Transactions of Society of Automotive Engineers of Japan
Online ISSN : 1883-0811
Print ISSN : 0287-8321
ISSN-L : 0287-8321
Technical Paper
An Inverse Design Method for Windshield Defrosting-Demisting Performance Using Machine Learning Techniques.
Vinh Long PhanYasuo YamamaeHiroshi TanakaTsuyoshi Yasuki
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2021 Volume 52 Issue 1 Pages 184-189

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Abstract
As vehicle electrification progresses, physical spaces inside instrumental panel for a defroster nozzle become narrower and nozzle sizes are required to reduced. Under such constraint conditions, it is important to design a defroster nozzle, satisfying requirements of windshield defrosting-demisting performance with low costs. In this paper, a reduced order model (ROM) is developed to predict instantly windshield velocity distribution, namely windshield defrosting-demisting performance. An inverse design method utilizing decision tree algorithm and ROM is established to find out instantly design conditions of defroster nozzle that fulfill performance requirements. Effectiveness of the method is validated by CFD performance results of a small defroster nozzle derived from the method results.
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© 2021 Society of Automotive Engineers of Japan, Inc.
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