Maximum variation analysis based analytical target cascading for multidisciplinary robust design optimization under interval uncertainty

2019 ◽  
Vol 40 ◽  
pp. 81-92 ◽  
Author(s):  
Wei Li ◽  
Mi Xiao ◽  
Yongsheng Yi ◽  
Liang Gao
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Interval Uncertainty ◽  
Robust Design Optimization ◽  
Analytical Target Cascading ◽  
Variation Analysis ◽  
Maximum Variation ◽  
Target Cascading

scholarly journals A set strategy approach for multidisciplinary robust design optimization under interval uncertainty

2019 ◽  
Vol 11 (1) ◽  
pp. 168781401882038 ◽  
Author(s):  
Yongsheng Yi ◽  
Wei Li ◽  
Mi Xiao ◽  
Liang Gao
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Design Problem ◽  
Design Space ◽  
System Optimization ◽  
Interval Uncertainty ◽  
Robust Design Optimization ◽  
Sequential Optimization ◽  
Air Cooling ◽  
Wide Range

Uncertainties widely exist in complex engineering systems. Robust design is one of the most used method for designing under uncertainty and has been gaining more attention. For the wide range of uncertainties, this article proposes a multidisciplinary robust design optimization method based on the set strategy. In this method, a robust design model that utilizes the maximum variation analysis is developed for uncertainty analysis. Then, a set strategy–based approach is employed to build a system optimization model, which is used to coordinate the coupling variables between full autonomy subsystems and obtains a new design space. Finally, the system robust optimal solution and the optimal robust design space are obtained through the sequential optimization, which provide a direction for the subsequent analysis. Two mathematics examples and the speed reducer design problem are taken to verify the validity and accuracy of the proposed method. A practical engineering problem, namely, air cooling battery thermal management system design problem, is successfully solved by the proposed method.

Robust design optimization of suspension system by using target cascading method

2011 ◽  
Vol 13 (1) ◽  
pp. 109-122 ◽  
Author(s):  
D. O. Kang ◽  
S. J. Heo ◽  
M. S. Kim ◽  
W. C. Choi ◽  
I. H. Kim
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Suspension System ◽  
Robust Design Optimization ◽  
Target Cascading

scholarly journals An Enhanced Analytical Target Cascading and Kriging Model Combined Approach for Multidisciplinary Design Optimization

2015 ◽  
Vol 2015 ◽  
pp. 1-11 ◽  
Author(s):  
Ping Jiang ◽  
Jianzhuang Wang ◽  
Qi Zhou ◽  
Xiaolin Zhang
Keyword(s):  
Design Optimization ◽  
Multidisciplinary Design Optimization ◽  
Kriging Model ◽  
Multidisciplinary Design ◽  
High Approximation ◽  
Engineering Systems ◽  
Analytical Target Cascading ◽  
Combined Approach ◽  
Coordination Strategy ◽  
Target Cascading

Multidisciplinary design optimization (MDO) has been applied widely in the design of complex engineering systems. To ease MDO problems, analytical target cascading (ATC) organizes MDO process into multilevels according to the components of engineering systems, which provides a promising way to deal with MDO problems. ATC adopts a coordination strategy to coordinate the couplings between two adjacent levels in the design optimization process; however, existing coordination strategies in ATC face the obstacles of complicated coordination process and heavy computation cost. In order to conquer this problem, a quadratic exterior penalty function (QEPF) based ATC (QEPF-ATC) approach is proposed, where QEPF is adopted as the coordination strategy. Moreover, approximate models are adopted widely to replace the expensive simulation models in MDO; a QEPF-ATC and Kriging model combined approach is further proposed to deal with MDO problems, owing to the comprehensive performance, high approximation accuracy, and robustness of Kriging model. Finally, the geometric programming and reducer design cases are given to validate the applicability and efficiency of the proposed approach.

Robust Design Optimization for Crashworthiness of Vehicle Side Impact

2017 ◽  
Vol 3 (3) ◽  
Author(s):  
Souvik Chakraborty ◽  
Tanmoy Chatterjee ◽  
Rajib Chowdhury ◽  
Sondipon Adhikari
Keyword(s):  
Design Optimization ◽  
New Product Development ◽  
Robust Design ◽  
Automobile Industry ◽  
Human Error ◽  
Differential Evolution Algorithm ◽  
Side Impact ◽  
Robust Design Optimization ◽  
Parameter Uncertainties ◽  
Design Engineers

Optimization for crashworthiness is of vast importance in automobile industry. Recent advancement in computational prowess has enabled researchers and design engineers to address vehicle crashworthiness, resulting in reduction of cost and time for new product development. However, a deterministic optimum design often resides at the boundary of failure domain, leaving little or no room for modeling imperfections, parameter uncertainties, and/or human error. In this study, an operational model-based robust design optimization (RDO) scheme has been developed for designing crashworthiness of vehicle against side impact. Within this framework, differential evolution algorithm (DEA) has been coupled with polynomial correlated function expansion (PCFE). An adaptive framework for determining the optimum basis order in PCFE has also been presented. It is argued that the coupled DEA–PCFE is more efficient and accurate, as compared to conventional techniques. For RDO of vehicle against side impact, minimization of the weight and lower rib deflection of the vehicle are considered to be the primary design objectives. Case studies by providing various emphases on the two objectives have also been performed. For all the cases, DEA–PCFE is found to yield highly accurate results.

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