Optimizing the Cooling System of High-Speed Train Environmental Wind Tunnels Using the Gene-Directed Change Genetic Algorithm

• Published in: Entropy (Basel, Switzerland) Switzerland), 2023-09, Vol.25 (10), p.1386
• Main Authors: Zhuang, Junjun, Liu, Meng, Wu, Hao, Wang, Jun
• Format: Article
• Language: English
• Subjects: Algorithms; Bottlenecks; Coefficients; Compressed air; Construction costs; Cooling; Cooling systems; Design and construction; Efficiency; Energy consumption; Energy economics; exergoeconomic evaluation coefficient; Gene mutations; Genes; genetic algorithm; Genetic algorithms; Heat; High speed rail; High speed trains; Mathematical analysis; Mutation; Optimization; optimizing the cooling system; R&D; Railroads; Railway engineering; Railway tunnels; Refrigeration; Refrigeration equipment; Research & development; Subsystems; Trains; Wind tunnels
• ISSN: 1099-4300; 1099-4300
• DOI: 10.3390/e25101386

Environmental wind tunnels play a crucial role in the research and development of high-speed railways. However, constructing and operating these wind tunnels requires significant resources, especially with respect to the cooling system, which serves as a vital subsystem. The cooling system utilizes an air compression refrigeration cycle and consists of multiple components. The efficient operation of these components, along with the adoption of appropriate strategies, greatly enhances the efficiency of the wind tunnel refrigeration system. Despite this, the existing methods for evaluating the refrigeration system do not fully capture the energy consumption of an air compression refrigeration system during practical use. To address this issue and effectively evaluate the wind tunnel refrigeration system, we propose using an exergoeconomic evaluation coefficient with experimental cycles to establish the system. This method incorporates the use of frequency coefficients and related parameters. By employing the newly developed evaluation coefficient as an objective function, we utilize the adaptive value-sharing congestion genetic algorithm to optimize the wind tunnel for high-speed trains. Furthermore, we compare the advantages and disadvantages of different optimization schemes. Traditional optimization methods prove inefficient because of the system’s numerous variables and the presence of multiple peaks in the objective function. Inspired by the biogenetic breeding method, we introduce an optimization approach based on a specific gene mutation. This innovative method significantly reduces optimization time and improves efficiency by approximately 17%.