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Performance improvement of CO2 two-phase ejector by combining CFD modeling, artificial neural network and genetic algorithm.

Author(s) : LIU G., ZHAO H., DENG J., WANG L., ZHANG H.

Type of article: IJR article

Summary

The ejector, as one of the core components of the CO2 trans-critical ejection refrigeration system, plays an important role in improving refrigeration capacity and reducing compressor power consumption. Although the ejector can be optimized by experiment and Computational Fluid Dynamics (CFD) simulation to improve its performance, these methods are time-consuming and complicated. This study aims to propose a method to improve the performance of the CO2 trans-critical two-phase ejector assisted by using CFD, artificial neural network (ANN), and genetic algorithm (GA). Firstly, the influence of geometric parameters on ejector efficiency was analyzed by using CFD technology, and the database was generated. Next, the complex and time-consuming CFD model was replaced by the ANN surrogate model established to predict the ejector performance. Finally, the GA method was used to optimize the ejector to maximize the ejector efficiency. The results show that the efficiency of the optimized ejector is 35.39%. The optimized ejector increases the secondary flow velocity and eliminates the vortex in ejector, and the efficiency of the optimized ejector is raised by more than 8% on average than that of initial ejector under different primary and secondary flow conditions. The research shows that the combination of CFD, ANN, and GA has higher reliability and better performance in the optimization design of CO2 two-phase ejector.

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Pages: p. 151-167

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Details

  • Original title: Performance improvement of CO2 two-phase ejector by combining CFD modeling, artificial neural network and genetic algorithm.
  • Record ID : 30032017
  • Languages: English
  • Subject: Technology
  • Source: International Journal of Refrigeration - Revue Internationale du Froid - vol. 154
  • Publication date: 2023/10
  • DOI: http://dx.doi.org/10.1016/j.ijrefrig.2023.07.005

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