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Multiscale contact homogenisation: A novel perspective through the method of multiscale virtual power Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-21 António M. Couto Carneiro, Francisco M. Andrade Pires, Eduardo A. de Souza Neto
The interaction between deformable bodies and rigid foundations undergoing finite strains is explored in this work with the Method of Multiscale Virtual Power, unlocking novel insights into contact homogenisation theories. The focus lies in establishing the foundational kinematical links across scales and achieving seamless homogenisation of the traction vector through rigorous duality arguments. Two
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Enhancing subset simulation through Bayesian inference Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-21 Zihan Liao, Xiao He, Weili Xia
Analyzing and reducing uncertainty in estimating failure probability has always been a crucial part of reliability analysis using Monte Carlo simulation. This paper employs Bayesian inference to capture uncertainty within Subset Simulation (SuS), with a specific emphasis on constructing the posterior distribution of failure probability. Two types of Bayesian models are discussed. The first type is
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A novel reliability-based design optimization method through instance-based transfer learning Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-21 Zhe Zhang, Haibo Liu, Tianhao Wu, Jingyu Xu, Chao Jiang
The RBDO optimization process consists of two main steps: iterative updating of design points and repeated reliability analysis at these different design points. A large number of performance function calls are usually necessary for each reliability analysis, and it involves with the repeated reliability analysis at different design pints, leading to potentially prohibitive computational cost when
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Mechanical identification with the reconditioned equilibrium gap method: Formulation, analysis and comparisons Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-21 Rémi Haustrate, François Hild, Amélie Fau
Full-field measurements are used to calibrate material parameters. The Equilibrium Gap Method (EGM), like other identification formulations that use full-field data has the advantage of being direct for linear behavior and some nonlinearities, thereby being computationally cheaper than iterative methods. However, it has a high sensitivity to measurement uncertainties, which is detrimental when dealing
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KAN-ODEs: Kolmogorov–Arnold network ordinary differential equations for learning dynamical systems and hidden physics Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-19 Benjamin C. Koenig, Suyong Kim, Sili Deng
Kolmogorov–Arnold networks (KANs) as an alternative to multi-layer perceptrons (MLPs) are a recent development demonstrating strong potential for data-driven modeling. This work applies KANs as the backbone of a neural ordinary differential equation (ODE) framework, generalizing their use to the time-dependent and temporal grid-sensitive cases often seen in dynamical systems and scientific machine
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A variationally consistent membrane wrinkling model based on spectral decomposition of the strain tensor Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-19 Daobo Zhang, Josef Kiendl
We propose a novel variationally consistent membrane wrinkling model for analyzing the mechanical responses of wrinkled thin membranes. The elastic strain energy density is split into tensile and compressive terms via a spectral decomposition of the strain tensor. Tensile and compressive parts of the stress and constitutive tensors are then obtained via consistent variation from the respective strain
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Predicting non-linear stress–strain response of mesostructured cellular materials using supervised autoencoder Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-19 Sushan Nakarmi, Jeffery A. Leiding, Kwan-Soo Lee, Nitin P. Daphalapurkar
Recent breakthroughs in advanced manufacturing capabilities have made it possible to design and print sophisticated topologies of cellular structures using diverse engineering materials such as metals, polymers, and ceramics. In these architectured materials, it is often desirable to tailor the mechanical properties by altering the unit cell topology. This necessitates an in-depth understanding of
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A novel sensitivity analysis method for multi-input-multi-output structures considering non-probabilistic correlations Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-19 Heng Ouyang, Hongbin Zhou, Haoyang Wang, Shuyong Duan, Xu Han
In practical engineering, a multi-input-multi-output (MIMO) structure generally features a significant number of correlated input parameters and output responses. Sensitivity analysis is usually adopted to select key parameters for improving the computational efficiency of structural analysis and design processes. Traditional sensitivity analysis methods based on probabilistic models for MIMO structures
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An immersed multi-material arbitrary Lagrangian–Eulerian finite element method for fluid–structure-interaction problems Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-18 Zixian Sun, Zhixin Zeng, Jiasheng Li, Xiong Zhang
Fluid–structure-interaction (FSI) phenomena are widely concerned in engineering practice and challenge current numerical methods. In this article, the finite element method is strongly coupled with the multi-material arbitrary Lagrangian–Eulerian (MMALE) method to develop a monolithic FSI method named the immersed multi-material arbitrary Lagrangian–Eulerian finite element method (IALEFEM). By immersing
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A parameter-free and locking-free enriched Galerkin method of arbitrary order for linear elasticity Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-18 Shuai Su, Siyuan Tong, Mingyan Zhang, Qian Zhang
We propose a parameter-free and locking-free enriched Galerkin method of arbitrary order for solving the linear elasticity problem in both two and three space dimensions. Our method uses an approximation space that enriches the vector-valued continuous Galerkin space of order k with some discontinuous piecewise polynomials. To the best of our knowledge, it extends the locking-free enriched Galerkin
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Active learning inspired multi-fidelity probabilistic modelling of geomaterial property Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-18 Geng-Fu He, Pin Zhang, Zhen-Yu Yin
The identification of geomaterial properties is costly but pivotal for engineering design. A wide range of approaches perform well with sufficiently measured data but their performance is problematic for sparse data. To address this issue, this study proposes an active learning based multi-fidelity residual Gaussian process (AL-MR-GP) modelling framework. A low-fidelity (LF) prediction model is first
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Learning the Hodgkin–Huxley model with operator learning techniques Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-17 Edoardo Centofanti, Massimiliano Ghiotto, Luca F. Pavarino
We construct and compare three operator learning architectures, DeepONet, Fourier Neural Operator, and Wavelet Neural Operator, in order to learn the operator mapping a time-dependent applied current to the transmembrane potential of the Hodgkin–Huxley ionic model. The underlying non-linearity of the Hodgkin–Huxley dynamical system, the stiffness of its solutions, and the threshold dynamics depending
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An iterative split scheme for steady flows with heterogeneous viscosity Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-16 J. Deteix, D. Yakoubi
This paper proposes a numerical scheme for the approximation of the solution of the Stokes or steady Navier–Stokes system for fluids with heterogeneous viscosity (generic bounded viscosity or shear thinning fluids). The scheme is based on a velocity–pressure splitting resembling a Uzawa approach combined with a grad-div stabilizing term. We establish the validity, convergence and a priori estimates
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Topology optimization using immersed isogeometric analysis and its software implementation Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-16 Xianda Xie, Shuting Wang, Qingtian Xie, Can Liu, Yuhang Ren, Aodi Yang
This work integrates the immersed isogeometric analysis (IGA) with topology optimization (IITO), which paves the way of seamless integration between CAD and CAE as well as topology optimization for complex engineering structures. A truncated hierarchical B-spline (THB) based local adaptivity strategy is proposed to improve the integral accuracy of trimmed elements for immersed IGA, and an adaptive
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Development of free-field and compliant base SPH boundary conditions for large deformation seismic response analysis of geomechanics problems Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-16 Trieu N. Hoang, Ha H. Bui, Thang T. Nguyen, Tien V. Nguyen, Giang D. Nguyen
Earthquake-induced geohazards are natural disasters that have the potential to cause severe damage to infrastructure and endanger human lives. To mitigate these natural disasters, advanced computational methods capable of dealing with large deformation and failure of geomaterials have been developed for years. Among those methods, the Smoothed Particle Hydrodynamics (SPH) method has been demonstrated
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3D decomposition optimization of topology-optimized structures considering a build volume constraint for additive manufacturing Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-14 Hansu Kim, Il Yong Kim
The integration of topology optimization and additive manufacturing (AM) offers a transformative approach to designing and fabricating complex structures across various industries. This synergy enables engineers to produce lightweight, high-performance designs with intricate, organic geometries that push the boundaries of conventional manufacturing methods. However, printing large 3D objects that exceed
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Dynamic and modal analysis of nearly incompressible structures with stabilised displacement-volumetric strain formulations Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-13 Rubén Zorrilla, Riccardo Rossi, Ramon Codina
This paper presents a dynamic formulation for the simulation of nearly incompressible structures using a mixed finite element method with equal-order interpolation pairs. Specifically, the nodal unknowns are the displacement and the volumetric strain component, something that makes possible the reconstruction of the complete stain at the integration point level and thus enables the use of strain-driven
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Structural reliability analysis with parametric p-box uncertainties via a Bayesian updating BDRM Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-13 Jun Xu, Ting Zhang, Long Li, Quanfu Yu
The parametric probability-box model, often abbreviated as “p-box” is frequently used to characterize epistemic uncertainties. However, structural reliability analysis with p-box uncertainties can often be computationally intensive. This paper presents an efficient method to accurately compute the bounds of failure probabilities within this context. The method’s key innovation lies in its ability to
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Tailored Functionally Graded Materials design and concurrent topology optimization with implicit fields Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-13 Lingfeng Li, Qiong Pan, Xiaoya Zhai, Falai Chen
Tailored unctionally raded aterials (FGMs) offer the ability to design and engineer materials with specific properties at a changing volume fraction and are widely used in various fields such as aerospace, biomedical engineering, etc. The precise control of physical properties and the connectivity of microstructural sequences are two main challenges in multiscale problems. This paper constructs a novel
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Quantum computing and tensor networks for laminate design: A novel approach to stacking sequence retrieval Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-12 Arne Wulff, Boyang Chen, Matthew Steinberg, Yinglu Tang, Matthias Möller, Sebastian Feld
As with many tasks in engineering, structural design frequently involves navigating complex and computationally expensive problems. A prime example is the weight optimization of laminated composite materials, which to this day remains a formidable task, due to an exponentially large configuration space and non-linear constraints. The rapidly developing field of quantum computation may offer novel approaches
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Learning macroscopic equations of motion from dissipative particle dynamics simulations of fluids Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-12 Matevž Jug, Daniel Svenšek, Tilen Potisk, Matej Praprotnik
Macroscopic descriptions of both natural and engineered materials usually include a number of phenomenological parameters that have to be estimated from experiments or large-scale microscopic simulations. When dealing with advanced complex materials, these descriptions are sometimes not available or not even known. Using sparsity-promoting techniques one can extract macroscopic dynamic models directly
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Particle inverse method for full-field displacement and crack propagation monitoring from discrete sensor measurements Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-12 A. Kefal, M.H. Bilgin, A. Kendibilir
This study presents the Particle Inverse Method (PIM), a novel structural health monitoring technique for real-time, full-field monitoring of deformations and damages/cracks in structures using discrete sensor data. Towards this end, the PIM mathematically unifies the concepts of the inverse finite element method and peridynamics differential operator for the first time, thus creating a fully meshless
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Improving the performance of Stein variational inference through extreme sparsification of physically-constrained neural network models Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-12 Govinda Anantha Padmanabha, Jan Niklas Fuhg, Cosmin Safta, Reese E. Jones, Nikolaos Bouklas
Most scientific machine learning (SciML) applications of neural networks involve hundreds to thousands of parameters, and hence, uncertainty quantification for such models is plagued by the curse of dimensionality. Using physical applications, we show that sparsification prior to Stein variational gradient descent (+SVGD) is a more robust and efficient means of uncertainty quantification, in terms
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Correlation structures of statistically isotropic stiffness and compliance TRFs through upscaling Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-12 Yaswanth Sai Jetti, Martin Ostoja-Starzewski
This paper reports a procedure to develop random fields of material properties on a mesoscale level, coarser than the microscale level of heterogeneous material microstructure. Since the anisotropy of properties at the mesoscale level is unavoidable, tensor-valued random fields (TRFs) need to be constructed. The construction satisfies three criteria: (i) the passage from the micro to mesoscale must
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Physics informed self-supervised segmentation of elastic composite materials Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-12 Guilherme Basso Della Mea, Cristian Ovalle, Lucien Laiarinandrasana, Etienne Decencière, Petr Dokládal
This work presents the application of Physics Informed Deep Learning models for both surrogate modelling and segmentation of composite materials. The segmentation is performed in a self-supervised manner, where, in the absence of ground truth images, the predicted stress field is used as the target of the deep learning model with a novel loss function. Our surrogate modelling approach prioritises model
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Numerical methods for shape optimal design of fluid–structure interaction problems Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-12 Johannes Haubner, Michael Ulbrich
We consider the method of mappings for performing shape optimization for unsteady fluid–structure interaction (FSI) problems. In this work, we focus on the numerical implementation. We model the optimization problem such that it takes several theoretical results into account, such as regularity requirements on the transformations and a differential geometrical point of view on the manifold of shapes
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A new mixed variational approach for Kirchhoff shells and [formula omitted] discretization with finite element exterior calculus Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-12 Jamun Kumar N., J.N. Reddy, Arun R. Srinivasa, Debasish Roy
We propose a geometrically-inspired mixed variational approach for nonlinear analysis of Kirchhoff shells based on Cartan’s moving frames. We use a two-parameter family of points (the mid-surface) and a two-parameter family of orthonormal frames (Cartan’s moving frames) introduced independently. Compatibility of the mid-surface vis-á-vis the frame field is enforced by appropriately constructing a functional
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An efficient phase-field framework for contact dynamics between deformable solids in fluid flow Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-12 Biswajeet Rath, Xiaoyu Mao, Rajeev K. Jaiman
Elastic contact in hydrodynamic environments is a complex multiphysics phenomenon and can be found in applications ranging from engineering to biological systems. Understanding the intricacies of this coupled problem requires the development of a generalized framework capable of handling topological changes and transitioning implicitly from fluid–structure interaction (FSI) conditions to solid–solid
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Viscous stress approximations in diffuse interface methods for two-phase flow based on mechanical jump conditions Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-12 Martin Reder, Andreas Prahs, Daniel Schneider, Britta Nestler
Diffuse interface approaches for multi-phase flow such as Hohenberg–Halperin type models require the approximation of material properties in the diffuse transition region. Different interpolation schemes achieving this are employed in literature. The present work focuses on such diffuse interface approximation of viscous stress. It is shown, that a viscosity interpolation based on the arithmetic mean
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Two Nitsche-based mixed finite element discretizations for the seepage problem in Richards’ equation Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-10 Federico Gatti, Andrea Bressan, Alessio Fumagalli, Domenico Gallipoli, Leonardo Maria Lalicata, Simone Pittaluga, Lorenzo Tamellini
This paper proposes two algorithms to impose seepage boundary conditions in the context of Richards’ equation for groundwater flows in unsaturated media. Seepage conditions are non-linear boundary conditions, that can be formulated as a set of unilateral constraints on both the pressure head and the water flux at the ground surface, together with a complementarity condition: these conditions in practice
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Mixed displacement–pressure formulations and suitable finite elements for multimaterial problems with compressible and incompressible models Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-10 Chennakesava Kadapa
Multimaterial problems in linear and nonlinear elasticity are some of the least explored using mixed finite element formulations with higher-order elements. The fundamental issue in adapting the mixed displacement–pressure formulations with linear and higher-order continuous elements for the pressure field is their inability to capture pressure and stress jumps across material interfaces. In this paper
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Enhancing topology optimization with colored body-fitted mesh using adaptive filter, dual re-meshing strategy, and OOP programming paradigm Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-10 Zicheng Zhuang, Tong Liu, Wei Tong, Fengming Xu, Yiwei Weng
This study introduces a novel topology optimization approach by employing power law-based material interpolation and adaptive filtering in the framework of the unstructured grids. As an extension of the established Solid Isotropic Material with Penalization (SIMP) method that utilizes the fixed structured mesh, the proposed Colored Body-Fitted Optimization (CBFO) method adopts the body-fitted grids
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Discrepancy-informed quadrature strategy for the nonlocal macro-meso-scale consistent damage model Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-10 Weifan Lv, Guangda Lu, Xiaozhou Xia, Xin Gu, Qing Zhang
The nonlocal macro-meso damage (NMMD) model has shown promising results in simulating the fracture process of materials. However, due to the inherent limitations of the nonlocal methods, its stability depends on whether the number of elements/nodes within the nonlocal region is sufficient. This paper proposes a discrepancy-informed quadrature strategy for NMMD to address its inherent limitations. Concretely
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Thermoelastic topology optimization for stiffened thin-walled structures under design-dependent thermal loading problems Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-08 Shili Xue, Xiangtao Ma, Dachuan Liu, ZeKai Huo, Peng Hao, Bo Wang
Due to their high specific strength and stiffness, stiffened thin-walled structures are extensively utilized in aerospace applications to maintain a high load-bearing capacity in a complex thermo-mechanical coupled environment. Thermal deformation significantly impacts the intake and exhaust performances, aerodynamic profiles, and even structural safety, hence how to design a reasonable stiffener layout
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Data-driven conditional probability to predict fatigue properties of multi-principal element alloys (MPEAs) Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-07 Halid Can Yıldırım, Peter K. Liaw
Traditional fatigue assessment methods for new and unexplored metallic alloys is challenging due to very limited experimental data. To address this, we formulate the assessment within a conditional probability framework, allowing us to capture the complexities of uncertainty in fatigue predictions. We employ advanced probabilistic methods to account for both inherent material variability and model
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On the automatic construction of interface coupling operators for non-matching meshes by optimization methods Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-07 Radim Dvořák, José A. González
We propose a novel optimization technique for the automatic construction of interface operators for coupling non-matching 3D meshes. The core of the method lies in the use of localized Lagrange multipliers and least-squares approximation to find the optimal location of additional interface nodes, allowing the problem to be solved without modifying the meshes of the coupled subdomains and passing the
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A scaled boundary finite element approach for elastoplastic analysis and implementation in ABAQUS Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-05 Yunxuan Cui, Shukai Ya, Chongmin Song
In this study, a revised formulation based on the uniform strain method (Flanagan and Belytschko, 1981) and the scaled boundary finite element method (SBFEM) — a numerical method with arbitrarily shaped polyhedral elements — is introduced for three-dimensional elastoplastic analysis. The proposed formulation uses the average strain of each polyhedral element. By employing the octree decomposition algorithm
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Probabilistic entropy and relative entropy for the effective characteristics of the fiber-reinforced composites with stochastic interface defects Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-05 Marcin Kamiński
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Physics-Aware Neural Implicit Solvers for multiscale, parametric PDEs with applications in heterogeneous media Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-04 Matthaios Chatzopoulos, Phaedon-Stelios Koutsourelakis
We propose Physics-Aware Neural Implicit Solvers (PANIS), a novel, data-driven framework for learning surrogates for parametrized Partial Differential Equations (PDEs). It consists of a probabilistic, learning objective in which weighted residuals are used to probe the PDE and provide a source of data i.e. the actual PDE never needs to be solved. This is combined with a physics-aware implicit solver
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A variational-based non-smooth contact dynamics approach for the seismic analysis of historical masonry structures Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-03 Nicola A. Nodargi, Paolo Bisegna
A variational formulation of the non-smooth contact dynamics method is proposed to address the dynamic response of historical masonry structures modeled as systems of 3D rigid blocks and subjected to ground excitation. Upon assuming a unilateral-frictional contact law between the blocks, the equations of motions are formulated in a time-discrete impulse theorem format in the unknown block velocities
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Attention-based multi-fidelity machine learning model for fractional flow reserve assessment Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-02 Haizhou Yang, Brahmajee K. Nallamothu, C. Alberto Figueroa, Krishna Garikipati
Coronary Artery Disease (CAD) is one of the most common forms of heart disease, caused by a buildup of atherosclerotic plaque in the coronary arteries. When this buildup is extensive, it can result in obstructions in the lumen of the blood vessels (known as stenosis) that lead to insufficient delivery of essential molecules like oxygen to the heart. Fractional Flow Reserve (FFR), defined as the ratio
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Time-variant reliability-based robust optimization for structures with material degradation Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-02 Meide Yang, Hongfei Zhang, Dequan Zhang, Xu Han, Qing Li
Time-variant reliability-based robust design optimization (TRBRDO) has achieved certain progress recently for its ability to ensure both robustness of design and feasibility of time-variant probabilistic constraints. However, the existing TRBRDO methods have not specifically addressed the dynamic uncertainty of material degradation, and there is lack of a universal and efficient approach for this class
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Design and optimization of functionally-graded triangular lattices for multiple loading conditions Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-02 Junpeng Wang, Rüdiger Westermann, Xifeng Gao, Jun Wu
Aligning lattice infills with the principal stress directions in loaded objects is crucial for improving stiffness. However, this principle only works for a single loading condition, where the stress field in 2D is described by two orthogonal principal stress directions. In this paper, we introduce a novel approach for designing and optimizing triangular lattice structures to accommodate multiple loading
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Designing brittle fracture-resistant structures:A tensile strain energy-minimized topology optimization Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-02 Wenke Qiu, Qifu Wang, Liang Xia, Zhaohui Xia
This research proposes a novel method for designing fracture-resistant structures. By analyzing the relationship between tensile strain energy and phase field brittle fracture, it has been found that minimizing tensile strain energy can delay fracture and enhance resistance. Capitalizing on this insight, a new topology optimization method is proposed. This method focuses on minimizing tensile strain
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Parallel isogeometric boundary element analysis with T-splines on CUDA Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-09-02 M.A. Peres, G. Sanches, A. Paiva, P. Pagliosa
We present a framework for parallel isogeometric boundary element analysis (BEA) of elastic solids on CUDA. To deal with traction discontinuities, we propose a BEA model that supports multiple nodes and semi-discontinuous elements. The multiplicity of a node is defined by the number of regions containing any element influenced by the node. A region is a group of connected elements delimited by a closed
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Concurrent topology optimization of sandwich structures with multi-configuration and variable-diameter lattice infill Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-08-31 Wei Ji, Yingchun Bai, Chao Jiang, Jianhua Liu, Qingdong Yan, Xu Han
The superior stiffness-to-weight and strength-to-weight mechanical advantages of sandwich structures can be fully exploited through concurrent design of entire topology, infill configuration and density, where the high-performance yet complicated structure can be fabricated through additive manufacturing. However, the emerging design challenges are concurrent design updating related to sandwich topology
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Hierarchical rank-one sequence convexification for the relaxation of variational problems with microstructures Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-08-31 M. Köhler, T. Neumeier, M.A. Peter, D. Peterseim, D. Balzani
This paper presents an efficient algorithm for the approximation of the rank-one convex hull in the context of nonlinear solid mechanics. It is based on hierarchical rank-one sequences and simultaneously provides first and second derivative information essential for the calculation of mechanical stresses and the computational minimisation of discretised energies. For materials, whose microstructure
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Free-Form Deformation as a non-invasive, discrete unfitted domain method: Application to the time-harmonic acoustic response of a saxophone Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-08-30 Marie Jeanneteau, Théo Sentagne, Paul Oumaziz, Robin Bouclier, Jean-Charles Passieux
The Finite Element method, widely used for solving Partial Differential Equations, may result in suboptimal computational costs when computing smooth fields within complex geometries. In such situations, IsoGeometric Analysis often offers improved per degree-of-freedom accuracy but building analysis-suitable representation of complex shapes is generally not obvious. This paper introduces a non-invasive
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Application of proper orthogonal decomposition to flow fields around various geometries and reduced-order modeling Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-08-30 Yuto Nakamura, Shintaro Sato, Naofumi Ohnishi
This study is focused on a reduced-order model (ROM) based on proper orthogonal decomposition (POD) for unsteady flow around a stationary object, which allows prediction with different object geometry as a parameter. The conventional POD method is applicable only to data with the same computational grid for all snapshots. This study proposed a novel POD methodology that performs on flow snapshots,
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Dynamical system prediction from sparse observations using deep neural networks with Voronoi tessellation and physics constraint Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-08-30 Hanyang Wang, Hao Zhou, Sibo Cheng
Despite the success of various methods in addressing the issue of spatial reconstruction of dynamical systems with sparse observations, spatio-temporal prediction for sparse fields remains a challenge. Existing Kriging-based frameworks for spatio-temporal sparse field prediction fail to meet the accuracy and inference time required for nonlinear dynamic prediction problems. In this paper, we introduce
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A mixed-dimensional formulation for the simulation of slender structures immersed in an incompressible flow Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-08-30 Fabien Lespagnol, Céline Grandmont, Paolo Zunino, Miguel A. Fernández
We consider the simulation of slender structures immersed in a three-dimensional (3D) flow. By exploiting the special geometric configuration of the slender structures, this particular problem can be modeled by mixed-dimensional coupled equations. Taking advantage of the slenderness of the structure and thus considering 3D/1D coupled problems raise several challenges and difficulties. From a mathematical
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Three-dimensional continuum point cloud method for large deformation and its verification Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-08-30 Peter M. Schaefferkoetter, Young-Cheol Yoon, Jeong-Hoon Song
This study presents a strong form based meshfree collocation method, which is named Continuum Point Cloud Method, to solve nonlinear field equations derived from classical mechanics for deformed bodies in three-dimensional Euclidean space. The method and its implementation are benchmarked against a nonlinear vector field using manufactured solutions. The analysis of mechanical fields firstly focuses
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Unsupervised machine learning classification for accelerating FE[formula omitted] multiscale fracture simulations Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-08-30 Souhail Chaouch, Julien Yvonnet
An approach is proposed to accelerate multiscale simulations of heterogeneous quasi-brittle materials exhibiting an anisotropic damage response. The present technique uses unsupervised machine learning classification based on k-means clustering to select integration points in the macro mesh within an FE strategy to track redundant micro nonlinear problems and to avoid unnecessary Representative Volume
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Peridynamics-fueled convolutional neural network for predicting mechanical constitutive behaviors of fiber reinforced composites Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-08-29 Binbin Yin, Jiasheng Huang, Weikang Sun
Despite advancements in predicting the constitutive relationships of composite materials, characterizing the effects of microstructural randomness on their mechanical behaviors remains challenging. In this study, we propose a data-driven convolutional neural network (CNN) to efficiently predict the stress-strain curves containing three key material features (Tensile strength, modulus, and toughness)
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Damage identification method based on interval regularization theory Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-08-29 Shuwei Qian, Qinghe Shi, Chen Yang, Zhenxian Luo, Liuyang Duan, Fengling Zhao
In the field of damage identification, traditional regularization methods neglect the impact of uncertainty factors on the selection of regularization parameters, leading to a decrease in the accuracy of damage identification. Therefore, this study proposes a damage identification based on interval truncated singular value decomposition (DI-ITSVD) method that considers the uncertainty in the selection
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A novel weight index-based uniform partition technique of multi-dimensional probability space for structural uncertainty quantification Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-08-28 Hanshu Chen, Yongxin Gao, Dixiong Yang, Zeng Meng, Zhuojia Fu
Accurately and efficiently achieving the uncertainty quantification of engineering structures is a challenging issue. The direct probability integral method (DPIM) provides an effective pathway to address this issue. However, the key partition technique via Voronoi cell of DPIM requires a prohibitive computational burden for multi-dimensional probability space. Moreover, due to the distributed nonuniformity
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Equivariant graph convolutional neural networks for the representation of homogenized anisotropic microstructural mechanical response Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-08-27 Ravi Patel, Cosmin Safta, Reese E. Jones
Composite materials with different microstructural material symmetries are common in engineering applications where grain structure, alloying and particle/fiber packing are optimized via controlled manufacturing. In fact these microstructural tunings can be done throughout a part to achieve functional gradation and optimization at a structural level. To predict the performance of particular microstructural
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Topology optimization with a finite strain nonlocal damage model using the continuous adjoint method Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-08-27 Jike Han, Kozo Furuta, Tsuguo Kondoh, Kazuhiro Izui, Shinji Nishiwaki, Kenjiro Terada
This study presents a unified formulation of topology optimization with a finite strain nonlocal damage model using the continuous adjoint method. For the primal problem to describe the material response including deterioration, we consider the standard Neo–Hookean constitutive model and incorporate crack phase-field theory for brittle fracture within the finite strain framework. For the optimization
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Greedy identification of latent dynamics from parametric flow data Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-08-27 M. Oulghelou, A. Ammar, R. Ayoub
Projection-based reduced-order models (ROMs) play a crucial role in simplifying the complex dynamics of fluid systems. Such models are achieved by projecting the Navier-Stokes equations onto a lower-dimensional subspace while preserving essential dynamics. However, this approach requires prior knowledge of the underlying high-fidelity model, limiting its effectiveness when applied to black-box data
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CMA-ES-based topology optimization accelerated by spectral level-set-boundary modeling Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-08-27 Shin Tanaka, Garuda Fujii
Topology optimization commonly encounters several challenges, such as ill-posedness, grayscale issues, interdependencies among design variables, , and . Furthermore, addressing the latter two concurrently presents considerable difficulty. In this study, we introduce a framework aimed at mitigating all the above obstacles . The objective is to achieve optimal configurations in a notably reduced timeframe