Skip to content Skip to navigation
University of Warwick
  • Study
  • |
  • Research
  • |
  • Business
  • |
  • Alumni
  • |
  • News
  • |
  • About

University of Warwick
Publications service & WRAP

Highlight your research

  • WRAP
    • Home
    • Search WRAP
    • Browse by Warwick Author
    • Browse WRAP by Year
    • Browse WRAP by Subject
    • Browse WRAP by Department
    • Browse WRAP by Funder
    • Browse Theses by Department
  • Publications Service
    • Home
    • Search Publications Service
    • Browse by Warwick Author
    • Browse Publications service by Year
    • Browse Publications service by Subject
    • Browse Publications service by Department
    • Browse Publications service by Funder
  • Help & Advice
University of Warwick

The Library

  • Login
  • Admin

Predictive analysis of large-scale coupled CFD simulations with the CPX mini-app

Tools
- Tools
+ Tools

Powell, Archie, Choudry, Kabir, Prabhakar, A., Reguly, I. Z., Amirante, D., Jarvis, S. A. and Mudalige, Gihan R. (2021) Predictive analysis of large-scale coupled CFD simulations with the CPX mini-app. In: IEEE International Conference on High Performance Computing, Data and Analytics (HiPC 2021), Bangalore, India, 17-20 Dec 2021. Published in: 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC) ISBN 9781665410168. ISSN 2640-0316. doi:10.1109/HiPC53243.2021.00028

[img]
Preview
PDF
WRAP-Predictive-analysis-large-scale-coupled-CFD-2021.pdf - Accepted Version - Requires a PDF viewer.

Download (1358Kb) | Preview
Official URL: https://doi.org/10.1109/HiPC53243.2021.00028

Request Changes to record.

Abstract

As the complexity of multi-physics simulations increases, there is a need for efficient flow of information between components. Discrete ‘coupler’ codes can abstract away this process, improving solver interoperability. One such multi-physics problem is modelling the high pressure compressor of turbofan engines, where instances of rotor/stator CFD simulations are coupled. Configuring couplers and allocating resources correctly can be challenging for such problems due to the sliding interfaces between codes. In this research, we present CPX, a mini-coupler designed to model the performance behaviour of a production coupler framework at Rolls-Royce plc., used for coupling rotor/stator simulations. CPX, the first mini-coupler framework of its kind, is combined with a CFD mini-app to predict the run-time and scaling behaviour of large scale coupled CFD simulations. We demonstrate high qualitative and quantitative predictive accuracy with a less than 17 % mean error. A performance model is developed to predict the ‘optimum’ configuration of resources, and is tested to show the high accuracy of these predictions. The model is also used to project the ‘optimum’ configuration for a 6 Billion cell test case, a problem size representative of current leading-edge production workloads, on a 100,000 core cluster and a 400 GPU cluster. Further testing reveals that the ‘optimum’ configuration is unstable if not set up correctly, and therefore a trade-off needs to be made with a marginally less-than-optimal setup to ensure stability. The work illustrates the significant utility of CPX to carry out such rapid design space and run-time setup exploration studies to obtain the best performance from production CFD coupled simulations.

Item Type: Conference Item (Paper)
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
T Technology > TJ Mechanical engineering and machinery
Divisions: Faculty of Science, Engineering and Medicine > Science > Computer Science
Library of Congress Subject Headings (LCSH): Computational fluid dynamics , Turbomachines -- Performance
Journal or Publication Title: 2021 IEEE 28th International Conference on High Performance Computing, Data, and Analytics (HiPC)
Publisher: IEEE
ISBN: 9781665410168
ISSN: 2640-0316
Official Date: December 2021
Dates:
DateEvent
December 2021Published
27 September 2021Accepted
DOI: 10.1109/HiPC53243.2021.00028
Status: Peer Reviewed
Publication Status: Published
Reuse Statement (publisher, data, author rights): © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
Access rights to Published version: Restricted or Subscription Access
RIOXX Funder/Project Grant:
Project/Grant IDRIOXX Funder NameFunder ID
UNSPECIFIEDRolls-Roycehttp://dx.doi.org/10.13039/501100000767
EP/S005072/1[EPSRC] Engineering and Physical Sciences Research Councilhttp://dx.doi.org/10.13039/501100000266
INF/R1/1800 12Royal Societyhttp://dx.doi.org/10.13039/501100000288
Conference Paper Type: Paper
Title of Event: IEEE International Conference on High Performance Computing, Data and Analytics (HiPC 2021)
Type of Event: Conference
Location of Event: Bangalore, India
Date(s) of Event: 17-20 Dec 2021
Related URLs:
  • Organisation

Request changes or add full text files to a record

Repository staff actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics

twitter

Email us: wrap@warwick.ac.uk
Contact Details
About Us