifidelity topology design with multi-channel variational auto-encoder for concurrent optimization of multiple design variable fields Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-21 Hiroki Kawabe, Kentaro Yaji, Yuichiro Aoki
Topology optimization can generate high-performance structures with a high degree of freedom. Regardless, it generally confronts entrapment in undesirable local optima especially in problems characterized by strong non-linearity. This study aims to establish a gradient-free topology optimization framework that facilitates more global solution searches to avoid the entrapment. The framework utilizes
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Phase-field hydraulic fracturing operator network based on En-DeepONet with integrated physics-informed mechanisms Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-21 Xiaoqiang Wang, Peichao Li, Detang Lu
Hydraulic fracturing in porous media, driven by fluid injection, presents a formidable computational challenge due to the intricate interplay of fluid flow and fracture mechanics. The phase-field method offers a powerful approach for modeling such complex phenomena, but its high computational demands limit its practical application in large-scale scenarios. This work introduces a phase-field hydraulic
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On the use of physics-based constraints and validation KPI for data-driven elastoplastic constitutive modelling Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-21 Rúben Lourenço, Aiman Tariq, Petia Georgieva, A. Andrade-Campos, Babür Deliktaş
Constitutive modelling based on machine learning (ML) approaches has surged in the last couple of decades due to novel and more robust model architectures and computational power. The dependency of these models on large amounts of training data can be mitigated by imposing some phenomenological knowledge as constraints, which also helps maintain the quality of learning. This paper highlights the importance
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Tree–cotree-based tearing and interconnecting for 3D magnetostatics: A dual–primal approach Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-21 Mario Mally, Bernard Kapidani, Melina Merkel, Sebastian Schöps, Rafael Vázquez
The simulation of electromagnetic devices with complex geometries and large-scale discrete systems benefits from advanced computational methods like IsoGeometric Analysis and Domain Decomposition. In this paper, we employ both concepts in an Isogeometric Tearing and Interconnecting method to enable the use of parallel computations for magnetostatic problems. We address the underlying non-uniqueness
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Neural networks meet anisotropic hyperelasticity: A framework based on generalized structure tensors and isotropic tensor functions Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-21 Karl A. Kalina, Jörg Brummund, WaiChing Sun, Markus Kästner
We present a data-driven framework for the multiscale modeling of anisotropic finite strain elasticity based on physics-augmented neural networks (PANNs). Our approach allows the efficient simulation of materials with complex underlying microstructures which reveal an overall anisotropic and nonlinear behavior on the macroscale. By using a set of invariants as input, an energy-type output and by adding
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Learning the physics-consistent material behavior from measurable data via PDE-constrained optimization Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-20 Xinxin Wu, Yin Zhang, Sheng Mao
Constitutive models play a crucial role in materials science as they describe the behavior of the materials in mathematical forms. Over the last few decades, the rapid development of manufacturing technologies has led to the discovery of many advanced materials with complex and novel behavior, which in the meantime, has also posed great challenges for constructing accurate and reliable constitutive
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A novel design update framework for topology optimization with quantum annealing: Application to truss and continuum structures Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-20 Naruethep Sukulthanasorn, Junsen Xiao, Koya Wagatsuma, Reika Nomura, Shuji Moriguchi, Kenjiro Terada
This paper presents a novel design update strategy for topology optimization, as an iterative optimization. The key contribution lies in incorporating a design updater concept with quantum annealing, applicable to both truss and continuum structures. To align with density-based approaches in topology optimization, these updaters are formulated through a multiplicative relationship to represent the
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A new finite element interpolation of the Cosserat directors for nonlinear three-dimensional Kirchhoff rods Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-20 F. Armero
We present in this contribution an interpolation of the rotations describing the Cosserat frame for a Kirchhoff rod. The frames are characterized by the director perpendicular to the cross section being also aligned with the tangential direction of the rod’s axis. The formulation is based on a G1 Hermite interpolation of the axis, described by the position of the rod itself but also with the above
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A deep learning framework to predict microstructurally small fatigue crack growth in three-dimensional polycrystals Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-20 Vignesh Babu Rao, Ashley D. Spear
Accurately predicting the growth of microstructurally small cracks (MSCs) is vital for materials design and structural prognosis. Traditional physics-based simulations involving crystal plasticity, though precise, are computationally intensive and impractical for applications requiring high-throughput or real-time predictions, such as digital twins. This study introduces a deep learning framework using
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A transient dynamic topology optimization method with approximate dynamic response sensitivity using equivalent static loads Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-20 Delin Cao, Yan Zeng, Zeng Meng, Gang Li
Transient dynamic response topology optimization methods always face the challenge of high computational costs due to the need for repeated time-domain discrete structural response calculations. To address this issue, the Equivalent Static Loads Method (ESLM) calculates structural responses with Equivalent Static Loads (ESLs), and solves a sequence of static response optimization problems to approximate
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A Data-Driven-based homogenization method to simulate the anisotropic damage of brittle heterogeneous structures Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-18 Zakaria Chafia, Julien Yvonnet, Jérémy Bleyer
An efficient data-driven multiscale framework for modeling anisotropic damage (M-DDHAD) in heterogeneous structures is proposed, where the anisotropic damage model at the macro scale is constructed purely on the knowledge of Representative Volume Elements (RVE) of the material microstructure. The technique involves three main steps: the construction of a database, obtained by performing off-line calculations
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A third medium approach for contact using first and second order finite elements Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-18 P. Wriggers, J. Korelc, Ph. Junker
Third medium contact can be applied in situations where large deformations occur and self-contact is possible. Starting with Wriggers et al. (2013), this approach has been further developed and often applied in the area of topology optimization. Lately approaches have been discussed which use the gradient of the deformation measure to enhance the performance of the algorithm. Such approaches, however
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An improved explicit MPM formulation and its coupling scheme with FEM Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-17 Xi-Wen Zhou, Yin-Fu Jin, Kai-Yuan He, Zhen-Yu Yin
Accurately imposing boundary conditions and contact constraints in the Material Point Method (MPM) and its coupling with the Finite Element Method (FEM-MPM) is challenging, especially when dealing with complex geometrical shapes and misalignment between material boundaries and the computational grid. To address these issues, an improved explicit penalty formulation based on particle positions is developed
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Fully-discrete decoupled Subdivision-based IGA-IEQ-ZEC numerical scheme for the binary surfactant phase-field model coupled with Darcy flow equations on Surfaces Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-17 Qing Pan, Yunqing Huang, Timon Rabczuk, Yin Yang, Xiaofeng Yang
In this paper, we present a comprehensive numerical investigation of the binary phase-field surfactant model coupled with the Darcy flow equation to explore the impact of surfactant addition on the evolution of Saffman–Taylor fingering patterns within a Hele-Shaw cell on surfaces. We develop an efficient and robust spatiotemporal discretization framework that effectively addresses the highly nonlinear
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An end-to-end deep learning method for solving nonlocal Allen–Cahn and Cahn–Hilliard phase-field models Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-17 Yuwei Geng, Olena Burkovska, Lili Ju, Guannan Zhang, Max Gunzburger
We propose an efficient end-to-end deep learning method for solving nonlocal Allen–Cahn (AC) and Cahn–Hilliard (CH) phase-field models. One motivation for this effort emanates from the fact that discretized partial differential equation-based AC or CH phase-field models result in diffuse interfaces between phases, with the only recourse for remediation is to severely refine the spatial grids in the
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Metamaterial design with vibroacoustic bandgaps through topology optimization Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-16 Vanessa Cool, Ole Sigmund, Niels Aage
Metamaterials have shown potential to achieve strong noise or vibration reduction in predefined frequency ranges. Targeting both wave types simultaneously remains, however, a cumbersome design task requiring complex geometries which often only enable a wide bandgap for one type while limited attenuation for the other. To overcome this hurdle, this work presents a 2D topology optimization framework
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Analysis of stress intensity factor oscillations in 3D cracks using domain integrals and the extended finite element method Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-16 Vicente F. González-Albuixech, Eugenio Giner, Anthony Gravouil
Fracture-related failure of structural integrity can be evaluated using stress intensity factors (SIFs), and complex fractured geometries can be modeled using the extended finite element method (XFEM). Typically, domain integrals — especially J-integrals and interaction integrals — are used to compute SIFs. Although these integrals produce accurate estimates with the finite element method, they exhibit
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Modeling of cardiac fibers as oriented liquid crystals Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-16 Nicolás A. Barnafi, Axel Osses
In this work we propose a mathematical model that describes the orientation of ventricular cardiac fibers. These fibers are commonly computed as the normalized gradient of certain harmonic potentials, so our work consisted in finding the equations that such a vector field satisfies, considering the unitary norm constraint. The resulting equations belong to the Frank–Oseen theory of nematic liquid crystals
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Learning implicit yield surface models with uncertainty quantification for noisy datasets Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-15 Donovan Birky, John Emery, Craig Hamel, Jacob Hochhalter
Materials often exhibit stochastic mechanical behaviors due to their inherent intrinsic variability. Data acquisition also introduces extrinsic noise into data. To learn yield surface models under uncertainty, we present a method that uses genetic programming based symbolic regression (GPSR) and a multi-objective fitness function (MOSR). Previous works have demonstrated using an implicit fitness metric
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Constructing boundary-identical microstructures via guided diffusion for fast multiscale topology optimization Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-14 Jingxuan Feng, Lili Wang, Xiaoya Zhai, Kai Chen, Wenming Wu, Ligang Liu, Xiao-Ming Fu
Hierarchical structures exhibit critical features across multiple scales. However, designing multiscale structures demands significant computational resources, and ensuring connectivity between microstructures remains a key challenge. To address these issues, large-range, boundary-identical microstructure datasets are successfully constructed, where the microstructures share the same boundaries and
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Constraints adjusted material with penalization method for topology optimization with minimum and maximum length controls Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-14 Chuan Luo
This paper presents the Constraints Adjusted Material with Penalization (CAMP) method, a novel approach to computational design for manufacturing, e.g. minimum and maximum length controls in topology optimization, without the need for additional explicit constraints. The proposed method is demonstrated on benchmark problems of minimum compliance and heat transfer, demonstrating its ability to yield
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SUPG-stabilized time-DG finite and virtual elements for the time-dependent advection–diffusion equation Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-14 L. Beirão da Veiga, F. Dassi, S. Gómez
We carry out a stability and convergence analysis for the fully discrete scheme obtained by combining a finite or virtual element spatial discretization with the upwind-discontinuous Galerkin time-stepping applied to the time-dependent advection–diffusion equation. A space–time streamline-upwind Petrov–Galerkin term is used to stabilize the method. More precisely, we show that the method is inf–sup
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DeepOKAN: Deep operator network based on Kolmogorov Arnold networks for mechanics problems Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-14 Diab W. Abueidda, Panos Pantidis, Mostafa E. Mobasher
The modern digital engineering design often requires costly repeated simulations for different scenarios. The prediction capability of neural networks (NNs) makes them suitable surrogates for providing design insights. However, only a few NNs can efficiently handle complex engineering scenario predictions. We introduce a new version of the neural operators called DeepOKAN, which utilizes Kolmogorov
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Hybrid-PFC: Coupling the phase-field crystal model and its amplitude-equation formulation Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-13 Maik Punke, Marco Salvalaglio
The phase-field crystal (PFC) model describes crystal structures on diffusive timescales through a periodic, microscopic density field. It has been proposed to model elasticity in crystal growth and encodes most of the phenomenology related to the mechanical properties of crystals like dislocation nucleation and motion, grain boundaries, and elastic or interface-energy anisotropies. To overcome limitations
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Shear-flexible geometrically exact beam element based on finite differences Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-13 Milan Jirásek, Martin Horák, Emma La Malfa Ribolla, Chiara Bonvissuto
The proposed two-dimensional geometrically exact beam element extends our previous work by including the effects of shear distortion, and also of distributed forces and moments acting along the beam. The general flexibility-based formulation exploits the kinematic equations combined with the inverted sectional equations and the integrated form of equilibrium equations. The resulting set of three first-order
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Exploring energy minimization to model strain localization as a strong discontinuity using Physics Informed Neural Networks Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-11 Omar León, Víctor Rivera, Angel Vázquez-Patiño, Jacinto Ulloa, Esteban Samaniego
We explore the possibilities of using energy minimization for the numerical modeling of strain localization in solids as a sharp discontinuity in the displacement field. For this purpose, we consider (regularized) strong discontinuity kinematics in elastoplastic solids. The corresponding mathematical model is discretized using Artificial Neural Networks (ANNs), aiming to predict both the magnitude
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A multi-GPU based high-performance computing framework in elastodynamics simulation using octree meshes Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-10 Shayan Mohammadian, Ankit S. Kumar, Chongmin Song
This paper proposes a high-performance computing framework for large-scale elastodynamic analysis utilizing Graphics Processor Units (GPUs). The study adopts an octree algorithm for automatic mesh generation. The scaled boundary finite element method (SBFEM) is employed with the octree mesh, eliminating hanging nodes between octree cells with different sizes. This approach significantly reduces the
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Intermediate flexural crack debonding of externally bonded FRP in RC beams through a FEM formulation based on positions Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-10 Danilo Silva Bomfim, Humberto Breves Coda, Rodrigo Ribeiro Paccola
This study presents a new strategy for simulating the coupling of internal and external reinforcements in continuum media, accounting for large translations and rotations, using High Aspect Ratio (HAR) interface elements combined with a tailored technique. In the proposed approach, a J2 damage model is applied to HAR interface elements to simulate the bond–slip behavior of reinforcements. Crack growth
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A fully decoupled, iteration-free, unconditionally stable fractional-step scheme for dispersed multi-phase flows Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-10 Douglas R.Q. Pacheco
Volume-averaged flow equations model fluid systems with two or more interpenetrating phases, as used in various engineering and science applications. Each fluid obeys its own set of Navier–Stokes equations, and the interphase coupling occurs via mass conservation, drag forces, and a common pressure shared by all phases. Therefore, designing decoupling schemes to avoid costly monolithic solvers is a
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AT-PINN-HC: A refined time-sequential method incorporating hard-constraint strategies for predicting structural behavior under dynamic loads Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-10 Zhaolin Chen, Siu-Kai Lai, Zhicheng Yang, Yi-Qing Ni, Zhichun Yang, Ka Chun Cheung
Physics-informed neural networks (PINNs) have been rapidly developed and offer a new computational paradigm for solving partial differential equations (PDEs) in various engineering fields. Hard constraints on boundary and initial conditions represent a significant advancement in PINNs. Given that existing hard-constraint strategies are unsuitable for structural vibration problems, this work addresses
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Isogeometric multipatch surface fitting in tomographic images: Application to lattice structures Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-09 D. Bichet, J.C. Passieux, J.N. Périé, R. Bouclier
Additive manufacturing has enabled the production of cellular architected (lattice) structures known for their exceptional mechanical performances. However, the printed components often exhibit geometric defects on a scale close to that of lattice struts, leading to significant deviations in mechanical behavior when comparing simulations based on the as-designed (defect-free) geometry with experimental
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Physics informed neural networks for learning the horizon size in bond-based peridynamic models Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-09 Fabio V. Difonzo, Luciano Lopez, Sabrina F. Pellegrino
This paper broaches the peridynamic inverse problem of determining the horizon size of the kernel function in a one-dimensional model of a linear microelastic material. We explore different kernel functions, including V-shaped, distributed, and tent kernels. The paper presents numerical experiments using PINNs to learn the horizon parameter for problems in one and two spatial dimensions. The results
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Dream Optimization Algorithm (DOA): A novel metaheuristic optimization algorithm inspired by human dreams and its applications to real-world engineering problems Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-09 Yifan Lang, Yuelin Gao
As optimization problems grow increasingly complex, traditional deterministic algorithms often struggle to address these challenges. Metaheuristic algorithms, with their flexibility and low problem dependency, have emerged as a competitive alternative. This paper introduces the Dream Optimization Algorithm (DOA), inspired by human dreams, which exhibit partial memory retention, forgetting, and logical
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A path-following approach for quasi-static structural and material instability phenomena Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2025-01-09 Anton Köllner, Nicholas Lüer
A computational framework for investigating the stability landscape of structures is presented. A path-following algorithm is developed for mechanical systems described by sets of generalized coordinates, control/load and damage parameters undergoing quasi-static deformation. The framework makes use of an extended total potential energy functional that enables the study of structural/geometric instabilities