This paper presents an improved mathematical expression for semi-empirical wall pressure spectra modeling based on gene expression programming (GEP). The main focus of this work is to obtain a model that applies to a wide range of cases in terms of parameters and the source of data. The dataset comprises flat plate and airfoil cases with adverse and favorable pressure gradients at various Reynolds numbers. First, a characterization of the dataset is performed to understand the low-dimensional distribution of parameters. Then, a feature importance study is conducted to choose the most suitable model input variables from the exhaustive list of nondimensional parameters. The GEP algorithm is modified to ensure that trained models adhere to the basic structure of previously published semi-empirical models. Following training on the diverse database, the new model is compared against existing, best-performing empirical models to quantify the performance improvements. The models are tested on cases with completely different configurations and parameter ranges, unseen during training, and maintain their superior performance. Finally, a comparison is made between models developed with GEP and neural networks in terms of their efficacy, complexity, and interpretability.

More General Wall Pressure Spectra Models: Combining Feature Engineering with Evolutionary Algorithms