Artificial neural networks as a surrogate model for linear stability analysis of compressible, three-dimensional boundary layers

Abstract

One impactful factor for the reduction of carbon emissions in aviation is the reduction of viscous drag, which in turn is heavily influenced by the boundary-layer state. Due to this dependency, a crucial design parameter in this context is the position of laminar-turbulent transition. In order to estimate the transition location, the semi-empirical e^N-method is commonly used, which relies on stability characteristics of the laminar boundary layer computed based on the linear stability theory (LST). Transition is predicted, where the integrated growth rate of disturbances modes, the N-factor, reaches an experimentally derived critical limit. How-ever, the transition prediction based on LST is so far mostly used by expert users only. In order to make this method accessible to a wider range of potential users and to profit from improved performance, various strategies to construct an according surrogate model, such as lookup tables [1], have been proposed in the past. After having proven their strong potential in different branches and fields of application, artificial neural networks (ANN) have lately gained attention as a suitable candidate for surrogate models for boundary-layer stability predictions again [2]. In the present work, an ANN-based approach for surrogate modelling of LST-based stability computation is presented for three-dimensional compressible boundary layers. Within the scope of this work, two different instability mechanisms, two-dimensional (2D) Tollmien-Schlichting waves (TS) and stationary cross-flow instabilities (CFI), are covered.