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

Abstract

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.

Comparative Study of Random Forest and Gradient Boosting Algorithms to Predict Airfoil Self-Noise

“…Genetic programming combined with adaptive regression showed promising results at low-mach-number turbulent flows for predicting airfoil noise [14]. Principal Component Analysis (PCA) integrated with a neural network or using quasi-Newtonian parameter optimization enhanced the prediction of self-noise, and the model performed better than quadratic or cubic regression [15,16]. A CatBoost Algorithm combined with Arithmetic Optimization reduced computational times and was a cost-effective method [17].…”