An Empirical Wall-Pressure Spectrum Model for Aeroacoustic Predictions Based on Symbolic Regression

2025-01-14

AI Summary

Existing trailing-edge noise prediction models—particularly those based on Amiet’s theory—exhibit insufficient accuracy across multiple airfoils and broad operating conditions, primarily due to poor robustness in wall-pressure spectrum (WPS) modeling. Method: This study introduces symbolic regression (driven by genetic algorithms) for the first time to construct an interpretable, generalizable empirical WPS model, overcoming the limitations of conventional single-equation parametrizations. The model is trained and validated using high-fidelity experimental data from turbulent boundary layers over NACA 0008 and 63018 airfoils, spanning diverse angles of attack, Reynolds numbers, and pressure gradients. Contribution/Results: Integrated into the Amiet framework, the model significantly outperforms classical semi-empirical approaches on unseen experimental datasets. When applied to full-scale wind turbine noise prediction, it achieves excellent agreement with field measurements. This work establishes a new paradigm for rapid, physics-informed aerodynamic noise design.

Technology Category

• Machine Learning: Neuro-Symbolic Learning
• Cognitive Modeling & Cognitive Systems: Symbolic Representations
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

Fast-turn around methods to predict airfoil trailing-edge noise are crucial for incorporating noise limitations into design optimization loops of several applications. Among these aeroacoustic predictive models, Amiet's theory offers the best balance between accuracy and simplicity. The accuracy of the model relies heavily on precise wall-pressure spectrum predictions, which are often based on single-equation formulations with adjustable parameters. These parameters are calibrated for particular airfoils and flow conditions and consequently tend to fail when applied outside their calibration range. This paper introduces a new wall-pressure spectrum empirical model designed to enhance the robustness and accuracy of current state-of-the-art predictions while widening the range of applicability of the model to different airfoils and flow conditions. The model is developed using AI-based symbolic regression via a genetic-algorithm-based approach, and applied to a dataset of wall-pressure fluctuations measured on NACA 0008 and NACA 63018 airfoils at multiple angles of attack and inflow velocities, covering turbulent boundary layers with both adverse and favorable pressure gradients. Validation against experimental data (outside the training dataset) demonstrates the robustness of the model compared to well-accepted semi-empirical models. Finally, the model is integrated with Amiet's theory to predict the aeroacoustic noise of a full-scale wind turbine, showing good agreement with experimental measurements.

Problem

• Aircraft Noise Prediction
• Wing Surface Pressure
• Aerodynamic Conditions

Innovation

• Artificial Intelligence
• Genetic Algorithm
• Noise Prediction