Advances in Computational Design

  • 제1권3호
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  • Pages.235-251
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  • 2016
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  • 2383-8477(pISSN)
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  • 2466-0523(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

An artificial neural network residual kriging based surrogate model for curvilinearly stiffened panel optimization

  • Sunny, Mohammed R. (Department of Aerospace Engineering, Indian Institute of Technology) ;
  • Mulani, Sameer B. (Department of Aerospace Engineering and Mechanics, The University of Alabama) ;
  • Sanyal, Subrata (Measurement Science and Engineering Department, Naval Surface Warfare Center (NSWC)) ;
  • Kapania, Rakesh K. (Department of Aerospace and Ocean Engineering, Virginia Polytechnic Institute and State University)
  • 투고 : 2016.01.01
  • 심사 : 2016.06.07
  • 발행 : 2016.07.25
⟨ 이전 논문 다음 논문 ⟩

초록

We have performed a design optimization of a stiffened panel with curvilinear stiffeners using an artificial neural network (ANN) residual kriging based surrogate modeling approach. The ANN residual kriging based surrogate modeling involves two steps. In the first step, we approximate the objective function using ANN. In the next step we use kriging to model the residue. We optimize the panel in an iterative way. Each iteration involves two steps-shape optimization and size optimization. For both shape and size optimization, we use ANN residual kriging based surrogate model. At each optimization step, we do an initial sampling and fit an ANN residual kriging model for the objective function. Then we keep updating this surrogate model using an adaptive sampling algorithm until the minimum value of the objective function converges. The comparison of the design obtained using our optimization scheme with that obtained using a traditional genetic algorithm (GA) based optimization scheme shows satisfactory agreement. However, with this surrogate model based approach we reach optimum design with less computation effort as compared to the GA based approach which does not use any surrogate model.

키워드

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