Predicting the passive control of fluid forces over circular cylinder in a time dependent flow using neuro-computing
The objective of this research is to combine Artificial Neural Networks (ANNs) and Computational Fluid Dynamics (CFD) approaches to leverage the advantages of both methods. To achieve this goal, we introduce a new artificial neural network architecture designed specifically for predicting fluid forc...
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| Format: | Article |
| Language: | English |
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AIP Publishing LLC
2024-12-01
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| Series: | AIP Advances |
| Online Access: | http://dx.doi.org/10.1063/5.0235129 |
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| author | Atif Asghar Rashid Mahmood Afraz Hussain Majeed Hammad Alotaibi Ahmed Refaie Ali |
| author_facet | Atif Asghar Rashid Mahmood Afraz Hussain Majeed Hammad Alotaibi Ahmed Refaie Ali |
| author_sort | Atif Asghar |
| collection | DOAJ |
| description | The objective of this research is to combine Artificial Neural Networks (ANNs) and Computational Fluid Dynamics (CFD) approaches to leverage the advantages of both methods. To achieve this goal, we introduce a new artificial neural network architecture designed specifically for predicting fluid forces within the CFD framework, aiming to reduce computational costs. Initially, time-dependent simulations around a rigid cylinder and a passive device (attached and detached) were conducted, followed by a thorough analysis of the hydrodynamic drag and lift forces encountered by the cylinder and passive device with various length L=0.1,0.2,0.3 and gap spacing Gi=0.1,0.2,0.3. The inhibition of vortex shedding is noted for gap separations of 0.1 and 0.2. However, a splitter plate of insufficient length or placed at an unsuitable distance from an obstacle yields no significant benefits. The finite element method is employed as a computational technique to address complex nonlinear governing equations. The nonlinear partial differential equations are spatially discretized with the finite element method, while temporal derivatives are addressed using a backward implicit Euler scheme. Velocity and pressure plots are provided to illustrate the physical aspects of the problem. The results indicate that the introduction of a splitter plate has reduced vortex shedding, leading to a steady flow regime, as evidenced by the stable drag and lift coefficients. The data obtained from simulations were utilized to train a neural network architecture based on the feed-forward backpropagation algorithm of Levenberg–Marquardt. Following training and validation stages, predictions for drag and lift coefficients were made without the need for additional CFD simulations. These results show that the mean square error values are very close to zero, indicating a strong correlation between the fluid force coefficients obtained from CFD and those predicted by the ANN. Additionally, a significant reduction in computational time was achieved without sacrificing the accuracy of the drag and lift coefficient predictions. |
| format | Article |
| id | doaj-art-6cd013cbc63b4a569ad4c2334f70725e |
| institution | DOAJ |
| issn | 2158-3226 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | AIP Publishing LLC |
| record_format | Article |
| series | AIP Advances |
| spelling | doaj-art-6cd013cbc63b4a569ad4c2334f70725e2025-08-20T02:56:31ZengAIP Publishing LLCAIP Advances2158-32262024-12-011412125103125103-1110.1063/5.0235129Predicting the passive control of fluid forces over circular cylinder in a time dependent flow using neuro-computingAtif Asghar0Rashid Mahmood1Afraz Hussain Majeed2Hammad Alotaibi3Ahmed Refaie Ali4Department of Mathematics, Air University, Islamabad 44000, PakistanDepartment of Mathematics, Namal University Mianwali, Mianwali 42250, PakistanSchool of Energy and Power Engineering, Jiangsu University, Zhenjiang, Jiangsu 212013, People’s Republic of ChinaDepartment of Mathematics and Statistics, College of Science, Taif University, P.O. Box 11099, Taif 21944, Saudi ArabiaDepartment of Mathematics and Computer Science, Faculty of Science, Menoufia University, Shebin El Kom 32511, Menofia, EgyptThe objective of this research is to combine Artificial Neural Networks (ANNs) and Computational Fluid Dynamics (CFD) approaches to leverage the advantages of both methods. To achieve this goal, we introduce a new artificial neural network architecture designed specifically for predicting fluid forces within the CFD framework, aiming to reduce computational costs. Initially, time-dependent simulations around a rigid cylinder and a passive device (attached and detached) were conducted, followed by a thorough analysis of the hydrodynamic drag and lift forces encountered by the cylinder and passive device with various length L=0.1,0.2,0.3 and gap spacing Gi=0.1,0.2,0.3. The inhibition of vortex shedding is noted for gap separations of 0.1 and 0.2. However, a splitter plate of insufficient length or placed at an unsuitable distance from an obstacle yields no significant benefits. The finite element method is employed as a computational technique to address complex nonlinear governing equations. The nonlinear partial differential equations are spatially discretized with the finite element method, while temporal derivatives are addressed using a backward implicit Euler scheme. Velocity and pressure plots are provided to illustrate the physical aspects of the problem. The results indicate that the introduction of a splitter plate has reduced vortex shedding, leading to a steady flow regime, as evidenced by the stable drag and lift coefficients. The data obtained from simulations were utilized to train a neural network architecture based on the feed-forward backpropagation algorithm of Levenberg–Marquardt. Following training and validation stages, predictions for drag and lift coefficients were made without the need for additional CFD simulations. These results show that the mean square error values are very close to zero, indicating a strong correlation between the fluid force coefficients obtained from CFD and those predicted by the ANN. Additionally, a significant reduction in computational time was achieved without sacrificing the accuracy of the drag and lift coefficient predictions.http://dx.doi.org/10.1063/5.0235129 |
| spellingShingle | Atif Asghar Rashid Mahmood Afraz Hussain Majeed Hammad Alotaibi Ahmed Refaie Ali Predicting the passive control of fluid forces over circular cylinder in a time dependent flow using neuro-computing AIP Advances |
| title | Predicting the passive control of fluid forces over circular cylinder in a time dependent flow using neuro-computing |
| title_full | Predicting the passive control of fluid forces over circular cylinder in a time dependent flow using neuro-computing |
| title_fullStr | Predicting the passive control of fluid forces over circular cylinder in a time dependent flow using neuro-computing |
| title_full_unstemmed | Predicting the passive control of fluid forces over circular cylinder in a time dependent flow using neuro-computing |
| title_short | Predicting the passive control of fluid forces over circular cylinder in a time dependent flow using neuro-computing |
| title_sort | predicting the passive control of fluid forces over circular cylinder in a time dependent flow using neuro computing |
| url | http://dx.doi.org/10.1063/5.0235129 |
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