Parametric nonlinear Volterra series via machine learning: transonic aerodynamics

Abstract

This study introduces an approach for modeling unsteady transonic aerodynamics within a parametric space using the Volterra series to capture aerodynamic responses and machine learning to enable interpolation. The first- and second-order Volterra kernels were derived from indicial aerodynamic responses obtained through computational fluid dynamics, with the second-order kernel calculated as a correction to the dominant linear response. Machine learning algorithms, specifically artificial neural network and Gaussian process regression, were used to interpolate kernel coefficients within a parameter space defined by the Mach number and angle of attack. The methodology was applied to two- and three-dimensional test cases in the transonic regime. The results underscore the benefit of including a second-order kernel to address strong nonlinearity and demonstrate the effectiveness of neural networks. The approach achieved a level of accuracy that appeared sufficient for use in conceptual design.

More information

Identifiers

ORCID for Gabriele Immordino: orcid.org/0000-0003-2718-0120

ORCID for Andrea Da Ronch: orcid.org/0000-0001-7428-6935

Catalogue record

Export record

Altmetrics