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| Multi-objective performance optimization method of automotive air-conditioning condenser |
| GUO Hai1, NI Yi-hua2, WANG Jin1, LU Guo-dong1 |
| 1. Department of Mechanical Engineering, Zhejiang University, Hangzhou 310027, China; 2. School of Engineering, Zhejiang A&F University, Hangzhou 311300, China |
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Abstract Aiming at the performance optimization problem of an automotive air-conditioning parallel flow condenser, the condenser was divided into three regions which were respectively overheated, two-phase and sub-cooled. The mathematic relationship between the structure and the performance parameters was built based on the ε-NTU method by dividing flow path into micro units and calculating one by one after the region type was judged by enthalpy or dryness fraction, and the heat exchanging calculation model was verified by running experiments. The Pareto optimal solutions of double-objective and multi-objective optimizations were obtained by the multi-objective genetic algorithm (MOGA) and were compared. Results show that the MOGA can solve the performance optimization problem of an automotive air-conditioning parallel flow condenser, and the optimization effect of multi-objective optimization is better than that of double-objective. Through analysis of the optimizing points, three optimizing plans which respectively promoted the overall performance, the operating performance, and decreased the weight with volume were obtained. The overall optimizing plan increased heat transfer efficiency by 4.7% and decreased pressure drop by 4.5%, volume by 10.5% and mass by 6.4%．
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Published: 06 June 2018
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车用空调冷凝器性能多目标优化方法
针对车用空调平行流冷凝器的性能优化问题,将冷凝器划分为3个相区,分别为过热区、两相区和过冷区.通过划分微元并通过比焓或干度逐一判断所属相区并进行计算的方式,基于ε-NTU法建立结构参数与性能参数之间的数学关系,通过运行实验验证了该换热计算模型.利用多目标遗传算法(MOGA)分别获得双目标优化与多目标优化的Pareto最优解集,比较了两者的优劣.结果表明,采用MOGA能够解决车用空调平行流冷凝器性能优化问题,相对双目标优化具有更好的优化效果.通过对优化点进行分析,分别获得最佳综合性能、最佳运行性能、小型轻量化3种优化方案,其中综合性能优化方案提升换热效率4.7%、降低压降4.5%,体积和质量分别减小10.5%和6.4%.
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