Numerical prediction of heat transfer performance of plate heat exchanger based on experimental data assimilation to calibrate turbulence model constants. (1st September 2022)

• Record Type: Journal Article
• Authors: Yu, Jianping; Qiu, Hongwei; Jiao, Yanping; Tian, Youwen; Meng, Yue; Wang, Wenjiao; Min, Huaiyu; Li, Xuanxuan
• Is Part Of: Thermal science and engineering progress. Volume 34(2022)
• Publication Date: 2022-09-01
• Subjects: Plate heat exchanger -- Data assimilation model -- Constant calibration; Heat engineering -- Periodicals; Heat engineering; Thermodynamics; Periodicals; 621.402
• DOI: 10.1016/j.tsep.2022.101433
• Languages: English
• ISSNs: 2451-9049

Abstract

Highlights: Apply Data Assimilation to the study of heat transfer characteristics. Use the ensemble Kalman filter to calibrate turbulence model constants. Realize the perfect integration of experimental and numerical research. The application of Data Assimilation in engineering is expanded. Abstract: The heat transfer effect of plate heat exchangers is different due to its various plate structures. To accurately predict the heat transfer characteristics of a plate heat exchanger, this paper makes an important attempt to apply data assimilation technology, namely the data-driven model constant optimization method, to the study of the flow and heat transfer characteristics of a plate heat exchanger. Based on the experimental data, the ensemble Kalman filter algorithm is used to calibrate turbulence model constants and predict the heat transfer performance of a plate heat exchanger. The results show that the data assimilation technology maximizes the value of experimental data, realizes the perfect integration of experimental research and numerical simulation research, and gives full play to the advantages of experimental research and numerical simulation research of plate heat exchangers. At the same time, the reliability of predicting heat transfer characteristics of a plate heat exchanger is improved, the application of data assimilation in engineering is expanded, and the application potential of data assimilation in future turbulence research is highlighted.