Int. J. Simul. Multidisci. Des. Optim.
Volume 14, 2023
Advances in Modeling and Optimization of Manufacturing Processes
|Number of page(s)||7|
|Published online||08 May 2023|
A novel approach for noise prediction using Neural network trained with an efficient optimization technique
Department of Aeronautical Engineering, Vel Tech Rangarajan Dr. Sagunthala R&D Institute of Science and Technology, Chennai, TN, India
* e-mail: firstname.lastname@example.org
Accepted: 20 March 2023
Aerofoil noise as self-noise is detrimental to system performance, in this paper NACA 0012 optimization parameters are presented for reduction in noise. Designing an aerofoil with little noise is a fundamental objective of designing an aircraft that physically and functionally meets the requirements. Aerofoil self-noise is the noise created by aerofoils interacting with their boundary layers. Using neural networks, the suggested method predicts aerofoil self-noise. For parameter optimization, the quasi-Newtonian method is utilised. The input variables, such as angle of attack and chord length, are used as training parameters for neural networks. The output of a neural network is the sound pressure level, and the Quasi Newton method further optimises these parameters. When compared to the results of regression analysis, the values produced after training a neural network are enhanced.
Key words: Aerofoil self-noise / noise prediction / neural network training / optimization / quasi Newton method
© N.S. Radha Krishnan and S.P. Uppu, Published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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