Modeling Cavitation in Converging-Diverging Nozzle Using Computational Fluid Dynamics and Machine Learning Model

被引：0

作者：

Lu, You-Cheng ^{[1
]}

Mohammadzaheri, Morteza ^{[2
]}

Cheng, Way Lee ^{[1
]}

机构：

[1] Natl Sun Yat sen Univ, Dept Mech & Electromech Engn, 70 Lien hai Rd, Kaohsiung 804, Taiwan

[2] German Univ Technol Oman, Engn Dept, POB 1816, Athaibah 130, Sultanate Of Om, Oman

来源：

CHEMICAL ENGINEERING & TECHNOLOGY | 2025年

关键词：

Cavitation; Computational fluid dynamic; Machine learning; Venturi tube; HYDRODYNAMIC CAVITATION; FLOWS; CFD;

D O I：

10.1002/ceat.12011

中图分类号：

TQ [化学工业];

学科分类号：

0817 ;

摘要：

Cavitation occurs when the pressure drops below the saturation pressure. In this study, computational fluid dynamics (CFD) is used to model the cavitation behavior in the Venturi tube under high pressure and to investigate the impact of geometric parameters on steam generation. In recent years, there has been a shift toward exploring machine learning as an alternative to traditional CFD. This work aims to establish an artificial neural network (ANN) using numerical analysis results to predict flow characteristics for various geometrical shapes of nozzles. This including the prediction of pressure drop and steam generation. The final results demonstrate a high accuracy in prediction.

引用

页数：8

共 29 条

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