Random Forest Regression-Based Machine Learning Model for Accurate Estimation of Fluid Flow in Curved Pipes

In industrial piping systems, turbomachinery, heat exchangers etc., pipe bends are essential components. Computational fluid dynamics (CFD), which is frequently used to analyse the flow behaviour in such systems, provides extremely precise estimates but is computationally expensive. As a result, a c...

Descripción completa

Guardado en:

Detalles Bibliográficos
Autores principales:	Ganesh N., Paras Jain, Amitava Choudhury, Prasun Dutta, Kanak Kalita, Paolo Barsocchi
Formato:	article
Lenguaje:	EN
Publicado:	MDPI AG 2021
Materias:	computational fluid dynamics (CFD) random forest regression (RFR) machine learning curved pipe turbulent flow Chemical technology TP1-1185 Chemistry QD1-999
Acceso en línea:	https://doaj.org/article/3a634bd9bb5446c5aa0f428699179932
Etiquetas:	Agregar Etiqueta Sin Etiquetas, Sea el primero en etiquetar este registro!

id	oai:doaj.org-article:3a634bd9bb5446c5aa0f428699179932
record_format	dspace
spelling	oai:doaj.org-article:3a634bd9bb5446c5aa0f4286991799322021-11-25T18:52:19ZRandom Forest Regression-Based Machine Learning Model for Accurate Estimation of Fluid Flow in Curved Pipes10.3390/pr91120952227-9717https://doaj.org/article/3a634bd9bb5446c5aa0f4286991799322021-11-01T00:00:00Zhttps://www.mdpi.com/2227-9717/9/11/2095https://doaj.org/toc/2227-9717In industrial piping systems, turbomachinery, heat exchangers etc., pipe bends are essential components. Computational fluid dynamics (CFD), which is frequently used to analyse the flow behaviour in such systems, provides extremely precise estimates but is computationally expensive. As a result, a computationally efficient method is developed in this paper by leveraging machine learning for such computationally expensive CFD problems. Random forest regression (RFR) is used as the machine learning algorithm in this work. Four different fluid flow characteristics (i.e., axial velocity, x-velocity, y-velocity and z-velocity) are studied in this work. The accuracy of the RFR models is assessed by using a number of statistical metrics such as mean-absolute error (MAE), mean-squared-error (MSE), root-mean-squared-error (RMSE), maximum error (Max.Error) and median error (Med.Error) etc. It is observed that the RFR models can produce considerable cost reductions in computing by surrogating the CFD model. Minor loss in estimation accuracy as compared to the CFD models is observed. While the magnitude of intricate flow characteristics such as the additional vortices are correctly predicted, some error in their location is observed.Ganesh N.Paras JainAmitava ChoudhuryPrasun DuttaKanak KalitaPaolo BarsocchiMDPI AGarticlecomputational fluid dynamics (CFD)random forest regression (RFR)machine learningcurved pipeturbulent flowChemical technologyTP1-1185ChemistryQD1-999ENProcesses, Vol 9, Iss 2095, p 2095 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	computational fluid dynamics (CFD) random forest regression (RFR) machine learning curved pipe turbulent flow Chemical technology TP1-1185 Chemistry QD1-999
spellingShingle	computational fluid dynamics (CFD) random forest regression (RFR) machine learning curved pipe turbulent flow Chemical technology TP1-1185 Chemistry QD1-999 Ganesh N. Paras Jain Amitava Choudhury Prasun Dutta Kanak Kalita Paolo Barsocchi Random Forest Regression-Based Machine Learning Model for Accurate Estimation of Fluid Flow in Curved Pipes
description	In industrial piping systems, turbomachinery, heat exchangers etc., pipe bends are essential components. Computational fluid dynamics (CFD), which is frequently used to analyse the flow behaviour in such systems, provides extremely precise estimates but is computationally expensive. As a result, a computationally efficient method is developed in this paper by leveraging machine learning for such computationally expensive CFD problems. Random forest regression (RFR) is used as the machine learning algorithm in this work. Four different fluid flow characteristics (i.e., axial velocity, x-velocity, y-velocity and z-velocity) are studied in this work. The accuracy of the RFR models is assessed by using a number of statistical metrics such as mean-absolute error (MAE), mean-squared-error (MSE), root-mean-squared-error (RMSE), maximum error (Max.Error) and median error (Med.Error) etc. It is observed that the RFR models can produce considerable cost reductions in computing by surrogating the CFD model. Minor loss in estimation accuracy as compared to the CFD models is observed. While the magnitude of intricate flow characteristics such as the additional vortices are correctly predicted, some error in their location is observed.
format	article
author	Ganesh N. Paras Jain Amitava Choudhury Prasun Dutta Kanak Kalita Paolo Barsocchi
author_facet	Ganesh N. Paras Jain Amitava Choudhury Prasun Dutta Kanak Kalita Paolo Barsocchi
author_sort	Ganesh N.
title	Random Forest Regression-Based Machine Learning Model for Accurate Estimation of Fluid Flow in Curved Pipes
title_short	Random Forest Regression-Based Machine Learning Model for Accurate Estimation of Fluid Flow in Curved Pipes
title_full	Random Forest Regression-Based Machine Learning Model for Accurate Estimation of Fluid Flow in Curved Pipes
title_fullStr	Random Forest Regression-Based Machine Learning Model for Accurate Estimation of Fluid Flow in Curved Pipes
title_full_unstemmed	Random Forest Regression-Based Machine Learning Model for Accurate Estimation of Fluid Flow in Curved Pipes
title_sort	random forest regression-based machine learning model for accurate estimation of fluid flow in curved pipes
publisher	MDPI AG
publishDate	2021
url	https://doaj.org/article/3a634bd9bb5446c5aa0f428699179932
work_keys_str_mv	AT ganeshn randomforestregressionbasedmachinelearningmodelforaccurateestimationoffluidflowincurvedpipes AT parasjain randomforestregressionbasedmachinelearningmodelforaccurateestimationoffluidflowincurvedpipes AT amitavachoudhury randomforestregressionbasedmachinelearningmodelforaccurateestimationoffluidflowincurvedpipes AT prasundutta randomforestregressionbasedmachinelearningmodelforaccurateestimationoffluidflowincurvedpipes AT kanakkalita randomforestregressionbasedmachinelearningmodelforaccurateestimationoffluidflowincurvedpipes AT paolobarsocchi randomforestregressionbasedmachinelearningmodelforaccurateestimationoffluidflowincurvedpipes
_version_	1718410579523141632

Random Forest Regression-Based Machine Learning Model for Accurate Estimation of Fluid Flow in Curved Pipes

Ejemplares similares