https://doi.org/10.1140/epjp/s13360-024-05047-9
Regular Article
Water jet angle prediction in supersonic crossflows: Euler–Lagrange and machine learning approaches
1
School of Chemical Engineering and Technology, Xi’an JiaoTong University, 28 Xianning West Road, 710049, Xi’an, Shaanxi, People’s Republic of China
2
Mechanical Engineering Department, Ferdowsi University of Mashhad, Mashhad, Iran
Received:
19
January
2024
Accepted:
27
February
2024
Published online:
14
March
2024
This study presents a comprehensive investigation into water jet injection dynamics in supersonic crossflows, employing a hybrid approach that integrates machine learning techniques, specifically random forest, with traditional discrete phase methods. The aim is to accurately model water jet structures and understand the underlying dynamics for optimizing flow control strategies. Through extensive numerical analysis, the study examines the influence of key parameters such as pressure levels, Weber number, droplet diameter and water jet velocity on penetration depth. Additionally, machine learning techniques are employed to analyze the impact of momentum, mass flow, pressure, Mach number and injection angle on penetration height. The findings reveal intricate interactions between pressure levels and penetration depth, driven by factors such as momentum transfer, evaporation efficiency and shock wave behavior. A direct correlation is observed between Weber number and penetration depth, emphasizing the role of inertial forces in determining penetration characteristics. Droplet diameter and water jet velocity emerge as critical factors affecting penetration depth, with smaller droplets and higher velocities resulting in deeper penetration into the crossflow. Machine learning analysis highlights the significance of momentum in influencing penetration height, while also indicating comparable effects of pressure, Mach number and injection angle. The random forest model demonstrates robust performance, achieving an accuracy exceeding 86.7% with an average absolute error of 0.00282, underscoring its reliability in predicting infiltration height.
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© The Author(s), under exclusive licence to Società Italiana di Fisica and Springer-Verlag GmbH Germany, part of Springer Nature 2024. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
