Genetic Algorithm-Enabled Rapid Prediction of Condenser Simulation Models

Wang Cheng; Pu Liang; Yuan Fengming; Liu Gao; Zhu Qing; Duan Jiru; Wang Xinyang; Liu Guangdi; Chen Yu; Su Weixun

doi:10.12465/issn.0253-4339.20250704001

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Genetic Algorithm-Enabled Rapid Prediction of Condenser Simulation Models

更新时间：2026-10-13

- Genetic Algorithm-Enabled Rapid Prediction of Condenser Simulation Models
- Journal of Refrigeration Vol. 47, Issue 5, Pages: 150-159(2026)
- 作者机构：
  
  1.宁波奥克斯电气有限公司宁波 315100
  2. 西安交通大学能源与动力工程学院西安 710049
  3. 奥克斯集团有限公司宁波 315100
- 作者简介：
  
  Wang Cheng， male， Assistant Director of Commercial Research & Development， Ningbo Aux Electric Co.， Ltd.， 86-13736095897， E-mail：15888506735@139.com. Research fields： research and development of heat pump air-conditioning systems.
- 基金信息：
- DOI：10.12465/issn.0253-4339.20250704001
  CLC： TK011;TK112
- Received：04 July 2025，
  
  Revised：2025-07-31，
  
  Accepted：16 September 2025，
  
  Published：16 October 2026
- 稿件说明：
移动端阅览
王成,蒲亮,袁封明等.遗传算法驱动冷凝器仿真模型的快速预测[J].制冷学报,2026,47(05):150-159.

Wang Cheng,Pu Liang,Yuan Fengming,et al.Genetic Algorithm-Enabled Rapid Prediction of Condenser Simulation Models[J].Journal of Refrigeration,2026,47(05):150-159.
王成,蒲亮,袁封明等.遗传算法驱动冷凝器仿真模型的快速预测[J].制冷学报,2026,47(05):150-159. DOI： 10.12465/issn.0253-4339.20250704001.

Wang Cheng,Pu Liang,Yuan Fengming,et al.Genetic Algorithm-Enabled Rapid Prediction of Condenser Simulation Models[J].Journal of Refrigeration,2026,47(05):150-159. DOI： 10.12465/issn.0253-4339.20250704001.

摘要

冷凝器作为制冷与热泵系统中的关键部件，其性能的快速、准确预测对于系统的选型与优化设计具有重要意义。针对现有冷凝器性能计算方法（如计算流体动力学仿真与实验测试）存在计算效率低、周期长等问题，本文提出一种基于遗传算法与冷凝器仿真模型相结合的优化方法，对单相区的传热关联式中的关键参数进行修正，从而提升模型预测精度。研究结果表明：在该优化方法下，板式冷凝器和翅片管冷凝器的过冷度平均绝对误差分别为1.83 ℃和2.23 ℃。此外，基于32组宽工况实验数据的验证结果显示，换热量与制冷剂出口温度的平均相对误差均控制在5%以内。该方法在一定程度上提升了冷凝器仿真模型的预测精度，为制冷系统的快速仿真与工程应用提供了有效的技术支撑。

Abstract

Condensers， a critical component in refrigeration and heat pump systems， require rapid and accurate performance prediction， which is crucial for system selection and design optimization. Conventional condenser performance evaluation methods， such as computational fluid dynamics simulations and experimental testing， suffer from computational inefficiency and long cycle times. To address such technical limitations， this study developed a synergistic optimization framework integrating genetic algorithms and the condenser models. This approach refined the heat transfer correlations in the single-phase region to enhance the predictive accuracy of the model. The results show that under this optimization method， the average absolute errors in the subcooling degree for the plate and finned-tube condensers are 1.83 ℃ and 2.23 ℃， respectively. In addition， validation results based on 32 sets of experimental data covering a wide range of operating conditions showed that the average relative errors for both the heat transfer capacity and the outlet temperature of the refrigerant were maintained within 5%. This method improved the prediction accuracy of simulation models to some extent， providing effective technical support for the rapid simulation and engineering applications of refrigeration systems.

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references

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