What type of study is this?

This is a Multi-objective optimization study (also classified as: Solver based on XFOIL, Non-dominated Sorting Genetic Algorithm III).

September 10, 2025

An integrated framework for multi-objective airfoil aerodynamic and aeroacoustic performance optimization combining a fast airfoil performance solver

Key Points

The optimization led to a 26.04%–62.52% increase in the lift-to-drag ratio of the NACA0012 airfoil.
A reduction of 0.68–2.07 dB in overall sound pressure level was achieved through the optimization process.
The framework integrates a solver using XFOIL and Amiet's theory for performance evaluation and design.
Combining machine learning with genetic algorithms streamlines the design process for more efficient airfoils.

Abstract

As the basic blade component, the aerodynamic and aeroacoustic performance of an airfoil significantly influences the overall aerodynamic efficiency and noise characteristics of offshore wind turbines. However, due to the trade-off between aerodynamic efficiency and noise reduction, achieving both high aerodynamic efficiency and low noise is challenging. To address this issue, this paper employs a solver to evaluate both the aerodynamic and aeroacoustic performances of the airfoil, which is based on the XFOIL, Amiet's theory, and the wall-pressure spectrum model. Moreover, a multi-objective optimization framework combining the Light Gradient Boosting Machine algorithm with the Non-dominated Sorting Genetic Algorithm III was proposed in order to get a quick design. The solver and the framework were applied to optimize the National Advisory Committee for Aeronautics (NACA) model NACA0012 airfoil, i.e., simultaneously enhancing the lift-to-drag ratio and reducing trailing-edge noise. The optimized airfoils showed significant improvements: a 26.04%–62.52% increase in the lift-to-drag ratio and a 0.68–2.07 dB reduction in the overall sound pressure level compared to the NACA0012 airfoil.

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