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Automatic extraction of reduced-order models from CFD simulations for building energy modelling

  • D.T. Mullen
  • , M. M. Keane
  • , M. Geron
  • , Rory F. D. Monaghan
  • University of Galway
  • Queen's University of Belfast

Research output: Contribution to a Journal (Peer & Non Peer)Articlepeer-review

11 Citations (Scopus)

Abstract

Accurate modelling of the internal climate of buildings is essential if building energy management systems (BEMS) are to efficiently maintain adequate thermal comfort. Computational fluid dynamics (CFD) models are usually utilised to predict internal climate. Nevertheless CFD models, although providing the necessary level of accuracy, are highly computationally expensive, and cannot practically be integrated in BEMS. This paper presents and describes validation of a CFD-ROM method for real-time simulations of building thermal performance. The CFD-ROM method involves the automatic extraction and solution of reduced order models (ROMs) from validated CFD simulations. ROMs are shown to be adequately accurate with a total error below 5% and to retain satisfactory representation of the phenomena modelled. Each ROM has a time to solution under 20 s, which opens the potential of their integration with BEMS, giving real-time physics-based building energy modelling. A parameter study was conducted to investigate the applicability of the extracted ROM to initial boundary conditions different from those from which it was extracted. The results show that the ROMs retained satisfactory total errors when the initial conditions in the room were varied by ±5 °C. This allows the production of a finite number of ROMs with the ability to rapidly model many possible scenarios.

Original languageEnglish
Pages (from-to)313-326
Number of pages14
JournalEnergy and Buildings
Volume99
DOIs
Publication statusPublished - 30 May 2015

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • Computational modelling
  • Energy efficient building
  • Natural ventilation
  • Reduced order models
  • Thermal comfort

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