Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/137038
Title: Data-driven subgrid scale modelling with neural networks
Authors: Gangu,Vaishnavi
Keywords: Engineering::Aeronautical engineering::Aerodynamics
Issue Date: 2019
Publisher: Nanyang Technological University
Abstract: An exploratory study is performed to assess the proficiency of the neural networks in prediction of the non-linear mapping of the closure terms of LES and the coarse grid components in the flow, with \textit{a priori} assumptions. Two distinct frameworks of convolutional neural networks are built to interpret the relation between the subgrid scale stress and the filtered velocity components. The first approach being the super resolution convolutional neural networks (SRCNN), originally a design for image super resolution, is found to reconstruct the high resolution flow field with a remarkable level of accuracy. Subsequent measures involved the extraction of SGS stress from this high resolution flow field. The second framework involves a direct prediction of the SGS behaviour from the filtered velocity components, exhibiting satisfactory performance. Additional examination of the model architecture encompassed the altering of the convolution kernel width of the intermediate layer. With positive and favourable results, the proposed convolutional neural network frameworks could establish a foundation for the development of potential data-driven subgrid scale models of more complex turbulent flows.
URI: https://hdl.handle.net/10356/137038
Fulltext Permission: restricted
Fulltext Availability: With Fulltext
Appears in Collections:MAE Theses

Files in This Item:
File Description SizeFormat 
VaishnaviGangu_Master'sThesis.pdf
  Restricted Access
4.27 MBAdobe PDFView/Open

Page view(s)

240
Updated on May 16, 2022

Download(s)

14
Updated on May 16, 2022

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.