Simulation and Robust Optimization Design for Electromagnetic Railgun Performance

Toward a multi-objective optimization robust problem, the variations in design variables (DVs) and design environment parameters (DEPs) include the small variations and the large variations. The former have small effect on the performance functions and/or the constraints, and the latter refer to the ones that have large effect on the performance functions and/or the constraints. The robustness of performance functions is discussed in this paper. A postoptimality sensitivity analysis technique for multi-objective robust optimization problems (MOROPs) is discussed, and two robustness indices (RIs) are introduced. The first one considers the robustness of the performance functions to small variations in the DVs and the DEPs. The second RI characterizes the robustness of the performance functions to large variations in the DEPs. It is based on the ability of a solution to maintain a good Pareto ranking for different DEPs due to large variations. The robustness of the solutions is treated as vectors in the robustness function space (RF-Space), which is defined by the two proposed RIs. As a result, the designer can compare the robustness of all Pareto optimal solutions and make a decision. Finally, two illustrative examples are given to highlight the contributions of this paper. The first example is about a numerical problem, whereas the second problem deals with the multi-objective robust optimization design of a floating wind turbine.

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A robust optimization design method for sheet metal roll forming and its application in roll forming circular cross-section pipe

The International Journal of Advanced Manufacturing Technology ◽

10.1007/s00170-019-03773-4 ◽

2019 ◽

Vol 103 (5-8) ◽

pp. 2903-2916 ◽

Cited By ~ 4

Author(s):

Lemiao Qiu ◽

Shuyou Zhang ◽

Zili Wang ◽

Xingxing Hu ◽

Xiaojian Liu

Keyword(s):

Robust Optimization ◽

Sheet Metal ◽

Cross Section ◽

Optimization Design ◽

Design Method ◽

Circular Cross Section ◽

Roll Forming ◽

Robust Optimization Design

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Application of Parallel Computing in Robust Optimization Design Using MATLAB

2015 Fifth International Conference on Instrumentation and Measurement, Computer, Communication and Control (IMCCC) ◽

10.1109/imccc.2015.263 ◽

2015 ◽

Cited By ~ 1

Author(s):

Huijing Wang ◽

Zhigang Lu ◽

Hao Zhao ◽

Huibo Feng

Keyword(s):

Parallel Computing ◽

Robust Optimization ◽

Optimization Design ◽

Robust Optimization Design

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The Effect of Different Optimization Methods on Robustness for Robust Optimization Design

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.721.464 ◽

2014 ◽

Vol 721 ◽

pp. 464-467

Author(s):

Tao Fu ◽

Qin Zhong Gong ◽

Da Zhen Wang

Keyword(s):

Robust Optimization ◽

Objective Function ◽

Robust Design ◽

Optimization Design ◽

Optimization Methods ◽

Optimization Method ◽

Design Parameters ◽

Hybrid Particle Swarm Optimization ◽

Design Variables ◽

Robust Optimization Design

In view of robustness of objective function and constraints in robust design, the method of maximum variation analysis is adopted to improve the robust design. In this method, firstly, we analyses the effect of uncertain factors in design variables and design parameters on the objective function and constraints, then calculate maximum variations of objective function and constraints. A two-level optimum mathematical model is constructed by adding the maximum variations to the original constraints. Different solving methods are used to solve the model to study the influence to robustness. As a demonstration, we apply our robust optimization method to an engineering example, the design of a machine tool spindle. The results show that, compared with other methods, this method of HPSO(hybrid particle swarm optimization) algorithm is superior on solving efficiency and solving results, and the constraint robustness and the objective robustness completely satisfy the requirement, revealing that excellent solving method can improve robustness.

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