Prediction of Base Pressure in a Suddenly Expanded Flow Process at Supersonic Mach Number Regimes using ANN and CFD
Sudden expansion of flow in supersonic flow regime has gained relevance in the recent pasts for a wide run of applications. A number of kinematic as well as geometric parameters have been significantly found to impact the base pressure created within the suddenly expanded stream. The current researc...
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| Format: | Article |
| Language: | English |
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Isfahan University of Technology
2020-01-01
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| Series: | Journal of Applied Fluid Mechanics |
| Subjects: | |
| Online Access: | http://jafmonline.net/JournalArchive/download?file_ID=51088&issue_ID=1005 |
| _version_ | 1828377640972582912 |
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| author | J. D. Quadros S. A. Khan |
| author_facet | J. D. Quadros S. A. Khan |
| author_sort | J. D. Quadros |
| collection | DOAJ |
| description | Sudden expansion of flow in supersonic flow regime has gained relevance in the recent pasts for a wide run of applications. A number of kinematic as well as geometric parameters have been significantly found to impact the base pressure created within the suddenly expanded stream. The current research intends to create a predictive model for base pressure that is established in the abruptly extended stream. The artificial neural network (ANN) approach is being utilized for this purpose. The database utilized for training the network was assembled utilizing computational fluid dynamics (CFD). This was done by the design of experiments based L27 Orthogonal array. The three input parameters were Mach number (M), nozzle pressure ratio (NPR) and area ratio (AR) and base pressure was the output parameter. The CFD numerical demonstrate was approved by an experimental test rig that developed results for base pressure, and used a nozzle and sudden extended axis-symmetric duct to do so. The ANN architecture comprised of three layers with eight neurons in the hidden layer. The algorithm for optimization was Levenberg-Marquardt. The ANN was able to successfully predict the base pressure with a regression coefficient R2 of less than 0.99 and RMSE=0.0032. The importance of input parameters influencing base pressure was estimated by using the ANN weight coefficients. Mach number obtained a relative importance of 47.16% claiming to be the most dominating factor. |
| first_indexed | 2024-04-14T08:15:14Z |
| format | Article |
| id | doaj.art-0cf632ee863141d2bd49ca3a4edc3ac1 |
| institution | Directory Open Access Journal |
| issn | 1735-3572 |
| language | English |
| last_indexed | 2024-04-14T08:15:14Z |
| publishDate | 2020-01-01 |
| publisher | Isfahan University of Technology |
| record_format | Article |
| series | Journal of Applied Fluid Mechanics |
| spelling | doaj.art-0cf632ee863141d2bd49ca3a4edc3ac12022-12-22T02:04:25ZengIsfahan University of TechnologyJournal of Applied Fluid Mechanics1735-35722020-01-01132499511.Prediction of Base Pressure in a Suddenly Expanded Flow Process at Supersonic Mach Number Regimes using ANN and CFDJ. D. Quadros0S. A. Khan1Department of Mechanical Engineering, Birla Institute of Technology, Off-shore campus, Ras Al Khaimah, UAEDepartment of Mechanical Engineering, International Islamic University Malaysia, Kuala Lampur, MalaysiaSudden expansion of flow in supersonic flow regime has gained relevance in the recent pasts for a wide run of applications. A number of kinematic as well as geometric parameters have been significantly found to impact the base pressure created within the suddenly expanded stream. The current research intends to create a predictive model for base pressure that is established in the abruptly extended stream. The artificial neural network (ANN) approach is being utilized for this purpose. The database utilized for training the network was assembled utilizing computational fluid dynamics (CFD). This was done by the design of experiments based L27 Orthogonal array. The three input parameters were Mach number (M), nozzle pressure ratio (NPR) and area ratio (AR) and base pressure was the output parameter. The CFD numerical demonstrate was approved by an experimental test rig that developed results for base pressure, and used a nozzle and sudden extended axis-symmetric duct to do so. The ANN architecture comprised of three layers with eight neurons in the hidden layer. The algorithm for optimization was Levenberg-Marquardt. The ANN was able to successfully predict the base pressure with a regression coefficient R2 of less than 0.99 and RMSE=0.0032. The importance of input parameters influencing base pressure was estimated by using the ANN weight coefficients. Mach number obtained a relative importance of 47.16% claiming to be the most dominating factor.http://jafmonline.net/JournalArchive/download?file_ID=51088&issue_ID=1005base pressure mach number artificial neural network (ann) computational fluid dynamics (cfd). |
| spellingShingle | J. D. Quadros S. A. Khan Prediction of Base Pressure in a Suddenly Expanded Flow Process at Supersonic Mach Number Regimes using ANN and CFD Journal of Applied Fluid Mechanics base pressure mach number artificial neural network (ann) computational fluid dynamics (cfd). |
| title | Prediction of Base Pressure in a Suddenly Expanded Flow Process at Supersonic Mach Number Regimes using ANN and CFD |
| title_full | Prediction of Base Pressure in a Suddenly Expanded Flow Process at Supersonic Mach Number Regimes using ANN and CFD |
| title_fullStr | Prediction of Base Pressure in a Suddenly Expanded Flow Process at Supersonic Mach Number Regimes using ANN and CFD |
| title_full_unstemmed | Prediction of Base Pressure in a Suddenly Expanded Flow Process at Supersonic Mach Number Regimes using ANN and CFD |
| title_short | Prediction of Base Pressure in a Suddenly Expanded Flow Process at Supersonic Mach Number Regimes using ANN and CFD |
| title_sort | prediction of base pressure in a suddenly expanded flow process at supersonic mach number regimes using ann and cfd |
| topic | base pressure mach number artificial neural network (ann) computational fluid dynamics (cfd). |
| url | http://jafmonline.net/JournalArchive/download?file_ID=51088&issue_ID=1005 |
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