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About this paper
Laura Botero Bolívar, David Huergo, Fernanda L. dos Santos, Cornelis H. Venner, Leandro D. de Santana, Esteban Ferrer
A symbolic-regression model predicts trailing-edge wall-pressure spectra by learning separate analytical relationships for low-, mid-, and high-frequency bands from boundary-layer quantities. Trained on measurements from two airfoils and tested on four additional configurations, it achieved a mean external-validation error of 9.86 versus 43.44, 66.39, and 111.41 for the Kamruzzaman, Lee, and Goody models, and reproduced a full-scale wind-turbine noise spectrum to within 1 dB overall.
Key insights
- Different parts of the wall-pressure spectrum are governed by different boundary-layer scales: thickness and velocity dominate the low-frequency region, whereas momentum thickness and wall shear stress control the high-frequency tail.
- Separating the spectrum into frequency bands lets symbolic regression capture these mechanisms with interpretable expressions rather than forcing one equation to fit the full spectrum.
- Model transferability depends strongly on the quality of boundary-layer estimates, especially wall shear stress under strong adverse pressure gradients and near-stall conditions.
Summary by alphaXiv.
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