Zonal Reduced-Order Modelling Toward Prediction of Transitional Flow Fields

T. Misaka, S. Obayashi

Research output: Contribution to journalConference articlepeer-review

1 Citation (Scopus)

Abstract

The utilization of measurement data is becoming attractive in various fields due to the massive growth of sensing and networking technologies. It is expected to utilize such a data-rich environment to improve engineering simulations in computer-aided engineering (CAE). Data assimilation is one of methodologies to statistically integrate a numerical model and measurement data, and it is expected to be a key technology to take advantage of measured data in CAE. However, the additional cost of data assimilation is not always affordable in CAE simulations. In this study, we consider the cost reduction of numerical flow simulation by the help of a reduced-order model. Since the prediction accuracy of existing ROMs are limited in transitional flow problems such as two- to three-dimensional flow transition, we investigate here a zonal hybrid approach of a full-order model and a reduced-order model.

Original languageEnglish
Article number012012
JournalJournal of Physics: Conference Series
Volume1036
Issue number1
DOIs
Publication statusPublished - 2018 Jun 27
EventInternational Meeting on High-Dimensional Data-Driven Science, HD3 2017 - Kyoto, Japan
Duration: 2017 Sep 102017 Sep 13

ASJC Scopus subject areas

  • Physics and Astronomy(all)

Fingerprint Dive into the research topics of 'Zonal Reduced-Order Modelling Toward Prediction of Transitional Flow Fields'. Together they form a unique fingerprint.

Cite this