Machine Learning Applications in Modelling and Analysis of Base Pressure in Suddenly Expanded Flows

Base pressure becomes a decisive factor in governing the base drag of aerodynamic vehicles. While several experimental and numerical methods have already been used for analysis suddenly expanded flows, their implementation is quite time consuming. Therefore, we must develop progressive approach to determine (?). Furthermore, direct consideration influence flow geometric parameters cannot be studied by using these methods. This study develops platform data-driven (?) prediction which including Mach number (M), nozzle ratio (?), area (?), length diameter (?) studied. Three different machine learning (ML) models, namely, artificial neural networks (ANN), support vector (SVM), random forest (RF), trained large amount data developed from response equations. The equations were created surface methodology (RSM) approach. predicted results are compared with validate proposed platform. obtained this work can applied right way maximize rockets missiles minimize drag.