Noise Prediction of Cylinder Flow using Machine Learning
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Abstract
The accurate prediction of aerodynamic noise generated by cylinder flow is a critical challenge in various engineering applications, including automotive and aerospace industries. Traditional Computational Fluid Dynamics (CFD) methods, such as Direct Numerical Simulation (DNS), often require significant computational resources and time to simulate the complex interactions within flow. This study successfully creates a model using Machine Learning (ML) techniques to predict the pressure fluctuation in flow over a cylinder which provides a faster and equally reliable alternative to conventional methods.
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This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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