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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Bibliographic Details
Published in:Journal of mechanical science and technology Vol. 38; no. 8; pp. 3899 - 3919
Main Authors: Shirvani, Ahmad, Nili-Ahmadabadi, Mahdi, Ha, Man Yeong
Format: Journal Article
Language:English
Published: Seoul Korean Society of Mechanical Engineers 01.08.2024
Springer Nature B.V
대한기계학회
Subjects:
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
Aerodynamic design
Deep learning
Neural network design
Data-driven computational cost reduction
Target prediction
ISSN:1738-494X, 1976-3824
Online Access:Get full text
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Summary: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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ISSN:1738-494X
1976-3824
DOI:10.1007/s12206-024-2104-7