Deep neural networks for predicting and analyzing the performance of air-jets for active flow control in an axial compressor

Authors

M. A. Elhawary  - Univ. Lille, CNRS, ONERA, Arts et Métiers Institute of Technology, Centrale Lille, UMR 9014 -LMFL – Laboratoire de Mécanique des Fluides de Lille – Kampé de Fériet, F-59000 Lille, France
Francesco Romanò - Univ. Lille, CNRS, ONERA, Arts et Métiers Institute of Technology, Centrale Lille, UMR 9014 -LMFL – Laboratoire de Mécanique des Fluides de Lille – Kampé de Fériet, F-59000 Lille, France
Jean-christophe Loiseau - Arts et Métiers Institute of Technology, CNAM, DynFluid, HESAM Université, F-75013 Paris, France
Antoine Dazin - Univ. Lille, CNRS, ONERA, Arts et Métiers Institute of Technology, Centrale Lille, UMR 9014 -LMFL – Laboratoire de Mécanique des Fluides de Lille – Kampé de Fériet, F-59000 Lille, France

Abstract

Active flow control (AFC) via air-jets has proved effectiveness in mitigating rotating stall in axial compressors. However, achieving robust and reliable control strategies remains challenging. In this study, a deep neural network (DNN) is proposed to investigate the effect of air-jets control parameters on the performance of an axial compressor (CME2). We consider a control system consisting of 20 pairs of air-jets positioned circumferentially upstream of the leading edge of the rotor blades. Three control parameters are investigated: the absolute injection angle (αinj), the number of injector pairs (Ninj), and the injection velocity (Vinj). The performances of the compressor are evaluated in terms of the power balance (PB) and the surge margin improvement (SMItot) at three rotational velocities Ω = 3200RPM, 4500RPM, and 6000RPM. We build on a set of experiments previously carried-out in our lab and use it to train and validate the DNN model. The results show that the optimal air-jet parameters giving high PB can be obtained at a velocity ratio (VR) and an angle of attack (βatt) between 1.0 to 1.7 and -14◦ to 12◦, respectively. High SMItot values are obtained at VR and βatt that lie between 1.4 to 1.9 and -7◦ to 5◦, respec tively. It is also shown that the surge margin improvement based on mass flow rate (SMIQ) increases as the injection mass flow rate (Qinj) or the injection velocity (Vinj) increases, while the total-to-total pressure rise (ptt) tends to decrease at the same conditions. These outcomes open up to the possibility of generalizing our findings by interpreting the underlying physics of the control process, with the ultimate goal of proposing an AFC strategy to other compressor geometries.