Title: Prediction of airfoil self-noise using polynomial regression, multivariate adaptive regression splines, gradient boosting technique and deep learning technique

Authors: Sanjiban Sekhar Roy; Paridhi Singh; Gobind Manuja; Raghav Sikaria; Maharishi Parekh

Addresses: School of Computer Science and Engineering, Vellore Institute of Technology, SJT-116A29, Vellore, Tamilnadu 632014, India ' Data Science Institute, Columbia University, New York, NY 10027, USA ' School of Computer Science and Engineering, Vellore Institute of Technology, SJT-116A29, Vellore, Tamilnadu 632014, India ' School of Computer Science and Engineering, Vellore Institute of Technology, SJT-116A29, Vellore, Tamilnadu 632014, India ' School of Computer Science and Engineering, Vellore Institute of Technology, SJT-116A29, Vellore, Tamilnadu 632014, India

Abstract: Recently, studies have been made regarding airfoil self-noise, its generation, its prediction and how to curb the noise and the various ill-effects of it. Estimation of noise needs to be accurate so that the further studies to reduce noise from the airfoil models can be performed efficiently. Thus, the development of a coherent noise prediction tool is vital. This paper, proposes polynomial regression, multivariate adaptive regression splines, gradient boosting technique and deep learning model to estimate the airfoil self-noise noise prediction. It is observed that multivariate adaptive regression splines (MARS) and polynomial regression models have shown reasonable output whereas outstanding results have been obtained by applying deep neural networks and gradient boosting machine, for the airfoil self-noise prediction problem.

Keywords: airfoil acoustic noise; prediction; multivariate adaptive regression splines; MARS; deep neural network; polynomial regression; gradient boosting.

DOI: 10.1504/IJAIP.2025.147913

International Journal of Advanced Intelligence Paradigms, 2025 Vol.30 No.4, pp.296 - 310

Received: 18 Oct 2017
Accepted: 23 Jan 2019
Published online: 08 Aug 2025 *

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