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A PINN-DeepONet framework for extracting turbulent combustion closure from multiscalar measurements Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-28 Arsalan Taassob, Anuj Kumar, Kevin M. Gitushi, Rishikesh Ranade, Tarek Echekki
In this study, we develop a novel framework to extract turbulent combustion closure, including closure for species chemical source terms, from multiscalar and velocity measurements in turbulent flames. The technique is based on a physics-informed neural network (PINN) that combines models for velocity and scalar measurements and a deep operator network (DeepONet) to accommodate spatial measurements
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Points of inflection of special eigenvalue functions as indicators of stiffness maxima/minima of proportionally loaded structures Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-28 A. Wagner, J. Kalliauer, M. Aminbaghai, H.A. Mang
The stiffness of a proportionally loaded structure may continuously increase or decrease. As a special exception, it may be constant. On the other hand, an initially stiffening (softening) structure may turn into a softening (stiffening) structure. At the load level of such a change the stiffness of the structure attains an extreme value. The task of this work is to present mathematical conditions
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Fatigue crack evaluation of butt weld joints in full-scale aluminum alloy joints: Experimental and numerical study of traction structural stress Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-28 Chao Wang, Tao Zhu, Bing Yang, Shoune Xiao, Guangwu Yang
In this study, the digital image correlation technique is used to determine the inconsistent mechanical properties and microstructure non-uniformity in different regions of aluminum alloy butt welded joints. A state monitoring method is proposed based on phased-array ultrasonic total focus imaging for crack evolution. A series of surface fatigue crack propagation tests on full-scale aluminum alloy
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Topology optimization of finite strain elastoplastic materials using continuous adjoint method: Formulation, implementation, and applications Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-27 Jike Han, Kozo Furuta, Tsuguo Kondoh, Shinji Nishiwaki, Kenjiro Terada
This study presents a unified formulation of topology optimization for finite strain elastoplastic materials. As the primal problem to describe the elastoplastic behavior, we consider the standard -plasticity model incorporated into Neo-Hookean elasticity within the finite strain framework. For the optimization problem, the objective function is set to accommodate both single and multiple objectives
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An improved approximate integral method for nonlinear reliability analysis Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-27 Zhenzhong Chen, Guiming Qiu, Xiaoke Li, Zan Yang, Ge Chen, Xuehui Gan
In order to evaluate the failure probability corresponding to the Limit State Function (LSF) in structural reliability, the First Order Reliability Method (FORM) linearizes the LSF and directly calculates the failure probability based on the Most Probable Point (MPP). But this method is unable to effectively handle nonlinear problems. The Second Order Reliability Method (SORM), on the other hand, utilizes
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Non-probabilistic reliability analysis with both multi-super-ellipsoidal input and fuzzy state Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-27 Linxiong Hong, Shizheng Li, Mu Chen, Pengfei Xu, Huacong Li, Jiaming Cheng
In real-world engineering scenarios, incomplete uncertainty information and ambiguous failure states persist and pose significant challenges for structural reliability analysis. This paper introduces a non-probabilistic fuzzy reliability analysis (NPFRA) model featuring fuzzy output states, where the input uncertainties are quantified by a multi-super-ellipsoidal model. Initially, we define both reliability
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Variational temporal convolutional networks for I-FENN thermoelasticity Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-27 Diab W. Abueidda, Mostafa E. Mobasher
Machine learning (ML) has been used to solve multiphysics problems like thermoelasticity through multi-layer perceptron (MLP) networks. However, MLPs have high computational costs and need to be trained for each prediction instance. To overcome these limitations, we introduced an integrated finite element neural network (I-FENN) framework to solve transient thermoelasticity problems in Abueidda and
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Refined cyclic R-curve determination through residual crack tip stress reduction by annealing Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-27 Lukas Walch, Thomas Klünsner, Gerald Ressel, Stefan Marsoner, Reinhard Pippan, Alfred Hackl, Harald Leitner, Anton Hohenwarter
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Influence of the as-built surface and a T6 heat treatment on the fatigue behavior of additively manufactured AlSi10Mg Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-27 Patrick Lehner, Bastian Blinn, Tong Zhu, Ali Al-Zuhairi, Marek Smaga, Roman Teutsch, Tilmann Beck
As the fatigue strength of materials manufactured via lase-based powder bed fusion (PBF-LB) is highly influenced by process-induced defects and the “as-built” surface, in this work fatigue tests at specimens made of AlSi10Mg with “as-built” and polished surface condition were conducted. In this context also the defect tolerance of the material, and hence its ability to counteract process-induced notch
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On the benefits of a multiscale domain decomposition method to model-order reduction for frictional contact problems Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-26 D. Zeka, P.-A. Guidault, D. Néron, M. Guiton
In this paper, the efficiency of a multiscale strategy based on a domain decomposition method (DDM) for model-order reduction of time-dependent frictional contact problems is presented. The proposed strategy relies on the LArge Time INcrement (LATIN) nonlinear solver combined with model reduction based on the Proper Generalized Decomposition (PGD). The LATIN presents a robust treatment of contact conditions
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Topology optimization with accessibility constraint from multiple bi-directions using fictitious anisotropic diffusion equation based on coupled fictitious physical model Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-26 Mikihiro Tajima, Takayuki Yamada
In this study, we focus on topology optimization considering the accessibility constraint, which is a constraint that removes inaccessible regions from multiple linear directions. To detect inaccessible regions, we propose a method using a fictitious anisotropic diffusion equation. The proposed equation can simultaneously consider access from a bi-direction, which means one access direction and its
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Multiscale formulation for materials composed by a saturated porous matrix and solid inclusions Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-26 Reinaldo A. Anonis, Javier L. Mroginski, Pablo J. Sánchez
Despite all the progress achieved in the characterization of heterogeneous materials by using multiscale paradigms based on the Representative Volume Element concept (RVE), there are still many aspects that demand ongoing development. We mention, for instance, in-homogeneous media with internal micro-structure comprising a mixture of components that require a dissimilar number/character of primary
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A robust radial point interpolation method empowered with neural network solvers (RPIM-NNS) for nonlinear solid mechanics Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-26 Jinshuai Bai, Gui-Rong Liu, Timon Rabczuk, Yizheng Wang, Xi-Qiao Feng, YuanTong Gu
In this work, we proposed a robust radial point interpolation method empowered with neural network solvers (RPIM-NNS) for solving highly nonlinear solid mechanics problems. It is enabled by neural network solvers via minimizing an energy-based functional loss. The RPIM-NNS has the following key ingredients: (1) It uses radial basis functions (RBFs) for displacement interpolation at arbitrary points
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Data-driven identification of stable sparse differential operators using constrained regression Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-26 Aviral Prakash, Yongjie Jessica Zhang
Identifying differential operators from data is essential for the mathematical modeling of complex physical and biological systems where massive datasets are available. These operators must be stable for accurate predictions for dynamics forecasting problems. In this article, we propose a novel methodology for learning differential operators that are theoretically linearly stable and have sparsity
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A phase-field fracture model in thermo-poro-elastic media with micromechanical strain energy degradation Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-24 Yuhao Liu, Keita Yoshioka, Tao You, Hanzhang Li, Fengshou Zhang
This work extends the hydro-mechanical phase-field fracture model to non-isothermal conditions with micromechanics based poroelasticity, which degrades Biot’s coefficient not only with the phase-field variable (damage) but also with the energy decomposition scheme. Furthermore, we propose a new approach to update porosity solely determined by the strain change rather than damage evolution as in the
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An MPMD approach coupling electromagnetic continuum mechanics approximations in ALEGRA Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-24 Allen C. Robinson, Richard R. Drake, Christopher B. Luchini, Ramón J. Moral, John H.J. Niederhaus, Sharon V. Petney
Two complementary approximations for describing aspects of continuum electromagnetics in moving media are discussed: electroquasistatic and magnetoquasistatic. Each has been implemented in the finite element shock code ALEGRA for modeling dynamic electromechanical phenomena on typical engineering time scales, with fully integrated circuit coupling (Niederhaus et al. 2023). The approximations can be
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Topological derivative based sensitivity analysis for three-dimensional discrete variable topology optimization Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-24 Kai Sun, Gengdong Cheng, Yuan Liang
This study introduces a novel Topological Derivative-based Sensitivity Analysis (TDSA) methodology for three-dimensional (3D) discrete variable topology optimization. Recently, the authors pointed out that the discrete variable sensitivity can be related to the topological derivative, and thus can be rationally approximated by the specially customized topological derivative for plane stress problems
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Finite elements for Matérn-type random fields: Uncertainty in computational mechanics and design optimization Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-24 Tobias Duswald, Brendan Keith, Boyan Lazarov, Socratis Petrides, Barbara Wohlmuth
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Wrinkle-free membranes through spatioselective exposure J. Mech. Phys. Solids (IF 5.0) Pub Date : 2024-06-24 Guangliang Qi, Heng Gao, Jianyue Wang, Guozhong Zhao, Dzianis Marmysh, Zhan Kang, Kexi Zhu, Ming Li
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Robustness and diversity of disordered structures on sound absorption and deformation resistance J. Mech. Phys. Solids (IF 5.0) Pub Date : 2024-06-23 Yong Liu, Baizhan Xia, Ke Liu, Ye Zhou, Kai Wei
Biostructures exhibit excellent physical properties, such as sound absorption, compression resistance, and fatigue resistance. These properties of the same species are robust in the same environment and diverse in distinct environments. Unlocking the robustness and diversity of biostructural properties will provide high flexibility in the design of engineering structures in different scenarios. However
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Machine learning predictive model for dynamic response of rising bubbles impacting on a horizontal wall Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-22 Xiangyu Zhang, Yang Zhang, K.M. Liew
The integrated behavior of the fluid flow, encompassing variation of some specific response, has received more attention than mere examination of velocity and pressure distributions. A machine learning framework is introduced for the first time to elucidate and predict the complex fluid dynamic responses across varying time scales. For the same class of fluid processes, the dynamic response curves
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Mesh-free SPH modelling of sediment scouring and flushing considering grains transport and transformation Eng. Appl. Comput. Fluid Mech. (IF 5.9) Pub Date : 2024-06-18 Rongzhao Zhang, Wen Xiong, Xiaolong Ma, C. S. Cai
Sediment scour numerical simulation plays a critical role in the design of water-resistant foundation engineering, and this paper addresses a significant gap in most related studies, which often ov...
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Projection-based reduced-order modelling of time-periodic problems, with application to flow past flapping hydrofoils Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-21 Jacob E. Lotz, Gabriel D. Weymouth, Ido Akkerman
Simulating forced time-periodic flows in industrial applications presents significant computational challenges, partly due to the need to overcome costly transients before achieving time-periodicity. Reduced-order modelling emerges as a promising method to speed-up computations. We extend upon the work of Lotz et al. (2024) where a time-periodic space–time model is introduced. We present a time-periodic
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Enhancing CFD solver with Machine Learning techniques Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-21 Paulo Sousa, Carlos Veiga Rodrigues, Alexandre Afonso
This study addresses the computational challenges in fluid flow simulations arising from demanding computational grids, required to capture the temporal and length scales involved. Our approach focuses on the pressure solver, as this is a resource-intensive component in Computational Fluid Dynamics (CFD) solvers. We achieve this by integrating a Machine Learning (ML) surrogate model with an incompressible
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Koopman dynamic-oriented deep learning for invariant subspace identification and full-state prediction of complex systems Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-21 Jiaxin Wu, Min Luo, Dunhui Xiao, Christopher C. Pain, Boo Cheong Khoo
One strategy for predicting the state of nonlinear dynamical systems (typically of high dimensionality) is global linearization, such as utilizing the Koopman analysis model to transform the system state into an invariant subspace that evolves linearly. A critical challenge in the Koopman model is designing or deriving observation functions, typically nonlinear, to linearize the dynamical systems.
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Exploring VHCF response in L-PBF AlSi7Mg Alloy: Influence of T6 heat treatment on microstructural characteristics and defect distribution Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-21 Md Mehide Hasan Tusher, Ayhan Ince
Laser Powder Bed Fusion (L-PBF) is a popular technique for making intricate parts in various fields. Assessing the load-bearing capacity of components fabricated through L-PBF technique is crucial for broadening their applications across various industrial sectors. While prior research has primarily concentrated on static loads, cyclic loading is critical in real-world engineering applications. The
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Investigation of fatigue durability and influencing factors of coil springs: A case study for metro vehicles Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-21 Yang Liu, Zefeng Wen, Xingwen Wu, Bo Peng, Yabo Zhou, Gongquan Tao
This study investigates the fatigue failure behavior of coil compression springs and various influencing factors. The intrinsic and induced causes of fatigue failure in metro steel springs were revealed by systematic tests. Macroscopic observation and metallographic analysis of the fracture indicate that decarburization, which leads to a reduction in the fatigue limit of the material, was the essential
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Fatigue crack propagation anisotropy of an Al–Zn–Mg–Cu super-thick plate Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-21 H. Wang, Z.J. Zhang, J.P. Hou, B.S. Gong, H.Z. Liu, H.R. Abedi, G. Purcek, H. Yanar, M. Demirtas, Z.F. Zhang
The fatigue crack propagation (FCP) rate is an important performance index in the aerospace industry for the safe lifespan evaluation. In this study, the FCP anisotropy of an Al–Zn–Mg–Cu super-thick rolled plate was analyzed. The results show that as the depth increases, the FCP anisotropy gradually becomes significant especially in the low ΔK region. The specimens tested along the transverse direction
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Toward developing remanufactured Ti6Al4V alloys with high fatigue crack growth resistance by in-situ cooling during laser remanufacturing Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-21 Wanli Ling, Xiaoping Wang, Qiyu Gao, Zhuanni Gao, Xiaoming Wang, Xiaohong Zhan
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Bayesian structural model updating with multimodal variational autoencoder Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-20 Tatsuya Itoi, Kazuho Amishiki, Sangwon Lee, Taro Yaoyama
A novel framework for Bayesian structural model updating is presented in this study. The proposed method utilizes the surrogate unimodal encoders of a multimodal variational autoencoder (VAE). The method facilitates an approximation of the likelihood when dealing with a small number of observations. It is particularly suitable for high-dimensional correlated simultaneous observations applicable to
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Interface PINNs (I-PINNs): A physics-informed neural networks framework for interface problems Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-20 Antareep Kumar Sarma, Sumanta Roy, Chandrasekhar Annavarapu, Pratanu Roy, Shriram Jagannathan
We present a novel physics-informed neural networks (PINNs) framework for modeling interface problems, termed Interface PINNs (I-PINNs). I-PINNs uses different neural networks for any two subdomains separated by a sharp interface such that the neural networks differ only through their activation functions while the other parameters remain identical. The performance of I-PINNs, conventional PINNs, and
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Multilevel domain decomposition-based architectures for physics-informed neural networks Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-20 Victorita Dolean, Alexander Heinlein, Siddhartha Mishra, Ben Moseley
Physics-informed neural networks (PINNs) are a powerful approach for solving problems involving differential equations, yet they often struggle to solve problems with high frequency and/or multi-scale solutions. Finite basis physics-informed neural networks (FBPINNs) improve the performance of PINNs in this regime by combining them with an overlapping domain decomposition approach. In this work, FBPINNs
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Fatigue life prediction of metal materials under random loads based on load spectrum extrapolation Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-20 Que Wu, Yongxiang Zhao, Xintian Liu
The impact of random loading on the fatigue life of mechanical components contains numerous uncertainties. This study aims to enhance the precision of life prediction by developing mean and amplitude distribution models based on the road load spectrum and extrapolating the operational load beyond the threshold value in the time domain. Additionally, it considers the influence of high cycle fatigue
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Fatigue response of wire-arc additive manufactured nickel-aluminum bronze (NAB) in the post-annealed condition Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-20 Shawkat I. Shakil, Sajad Shakerin, Keivan Rahmdel, Mohsen Mohammadi, Andrea Tridello, Davide S. Paolino, Shuai Shao, Nima Shamsaei, Meysam Haghshenas
Nickel-aluminum bronze (NAB) is chosen for critical applications like marine propellers, pump components, and offshore structures due to its exceptional mechanical properties and corrosion resistance. Considering the utmost importance of the fatigue performance of NAB in such applications, this study investigates the fatigue performance of wire arc additive manufactured (WAAM) NAB in the annealed (675 °C
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Effect of Mode II in the mixed-mode on the fatigue crack growth behaviour for SAPH440 material Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-20 Jong-Sung Kim, Dong-Jun Kim, Seok-Pyo Hong
In this study, the effect of mixed-mode loading on fatigue crack growth under varying applied loads for SAPH440 material was experimentally investigated. Previous research has either examined the effect of mixed-mode loading at a fixed angle or its effect on slanted cracks. This paper uses a CTS specimen and a loading device to apply varying angles of load during Mode I loading conditions. Initially
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Study on low fatigue damage behavior of TC17 titanium alloy with basket-weave microstructure Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-20 Yanli Lu, Jialiang Jiang, Hong Wang, Hanrui Dang, Menghan He
Room-temperature low-fatigue tests were conducted on TC17 titanium alloy, and the low-fatigue damage behavior of TC17 titanium alloy with a basket-weave microstructure at room temperature was investigated. The results show that TC17 titanium alloy exhibits different cyclic hardening/softening phenomena at different strain amplitude () levels, which was closely related to the magnitude of strain amplitude
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Hyperelasticity of blood clots: Bridging the gap between microscopic and continuum scales J. Mech. Phys. Solids (IF 5.0) Pub Date : 2024-06-20 Nicholas Filla, Beikang Gu, Jixin Hou, Kenan Song, He Li, Ning Liu, Xianqiao Wang
The biomechanical properties of blood clots, which are dictated by their compositions and micro-structures, play a critical role in determining their fates, i.e., occlusion, persistency, or embolization in the human circulatory system. While numerous constitutive models have emerged to describe the biomechanics of blood clots, most of these models have primarily focused on the macroscopic deformation
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Mechanics and thermal analyses of microfluidic nerve-cooler system J. Mech. Phys. Solids (IF 5.0) Pub Date : 2024-06-20 Dongjun Bai, Zichen Zhao, Raudel Avila, Danli Xia, Yonggang Huang, John A. Rogers, Zhaoqian Xie
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Solving large-scale variational inequalities with dynamically adjusting initial condition in physics-informed neural networks Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-19 Dawen Wu, Ludovic Chamoin, Abdel Lisser
This work aims to solve large-scale variational inequalities (VIs), which are equivalent to high-dimensional systems of ordinary differential equations (ODEs). The existing physics-informed neural network (PINN) approach (Wu and Lisser, 2023) shows superior performance for VIs with less than 1000 variables, but fails for VIs of larger size, due to the increasing number of equations and the requirement
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Bayesian reduced-order deep learning surrogate model for dynamic systems described by partial differential equations Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-19 Yuanzhe Wang, Yifei Zong, James L. McCreight, Joseph D. Hughes, Alexandre M. Tartakovsky
We propose a reduced-order deep-learning surrogate model for dynamic systems described by time-dependent partial differential equations. This method employs space–time Karhunen–Loève expansions (KLEs) of the state variables and space-dependent KLEs of space-varying parameters to identify the reduced (latent) dimensions. Subsequently, a deep neural network (DNN) is used to map the parameter latent space
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A rapid testing method for assessing mode I fatigue delamination of carbon fibre-reinforced polymer Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-18 Sergi Parareda, Daniel Casellas, Jordi Llobet, Jordi Renart, Antonio Mateo
Characterising fatigue delamination in composite materials following standardised testing protocols is often associated with high costs and significant time investment. Therefore, it can be helpful to have a testing strategy to reduce the testing time and easily assess the fatigue delamination of multiple materials and conditions. This work shows how to apply one of the new rapid testing techniques
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Grain size sensitive modelling of the nonlinear behaviour and fatigue damage of Inconel 718 superalloy Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-18 Serge Kruch, Louise Toualbi
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A unified discontinuous Galerkin formulation for interfacial multiphysics modeling of thermo-chemically driven fracture J. Mech. Phys. Solids (IF 5.0) Pub Date : 2024-06-18 Daniel Pickard, Raúl Radovitzky
Many engineering and natural materials exhibit coupled thermo-chemo-mechanical phenomena, which can result in embrittlement and fracture. These fractures, in turn, can alter the subsequent thermal, chemical, and mechanical response. We present a theoretical formulation and computational framework for the analysis of thermo-chemically fractured solids, with emphasis on the post-fracture thermal and
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An immersed boundary fast meshfree integration methodology with consistent weight learning Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-17 Jijun Ying, Dongdong Wang, Like Deng, Zhiwei Lin
An immersed boundary fast integration methodology featured by a consistent weight learning is proposed to accelerate Galerkin meshfree computation. In the proposed approach, the problem domain is embedded in a rectangular spatial domain discretized by regular distributions of meshfree nodes and integration sampling points with virtual integration cells. A trimming operation of the rectangular spatial
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Phase-field modeling of fracture with physics-informed deep learning Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-17 M. Manav, R. Molinaro, S. Mishra, L. De Lorenzis
We explore the potential of the deep Ritz method to learn complex fracture processes such as quasistatic crack nucleation, propagation, kinking, branching, and coalescence within the unified variational framework of phase-field modeling of brittle fracture. We elucidate the challenges related to the neural-network-based approximation of the energy landscape, and the ability of an optimization approach
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Investigating the influence of infill patterns and mesh modifiers on fatigue properties of 3D printed polymers Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-17 Mohamad Alagheband, Qian Zhang, Sungmoon Jung
This research examines the structural performance of Tough Polylactic Acid (PLA) components made through material extrusion, focusing on tensile and fatigue behaviors. Tough PLA, similar to regular PLA, exhibits improved toughness comparable to acrylonitrile butadiene styrene (ABS). As the application of 3D printed parts for functional use expands, understanding the mechanical attributes of materials
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The fatigue behavior of TC17 titanium alloy under different test environments: The effect of saltwater on fatigue failure characteristics and life estimation Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-17 Wang Jinlong, Peng Wenjie, Liu Tianlong, Shi Zeyu
The study of fatigue behavior in saltwater for TC17 titanium alloy has become a potential research issue in practical engineering and theoretical scientific research. In this study, it is found from the designed fatigue test that the corrosiveness of the test environment has a significant negative effect on the fatigue life of titanium alloy, and the distributions of fatigue life in 3.5%NaCl solution
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Non-stationary vibration fatigue life prediction of automotive components based on long short-term memory network Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-17 Chun Zhang, Ruoqing Wan, Junru He, Jian Yu, Yinjie Zhao
Automotive components are prone to fatigue failure as a result of the long-term effects of vibration loads. Due to the significant non-stationarity of irregular excitations from various road surfaces, the classical frequency-domain method struggles to accurately estimate the fatigue life of automotive components. Based on long short-term memory (LSTM) networks, an efficient time-domain method for non-stationary
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Vibration fatigue assessment and crack propagation mechanism of directionally solidified superalloy with film cooling holes Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-17 Hao Lu, Yeda Lian, Jundong Wang, Zhixun Wen, Zhenwei Li, Zhufeng Yue
Film cooling represents a critical protective measure for turbine blades, yet the presence of film cooling holes (FCHs) under vibrational loads can significantly impact structural strength and integrity. This study conducts random fatigue tests on DZ125L directionally solidified superalloy specimens with FCHs. It investigates how various FCH types and vibration signal intensities influence the vibration
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Comparative investigation of fatigue properties between additively and conventionally manufactured Invar 36 alloy Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-16 Xinxi Liu, Yuhao Zhou, Jie Chen, Dayong An, Xifeng Li, Jun Chen
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Nonlinear fatigue damage constrained topology optimization Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-15 Jinyu Gu, Zhuo Chen, Kai Long, Yingjun Wang
In engineering applications, plenty of components are subjected to variable-amplitude cyclic loading, resulting in fatigue damage, which is one of the main forms of structural damage. While the linear damage rule has long served as a fundamental approach, its limitations necessitate advancements for more accurate fatigue life predictions. Hence, this paper introduces a pioneering method termed nonlinear
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Data-driven aerodynamic shape design with distributionally robust optimization approaches Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-15 Long Chen, Jan Rottmayer, Lisa Kusch, Nicolas Gauger, Yinyu Ye
We formulate and solve data-driven aerodynamic shape design problems with distributionally robust optimization (DRO) approaches. DRO aims to minimize the worst-case expected performance in a set of distributions that is informed by observed data with uncertainties. Building on the findings of the work Gotoh, et al. (2018), we study the connections between a class of DRO and robust design optimization
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Topology optimization of smart structures with embedded piezoelectric stack actuators using a composite geometry projection method Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-15 Breno Vincenzo de Almeida, Renato Pavanello, Matthijs Langelaar
The design of smart structures is challenging because of the integrated electromechanical modelling and optimization of actuators, sensors and load-bearing structures. To simplify the design process, it is common to decouple some of the components and physics and develop each part separately, which could lead to suboptimal systems. To improve the overall design of active structures, we propose an integrated
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Physics-informed MeshGraphNets (PI-MGNs): Neural finite element solvers for non-stationary and nonlinear simulations on arbitrary meshes Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-15 Tobias Würth, Niklas Freymuth, Clemens Zimmerling, Gerhard Neumann, Luise Kärger
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Fatigue performance testing and life prediction of welded fuel pipes Int. J. Fatigue (IF 5.7) Pub Date : 2024-06-15 Xingkeng Shen, Ying Dai, Xinmin Chen, Wei Liu, Yishang Zhang, Hongmin Zhou
The external piping system which connects power devices, valve control devices, actuators, etc. is an essential system on the aero engine. Under the combined effects of high fluid pressure and external vibration, multiaxial fatigue failure is the main factor that seriously affects the safety of the piping system. The objective of this paper is to establish the multiaxial fatigue life model for the
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Modified error-in-constitutive-relation (MECR) framework for the characterization of linear viscoelastic solids J. Mech. Phys. Solids (IF 5.0) Pub Date : 2024-06-15 Marc Bonnet, Prasanna Salasiya, Bojan B. Guzina
We develop an error-in-constitutive-relation (ECR) approach toward the full-field characterization of linear viscoelastic solids described within the framework of standard generalized materials. To this end, we formulate the viscoelastic behavior in terms of the (Helmholtz) free energy potential and a dissipation potential. Assuming the availability of full-field interior kinematic data, the constitutive
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Mineral asperities reinforce nacre through interlocking and friction-like sliding J. Mech. Phys. Solids (IF 5.0) Pub Date : 2024-06-15 Hao Li, Kun Geng, Bingzhan Zhu, Qiang Zhang, Yi Wen, Zuoqi Zhang, Yanan Yuan, Huajian Gao
While the surface asperities of mineral platelets are widely believed to play important roles in stiffening, strengthening, and toughening nacre, their effects have not been thoroughly investigated. Here, a computationally efficient bar-spring model is adopted to simulate, as platelets with multiple interfacial asperities slide over each other, the tensile force versus elongation behaviors as well
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Buckling mode constraints for topology optimization using eigenvector aggregates Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-14 Bao Li, Graeme J. Kennedy
Buckling-constrained structural design problems have conventionally prioritized optimizing the buckling load factor with less consideration given to the buckling mode shape. In this work, mode shape constraints are imposed within a topology optimization problem using an eigenvector aggregate constraint that is a weighted sum of homogeneous quadratic functions of the linearized buckling eigenvectors
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A novel section–section potential for short-range interactions between plane beams Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-14 A. Borković, M.H. Gfrerer, R.A. Sauer, B. Marussig, T.Q. Bui
We derive a novel formulation for the interaction potential between deformable fibers due to short-range fields arising from intermolecular forces. The formulation improves the existing section–section interaction potential law for in-plane beams by considering an offset between interacting cross sections. The new law is asymptotically consistent, which is particularly beneficial for computationally
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Multiscale topology optimization for the design of spatially-varying three-dimensional lattice structure Comput. Methods Appl. Mech. Eng. (IF 6.9) Pub Date : 2024-06-14 Dongjin Kim, Jaewook Lee
This paper introduces three dimensional (3D) topology optimization specifically tailored for designing spatially-varying primitive-cubic (CP) type lattice structures. The developed design process consists of three steps: pre-processing, main processing, and post-processing. In the pre-processing step, a surrogate model between lattice geometry variables and effective elasticity tensor is constructed