Open Access

ARTICLE

On the Efficiency of a CFD-Based Full Convolution Neural Network for the Post-Processing of Field Data

Sheng Bai, Feng Bao^*, Fengzhi Zhao

School of Computer and Information Technology, Northeast Petroleum University, Daqing, 163318, China

* Corresponding Author: Feng Bao. Email:

Fluid Dynamics & Materials Processing 2021, 17(1), 39-47. https://doi.org/10.32604/fdmp.2021.010376

Received 19 March 2020; Accepted 18 December 2020; Issue published 09 February 2021

Abstract

The present study aims to improve the efficiency of typical procedures used for post-processing flow field data by applying a neural-network technology. Assuming a problem of aircraft design as the workhorse, a regression calculation model for processing the flow data of a FCN-VGG19 aircraft is elaborated based on VGGNet (Visual Geometry Group Net) and FCN (Fully Convolutional Network) techniques. As shown by the results, the model displays a strong fitting ability, and there is almost no over-fitting in training. Moreover, the model has good accuracy and convergence. For different input data and different grids, the model basically achieves convergence, showing good performances. It is shown that the proposed simulation regression model based on FCN has great potential in typical problems of computational fluid dynamics (CFD) and related data processing.

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