Finite element analysis and artificial neural network for stress distribution of an aircraft model in a wind tunnel
Wind tunnels are instrumental in the aerodynamic analysis of aircraft model structures, enabling the replication of real circumstances for better design and performance evaluation. This paper presents a novel enhancement to stress distribution predictions in wind tunnel simulations by combining Fini...
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| Format: | Article |
| Language: | English |
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Unviversity of Technology- Iraq
2025-01-01
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| Series: | Engineering and Technology Journal |
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| Online Access: | https://etj.uotechnology.edu.iq/article_184442_609279f58b4f7d827ffc50c7668a4106.pdf |
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| author | Ahmed Al-Mulla Khalaf Sinan Al-Haddad Bilal Al-Oubaidi Naseem Ibrahim Fawaz Abdulwahed Athraa Hilal |
| author_facet | Ahmed Al-Mulla Khalaf Sinan Al-Haddad Bilal Al-Oubaidi Naseem Ibrahim Fawaz Abdulwahed Athraa Hilal |
| author_sort | Ahmed Al-Mulla Khalaf |
| collection | DOAJ |
| description | Wind tunnels are instrumental in the aerodynamic analysis of aircraft model structures, enabling the replication of real circumstances for better design and performance evaluation. This paper presents a novel enhancement to stress distribution predictions in wind tunnel simulations by combining Finite Element Analysis (FEA) and Artificial Neural Networks (ANN). First, the research focuses on analyzing ANSYS Fluent data, which provides insights into the complex fluid dynamics inside the wind tunnel. The proposed approach combines the best available FEA and ANN techniques regarding prediction accuracy and computational efficiency. Such findings are those that evidence that predictions of real stress levels using ANN are quite near, with RMSE 12%, and, hence, quite accurate. The results indicated agreement between the functions generated by ANN and real stress levels and, therefore, were considered to manifest a very low error percentage. The methodology shows that it is significant for being computationally efficient since the ANN works much quicker compared to the conventional FEA approach. In addition, the methodology is significant in computations since the ANN works quicker than conventional FEA. These results thus indicate that the integrated FEA-ANN approach is beneficial and holds much promise in accurately and efficiently predicting stress distributions. Herewith, the provided method advances engineering simulations by making exact predictions of stress distributions necessary to improve design and structural analysis. |
| format | Article |
| id | doaj-art-718c90ef5a2940d39941d72a7a4dcb7a |
| institution | Kabale University |
| issn | 1681-6900 2412-0758 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Unviversity of Technology- Iraq |
| record_format | Article |
| series | Engineering and Technology Journal |
| spelling | doaj-art-718c90ef5a2940d39941d72a7a4dcb7a2025-02-02T07:51:22ZengUnviversity of Technology- IraqEngineering and Technology Journal1681-69002412-07582025-01-01431172410.30684/etj.2024.149979.1755184442Finite element analysis and artificial neural network for stress distribution of an aircraft model in a wind tunnelAhmed Al-Mulla Khalaf0Sinan Al-Haddad1Bilal Al-Oubaidi2Naseem Ibrahim3Fawaz Abdulwahed4Athraa Hilal5Institute of Technology - Baghdad, Middle Technical University, Baghdad, Iraq.Civil Engineering Dept., University of Technology-Iraq, Alsina’a street, 10066 Baghdad, Iraq.Civil Engineering Dept., Istanbul Technical University, Istanbul, Turkey.Training and Workshops Center, University of Technology-Iraq, Alsinaa Street, 10066 Baghdad, Iraq.Training and Workshops Center, University of Technology-Iraq, Alsinaa Street, 10066 Baghdad, Iraq.Training and Workshops Center, University of Technology-Iraq, Alsinaa Street, 10066 Baghdad, Iraq.Wind tunnels are instrumental in the aerodynamic analysis of aircraft model structures, enabling the replication of real circumstances for better design and performance evaluation. This paper presents a novel enhancement to stress distribution predictions in wind tunnel simulations by combining Finite Element Analysis (FEA) and Artificial Neural Networks (ANN). First, the research focuses on analyzing ANSYS Fluent data, which provides insights into the complex fluid dynamics inside the wind tunnel. The proposed approach combines the best available FEA and ANN techniques regarding prediction accuracy and computational efficiency. Such findings are those that evidence that predictions of real stress levels using ANN are quite near, with RMSE 12%, and, hence, quite accurate. The results indicated agreement between the functions generated by ANN and real stress levels and, therefore, were considered to manifest a very low error percentage. The methodology shows that it is significant for being computationally efficient since the ANN works much quicker compared to the conventional FEA approach. In addition, the methodology is significant in computations since the ANN works quicker than conventional FEA. These results thus indicate that the integrated FEA-ANN approach is beneficial and holds much promise in accurately and efficiently predicting stress distributions. Herewith, the provided method advances engineering simulations by making exact predictions of stress distributions necessary to improve design and structural analysis.https://etj.uotechnology.edu.iq/article_184442_609279f58b4f7d827ffc50c7668a4106.pdffinite element analysisartificial neural networkwind tunnelstress distributiondeformation |
| spellingShingle | Ahmed Al-Mulla Khalaf Sinan Al-Haddad Bilal Al-Oubaidi Naseem Ibrahim Fawaz Abdulwahed Athraa Hilal Finite element analysis and artificial neural network for stress distribution of an aircraft model in a wind tunnel Engineering and Technology Journal finite element analysis artificial neural network wind tunnel stress distribution deformation |
| title | Finite element analysis and artificial neural network for stress distribution of an aircraft model in a wind tunnel |
| title_full | Finite element analysis and artificial neural network for stress distribution of an aircraft model in a wind tunnel |
| title_fullStr | Finite element analysis and artificial neural network for stress distribution of an aircraft model in a wind tunnel |
| title_full_unstemmed | Finite element analysis and artificial neural network for stress distribution of an aircraft model in a wind tunnel |
| title_short | Finite element analysis and artificial neural network for stress distribution of an aircraft model in a wind tunnel |
| title_sort | finite element analysis and artificial neural network for stress distribution of an aircraft model in a wind tunnel |
| topic | finite element analysis artificial neural network wind tunnel stress distribution deformation |
| url | https://etj.uotechnology.edu.iq/article_184442_609279f58b4f7d827ffc50c7668a4106.pdf |
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