Prediction of hydrodynamic instability in the curved ducts by means of semi-analytical and ANN approaches
Curved ducts with non-circular cross-sectional geometry have significant applications in different industries. Hydrodynamic stability in these curved ducts is an interesting issue in field of fluid mechanics. In the present study, the linear hydrodynamics stability of fluid flow in the curved rectan...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2020-09-01
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| Series: | Partial Differential Equations in Applied Mathematics |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666818120300048 |
| _version_ | 1818728142115700736 |
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| author | Hashem Nowruzi Hassan Ghassemi Mahdi Yousefifard |
| author_facet | Hashem Nowruzi Hassan Ghassemi Mahdi Yousefifard |
| author_sort | Hashem Nowruzi |
| collection | DOAJ |
| description | Curved ducts with non-circular cross-sectional geometry have significant applications in different industries. Hydrodynamic stability in these curved ducts is an interesting issue in field of fluid mechanics. In the present study, the linear hydrodynamics stability of fluid flow in the curved rectangular duct is semi-analytically investigated. Then, the hydrodynamic stability in these ducts is estimated via using artificial neural networks (ANNs). To this accomplishment, critical Dean number (Dnc) is estimated under various aspect ratios and curvature ratios. Based on the semi-analytical results, the Dncis increased by curvature ratio enhancement. In addition, irregular variation on trend of Dncis found by an enhancement in the aspect ratio. Moreover, maxima of mean square error and minima of correlation coefficient for intended ANN are obtained 0.00144 and 0.98621, respectively. Finally, predictive equation is suggested to estimate of Dncusing weights and bias of designed ANN. |
| first_indexed | 2024-12-17T22:25:17Z |
| format | Article |
| id | doaj.art-6395d368632c43d6acb89163423ebf95 |
| institution | Directory Open Access Journal |
| issn | 2666-8181 |
| language | English |
| last_indexed | 2024-12-17T22:25:17Z |
| publishDate | 2020-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Partial Differential Equations in Applied Mathematics |
| spelling | doaj.art-6395d368632c43d6acb89163423ebf952022-12-21T21:30:22ZengElsevierPartial Differential Equations in Applied Mathematics2666-81812020-09-011100004Prediction of hydrodynamic instability in the curved ducts by means of semi-analytical and ANN approachesHashem Nowruzi0Hassan Ghassemi1Mahdi Yousefifard2Department of Mechanical Engineering, Babol Noshirvani University of Technology, Babol, Iran; Correspondence to: Department of Maritime Engineering, Amirkabir University of Technology (Tehran Polytechnic), Hafez Ave, No 424, P.O. Box 15875-4413, Tehran, Iran.Department of Maritime Engineering, Amirkabir University of Technology, Tehran, IranDepartment of Mechanical Engineering, Babol Noshirvani University of Technology, Babol, IranCurved ducts with non-circular cross-sectional geometry have significant applications in different industries. Hydrodynamic stability in these curved ducts is an interesting issue in field of fluid mechanics. In the present study, the linear hydrodynamics stability of fluid flow in the curved rectangular duct is semi-analytically investigated. Then, the hydrodynamic stability in these ducts is estimated via using artificial neural networks (ANNs). To this accomplishment, critical Dean number (Dnc) is estimated under various aspect ratios and curvature ratios. Based on the semi-analytical results, the Dncis increased by curvature ratio enhancement. In addition, irregular variation on trend of Dncis found by an enhancement in the aspect ratio. Moreover, maxima of mean square error and minima of correlation coefficient for intended ANN are obtained 0.00144 and 0.98621, respectively. Finally, predictive equation is suggested to estimate of Dncusing weights and bias of designed ANN.http://www.sciencedirect.com/science/article/pii/S2666818120300048Hydrodynamic stabilityCurved ductSemi analytical methodArtificial neural networkCritical Dean number |
| spellingShingle | Hashem Nowruzi Hassan Ghassemi Mahdi Yousefifard Prediction of hydrodynamic instability in the curved ducts by means of semi-analytical and ANN approaches Partial Differential Equations in Applied Mathematics Hydrodynamic stability Curved duct Semi analytical method Artificial neural network Critical Dean number |
| title | Prediction of hydrodynamic instability in the curved ducts by means of semi-analytical and ANN approaches |
| title_full | Prediction of hydrodynamic instability in the curved ducts by means of semi-analytical and ANN approaches |
| title_fullStr | Prediction of hydrodynamic instability in the curved ducts by means of semi-analytical and ANN approaches |
| title_full_unstemmed | Prediction of hydrodynamic instability in the curved ducts by means of semi-analytical and ANN approaches |
| title_short | Prediction of hydrodynamic instability in the curved ducts by means of semi-analytical and ANN approaches |
| title_sort | prediction of hydrodynamic instability in the curved ducts by means of semi analytical and ann approaches |
| topic | Hydrodynamic stability Curved duct Semi analytical method Artificial neural network Critical Dean number |
| url | http://www.sciencedirect.com/science/article/pii/S2666818120300048 |
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