A novel approach for noise prediction using Neural network trained with an efficient optimization technique

Autor: Naren Shankar Radha Krishnan, Shiva Prasad Uppu
Rok vydání: 2023
Předmět:
Zdroj: International Journal for Simulation and Multidisciplinary Design Optimization. 14:3
ISSN: 1779-6288
Popis: 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.
Databáze: OpenAIRE
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