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  • Open Access


    A Multiscale Reliability-Based Design Optimization Method for Carbon-Fiber-Reinforced Composite Drive Shafts

    Huile Zhang1,2,*, Shikang Li2, Yurui Wu3, Pengpeng Zhi1, Wei Wang1,4, Zhonglai Wang1,4,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1975-1996, 2024, DOI:10.32604/cmes.2024.050185

    Abstract Carbon fiber composites, characterized by their high specific strength and low weight, are becoming increasingly crucial in automotive lightweighting. However, current research primarily emphasizes layer count and orientation, often neglecting the potential of microstructural design, constraints in the layup process, and performance reliability. This study, therefore, introduces a multiscale reliability-based design optimization method for carbon fiber-reinforced plastic (CFRP) drive shafts. Initially, parametric modeling of the microscale cell was performed, and its elastic performance parameters were predicted using two homogenization methods, examining the impact of fluctuations in microscale cell parameters on composite material performance. A finite… More >

  • Open Access


    Multi-Stage Multidisciplinary Design Optimization Method for Enhancing Complete Artillery Internal Ballistic Firing Performance

    Jipeng Xie1,2, Guolai Yang1,*, Liqun Wang1, Lei Li1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 793-819, 2024, DOI:10.32604/cmes.2024.048174

    Abstract To enhance the comprehensive performance of artillery internal ballistics—encompassing power, accuracy, and service life—this study proposed a multi-stage multidisciplinary design optimization (MS-MDO) method. First, the comprehensive artillery internal ballistic dynamics (AIBD) model, based on propellant combustion, rotation band engraving, projectile axial motion, and rifling wear models, was established and validated. This model was systematically decomposed into subsystems from a system engineering perspective. The study then detailed the MS-MDO methodology, which included Stage I (MDO stage) employing an improved collaborative optimization method for consistent design variables, and Stage II (Performance Optimization) focusing on the independent optimization More >

  • Open Access


    Probabilistic-Ellipsoid Hybrid Reliability Multi-Material Topology Optimization Method Based on Stress Constraint

    Zibin Mao1, Qinghai Zhao1,2,*, Liang Zhang1

    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.1, pp. 757-792, 2024, DOI:10.32604/cmes.2024.048016

    Abstract This paper proposes a multi-material topology optimization method based on the hybrid reliability of the probability-ellipsoid model with stress constraint for the stochastic uncertainty and epistemic uncertainty of mechanical loads in optimization design. The probabilistic model is combined with the ellipsoidal model to describe the uncertainty of mechanical loads. The topology optimization formula is combined with the ordered solid isotropic material with penalization (ordered-SIMP) multi-material interpolation model. The stresses of all elements are integrated into a global stress measurement that approximates the maximum stress using the normalized p-norm function. Furthermore, the sequential optimization and reliability assessment… More >

  • Open Access


    An Efficient Reliability-Based Optimization Method Utilizing High-Dimensional Model Representation and Weight-Point Estimation Method

    Xiaoyi Wang1, Xinyue Chang2, Wenxuan Wang1,*, Zijie Qiao3, Feng Zhang3,*

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 1775-1796, 2024, DOI:10.32604/cmes.2023.043913

    Abstract The objective of reliability-based design optimization (RBDO) is to minimize the optimization objective while satisfying the corresponding reliability requirements. However, the nested loop characteristic reduces the efficiency of RBDO algorithm, which hinders their application to high-dimensional engineering problems. To address these issues, this paper proposes an efficient decoupled RBDO method combining high dimensional model representation (HDMR) and the weight-point estimation method (WPEM). First, we decouple the RBDO model using HDMR and WPEM. Second, Lagrange interpolation is used to approximate a univariate function. Finally, based on the results of the first two steps, the original nested More >

  • Open Access


    Multi-Scale Topology Optimization Method Considering Multiple Structural Performances

    Wenjun Chen1, Yingjun Wang1,*

    The International Conference on Computational & Experimental Engineering and Sciences, Vol.27, No.3, pp. 1-1, 2023, DOI:10.32604/icces.2023.09096

    Abstract The rapid development of topology optimization has given birth to a large amount of different topology optimization methods, and each of them can manage a class of corresponding engineering problems. However, structures need to meet a variety of requirements in engineering application, such as lightweight and multiple load-bearing performance. To design composite structures that have multiple structural properties, a new multi-scale topology optimization method considering multiple structural performances is proposed in this paper. Based on the fitting functions of the result set and the bisection method, a new method to determine the weight coefficient is… More >

  • Open Access


    A Nonlinear Spatiotemporal Optimization Method of Hypergraph Convolution Networks for Traffic Prediction

    Difeng Zhu1, Zhimou Zhu2, Xuan Gong1, Demao Ye1, Chao Li3,*, Jingjing Chen4,*

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 3083-3100, 2023, DOI:10.32604/iasc.2023.040517

    Abstract Traffic prediction is a necessary function in intelligent transportation systems to alleviate traffic congestion. Graph learning methods mainly focus on the spatiotemporal dimension, but ignore the nonlinear movement of traffic prediction and the high-order relationships among various kinds of road segments. There exist two issues: 1) deep integration of the spatiotemporal information and 2) global spatial dependencies for structural properties. To address these issues, we propose a nonlinear spatiotemporal optimization method, which introduces hypergraph convolution networks (HGCN). The method utilizes the higher-order spatial features of the road network captured by HGCN, and dynamically integrates them More >

  • Open Access


    Research on Freezing of Gait Recognition Method Based on Variational Mode Decomposition

    Shoutao Li1,2,*, Ruyi Qu1, Yu Zhang1, Dingli Yu3

    Intelligent Automation & Soft Computing, Vol.37, No.3, pp. 2809-2823, 2023, DOI:10.32604/iasc.2023.036999

    Abstract Freezing of Gait (FOG) is the most common and disabling gait disorder in patients with Parkinson’s Disease (PD), which seriously affects the life quality and social function of patients. This paper proposes a FOG recognition method based on the Variational Mode Decomposition (VMD). Firstly, VMD instead of the traditional time-frequency analysis method to complete adaptive decomposition to the FOG signal. Secondly, to improve the accuracy and speed of the recognition algorithm, use the CART model as the base classifier and perform the feature dimension reduction. Then use the RUSBoost ensemble algorithm to solve the problem… More >

  • Open Access


    Research on Operation Optimization of Heating System Based on Electric Storage Coupled Solar Energy and Air Source Heat Pump

    Jingxiao Han1, Chuanzhao Zhang2, Lu Wang3,*, Zengjun Chang1, Qing Zhao1, Ying Shi4, Jiarui Wu5, Xiangfei Kong3

    Energy Engineering, Vol.120, No.9, pp. 1991-2011, 2023, DOI:10.32604/ee.2023.029749

    Abstract For heating systems based on electricity storage coupled with solar energy and an air source heat pump (ECSA), choosing the appropriate combination of heat sources according to local conditions is the key to improving economic efficiency. In this paper, four cities in three climatic regions in China were selected, namely Nanjing in the hot summer and cold winter region, Tianjin in the cold region, Shenyang and Harbin in the severe cold winter region. The levelized cost of heat (LCOH) was used as the economic evaluation index, and the energy consumption and emissions of different pollutants… More > Graphic Abstract

    Research on Operation Optimization of Heating System Based on Electric Storage Coupled Solar Energy and Air Source Heat Pump

  • Open Access


    3D Model Occlusion Culling Optimization Method Based on WebGPU Computing Pipeline

    Liming Ye1,2, Gang Liu1,2,3,4,*, Genshen Chen1,2, Kang Li1,2, Qiyu Chen1,2,3, Wenyao Fan1,2, Junjie Zhang1,2

    Computer Systems Science and Engineering, Vol.47, No.2, pp. 2529-2545, 2023, DOI:10.32604/csse.2023.041488

    Abstract Nowadays, Web browsers have become an important carrier of 3D model visualization because of their convenience and portability. During the process of large-scale 3D model visualization based on Web scenes with the problems of slow rendering speed and low FPS (Frames Per Second), occlusion culling, as an important method for rendering optimization, can remove most of the occluded objects and improve rendering efficiency. The traditional occlusion culling algorithm (TOCA) is calculated by traversing all objects in the scene, which involves a large amount of repeated calculation and time consumption. To advance the rendering process and… More >

  • Open Access


    Deep Learning Applied to Computational Mechanics: A Comprehensive Review, State of the Art, and the Classics

    Loc Vu-Quoc1,*, Alexander Humer2

    CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1069-1343, 2023, DOI:10.32604/cmes.2023.028130

    Abstract Three recent breakthroughs due to AI in arts and science serve as motivation: An award winning digital image, protein folding, fast matrix multiplication. Many recent developments in artificial neural networks, particularly deep learning (DL), applied and relevant to computational mechanics (solid, fluids, finite-element technology) are reviewed in detail. Both hybrid and pure machine learning (ML) methods are discussed. Hybrid methods combine traditional PDE discretizations with ML methods either (1) to help model complex nonlinear constitutive relations, (2) to nonlinearly reduce the model order for efficient simulation (turbulence), or (3) to accelerate the simulation by predicting… More >

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