To gain some basic knowledge about jet propulsion and Rocket Propulsion.
„h To understand the phenomenon of shock waves and its effect on flow. OBJECTIVE: „h To understand the basic difference between incompressible and compressible flow. The accuracy of base pressure predictions is between 84% and 99% during the training and testing of all NN models.ME2351 GAS DYNAMICS AND JET PROPULSION L T P C 3104 AIM: „h To impart knowledge to the students on compressible flow through ducts, jet propulsion and space propulsion. 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 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.
For the first time, CNN used to model high-speed data predicted accurately the flow characteristics. 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. Results shows nozzles flowing under favorable pressure becomes effective and significantly increases the base pressure.
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. Furthermore, predictions of compressible flow characteristics in duct expansion using machine learning are very rare. Ribs as passive control in suddenly expanded flow is exciting to enhance the base pressure around nozzle exit.
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