Data-driven reduced order modeling of flows around two-dimensional bluff bodies of various shapes

Kazuto Hasegawa, Kai Fukami, Takaaki Murata, Koji Fukagata

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We propose a reduced order model for predicting unsteady flows using a data-driven method. As preliminary tests, we use two-dimensional unsteady flow around bluff bodies with different shapes as the training datasets obtained by direct numerical simulation (DNS). Our machine-learned architecture consists of two parts: Convolutional Neural Network-based AutoEncoder (CNN-AE) and Long Short Term Memory (LSTM), respectively. First, CNN-AE is used to map into a low-dimensional space from the flow field data. Then, LSTM is employed to predict the temporal evolution of the low-dimensional data generated by CNN-AE. Proposed machine-learned reduced order model is applied to two-dimensional circular cylinder flows at various Reynolds numbers and flows around bluff bodies of various shapes. The flow fields reconstructed by the machine-learned architecture show reasonable agreement with the reference DNS data. Furthermore, it can be seen that our machine-learned reduced order model can successfully map the high-dimensional flow data into low-dimensional field and predict the flow fields against unknown Reynolds number fields and shapes of bluff body. As concluding remarks, we discuss the extension study of machine-learned reduced order modeling for various applications in experimental and computational fluid dynamics.

Original languageEnglish
Title of host publicationComputational Fluid Dynamics
PublisherAmerican Society of Mechanical Engineers (ASME)
ISBN (Electronic)9780791859032
DOIs
Publication statusPublished - 2019 Jan 1
EventASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference, AJKFluids 2019 - San Francisco, United States
Duration: 2019 Jul 282019 Aug 1

Publication series

NameASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference, AJKFluids 2019
Volume2

Conference

ConferenceASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference, AJKFluids 2019
CountryUnited States
CitySan Francisco
Period19/7/2819/8/1

ASJC Scopus subject areas

  • Fluid Flow and Transfer Processes

Fingerprint Dive into the research topics of 'Data-driven reduced order modeling of flows around two-dimensional bluff bodies of various shapes'. Together they form a unique fingerprint.

  • Cite this

    Hasegawa, K., Fukami, K., Murata, T., & Fukagata, K. (2019). Data-driven reduced order modeling of flows around two-dimensional bluff bodies of various shapes. In Computational Fluid Dynamics (ASME-JSME-KSME 2019 8th Joint Fluids Engineering Conference, AJKFluids 2019; Vol. 2). American Society of Mechanical Engineers (ASME). https://doi.org/10.1115/AJKFluids2019-5079