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

Jaimon Dennis Quadros,Mohammad Shohag Alam,Abdul Aabid,Sher Afghan Khan,Muneer Baig

doi:10.3390/aerospace8110318

Jaimon Dennis Quadros, Mohammad Shohag Alam + Show 3 more

Open Access

https://doi.org/10.3390/aerospace8110318

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Abstract

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

Highlights

A flow subjected to sudden expansion will always have flow separation and flow reattachment associated with it (Figure 1)
The results observed that the artificial neural networks (ANN) model could effectively predict base pressure as all the predicted base pressure data were within the three error bands (±5%)
The present study developed a platform for data-driven analysis of base pressure prediction

Summary

Introduction

A flow subjected to sudden expansion will always have flow separation and flow reattachment associated with it (Figure 1) Such flows with a detached and high-speed nature have not been thoroughly analyzed yet, even though much research has been carried out. Due to the complicated nature of these flows, their prediction becomes very uncertain This includes recirculation, shock, and high pressure and velocity gradients [1,2,3,4]. As the shear layer exits the nozzle, a sub-atmospheric recirculation region is developed at the base, with high turbulence, high Reynolds number, and highly compressed shear flow. Such situations are critically important at the base of aerodynamic vehicles like projectiles, missiles, and rockets. It has already been documented that the base drag values account for almost 60% of the total drag force for aerodynamic vehicles [5,6]

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Results

Conclusion