Change-point detection between two unsteady computational fluid dynamics simulation results by sparse structure learning

Nobuyuki Isoshima, Koji Shimoyama, Shigeru Obayashi

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

Abstract

High-resolution turbulent-flow simulations using unsteady computational fluid dynamics (CFD) have been widely applied to research and development in the aerospace and mechanical engineering industries. In this study, we propose a data-exploration method to detect anomaly elements between two unsteady simulation data sets which have different structures by sparse structure learning. This method is tested with unsteady pressure-distribution data for two models of an RAE 2822 airfoil with/without the transition trip. The method detects not only the obvious change elements, such as the transition trip, but also small change elements, such as delay of turbulent transition, which are easily overlooked with conventional visualization methods.

Original languageEnglish
Title of host publicationAIAA Aerospace Sciences Meeting
PublisherAmerican Institute of Aeronautics and Astronautics Inc, AIAA
Edition210059
ISBN (Print)9781624105241
DOIs
Publication statusPublished - 2018
EventAIAA Aerospace Sciences Meeting, 2018 - Kissimmee, United States
Duration: 2018 Jan 82018 Jan 12

Publication series

NameAIAA Aerospace Sciences Meeting, 2018
Number210059

Other

OtherAIAA Aerospace Sciences Meeting, 2018
CountryUnited States
CityKissimmee
Period18/1/818/1/12

ASJC Scopus subject areas

  • Aerospace Engineering

Fingerprint Dive into the research topics of 'Change-point detection between two unsteady computational fluid dynamics simulation results by sparse structure learning'. Together they form a unique fingerprint.

Cite this