Towards the prediction of wall-pressure fluctuations for space launchers using ZDES-based convolutional neural networks
Creators: name:  S. LECLER 
name:   P. WEISS 
name:   S. DECK 
Publisher: name: Proceedings of  the 9th European Conference for Aerospace Sciences. Lille, France, 27 June - 1 July, 2022
Year: 2022
Subjects: FLIGHT PHYSICS
COMPUTATIONAL FLUID DYNAMICS

description: Launch vehicles can experience high-level pressure fluctuations leading to unsteady aerodynamic loads during transonic flight. These fluctuations originate from physical phenomena such as boundary layer turbulence, shock-waves oscillations, separating/reattaching flows. In particular, the unsteady pressure field resulting from the latter phenomenon is highly dependent on the launcher geometry and needs to be assessed for each new configuration. Therefore, the knowledge of the fluctuating wall-pressure field is of major importance for the design of space launchers. Current advanced turbulence modelling methods (URANS, ZDES, LES, DNS) are able to predict the unsteady fields for a given configuration with a given fidelity level. However, they are still costly in terms of computational time, especially applied to realistic, high Reynolds flows. In the earlier phases of design, surrogate models could help providing insights into the unsteady wall-pressure field, namely the local root-mean-square pressure coefficient and the single-point power spectrum. In particular, machine learning methods can be leveraged to take advantage of the large amount of data produced by advanced numerical simulations. Data-driven models have indeed emerged as a promising way to solve aerodynamic problems. Among them, artificial neural networks have become very popular for their ability to learn highly nonlinear relationships between inputs and outputs. In the present work, we assess the capability of convolutional neural networks to accurately predict the wall pressure fluctuations for a semi-realistic, buffet-prone, launcher configuration. Convolutional neural networks are widely used in the field of computer vision, and are increasingly used in aerodynamics for flow reconstruction, control, feature identification and aerodynamic response prediction. They have drawn researcher\'s interest due to their ability to learn high-level features from data with strong spatial correlations and fit the nonlinear relations between these abstract, low-dimensional correlations and the output. In this study, we focus on the axisymmetric separating/reattaching flow resulting from a sudden geometrical change. Two neural networks were trained to predict the local root-means-square pressure coefficient and one-point spectrum of pressure fluctuations from mean flow data. The training dataset was built from a Zonal Detached Eddy Simulation (ZDES) of a Generic Afterbody (GA) configuration designed to fit the experiments of Depres and Meliga et al. conducted in ONERA\'s S3Ch wind tunnel. We show that our models are able to accurately predict the unsteady pressure field of this configuration. Then, a simulation of a transonic hammerhead payload fairing (cf figure 1) was realized and validated by comparison to available experimental results. A testing dataset is built from this simulation to assess the generalization capability of our models when applied to an unknown configuration. The model outputs have been compared to computed wall-pressure spectra and RMS pressure coefficients. We show that the proposed convolutional neural networks are able to locate regions of high-level pressure fluctuations and identify the most energetic frequencies in the flow for an arbitrary configuration presenting similar flow physics. The accurate levels of fluctuations can also be retrieved from the models outputs. The sensitivity of the results to the underlying turbulence model (ZDES, RANS SA, RANS SA-RC) used to get the input mean flow field is also examined. Finally, these results are discussed in terms of possible generalization and improvement by using multiple training configurations and flow conditions.