A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Multi-objective optimization of a Stirling cooler using particle swarm optimization algorithm
The Stirling cooler is a potential substitute for the vapor compression refrigeration system in a moderate-temperature zone. An isothermal model of a Stirling cooler based on finite time thermodynamics is established. The expressions of input power, cooling capacity, and coefficient of performance (COP) are derived. The input power, cooling capacity, and COP of the Stirling cooler are optimized simultaneously using the particle swarm optimization (PSO) algorithm. The performance of the multi-objective particle swarm optimization (MOPSO) algorithm is tested by four benchmark functions. The technique for order preference by similarity to an ideal solution (TOPSIS) is used to obtain the global optimal solution. According to the global optimal solution, the Stirling cooler obtains the performance with an input power of 106.5 W, a cooling capacity of 266.7 W, and a COP of 2.5. Compared with the results obtained by the single-objective optimization of cooling capacity, the COP increases by 42.0%, and the input power decreases by 59.3%. Finally, a sensitivity analysis of heat exchangers on the cooling capacity is carried out. The result shows that the cooling capacity is more sensitive to the hot-side heat exchanger in the optimal design point.
Multi-objective optimization of a Stirling cooler using particle swarm optimization algorithm
The Stirling cooler is a potential substitute for the vapor compression refrigeration system in a moderate-temperature zone. An isothermal model of a Stirling cooler based on finite time thermodynamics is established. The expressions of input power, cooling capacity, and coefficient of performance (COP) are derived. The input power, cooling capacity, and COP of the Stirling cooler are optimized simultaneously using the particle swarm optimization (PSO) algorithm. The performance of the multi-objective particle swarm optimization (MOPSO) algorithm is tested by four benchmark functions. The technique for order preference by similarity to an ideal solution (TOPSIS) is used to obtain the global optimal solution. According to the global optimal solution, the Stirling cooler obtains the performance with an input power of 106.5 W, a cooling capacity of 266.7 W, and a COP of 2.5. Compared with the results obtained by the single-objective optimization of cooling capacity, the COP increases by 42.0%, and the input power decreases by 59.3%. Finally, a sensitivity analysis of heat exchangers on the cooling capacity is carried out. The result shows that the cooling capacity is more sensitive to the hot-side heat exchanger in the optimal design point.
Multi-objective optimization of a Stirling cooler using particle swarm optimization algorithm
Wang, Lifeng (author) / Zheng, Pu (author) / Ji, Yuzhe (author) / Chen, Xi (author)
Science and Technology for the Built Environment ; 28 ; 379-390
2022-03-16
12 pages
Article (Journal)
Electronic Resource
Unknown
| British Library Online Contents | 2014
| British Library Conference Proceedings | 2010