High-speed shock–boundary-layer interaction over deformed surfaces

The multi-medium fluid flow around a supersonic sea-skimming flight is featured by the detached/attached shock wave, separation shock wave, and the reflected wave from the free surface. The complex wave structure and high nonlinearity pose a great challenge in accurate and stable numerical simulation. In this paper, a numerical model based on the high-order Runge–Kutta discontinuous Galerkin method is established to resolve the above problem. Based on the fact that the dimensionless vertical velocity is small, the air–water interface is linearized and a modified flux scheme is proposed to simplify the treatment of the multi-medium problem. A block-based adaptive mesh refinement scheme is adopted to capture the complex wave structure with the new nodes projected on the curved boundary. Finally, the numerical simulation of supersonic sea-skimming flight of the National Advisory Committee for Aeronautics 0012 airfoil is carried out by using the above-mentioned simplified numerical model based on the scheme of partition solution. The results show that the model can perform high-resolution simulations for the shock wave structure in various scenes. Meanwhile, the Mach number and distance between the airfoil and free surface are important factors affecting the structural characteristics of the shock wave systems and the airfoil loading characteristics. When the reflected shock wave acts on the airfoil's lower boundary, there will be a positive moment effect to make the airfoil dive, and the occurrence of this dangerous scene should be avoided. The relevant conclusions obtained can provide a reference for further research and engineering design.

Numerical simulation of supersonic sea-skimming flight based on discontinuous Galerkin method with adaptive mesh refinement framework

Kong,

Liu,

et al. 2023

Generalizability of transformer-based deep learning for multidimensional turbulent flow data

Drikakis,

Kokkinakis,

Fung

et al. 2024

Deep learning has been going through rapid advancement and becoming useful in scientific computation, with many opportunities to be applied to various fields, including but not limited to fluid flows and fluid–structure interactions. High-resolution numerical simulations are computationally expensive, while experiments are equally demanding and encompass instrumentation constraints for obtaining flow, acoustics and structural data, particularly at high flow speeds. This paper presents a Transformer-based deep learning method for turbulent flow time series data. Turbulent signals across spatiotemporal and geometrical variations are investigated. The pressure signals are coarsely-grained, and the Transformer creates a fine-grained pressure signal. The training includes data across spatial locations of compliant panels with static deformations arising from the aeroelastic effects of shock-boundary layer interaction. Different training approaches using the Transformer were investigated. Evaluations were carried out using the predicted pressure signal and their power spectra. The Transformer's predicted signals show promising performance. The proposed method is not limited to pressure fluctuations and can be extended to other turbulent or turbulent-like signals.

A deep learning super-resolution model for turbulent image upscaling and its application to shock wave–boundary layer interaction

Sofos,

Drikakis,

Kokkinakis

et al. 2024

Upscaling flow features from coarse-grained data is paramount for extensively utilizing computational physics methods across complex flow, acoustics, and aeroelastic environments where direct numerical simulations are computationally expensive. This study presents a deep learning flow image model for upscaling turbulent flow images from coarse-grained simulation data of supersonic shock wave–turbulent boundary layer interaction. It is shown for the first time that super-resolution can be achieved using only the coarsest-grained data as long as the deep learning training is performed using hundreds of fine-grained data. The unsteady pressure data are used in training due to their importance in aeroelasticity and acoustic fatigue occurring on aerospace structures. The effect on the number of images and their resolution features used in training, validation, and prediction is investigated regarding the model accuracy obtained. It is shown that the deep learning super-resolution model provides accurate spectra results, thus confirming the approach's effectiveness.