Using artificial neural network for computing the development length of MHD channel flows. (July 2019)
- Record Type:
- Journal Article
- Title:
- Using artificial neural network for computing the development length of MHD channel flows. (July 2019)
- Main Title:
- Using artificial neural network for computing the development length of MHD channel flows
- Authors:
- Taheri, Mohammad Hasan
Abbasi, Morteza
Khaki Jamei, Mehran - Abstract:
- Highlights: An artificial neural network (ANN) was applied for computing the development length of laminar magnetohydrodynamics (MHD) flow in the entrance region of a channel. The finite volume method (FVM) was conducted to collect the input data for ANN. A feed-forward back-propagation neural network contained one hidden layer including was trained and developed to predict the development length. Comparison of the ANN with FVM and proposed correlation results revealed that ANN can be employed for modeling the developing MHD channel flow and computing of the development length. Abstract: In the present study, an artificial neural network (ANN) was applied for computing the development length of laminar magnetohydrodynamics (MHD) flow in the entrance region of a channel. The finite volume method (FVM) was conducted to investigate the laminar MHD channel entrance flow. The investigation was applied for Reynolds number (Re) ranging from 600 to 1200 while Hartmann number ( Ha ) ranging from 4 to 14. 60 datasets were obtained from numerical solution and then, a feed-forward back-propagation neural network contained one hidden layer including was trained and developed to predict the development length. Using ANN, a correlation for predicting the MHD channel flow development length was proposed. Comparison of the ANN with FVM and proposed correlation results revealed that ANN can be employed for modeling the developing MHD channel flow and prediction of the development length. ItHighlights: An artificial neural network (ANN) was applied for computing the development length of laminar magnetohydrodynamics (MHD) flow in the entrance region of a channel. The finite volume method (FVM) was conducted to collect the input data for ANN. A feed-forward back-propagation neural network contained one hidden layer including was trained and developed to predict the development length. Comparison of the ANN with FVM and proposed correlation results revealed that ANN can be employed for modeling the developing MHD channel flow and computing of the development length. Abstract: In the present study, an artificial neural network (ANN) was applied for computing the development length of laminar magnetohydrodynamics (MHD) flow in the entrance region of a channel. The finite volume method (FVM) was conducted to investigate the laminar MHD channel entrance flow. The investigation was applied for Reynolds number (Re) ranging from 600 to 1200 while Hartmann number ( Ha ) ranging from 4 to 14. 60 datasets were obtained from numerical solution and then, a feed-forward back-propagation neural network contained one hidden layer including was trained and developed to predict the development length. Using ANN, a correlation for predicting the MHD channel flow development length was proposed. Comparison of the ANN with FVM and proposed correlation results revealed that ANN can be employed for modeling the developing MHD channel flow and prediction of the development length. It was found that with the increase of Ha, the velocity profile gets flatten and consequently, the development length becomes shorter. Moreover, by augmentation of Re, the development length increases. … (more)
- Is Part Of:
- Mechanics research communications. Volume 99(2019)
- Journal:
- Mechanics research communications
- Issue:
- Volume 99(2019)
- Issue Display:
- Volume 99, Issue 2019 (2019)
- Year:
- 2019
- Volume:
- 99
- Issue:
- 2019
- Issue Sort Value:
- 2019-0099-2019-0000
- Page Start:
- 8
- Page End:
- 14
- Publication Date:
- 2019-07
- Subjects:
- Artificial neural networks -- Channel -- Development length -- Magnetohydrodynamics
Mechanics, Applied -- Periodicals
Mécanique appliquée -- Périodiques
Mechanics, Applied
Periodicals
530 - Journal URLs:
- http://www.sciencedirect.com/science/journal/00936413 ↗
http://www.elsevier.com/journals ↗
http://www.elsevier.com/homepage/elecserv.htt ↗ - DOI:
- 10.1016/j.mechrescom.2019.06.003 ↗
- Languages:
- English
- ISSNs:
- 0093-6413
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library DSC - 5424.120000
British Library DSC - BLDSS-3PM
British Library HMNTS - ELD Digital store - Ingest File:
- 11357.xml