Zhiyuan Dai, Tian Li^*, Weihua Zhang, Jiye Zhang

CMES-Computer Modeling in Engineering & Sciences, Vol.137, No.2, pp. 1461-1489, 2023, DOI:10.32604/cmes.2023.028677

Abstract The aerodynamic optimization design of high-speed trains (HSTs) is crucial for energy conservation, environmental preservation, operational safety, and speeding up. This study aims to review the current state and progress of the aerodynamic multi-objective optimization of HSTs. First, the study explores the impact of train nose shape parameters on aerodynamic performance. The parameterization methods involved in the aerodynamic multiobjective optimization of HSTs are summarized and classified as shape-based and disturbance-based parameterization methods. Meanwhile, the advantages and limitations of each parameterization method, as well as the applicable scope, are briefly discussed. In addition, the NSGA-II algorithm, particle swarm optimization algorithm, standard… More >

Nazia Afrin^a,*, Jonathan Lopez, Juan Ocampo

Frontiers in Heat and Mass Transfer, Vol.18, pp. 1-7, 2022, DOI:10.5098/hmt.18.46

Abstract Large number of numerical computer simulations in engineering places is a serious burden on associated optimization problems nowadays. Kriging Surrogate based optimization (KSBO) becomes standard practice in analyzing expensive and time-consuming simulation. This paper aims to investigate the surrogate based analyze and optimization of thermal damage in living biological tissue by laser irradiation using a generalized duel phase model. The relationships of maximum temperature and thermal damage in living biological tissues of the response with two variables at a time are studied. The result shows that the surrogate model predicted response variables i.e, temperature and thermal damage are in good… More >

Tran-Hieu Nguyen^*, Anh-Tuan Vu

CMES-Computer Modeling in Engineering & Sciences, Vol.134, No.1, pp. 429-458, 2023, DOI:10.32604/cmes.2022.020819

Abstract Design constraints verification is the most computationally expensive task in evolutionary structural optimization due to a large number of structural analyses that must be conducted. Building a surrogate model to approximate the behavior of structures instead of the exact structural analyses is a possible solution to tackle this problem. However, most existing surrogate models have been designed based on regression techniques. This paper proposes a novel method, called CaDE, which adopts a machine learning classification technique for enhancing the performance of the Differential Evolution (DE) optimization. The proposed method is separated into two stages. During the first optimization stage, the… More >

João Pedro Ceniz, Rodrigo de Sá Martins, Marco Antonio Luersen^*, Tiago Cousseau

CMES-Computer Modeling in Engineering & Sciences, Vol.133, No.3, pp. 601-618, 2022, DOI:10.32604/cmes.2022.021011

Abstract Ore conveyor belt rollers operate in harsh environments, making them prone to premature failure. Their service lives are highly dependent on the stress field and bearing misalignment angle, for which limit values are defined in a standard. In this work, an optimization methodology using metamodels based on radial basis functions is implemented to reduce the mass of two models of rollers. From a structural point of view, one of the rollers is made completely of metal, while the other also has some components made of polymeric material. The objective of this study is to develop and apply a parametric structural… More >

Huanwei Xu^*, Wenzhang Wei, Hanjin He, Xuerui Yang

CMES-Computer Modeling in Engineering & Sciences, Vol.132, No.1, pp. 173-199, 2022, DOI:10.32604/cmes.2022.019424

Abstract The surrogate model technology has a good performance in solving black-box optimization problems, which is widely used in multi-domain engineering optimization problems. The adaptive surrogate model is the mainstream research direction of surrogate model technology, which can realize model fitting and global optimization of engineering problems by infilling criteria. Based on the idea of the adaptive surrogate model, this paper proposes an efficient global optimization algorithm based on the local remodeling method (EGO-LR), which aims at improving the accuracy and optimization efficiency of the model. The proposed algorithm firstly constructs the expectation improvement (EI) function in the local area and… More >

Haiquan Li¹, Qinghui Zhang^2,*, Xiaoqian Chen³

CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 605-621, 2021, DOI:10.32604/cmes.2021.015747

Abstract Flight load computations (FLC) are generally expensive and time-consuming. This paper studies deep learning (DL)-based surrogate models of FLC to provide a reliable basis for the strength design of aircraft structures. We mainly analyze the influence of Mach number, overload, angle of attack, elevator deflection, altitude, and other factors on the loads of key monitoring components, based on which input and output variables are set. The data used to train and validate the DL surrogate models are derived using aircraft flight load simulation results based on wind tunnel test data. According to the FLC features, a deep neural network (DNN)… More >

Tianyu Wang¹, Wael A. Altabey^1,2, Mohammad Noori^3,*, Ramin Ghiasi¹

Structural Durability & Health Monitoring, Vol.14, No.4, pp. 315-338, 2020, DOI:10.32604/sdhm.2020.011083

Abstract Beam-like structures are a class of common but important structures in engineering. Over the past few centuries, extensive research has been carried out to obtain the static and dynamic response of beam-like structures. Although building the finite element model to predict the response of these structures has proven to be effective, it is not always suitable in all the application cases because of high computational time or lack of accuracy. This paper proposes a novel approach to predict the deflection response of beam-like structures based on a deep neural network and the governing differential equation of Euler-Bernoulli beam. The Prandtl-Ishlinskii… More >

Fangyi Li^1,2, Zhen Luo³, Jianhua Rong¹, Lin Hu¹

CMES-Computer Modeling in Engineering & Sciences, Vol.95, No.6, pp. 453-482, 2013, DOI:10.3970/cmes.2013.095.453

Abstract This paper aims to propose a non-probabilistic reliability-based multiobjective optimization method for structures with uncertain-but-bounded parameters. A combination of the interval and ellipsoid convex models is used to account for the different groups of uncertain parameters, in which the interval model accounts for uncorrelated parameters, while the ellipsoid model is applied to correlated parameters. The design is then formulated as a nested double-loop optimization problem. A multi-objective genetic algorithm is used in the out loop optimization to optimize the design vector for evaluating the objectives, and the Sequential Quadratic Programming (SQP) algorithm is applied in the inner loop to evaluate… More >