Neural network design for data-driven prediction of target geometry for an aerodynamic inverse design algorithm
With the current advancements in artificial intelligence and machine learning, data has become a powerful tool for major improvements in various fields. In the field of aerodynamic design, most algorithms utilize an iterative method to reach their target function or geometry due to their robustness....
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| Vydané v: | Journal of mechanical science and technology Ročník 38; číslo 8; s. 3899 - 3919 |
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| Hlavní autori: | , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Seoul
Korean Society of Mechanical Engineers
01.08.2024
Springer Nature B.V 대한기계학회 |
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| ISSN: | 1738-494X, 1976-3824 |
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| Abstract | With the current advancements in artificial intelligence and machine learning, data has become a powerful tool for major improvements in various fields. In the field of aerodynamic design, most algorithms utilize an iterative method to reach their target function or geometry due to their robustness. Deep learning models enable us to exploit the data generated during those iterations to leverage the design algorithm. In this paper, design procedures and guidelines were presented for the use of multilayer feedforward neural network (MFNN) and long-short term memory (LSTM) network to predict the target geometry with early generated data of the design algorithm to reduce its computational cost. The impact of various parameters and hyperparameters on the quality of the target prediction was discussed and early results were presented for various representations of input data using the NACA-0011 airfoil aerodynamic design data. The results indicated that selecting the appropriate network and hyperparameters can yield a reliable estimate of the target geometry using only 20 % to 30 % of the available data. |
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| AbstractList | With the current advancements in artificial intelligence and machine learning, data has become a powerful tool for major improvements in various fields. In the field of aerodynamic design, most algorithms utilize an iterative method to reach their target function or geometry due to their robustness. Deep learning models enable us to exploit the data generated during those iterations to leverage the design algorithm. In this paper, design procedures and guidelines were presented for the use of multilayer feedforward neural network (MFNN) and long-short term memory (LSTM) network to predict the target geometry with early generated data of the design algorithm to reduce its computational cost. The impact of various parameters and hyperparameters on the quality of the target prediction was discussed and early results were presented for various representations of input data using the NACA-0011 airfoil aerodynamic design data. The results indicated that selecting the appropriate network and hyperparameters can yield a reliable estimate of the target geometry using only 20 % to 30 % of the available data. With the current advancements in artificial intelligence and machine learning, data has become a powerful tool for major improvements in various fields. In the field of aerodynamic design, most algorithms utilize an iterative method to reach their target function or geometry due to their robustness. Deep learning models enable us to exploit the data generated during those iterations to leverage the design algorithm. In this paper, design procedures and guidelines were presented for the use of multilayer feedforward neural network (MFNN) and long-short term memory (LSTM) network to predict the target geometry with early generated data of the design algorithm to reduce its computational cost. The impact of various parameters and hyperparameters on the quality of the target prediction was discussed and early results were presented for various representations of input data using the NACA-0011 airfoil aerodynamic design data. The results indicated that selecting the appropriate network and hyperparameters can yield a reliable estimate of the target geometry using only 20 % to 30 % of the available data. KCI Citation Count: 0 |
| Author | Ha, Man Yeong Shirvani, Ahmad Nili-Ahmadabadi, Mahdi |
| Author_xml | – sequence: 1 givenname: Ahmad surname: Shirvani fullname: Shirvani, Ahmad organization: Department of Mechanical Engineering, Isfahan University of Technology – sequence: 2 givenname: Mahdi surname: Nili-Ahmadabadi fullname: Nili-Ahmadabadi, Mahdi email: m.nili@iut.ac.ir organization: Department of Mechanical Engineering, Isfahan University of Technology – sequence: 3 givenname: Man Yeong surname: Ha fullname: Ha, Man Yeong email: myha@pusan.ac.kr organization: School of Mechanical Engineering, Pusan National University |
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| Keywords | Aerodynamic design Deep learning Neural network design Data-driven computational cost reduction Target prediction |
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| SubjectTerms | Aerodynamics Algorithms Artificial intelligence Artificial neural networks Computational efficiency Control Deep learning Design parameters Dynamical Systems Engineering Geometry Impact prediction Industrial and Production Engineering Inverse design Iterative methods Machine learning Mechanical Engineering Multilayers Network design Neural networks Original Article Vibration 기계공학 |
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