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

Area of Science:

• Engineering
• Thermodynamics
• Computational Science

Background:

• Environmental wind tunnels are essential for high-speed railway research.
• Cooling systems, vital subsystems, are critical for wind tunnel operation.
• Existing evaluation methods for refrigeration systems lack accuracy in practical energy consumption assessment.

Purpose of the Study:

• To develop an effective evaluation method for wind tunnel refrigeration systems.
• To optimize wind tunnel performance for high-speed trains using advanced computational techniques.
• To enhance the energy efficiency of air compression refrigeration cycles in experimental facilities.

Main Methods:

• Proposed an exergoeconomic evaluation coefficient incorporating frequency coefficients.
• Utilized an adaptive value-sharing congestion genetic algorithm for optimization.
• Introduced a novel optimization approach inspired by biogenetic breeding with gene mutation.

Main Results:

• The new evaluation coefficient accurately assesses practical energy consumption.
• The genetic algorithm optimization effectively addressed system complexity and multiple objective function peaks.
• The biogenetic-inspired method reduced optimization time and improved efficiency by approximately 17%.

Conclusions:

• The proposed exergoeconomic evaluation coefficient and genetic algorithm optimization offer a superior approach for wind tunnel refrigeration systems.
• The novel biogenetic-inspired optimization significantly enhances efficiency and reduces computational time.
• This research provides a pathway for more efficient and cost-effective high-speed railway research infrastructure.