Article
Multidisciplinary Sciences
Hui Wang, Wenming Cheng, Min Zhang, Run Du, Wei Xiang
Summary: A non-gradient RTO method was proposed by combining the IPTO algorithm with various methods, including the probabilistic approach, linear theory superposition, Monte Carlo method, and weighted combination method to address the robust topology optimization problem. The new method effectively tackles structural RTO problems considering loading uncertainty by minimizing the weighted sum of expectation and standard deviation of structural compliance. Different control parameters in the new RTO method also impact structural optimization results.
ARABIAN JOURNAL FOR SCIENCE AND ENGINEERING
(2021)
Article
Computer Science, Interdisciplinary Applications
Van-Nam Hoang, Trung Pham, Sawekchai Tangaramvong, Stephane P. A. Bordas, H. Nguyen-Xuan
Summary: This paper presents a novel robust concurrent topology optimization method for the design of uniform/non-uniform porous infills under the accidental change of loads. The method directly models multiscale structures and seeks robust designs by simultaneously optimizing macro- and microscopic structures through the minimization of the weighted sum of the expected compliance and standard deviation.
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2021)
Article
Engineering, Multidisciplinary
Khader M. Hamdia, Hamid Ghasemi, Xiaoying Zhuang, Timon Rabczuk
Summary: An efficient MLMC method for topology optimization of flexoelectric structures is proposed in this study, utilizing NURBS-based IGA to solve governing equations and GA for integer-valued optimization. Material properties and volume fraction uncertainties are taken into account, resulting in the determination of minimum number of simulations required under different error tolerances.
ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS
(2022)
Article
Engineering, Multidisciplinary
Sheng Chu, Mi Xiao, Liang Gao, Yan Zhang, Jinhao Zhang
Summary: This paper focuses on robust topology optimization for fiber-reinforced composite structures under loading uncertainty, presenting an effective method for simultaneous optimization of fiber angles and structural topology. The study uses a new parameterization scheme and Monte Carlo simulation method to handle the optimization problem, sensitivity analysis, and Kriging metamodel for reducing computational cost.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Engineering, Multidisciplinary
Takayuki Nishino, Junji Kato
Summary: This study proposes a robust topology optimization method that considers uncertainties in load magnitude and direction on geometrically nonlinear structures. The method combines expected value and standard deviation of end-compliance, using quadratic approximation to reduce computational cost. The importance of considering geometrical nonlinearity for obtaining robust structures is emphasized through numerical examples.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING
(2021)
Article
Engineering, Multidisciplinary
Seyyed Ali Latifi Rostami, Amin Kolahdooz, Jian Zhang
Summary: This research introduces a novel algorithm for robust topology optimization of continuous structures under material and loading uncertainties by combining ESO method with XFEM. The method eliminates the need for post-processing and improves reliability in material and loading uncertainty, showcasing advantages over traditional methods.
ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS
(2021)
Article
Physics, Fluids & Plasmas
Mauro Rigo, Benjamin Hall, Morten Hjorth-Jensen, Alessandro Lovato, Francesco Pederiva
Summary: We propose a variational Monte Carlo method that uses an artificial neural network representation of the ground-state wave function in the occupation number formalism for solving the nuclear many-body problem. We develop a memory-efficient version of the stochastic reconfiguration algorithm to train the network by minimizing the Hamiltonian's expectation value. By benchmarking against widely used nuclear many-body methods, we demonstrate that our method outperforms coupled-cluster and produces energies in excellent agreement with numerically exact full configuration-interaction values.
Article
Energy & Fuels
Neha Patankar, Hadi Eshraghi, Anderson Rodrigo de Queiroz, Joseph F. DeCarolis
Summary: This study extends and applies robust optimization methods to the energy system optimization model in the United States to explore low carbon pathways. By considering future uncertainty, the robust strategy has shown significant cost savings and improved cost control.
ENERGY STRATEGY REVIEWS
(2022)
Article
Computer Science, Interdisciplinary Applications
Van-Nam Hoang, Trung Pham, Duc Ho, H. Nguyen-Xuan
Summary: This paper presents a novel multiscale topology optimization approach that can optimize incompressible multi-material designs at both macro and micro scales, and demonstrates the effectiveness of the technique through examples of solving incompressible porous multi-material designs under single and multiple random loads.
ENGINEERING WITH COMPUTERS
(2022)
Article
Engineering, Multidisciplinary
Alberto P. Torres, James E. Warner, Miguel A. Aguilo, James K. Guest
Summary: This paper presents an efficient approach for topology optimization under uncertainty using SROMs, which improves efficiency and accuracy over traditional Monte Carlo methods.
INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING
(2021)
Article
Engineering, Multidisciplinary
Fernando Valentini, Olavo M. Silva, Eduardo Lenz Cardoso
Summary: This work investigates the robust design of structures with minimum dynamic response under uncertainties in excitation frequency, using topology optimization and Monte Carlo Simulation with stratified sampling. The proposed formulation leads to structures with minimum dynamic response and improved robustness, with mechanisms for increased robustness depending on the target frequency.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2021)
Article
Engineering, Multidisciplinary
Dong Wang
Summary: This study extensively explores the effects of different design sensitivity schemes on the incorporation of loading position uncertainty into a gradient-based topology optimization procedure, aiming to efficiently achieve robust optimal design of a continuum structure over a given uncertain region. By comparing different schemes, a unique strategy that provides the most reliable and competitive material layout for load-bearing components is obtained. Numerical results demonstrate that the scheme with the largest absolute design sensitivity can effectively integrate the load uncertainties into the topology optimization algorithm, making the structural performance less sensitive to perturbations in the loading points.
ENGINEERING OPTIMIZATION
(2023)
Article
Engineering, Civil
Kang Gao, Duy Minh Do, Sheng Chu, Gang Wu, H. Alicia Kim, Carol A. Featherston
Summary: This study presents a novel computational framework for Robust Topology Optimization (RTO) considering imprecise random field parameters. The proposed method provides upper and lower bounds for mean and standard deviation of compliance, and obtains optimized topological layouts for different scenarios. The validity and accuracy of the method are rigorously examined by comparing with other optimization methods.
THIN-WALLED STRUCTURES
(2022)
Article
Engineering, Manufacturing
T. Q. D. Pham, T. V. Hoang, X. V. Tran, Seifallah Fetni, L. Duche, H. S. Tran, A. M. Habraken
Summary: This paper presents a conceptual framework for robust optimization under uncertainty in the directed energy deposition (DED) of M4 High-Speed Steel. The aim is to identify optimal process parameters for robust manufacturing of printed parts with a stationary melt pool depth and low consumed energy within the multiple layers of a bulk sample. A deep learning-based surrogate model is built using training data generated by a validated high-fidelity DED two-dimensional FE model to increase computational efficiency. The robustness of the optimized result is verified using the Monte-Carlo method and compared with experiments and two other deterministic approaches. Furthermore, a global sensitivity analysis is conducted, revealing that thermal conductivity and convection have the most significant impact on melt pool depth variation among the six uncertain input variables. This study demonstrates the promising possibilities of the presented framework in optimizing the DED process.
JOURNAL OF MANUFACTURING PROCESSES
(2023)
Article
Computer Science, Artificial Intelligence
Wei Du, Wenjiang Song, Yang Tang, Yaochu Jin, Feng Qian
Summary: This article investigates how to find the robustness intervals of the goal solution in evolutionary robust optimization and proposes a novel algorithm framework to solve this problem. The proposed algorithm contains four key components to enhance the efficiency of searching for the target intervals. Experimental results show that all the robustness intervals can be successfully found using the proposed algorithm framework.
IEEE TRANSACTIONS ON EVOLUTIONARY COMPUTATION
(2022)
Article
Computer Science, Interdisciplinary Applications
Junpeng Zhao, Chunjie Wang
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2016)
Article
Mechanics
Zhifang Fu, Chunjie Wang, Junpeng Zhao
MECHANICS BASED DESIGN OF STRUCTURES AND MACHINES
(2017)
Article
Engineering, Mechanical
J. T. Wang, C. J. Wang, J. P. Zhao
MECHANICAL SYSTEMS AND SIGNAL PROCESSING
(2017)
Article
Computer Science, Interdisciplinary Applications
Junpeng Zhao, Byeng Dong Youn, Heonjun Yoon, Zhifang Fu, Chunjie Wang
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2018)
Article
Computer Science, Interdisciplinary Applications
Junpeng Zhao, Heonjun Yoon, Byeng D. Youn
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2018)
Article
Engineering, Aerospace
Junpeng Zhao, Chunjie Wang
Article
Computer Science, Interdisciplinary Applications
Junpeng Zhao, Chunjie Wang
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2014)
Article
Engineering, Multidisciplinary
Junpeng Zhao, Heonjun Yoon, Byeng D. Youn
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2019)
Article
Computer Science, Interdisciplinary Applications
Junpeng Zhao, Heonjun Yoon, Byeng D. Youn
COMPUTERS & STRUCTURES
(2019)
Article
Computer Science, Interdisciplinary Applications
Junpeng Zhao, Heonjun Yoon, Byeng D. Youn
STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION
(2020)
Article
Engineering, Aerospace
Haoyu Deng, Junpeng Zhao, Chunjie Wang
Summary: The lattice structure has shown excellent capabilities in aerospace and other fields. This study proves the feasibility and potential of the combination of bionic design and lattice structure to improve the performance of lattice-filled footpads.
Article
Engineering, Aerospace
Haoyu Deng, Junpeng Zhao, Chunjie Wang
Summary: This paper proposes a method to map the biometric model to the lattice structure, using leaf veins as bionic objects to generate a gradient lattice structure for improved performance of a heat exchanger. Transient thermal finite element simulations were conducted to evaluate and compare different designs' heat dissipation performance, showing that the combination of bionic design and lattice structure effectively enhances the heat dissipation performance. The results suggest that the application of bionic design in lattice structure design is feasible and has predictable potential.
Article
Computer Science, Interdisciplinary Applications
Junpeng Zhao, Chunjie Wang
COMPUTERS & STRUCTURES
(2017)
Proceedings Paper
Automation & Control Systems
Fu Zhifang, Zhao Junpeng, Wang Chunjie
PROCEEDINGS 2016 EIGHTH INTERNATIONAL CONFERENCE ON MEASURING TECHNOLOGY AND MECHATRONICS AUTOMATION ICMTMA 2016
(2016)
Article
Engineering, Multidisciplinary
Akshay J. Thomas, Mateusz Jaszczuk, Eduardo Barocio, Gourab Ghosh, Ilias Bilionis, R. Byron Pipes
Summary: We propose a physics-guided transfer learning approach to predict the thermal conductivity of additively manufactured short-fiber reinforced polymers using micro-structural characteristics obtained from tensile tests. A Bayesian framework is developed to transfer the thermal conductivity properties across different extrusion deposition additive manufacturing systems. The experimental results demonstrate the effectiveness and reliability of our method in accounting for epistemic and aleatory uncertainties.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
Zhen Zhang, Zongren Zou, Ellen Kuhl, George Em Karniadakis
Summary: In this study, deep learning and artificial intelligence were used to discover a mathematical model for the progression of Alzheimer's disease. By analyzing longitudinal tau positron emission tomography data, a reaction-diffusion type partial differential equation for tau protein misfolding and spreading was discovered. The results showed different misfolding models for Alzheimer's and healthy control groups, indicating faster misfolding in Alzheimer's group. The study provides a foundation for early diagnosis and treatment of Alzheimer's disease and other misfolding-protein based neurodegenerative disorders using image-based technologies.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
Jonghyuk Baek, Jiun-Shyan Chen
Summary: This paper introduces an improved neural network-enhanced reproducing kernel particle method for modeling the localization of brittle fractures. By adding a neural network approximation to the background reproducing kernel approximation, the method allows for the automatic location and insertion of discontinuities in the function space, enhancing the modeling effectiveness. The proposed method uses an energy-based loss function for optimization and regularizes the approximation results through constraints on the spatial gradient of the parametric coordinates, ensuring convergence.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
Bodhinanda Chandra, Ryota Hashimoto, Shinnosuke Matsumi, Ken Kamrin, Kenichi Soga
Summary: This paper proposes new and robust stabilization strategies for accurately modeling incompressible fluid flow problems in the material point method (MPM). The proposed approach adopts a monolithic displacement-pressure formulation and integrates two stabilization strategies to ensure stability. The effectiveness of the proposed method is validated through benchmark cases and real-world scenarios involving violent free-surface fluid motion.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
Chao Peng, Alessandro Tasora, Dario Fusai, Dario Mangoni
Summary: This article discusses the importance of the tangent stiffness matrix of constraints in multibody systems and provides a general formulation based on quaternion parametrization. The article also presents the analytical expression of the tangent stiffness matrix derived through linearization. Examples demonstrate the positive effect of this additional stiffness term on static and eigenvalue analyses.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
Thibaut Vadcard, Fabrice Thouverez, Alain Batailly
Summary: This contribution presents a methodology for detecting isolated branches of periodic solutions to nonlinear mechanical equations. The method combines harmonic balance method-based solving procedure with the Melnikov energy principle. It is able to predict the location of isolated branches of solutions near families of autonomous periodic solutions. The relevance and accuracy of this methodology are demonstrated through academic and industrial applications.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
Weisheng Zhang, Yue Wang, Sung-Kie Youn, Xu Guo
Summary: This study proposes a sketch-guided topology optimization approach based on machine learning, which incorporates computer sketches as constraint functions to improve the efficiency of computer-aided structural design models and meet the design intention and requirements of designers.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
Leilei Chen, Zhongwang Wang, Haojie Lian, Yujing Ma, Zhuxuan Meng, Pei Li, Chensen Ding, Stephane P. A. Bordas
Summary: This paper presents a model order reduction method for electromagnetic boundary element analysis and extends it to computer-aided design integrated shape optimization of multi-frequency electromagnetic scattering problems. The proposed method utilizes a series expansion technique and the second-order Arnoldi procedure to reduce the order of original systems. It also employs the isogeometric boundary element method to ensure geometric exactness and avoid re-meshing during shape optimization. The Grey Wolf Optimization-Artificial Neural Network is used as a surrogate model for shape optimization, with radar cross section as the objective function.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
C. Pilloton, P. N. Sun, X. Zhang, A. Colagrossi
Summary: This paper investigates the smoothed particle hydrodynamics (SPH) simulations of violent sloshing flows and discusses the impact of volume conservation errors on the simulation results. Different techniques are used to directly measure the particles' volumes and stabilization terms are introduced to control the errors. Experimental comparisons demonstrate the effectiveness of the numerical techniques.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
Ye Lu, Weidong Zhu
Summary: This work presents a novel global digital image correlation (DIC) method based on a convolution finite element (C-FE) approximation. The C-FE based DIC provides highly smooth and accurate displacement and strain results with the same element size as the usual finite element (FE) based DIC. The proposed method's formulation and implementation, as well as the controlling parameters, have been discussed in detail. The C-FE method outperformed the FE method in all tested examples, demonstrating its potential for highly smooth, accurate, and robust DIC analysis.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
Mojtaba Ghasemi, Mohsen Zare, Amir Zahedi, Pavel Trojovsky, Laith Abualigah, Eva Trojovska
Summary: This paper introduces Lung performance-based optimization (LPO), a novel algorithm that draws inspiration from the efficient oxygen exchange in the lungs. Through experiments and comparisons with contemporary algorithms, LPO demonstrates its effectiveness in solving complex optimization problems and shows potential for a wide range of applications.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
Jingyu Hu, Yang Liu, Huixin Huang, Shutian Liu
Summary: In this study, a new topology optimization method is proposed for structures with embedded components, considering the tension/compression asymmetric interface stress constraint. The method optimizes the topology of the host structure and the layout of embedded components simultaneously, and a new interpolation model is developed to determine interface layers between the host structure and embedded components.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
Qiang Liu, Wei Zhu, Xiyu Jia, Feng Ma, Jun Wen, Yixiong Wu, Kuangqi Chen, Zhenhai Zhang, Shuang Wang
Summary: In this study, a multiscale and nonlinear turbulence characteristic extraction model using a graph neural network was designed. This model can directly compute turbulence data without resorting to simplified formulas. Experimental results demonstrate that the model has high computational performance in turbulence calculation.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
Jacinto Ulloa, Geert Degrande, Jose E. Andrade, Stijn Francois
Summary: This paper presents a multi-temporal formulation for simulating elastoplastic solids under cyclic loading. The proper generalized decomposition (PGD) is leveraged to decompose the displacements into multiple time scales, separating the spatial and intra-cyclic dependence from the inter-cyclic variation, thereby reducing computational burden.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)
Article
Engineering, Multidisciplinary
Utkarsh Utkarsh, Valentin Churavy, Yingbo Ma, Tim Besard, Prakitr Srisuma, Tim Gymnich, Adam R. Gerlach, Alan Edelman, George Barbastathis, Richard D. Braatz, Christopher Rackauckas
Summary: This article presents a high-performance vendor-agnostic method for massively parallel solving of ordinary and stochastic differential equations on GPUs. The method integrates with a popular differential equation solver library and achieves state-of-the-art performance compared to hand-optimized kernels.
COMPUTER METHODS IN APPLIED MECHANICS AND ENGINEERING
(2024)