A Computational Toolchain for the Automatic Generation of Multiple Reduced-Order Models from CFD Simulations

Thibault Marzullo, Marcus Keane, Marco Geron, Rory F.D. Monaghan

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)
26 Downloads (Pure)

Abstract

This paper describes the development of a systematic tool chain capable of automatically extracting accurate and efficient Reduced-Order Models (ROMs) from Computational Fluid Dynamics (CFD) simulations. These ROMs can then be used to support the design and operation of Near-Zero Energy Buildings (NZEB), with a higher accuracy than traditional zonal models but at a fraction of the computational cost of CFD. This study assesses the accuracy and time to solution of these ROMs when solved for appropriate Boundary Conditions (BCs), found in the built environment, in order to define the usability envelope of the automatically extracted ROMs. The parameters used in this study are inlet temperatures (K) and mass flow rates (kg/s). Results demonstrate that the absolute error can be maintained at under 0.5 K for changes in temperature of up to ±15 K, and under 0.25 K for changes in mass flow rates of up to ±45% of the original value. The results show that this method has the potential for applications in the built environment where the ROM accuracy and low computational cost can bridge a gap between low order RC models and high order CFD, further improving the energy efficiency in smart buildings.
Original languageEnglish
Pages (from-to)511-519
Number of pages9
JournalEnergy
Volume180
Early online date18 May 2019
DOIs
Publication statusPublished - 01 Aug 2019

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