Data-driven approach to predict the flow boiling heat transfer coefficient of liquid hydrogen aviation fuel. (15th September 2022)

• Record Type: Journal Article
• Authors: He, Yichuan; Hu, Chengzhi; Jiang, Bo; Sun, Zhehao; Ma, Jing; Li, Hongyang; Tang, Dawei
• Is Part Of: Fuel. Volume 324:Part C(2022)
• Publication Date: 2022-09-15
• Subjects: Machine learning -- Neural networks -- Hydrogen flow boiling -- Heat transfer coefficient; Fuel -- Periodicals; Coal -- Periodicals; Coal; Fuel; Periodicals; 662.6
• DOI: 10.1016/j.fuel.2022.124778
• Languages: English
• ISSNs: 0016-2361

Abstract

Highlights: A consolidated database for hydrogen flow boiling heat transfer is amassed. Extra tree model outperformed published universal correlations and seven ML models. Ja number is indispensable to the input parameters as one fundamental descriptor. Abstract: The airline industry targets net-zero carbon emissions by 2050. Hydrogen, a fuel with high energy density and clean combustion products, is poised to substitute kerosene as a new kind of future aviation fuel. Nonetheless, hydrogen aviation fuel is extremely sensitive to heat leakage during the transportation from the fuel tank to the engine on account of the liquid hydrogen flow boiling. Up to now, a reliable tool to predicate the hydrogen flow boiling heat transfer coefficient with high accuracy remains inaccessible. Herein, we propose a data-driven machine-learning model to predict this coefficient via a non-linear regression approach. To this end, we first collected over 864 data points with a hydraulic diameter ranging from 4 mm to 6 mm and with a flow velocity ranging from 1.33 m/s to 11.56 m/s. Then, eight machine learning regression models are developed and compared with empirical correlations. Among them, the Extra Tree model exhibited the best prediction performance with a MSE <0.01% and a R 2 = 0.9933, significantly outperforming previously reported generalized prediction correlations. Finally, we found that the Ja number, indispensable to the input parameters, served as a fundamental descriptor in the accurate prediction of the hydrogen flow boiling heat transfer coefficient. The machine learning-based technique provides a potent tool to predict the hydrogen nucleate flow boiling heat transfer coefficients with great precision.