Open AccessOptimization of the automotive air conditioning system using radial basis function neural network
Author(s) -
Pingqing Fan,
Xipei Ma,
Yong Chen,
Tao Yuan,
Tianhong Liu
Publication year - 2022
Publication title -
thermal science/thermal science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.339
H-Index - 43
eISSN - 2334-7163
pISSN - 0354-9836
DOI - 10.2298/tsci210225280f
Subject(s) - defrosting , windshield , air conditioning , automotive engineering , automotive industry , airflow , computational fluid dynamics , artificial neural network , computer science , simulation , environmental science , mechanical engineering , engineering , aerospace engineering , artificial intelligence
The defrosting performance of automotive air conditioners plays an important role in driving safety. This paper uses computational fluid dynamics (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. Airflow 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% to 5% after optimization, and the frost layer of the front windshield has been completely melted in 25 min. 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.