Sharma, G. and Tran, T.H. and Tanimoto, J. (2024) APPLICATION OF CONVOLUTIONAL NEURAL NETWORKS IN DETERMINING THE VELOCITY AND PRESSURE FIELDS AROUND AIRFOIL MODELS. In: UNSPECIFIED.
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The article constructs a convolutional neural network for predicting pressure and velocity fields around a twodimensional aircraft wing model (airfoil model). Training data is computed using the Reynolds-averaged method,then extracted, focusing on the flow around the wing. Input data includes geometric parameters, airfoil inlet velocity, and output data includes pressure field and flow velocity around the airfoil. The convolutional neural network is based on improving the U-Net network model, commonly used in medical applications. The results show that the convolutional neural network accurately predicts flow around the airfoil, with an average error below 3. Therefore, this network can be used and further developed to predict flow around the wing. Results related to pressure distribution, velocity, and method error are presented and discussed in the study. © 2024, International Council of the Aeronautical Sciences. All rights reserved.
| Item Type: | Conference or Workshop Item (UNSPECIFIED) |
|---|---|
| Divisions: | Offices > Office of International Cooperation |
| Uncontrolled Keywords: | Air intakes; Aircraft models; Input output programs; Medical applications; Neural network models; Pressure distribution; Training aircraft; Wings, Convolutional neural network; Inlet velocity; Neural-networks; Output data; Pressure-field; Reynolds averaged; Training data; Velocities data; Velocity field; Wing models, Convolutional neural networks |
| Additional Information: | Conference of 34th Congress of the International Council of the Aeronautical Sciences, ICAS 2024 ; Conference Date: 9 September 2024 Through 13 September 2024; Conference Code:321499 |
| URI: | http://eprints.lqdtu.edu.vn/id/eprint/11454 |
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