THERMAL SCIENCE

International Scientific Journal

Pingqing Fan

,

Xipei Ma

,

Yong Chen

,

Tao Yuan

,

Tianhong Liu

OPTIMIZATION OF THE AUTOMOTIVE AIR CONDITIONING SYSTEM USING RADIAL BASIS FUNCTION NEURAL NETWORK

ABSTRACT
The defrosting performance of automotive air conditioners plays an important role in driving safety. This paper uses CFD to simulate the internal flow field of the automobile numerically. Simulation results show that the flow distribution is unreasonable. The horizontal grilles are added at the outlets to improve the defrosting performance of the automobile. Air-flow jet angle and the length of the air conditioning outlets (L1, L2) are selected as design variables based on the radial basis neural network to find the optimal combination scheme. The area of the defrosting dead corner has been reduced from 20-5% after optimization, and the frost layer of the front windshield has been completely melted in 25 minutes. The experiment test is conducted to verify the improvement of the defrosting performance of automotive air conditioners. The design methodology can be applied to the development of the air conditioner.
KEYWORDS
defrosting performance, automotive air conditioning, optimization, radial basis neural network
PAPER SUBMITTED: 2021-02-25
PAPER REVISED: 2021-07-01
PAPER ACCEPTED: 2021-07-06
PUBLISHED ONLINE: 2021-10-10
DOI REFERENCE: https://doi.org/10.2298/TSCI210225280F
CITATION EXPORT: view in browser or download as text file
THERMAL SCIENCE YEAR 2022, VOLUME 26, ISSUE Issue 4, PAGES [3477 - 3489]
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Optimization of the automotive air conditioning system using radial basis function neural network

© 2024 Society of Thermal Engineers of Serbia. Published by the Vinča Institute of Nuclear Sciences, National Institute of the Republic of Serbia, Belgrade, Serbia. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International licence