A flow feature detection method for modeling pressure distribution around a cylinder in non-uniform flows by using a convolutional neural network.

Abstract:

:In a myriad of engineering situations, we often hope to establish a model which can acquire load conditions around structures through flow features detection. A data-driven method is developed to predict the pressure on a cylinder from velocity distributions in its wake flow. The proposed deep learning neural network is constituted with convolutional layers and fully-connected layers: The convolutional layers can process the velocity information by features extraction, which are gathered by the fully-connected layers to obtain the pressure coefficients. By comparing the output data of the typical network with Computational Fluid Dynamics (CFD) results as reference values, it suggests that the present convolutional neural network (CNN) is able to predict the pressure coefficient in the vicinity of the trained Reynolds numbers with various inlet flow profiles and achieves a high overall precision. Moreover, a transfer learning approach is adopted to preserve the feature detection ability by keeping the parameters in the convolutional layers unchanged while shifting parameters in the fully-connected layers. Further results show that this transfer learning network has nearly the same precision while significantly lower cost. The active prospects of convolutional neural network in fluid mechanics have also been demonstrated, which can inspire more kinds of loads prediction in the future.

journal_name

Sci Rep

journal_title

Scientific reports

authors

Ye S,Zhang Z,Song X,Wang Y,Chen Y,Huang C

doi

10.1038/s41598-020-61450-z

subject

Has Abstract

pub_date

2020-03-10 00:00:00

pages

4459

issue

1

issn

2045-2322

pii

10.1038/s41598-020-61450-z

journal_volume

10

pub_type

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