Language:

Prediction of aerodynamic flow fields using convolutional neural networks

Computational mechanics, 2019-08, Vol.64 (2), p.525-545 [Peer Reviewed Journal]

Springer-Verlag GmbH Germany, part of Springer Nature 2019 ;COPYRIGHT 2019 Springer ;Copyright Springer Nature B.V. 2019 ;Computational Mechanics is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 0178-7675 ;EISSN: 1432-0924 ;DOI: 10.1007/s00466-019-01740-0

Full text available

Citations Cited by

Actions
1. Add to My Research
2. Remove from My Research
3. E-mail
4. Print
5. Permalink
6. Citation
7. EasyBib
8. EndNote
9. RefWorks
10. Delicious
11. Export RIS
12. Export BibTeX

Title:
Prediction of aerodynamic flow fields using convolutional neural networks
Author: Bhatnagar, Saakaar ; Afshar, Yaser ; Pan, Shaowu ; Duraisamy, Karthik ; Kaushik, Shailendra
Subjects: Aerodynamic forces ; Aerodynamics ; Analysis ; Angle of attack ; Artificial neural networks ; Classical and Continuum Physics ; Computational fluid dynamics ; Computational Science and Engineering ; Convolution ; Engineering ; Flow control ; Neural networks ; Original Paper ; Predictions ; Shape recognition ; Sharpening ; Theoretical and Applied Mechanics
Is Part Of: Computational mechanics, 2019-08, Vol.64 (2), p.525-545
Description: An approximation model based on convolutional neural networks (CNNs) is proposed for flow field predictions. The CNN is used to predict the velocity and pressure field in unseen flow conditions and geometries given the pixelated shape of the object. In particular, we consider Reynolds Averaged Navier–Stokes (RANS) flow solutions over airfoil shapes as training data. The CNN can automatically detect essential features with minimal human supervision and is shown to effectively estimate the velocity and pressure field orders of magnitude faster than the RANS solver, making it possible to study the impact of the airfoil shape and operating conditions on the aerodynamic forces and the flow field in near-real time. The use of specific convolution operations, parameter sharing, and gradient sharpening are shown to enhance the predictive capabilities of the CNN. We explore the network architecture and its effectiveness in predicting the flow field for different airfoil shapes, angles of attack, and Reynolds numbers.
Publisher: Berlin/Heidelberg: Springer Berlin Heidelberg
Language: English
Identifier: ISSN: 0178-7675
EISSN: 1432-0924
DOI: 10.1007/s00466-019-01740-0
Source: ProQuest Central

Back to results list


INSPIRE LIBRARY - TON DUC THANG UNIVERSITY	(84-028) 37 755 057	Feedback
19 Nguyen Huu Tho St. Dist.7, HCM	thuvien@tdtu.edu.vn	Feedback

Prediction of aerodynamic flow fields using convolutional neural networks

Springer-Verlag GmbH Germany, part of Springer Nature 2019 ;COPYRIGHT 2019 Springer ;Copyright Springer Nature B.V. 2019 ;Computational Mechanics is a copyright of Springer, (2019). All Rights Reserved. ;ISSN: 0178-7675 ;EISSN: 1432-0924 ;DOI: 10.1007/s00466-019-01740-0

Searching Remote Databases, Please Wait