Comparative Study of Future State Predictions of Unsteady Multiphase Flows Using DMD and Deep Learning

Neil Ashwin Raj, Danesh Tafti, Nikhil Muralidhar, Anuj Karpatne

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Flow across an array of solid obstructions is a common phenomenon observed in many applications such as multiphase flows, heat exchangers, and environmental flows. In this work, we aim to train deep learning models and to predict the time evolution of unsteady flow fields in a domain of randomly arranged 2D cylinders at Reynolds number 50. Two different approaches are used and compared in this paper, dynamic mode decomposition (DMD) which is a dimensionality-reduction algorithm based on singular value decomposition (SVD) and long short-term memory (LSTM) neural networks. In both cases, the model is trained on the first 165 time steps and then is tested on predicting the next 300 time steps. Two flow fields with different spectral characteristics are used to compare the performance of the two techniques. The LSTM architecture owing to its ability to learn nonlinear dynamics performs better than the DMD algorithm in the case with more temporal time scales present.

Original languageEnglish
Title of host publicationFluid Mechanics and Fluid Power, Volume 4 - Select Proceedings of FMFP 2022
EditorsKrishna Mohan Singh, Sushanta Dutta, Sudhakar Subudhi, Nikhil Kumar Singh
Pages923-935
Number of pages13
DOIs
StatePublished - 2024
Event9th International and 49th National Conference on Fluid Mechanics and Fluid Power, FMFP 2022 - Roorkee, India
Duration: 14 Dec 202216 Dec 2022

Publication series

NameLecture Notes in Mechanical Engineering
ISSN (Print)2195-4356
ISSN (Electronic)2195-4364

Conference

Conference9th International and 49th National Conference on Fluid Mechanics and Fluid Power, FMFP 2022
Country/TerritoryIndia
CityRoorkee
Period14/12/2216/12/22

Keywords

  • Convolutional auto-encoder (CAE)
  • Deep learning
  • Dynamic mode decomposition (DMD)
  • Long short-term memory (LSTM)
  • Multiphase flow
  • Unsteady flow

Fingerprint

Dive into the research topics of 'Comparative Study of Future State Predictions of Unsteady Multiphase Flows Using DMD and Deep Learning'. Together they form a unique fingerprint.

Cite this