Convolutional neural network modeling and response surface analysis of compressible flow at sonic and supersonic Mach numbers

Abstract

Ribs as passive control in suddenly expanded flow is exciting to enhance the base pressure around nozzle exit. Furthermore, predictions of compressible flow characteristics in duct expansion using machine learning are very rare. In this work, the evolution of compressible flow through a nozzle regulated by semi-circular rib passive control is analyzed experimentally using the data acquisition technique; to control the base pressure for sonic and four supersonic Mach numbers. Results shows nozzles flowing under favorable pressure becomes effective and significantly increases the base pressure. Artificial neural networks (ANN), deep ANN (DNN), convolutional NN (CNN), and deep CNN (DCNN) were used for the modeling of compressible flow data that are highly sensitive and non-linear. For the first time, CNN used to model high-speed data predicted accurately the flow characteristics. The base pressure continuously decreases, irrespective of Mach number and Nozzle Pressure Ratio, even when the flow is under-expanded due to a higher area ratio. DNN was found to be most effective, with R-squared above 0.9 for the base pressure and pressure loss, while for wall pressure, it is above 0.8. The accuracy of base pressure predictions is between 84% and 99% during the training and testing of all NN models.