Application of data assimilation for a fast-computing heat exchanger model

• Published in: Transactions of the Japan Society for Computational Engineering and Science Vol. 2023; p. 20230009
• Main Authors: YASHIKI, Tatsurou, MEDRANO, Katleya, AIKAWA, Ryoichi, KOGA, Mutsuki, YAMAGUCHI, Yohei, SHIMOKAWA, Atsuya, ISHIBASHI, Nozomu
• Format: Journal Article
• Language: English
• Published: JAPAN SOCIETY FOR COMPUTATIONAL ENGINEERING AND SCIENCE 20.12.2023; 一般社団法人 日本計算工学会
• Subjects: 0-dimensional model; EnKF; heat transfer coefficient; physics-based model; simulation
• ISSN: 1347-8826
• DOI: 10.11421/jsces.2023.20230009

The present study focuses on combining data assimilation with a simple heat exchanger model to enable real-time simulations. First, a 0-dimensional (0D) heat exchanger model is proposed and then an impact assessment of applying different ensemble Kalman filter (EnKF) conditions on the 0D model is presented. Finally, the performance of the combined 0D model and EnKF is benchmarked against a 1-dimensional (1D) model. The survey of EnKF conditions reveals that an ensemble size of at least 10 members and a parameter noise of 5% standard deviation are crucial to balance the accuracy and convergence speed of parameter and state estimation of the 0D model. Under these conditions, the 0D model accurately predicted the behavior of an actual heat exchanger with higher accuracy than the 1D model. The results also showed that the proposed method completed 1s of the simulation period almost 2000 times faster than the 1D model.