The Experts below are selected from a list of 463023 Experts worldwide ranked by ideXlab platform
Shuhong Wang - One of the best experts on this subject based on the ideXlab platform.
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system level Design Optimization method for electrical drive systems robust approach
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Gang Lei, Jianguo Zhu, Youguang Guo, Tianshi Wang, Shuhong WangAbstract:A system-level Design Optimization method under the framework of a deterministic approach was presented for electrical drive systems in our previous work, in which not only motors but also the integrated control schemes were Designed and optimized to achieve good steady and dynamic performances. However, there are many unavoidable uncertainties (noise factors) in the industrial manufacturing process, such as material characteristics and manufacturing precision. These will result in big fluctuations for the product's reliability and quality in mass production, which are not investigated in the deterministic approach. Therefore, a robust approach based on the technique of Design for six sigma is presented for the system-level Design Optimization of drive systems to improve the reliability and quality of products in batch production in this work. Meanwhile, two system-level Optimization frameworks are presented for the proposed method, namely, single-level (only at the system level) and multilevel frameworks. Finally, a drive system is investigated as an example, and detailed results are presented and discussed. It can be found that the reliability and quality levels of the investigated drive system have been greatly increased by using the proposed robust approach.
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system level Design Optimization methods for electrical drive systems deterministic approach
IEEE Transactions on Industrial Electronics, 2014Co-Authors: Tianshi Wang, Shuhong WangAbstract:Electrical drive systems are key components in modern appliances, industry equipment, and systems, e.g., hybrid electric vehicles. To obtain the best performance of these drive systems, the motors and their control systems should be Designed and optimized at the system level rather than the component level. This paper presents an effort to develop system-level Design and Optimization methods for electrical drive systems. Two system-level Design Optimization methods are presented in this paper: 1) single-level method (only at system level); and 2) multilevel method. Meanwhile, the approximate models, the Design of experiments technique, and the sequential subspace Optimization method are presented to improve the Optimization efficiency. Finally, a drive system consisting of a permanent-magnet transverse flux machine with a soft magnetic composite core is investigated, and detailed results are presented and discussed. This is a high-dimensional Optimization problem with 14 parameters mixed with both discrete and continuous variables. The finite-element analysis model and method are verified by the experimental results on the motor prototype. From the discussion, it can be found that the proposed multilevel method can increase the performance of the whole drive system, such as bigger output power and lower material cost, and decrease the computation cost significantly compared with those of single-level Design Optimization method.
Kyung K. Choi - One of the best experts on this subject based on the ideXlab platform.
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metamodeling method using dynamic kriging for Design Optimization
AIAA Journal, 2011Co-Authors: Liang Zhao, Kyung K. Choi, Ikji LeeAbstract:Metamodeling has been widely used for Design Optimization by building surrogate models for computationally intensive engineering application problems. Among all the metamodeling methods, the kriging method has gained significant interest for its accuracy.However, in traditional krigingmethods, themean structure is constructed using a fixed set of polynomial basis functions, and the Optimization methods used to obtain the optimal correlation parameter may not yield an accurate optimum. In this paper, a new method called the dynamic kriging method is proposed to fit the true model more accurately. In this dynamic kriging method, an optimal mean structure is obtainedusing thebasis functions that are selected bya genetic algorithm from the candidate basis functions based on a new accuracy criterion, and a generalized pattern search algorithm is used to find an accurate optimum for the correlation parameter. The dynamic kriging method generates a more accurate surrogate model than other metamodeling methods. In addition, the dynamic kriging method is applied to the simulation-based Design Optimization with multiple efficiency strategies. An engineering example shows that the optimal Design obtained by using the surrogate models from the dynamic kriging method can achieve the same accuracy as the one obtained by using the sensitivity-based Optimization method.
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reliability based Design Optimization using response surface method with prediction interval estimation
Journal of Mechanical Design, 2008Co-Authors: Kyung K. ChoiAbstract:Since variances in the input variables of the engineering system cause subsequent variances in the product output performance, reliability-based Design Optimization (RBDO) is getting much attention recently. However, RBDO requires expensive computational time. Therefore, the response surface method is often used for computational efficiency in solving RBDO problems. A method to estimate the effect of the response surface error on the RBDO result is developed in this paper. The effect of the error is expressed in terms of the prediction interval, which is utilized as the error metric for the response surface used for RBDO. The prediction interval provides upper and lower bounds for the confidence level that the Design engineer specified. Using the prediction interval of the response surface, the upper and lower limits of the reliability are computed. The lower limit of reliability is compared with the target reliability to obtain a conservative optimum Design and thus safeguard against the inaccuracy of the response surface. On the other hand, in order to avoid obtaining a Design that is too conservative, the developed method also constrains the upper limit of the reliability in the Design Optimization process. The proposed procedure is combined with an adaptive sampling strategy to refine the response surface. Numerical examples show the usefulness and the efficiency of the proposed method.
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dimension reduction method for reliability based robust Design Optimization
Computers & Structures, 2008Co-Authors: Kyung K. Choi, Liu Du, David GorsichAbstract:In reliability-based robust Design Optimization (RBRDO) formulation, the product quality loss function is minimized subject to probabilistic constraints. Since the quality loss function is expressed in terms of the first two statistical moments, mean and variance, three methods have been recently proposed to accurately and efficiently estimate the moments: the univariate dimension reduction method (DRM), performance moment integration (PMI) method, and percentile difference method (PDM). In this paper, a reliability-based robust Design Optimization method is developed using DRM and compared to PMI and PDM for accuracy and efficiency. The numerical results show that DRM is effective when the number of random variables is small, whereas PMI is more effective when the number of random variables is relatively large.
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enriched performance measure approach for reliability based Design Optimization
AIAA Journal, 2005Co-Authors: Byeng D Youn, Kyung K. Choi, Liu DuAbstract:An enriched performance measure approach is presented for reliability-based Design Optimization to substantially improve computational efficiency when applied to large-scale applications. In the enriched performance measure approach, four improvements are made over the original performance measure approach: as a way to launch reliability-based Design Optimization at a deterministic optimum Design, as a new enhanced hybrid-mean value method, as an efficient probabilistic feasibility check, and as a fast reliability analysis under the condition of Design closeness. It is found that deterministic Design Optimization helps improve numerical efficiency by reducing some reliability-based Design Optimization iterations. In reliability-based Design Optimization, a computational burden on the feasibility check of constraints can be significantly reduced by using a mean value first-order method and by carrying out the refined reliability analysis using the enhanced hybrid-mean value method for e-active and violated constraints. The enhanced hybrid-mean value method is developed to handle nonlinear and/or nonmonotonic constraints in reliability analysis. The fast reliability analysis method is proposed to efficiently evaluate probabilistic constraints under the condition of Design closeness. Moreover, two numerical examples are provided to compare the enriched performance measure approach to existing reliability-based Design Optimization methods from a numerical efficiency and stability point of view.
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performance moment integration pmi method for quality assessment in reliability based robust Design Optimization
Mechanics Based Design of Structures and Machines, 2005Co-Authors: Byeng D Youn, Kyung K. Choi, Kiyoung YiAbstract:The reliability-based robust Design Optimization deals with two objectives of structural Design methodologies subject to various uncertainties: reliability and robustness. The reliability constraints deal with the probability of failures, while the robustness minimizes the product quality loss. In general, the product quality loss is described by using the first two statistical moments: mean and standard deviation. In this paper, a performance moment integration (PMI) method is proposed by using a three-level numerical integration on the output range to estimate the product quality loss. For the reliability part of the reliability-based robust Design Optimization, the enriched performance measure approach (PMA+) and its numerical method, enhanced hybrid-mean value (HMV+) method, are used. New formulations of reliability-based robust Design Optimization are presented for three different types of robustness objectives, such as smaller-the-better, larger-the-better, and nominal-the-best types. Examples that include an engine rubber gasket are used to demonstrate the effectiveness of reliability-based robust Design Optimization using the proposed PMI method for different types of robust objective.
Niclas Strömberg - One of the best experts on this subject based on the ideXlab platform.
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reliability based Design Optimization by using metamodels
International Conference on Engineering Optimization, 2018Co-Authors: Niclas StrömbergAbstract:This paper summarize our work so far on reliability based Design Optimization (RBDO) by using metamodels and present some new ideas on RBDO using support vector machines. Design Optimization of complex models, such as non-linear finite element models, are treated by fitting metamodels to computer experiments. A new approach for radial basis function networks (RBFN) using a priori bias is suggested and compared to established RBFN, Kriging, polynomial chaos expansion, support vector machines (SVM), support vector regression (SVR), and least square SVM and SVR. Different types of computer experiments are also investigated such as e.g. S-optimal Design of experiments, Halton- and Hammersley sampling, and different adaptive sampling approaches. For instance, SVM-supported sampling is suggested in order to improve the limit surface by putting extra sampling points at the margin of the SVM. Uncertainties in Design variables and parameters are included in the Design Optimization by FORM- and SORM-based RBDO. By establishing the most probable point (MPP) at the limit surface using a Newton method with an inexact Jacobian, Taylor expansions of the metamodels are done at the MPP using intermediate variables defined by the iso-probabilistic transformation for several density distributions such as lognormal, gamma, Gumbel and Weibull. In such manner, LP- and QP-problems are derived which are solved in sequence until convergence. The implementation of the approaches in an in-house toolbox are very robust and efficient. This is demonstrated by solving several examples for a large number of variables and reliability constraints.
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reliability based Design Optimization by using a slp approach and radial basis function networks
Design Automation Conference, 2016Co-Authors: Niclas StrömbergAbstract:In this paper reliability based Design Optimization by using radial basis function networks (RBFN) as surrogate models is presented. The RBFN are treated as regression models. By taking the center ...
Tianshi Wang - One of the best experts on this subject based on the ideXlab platform.
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system level Design Optimization method for electrical drive systems robust approach
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Gang Lei, Jianguo Zhu, Youguang Guo, Tianshi Wang, Shuhong WangAbstract:A system-level Design Optimization method under the framework of a deterministic approach was presented for electrical drive systems in our previous work, in which not only motors but also the integrated control schemes were Designed and optimized to achieve good steady and dynamic performances. However, there are many unavoidable uncertainties (noise factors) in the industrial manufacturing process, such as material characteristics and manufacturing precision. These will result in big fluctuations for the product's reliability and quality in mass production, which are not investigated in the deterministic approach. Therefore, a robust approach based on the technique of Design for six sigma is presented for the system-level Design Optimization of drive systems to improve the reliability and quality of products in batch production in this work. Meanwhile, two system-level Optimization frameworks are presented for the proposed method, namely, single-level (only at the system level) and multilevel frameworks. Finally, a drive system is investigated as an example, and detailed results are presented and discussed. It can be found that the reliability and quality levels of the investigated drive system have been greatly increased by using the proposed robust approach.
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system level Design Optimization methods for electrical drive systems deterministic approach
IEEE Transactions on Industrial Electronics, 2014Co-Authors: Tianshi Wang, Shuhong WangAbstract:Electrical drive systems are key components in modern appliances, industry equipment, and systems, e.g., hybrid electric vehicles. To obtain the best performance of these drive systems, the motors and their control systems should be Designed and optimized at the system level rather than the component level. This paper presents an effort to develop system-level Design and Optimization methods for electrical drive systems. Two system-level Design Optimization methods are presented in this paper: 1) single-level method (only at system level); and 2) multilevel method. Meanwhile, the approximate models, the Design of experiments technique, and the sequential subspace Optimization method are presented to improve the Optimization efficiency. Finally, a drive system consisting of a permanent-magnet transverse flux machine with a soft magnetic composite core is investigated, and detailed results are presented and discussed. This is a high-dimensional Optimization problem with 14 parameters mixed with both discrete and continuous variables. The finite-element analysis model and method are verified by the experimental results on the motor prototype. From the discussion, it can be found that the proposed multilevel method can increase the performance of the whole drive system, such as bigger output power and lower material cost, and decrease the computation cost significantly compared with those of single-level Design Optimization method.
Gang Lei - One of the best experts on this subject based on the ideXlab platform.
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a review of Design Optimization methods for electrical machines
Energies, 2017Co-Authors: Gang Lei, Jianguo Zhu, Youguang Guo, Chengcheng LiuAbstract:Electrical machines are the hearts of many appliances, industrial equipment and systems. In the context of global sustainability, they must fulfill various requirements, not only physically and technologically but also environmentally. Therefore, their Design Optimization process becomes more and more complex as more engineering disciplines/domains and constraints are involved, such as electromagnetics, structural mechanics and heat transfer. This paper aims to present a review of the Design Optimization methods for electrical machines, including Design analysis methods and models, Optimization models, algorithms and methods/strategies. Several efficient Optimization methods/strategies are highlighted with comments, including surrogate-model based and multi-level Optimization methods. In addition, two promising and challenging topics in both academic and industrial communities are discussed, and two novel Optimization methods are introduced for advanced Design Optimization of electrical machines. First, a system-level Design Optimization method is introduced for the development of advanced electric drive systems. Second, a robust Design Optimization method based on the Design for six-sigma technique is introduced for high-quality manufacturing of electrical machines in production. Meanwhile, a proposal is presented for the development of a robust Design Optimization service based on industrial big data and cloud computing services. Finally, five future directions are proposed, including smart Design Optimization method for future intelligent Design and production of electrical machines.
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system level Design Optimization method for electrical drive systems robust approach
IEEE Transactions on Industrial Electronics, 2015Co-Authors: Gang Lei, Jianguo Zhu, Youguang Guo, Tianshi Wang, Shuhong WangAbstract:A system-level Design Optimization method under the framework of a deterministic approach was presented for electrical drive systems in our previous work, in which not only motors but also the integrated control schemes were Designed and optimized to achieve good steady and dynamic performances. However, there are many unavoidable uncertainties (noise factors) in the industrial manufacturing process, such as material characteristics and manufacturing precision. These will result in big fluctuations for the product's reliability and quality in mass production, which are not investigated in the deterministic approach. Therefore, a robust approach based on the technique of Design for six sigma is presented for the system-level Design Optimization of drive systems to improve the reliability and quality of products in batch production in this work. Meanwhile, two system-level Optimization frameworks are presented for the proposed method, namely, single-level (only at the system level) and multilevel frameworks. Finally, a drive system is investigated as an example, and detailed results are presented and discussed. It can be found that the reliability and quality levels of the investigated drive system have been greatly increased by using the proposed robust approach.
