Set-Based Design Method for Rigid Axle Suspension using Bayesian Active Learning

DOI

Bibliographic Information

Other Title
  • Bayesian Active Learning を用いたリジッドアクスルサスペンションのセットベース設計法

Abstract

It is important to discover feasible region that satisfy multiple performances in the early stage of vehicle development. In this paper, we propose a set-based design method of rigid type suspension by introducing machine learning. In the proposed design method, surrogate model of the characteristics of rigid axle suspension are trained by using Gaussian process (GP). By using the posterior distribution of GP, adaptive sampling strategy to find feasible region is introduced. To show effectiveness of the proposed design method, a numerical example is demonstrated. In the numerical example, feasible region of suspension characteristics that satisfy multiple performances was identified.

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Keywords

Details 詳細情報について

  • CRID
    1390857977632358784
  • DOI
    10.11351/jsaeronbun.54.259
  • ISSN
    18830811
    02878321
  • Text Lang
    ja
  • Data Source
    • JaLC
  • Abstract License Flag
    Disallowed

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