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.

Mini-Max Type Formulation of Strict Robust Design Optimization Under Correlative Variation

2002 ◽  
Author(s):  
Noriyasu Hirokawa ◽  
Kikuo Fujita
Keyword(s):  
Design Optimization ◽  
Robust Design ◽  
Design Space ◽  
Computational Cost ◽  
Robust Design Optimization ◽  
Maximization Problem ◽  
Quadratic Polynomials ◽  
Feasibility Assessment ◽  
Design Variation ◽  
Definition Of

This paper proposes a mini-max type formulation for strict robust design optimization under correlative variation based on design variation hyper sphere and quadratic polynomial approximation. While various types of formulations and techniques have been developed for computational robust design, they confront the compromise among modeling of parameter variation, feasibility assessment, definition of optimality such as sensitivity, and computational cost. The formulation of this paper aims that all points within the distribution region are thoroughly optimized. For this purpose, the design space with correlative variation is diagonalized and isoparameterized into a hyper sphere, and the functions of nominal constraints and the nominal objective are modeled as quadratic polynomials. These transformation and approximation enable the analytical discrimination of inner or boundary type on the worst design and its quantified values with less computation cost under a certain condition, and bring the procedural definition of the strictly robust optimality of a design as a maximization problem. The minimization of this formulation, that is, mini-max type optimization, can find the robust design under the above meaning. Its validity is ascertained through numerical examples.

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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