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 base pressure analysis in suddenly expanded flows, their implementation is quite time consuming. Therefore, we must develop a progre...

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Autores principales:	Jaimon Dennis Quadros, Sher Afghan Khan, Abdul Aabid, Mohammad Shohag Alam, Muneer Baig
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	base pressure machine learning artificial neural networks support vector machine random forest response surface methodology Motor vehicles. Aeronautics. Astronautics TL1-4050
Acceso en línea:	https://doaj.org/article/505c633c7e0048aea339d0e6f931deba
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spelling	oai:doaj.org-article:505c633c7e0048aea339d0e6f931deba2021-11-25T15:57:10ZMachine Learning Applications in Modelling and Analysis of Base Pressure in Suddenly Expanded Flows10.3390/aerospace81103182226-4310https://doaj.org/article/505c633c7e0048aea339d0e6f931deba2021-10-01T00:00:00Zhttps://www.mdpi.com/2226-4310/8/11/318https://doaj.org/toc/2226-4310Base 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.Jaimon Dennis QuadrosSher Afghan KhanAbdul AabidMohammad Shohag AlamMuneer BaigMDPI AGarticlebase pressuremachine learningartificial neural networkssupport vector machinerandom forestresponse surface methodologyMotor vehicles. Aeronautics. AstronauticsTL1-4050ENAerospace, Vol 8, Iss 318, p 318 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	base pressure machine learning artificial neural networks support vector machine random forest response surface methodology Motor vehicles. Aeronautics. Astronautics TL1-4050
spellingShingle	base pressure machine learning artificial neural networks support vector machine random forest response surface methodology Motor vehicles. Aeronautics. Astronautics TL1-4050 Jaimon Dennis Quadros Sher Afghan Khan Abdul Aabid Mohammad Shohag Alam Muneer Baig Machine Learning Applications in Modelling and Analysis of Base Pressure in Suddenly Expanded Flows
description	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.
format	article
author	Jaimon Dennis Quadros Sher Afghan Khan Abdul Aabid Mohammad Shohag Alam Muneer Baig
author_facet	Jaimon Dennis Quadros Sher Afghan Khan Abdul Aabid Mohammad Shohag Alam Muneer Baig
author_sort	Jaimon Dennis Quadros
title	Machine Learning Applications in Modelling and Analysis of Base Pressure in Suddenly Expanded Flows
title_short	Machine Learning Applications in Modelling and Analysis of Base Pressure in Suddenly Expanded Flows
title_full	Machine Learning Applications in Modelling and Analysis of Base Pressure in Suddenly Expanded Flows
title_fullStr	Machine Learning Applications in Modelling and Analysis of Base Pressure in Suddenly Expanded Flows
title_full_unstemmed	Machine Learning Applications in Modelling and Analysis of Base Pressure in Suddenly Expanded Flows
title_sort	machine learning applications in modelling and analysis of base pressure in suddenly expanded flows
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/505c633c7e0048aea339d0e6f931deba
work_keys_str_mv	AT jaimondennisquadros machinelearningapplicationsinmodellingandanalysisofbasepressureinsuddenlyexpandedflows AT sherafghankhan machinelearningapplicationsinmodellingandanalysisofbasepressureinsuddenlyexpandedflows AT abdulaabid machinelearningapplicationsinmodellingandanalysisofbasepressureinsuddenlyexpandedflows AT mohammadshohagalam machinelearningapplicationsinmodellingandanalysisofbasepressureinsuddenlyexpandedflows AT muneerbaig machinelearningapplicationsinmodellingandanalysisofbasepressureinsuddenlyexpandedflows
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Machine Learning Applications in Modelling and Analysis of Base Pressure in Suddenly Expanded Flows

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