Compressor Profile Optimization Based on Hybrid Intelligent Algorithm

Yang, Huadong (2021) Compressor Profile Optimization Based on Hybrid Intelligent Algorithm. Journal of Scientific Research and Reports, 27 (10). pp. 70-78. ISSN 2320-0227

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Abstract

In order to improve the working characteristics of the scroll compressor, according to the scroll profile of the compressor, the energy efficiency ratio (EER) of the scroll compressor is taken as the objective function, and the number of scroll turns N and knots are determined based on the genetic annealing algorithm. The distance p, the height of the scroll body h, and the thickness of the scroll profile t are optimized. In the optimized solution set, three sets of optimized profile and initial profile are selected for theoretical calculation of thermodynamic characteristics and volume characteristics, and the specific influence of scroll compressor profile parameters on compressor characteristics is explored in detail, and compared with the unoptimized scroll. The initial parameters of the rotary compressor are compared with the theoretical performance. The results show that the pitch p has a significant effect on the energy efficiency ratio and discharge volume of the scroll compressor, and the number of scroll turns N has a significant effect on the characteristic of suction volume. Three kinds of optimized scroll profile parameters S2, S3, S4 are selected in the optimal solution set. Compared with the initial value S1, the working characteristics are improved. The energy efficiency ratio was increased by 38.10%, 42.58%, and 50.26%; the suction volume was increased by 66.1%, 82.3%, and 73.9%; the exhaust volume was increased by 21.1%, 29.6%, and 50%; the internal volume ratio was increased by 36.4%. 40.9%, 27.3%. It is proved that the use of genetic annealing algorithm achieves the purpose of improving the compressor's operating characteristics.

Item Type: Article
Subjects: SCI Archives > Multidisciplinary
Depositing User: Managing Editor
Date Deposited: 18 Feb 2023 09:54
Last Modified: 01 Aug 2024 05:04
URI: http://science.classicopenlibrary.com/id/eprint/313

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