Bulletin of the American Physical Society
76th Annual Meeting of the Division of Fluid Dynamics
Sunday–Tuesday, November 19–21, 2023; Washington, DC
Session L15: CFD: Algorithms II
8:00 AM–10:23 AM,
Monday, November 20, 2023
Room: 144C
Chair: Gennaro Coppola, Università di Napoli 'Federico II'
Abstract: L15.00003 : A Combined CFD and Machine Learning Technique for Efficient Prediction of Flow Behavior in Venturi Nozzle*
8:26 AM–8:39 AM
Presenter:
Way Lee Cheng
(National Sun Yat-sen University)
Authors:
Way Lee Cheng
(National Sun Yat-sen University)
You-Cheng Lu
(National Sun Yat-sen University)
Machine learning is an efficient method that reduces the computational cost in many engineering problems. It has been shown that machine learning models can be used to speed up or even replace a part of CFD simulations. The primary goal is to maximize the steam quantity at the nozzle outlet for a given flow conditions. The main difficulty is that a large number of simulations is needed due to the highly non-linear nature of the problem. To reduce the computational cost, several artificial neural network models will be used to predict the cavitation efficiency based on the boundary conditions. The results are then verified against the simulated flow field. The ANN model is then combined with a genetic algorithm to find the optimized geometry.
*This work was supported by Taiwan NTSC Research Fund under Grant 111-2222-E-110-002.
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