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Computational Fluid Dynamics and Data-Based Mechanistic Modelling of a Forced Ventilation Chamber

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Computational Fluid Dynamics and Data-Based Mechanistic Modelling of a Forced Ventilation Chamber. / Tate, Oliver; Wilson, Emma Denise; Cheneler, David; Taylor, C. James.

In: IFAC-PapersOnLine, Vol. 51, No. 15, 2018, p. 263-268.

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@article{fe2d6f5ecb1c4b81ad0d29e459134c25,
title = "Computational Fluid Dynamics and Data-Based Mechanistic Modelling of a Forced Ventilation Chamber",
abstract = "The research behind this article ultimately concerns control system robustness and optimisation for the regulation of temperatures in multiple buildings that are linked to a controllable external heating supply network. Lancaster University campus is being used as a case study, for which the building management system provides data. Nonetheless, situations arise when it is difficult or expensive to obtain suitable data for specific rooms or buildings and, in such cases, computational fluid dynamics (CFD) models are utilised to investigate relevant heat transfer phenomena. Such models can be limited by their complexity and they are inappropriate for model-based control design. Hence, the present article investigates a hybrid approach based on both CFD and data-based mechanistic (DBM) models. DBM models are obtained initially from statistical analysis of observational time-series but are only considered credible if they can be interpreted in physically meaningful terms. A laboratory forced ventilation chamber is used to investigate the modelling issues arising and to make recommendations relating to the wider project. The chamber is first discretised into finite volumes and the associated Navier--Stokes equations are solved to determine the physical properties of each zone. The model responses are compared with experimental data and analysed using the DBM approach.",
keywords = "Computational fluid dynamics (CFD), data--based mechanistic (DBM), heating, ventilation and air conditioning (HVAC), micro-climate",
author = "Oliver Tate and Wilson, {Emma Denise} and David Cheneler and Taylor, {C. James}",
year = "2018",
doi = "10.1016/j.ifacol.2018.09.145",
language = "English",
volume = "51",
pages = "263--268",
journal = "IFAC-PapersOnLine",
issn = "2405-8963",
publisher = "IFAC Secretariat",
number = "15",
note = "18th IFAC Symposium on System Identification ; Conference date: 09-07-2018 Through 11-07-2018",

}

RIS

TY - JOUR

T1 - Computational Fluid Dynamics and Data-Based Mechanistic Modelling of a Forced Ventilation Chamber

AU - Tate, Oliver

AU - Wilson, Emma Denise

AU - Cheneler, David

AU - Taylor, C. James

PY - 2018

Y1 - 2018

N2 - The research behind this article ultimately concerns control system robustness and optimisation for the regulation of temperatures in multiple buildings that are linked to a controllable external heating supply network. Lancaster University campus is being used as a case study, for which the building management system provides data. Nonetheless, situations arise when it is difficult or expensive to obtain suitable data for specific rooms or buildings and, in such cases, computational fluid dynamics (CFD) models are utilised to investigate relevant heat transfer phenomena. Such models can be limited by their complexity and they are inappropriate for model-based control design. Hence, the present article investigates a hybrid approach based on both CFD and data-based mechanistic (DBM) models. DBM models are obtained initially from statistical analysis of observational time-series but are only considered credible if they can be interpreted in physically meaningful terms. A laboratory forced ventilation chamber is used to investigate the modelling issues arising and to make recommendations relating to the wider project. The chamber is first discretised into finite volumes and the associated Navier--Stokes equations are solved to determine the physical properties of each zone. The model responses are compared with experimental data and analysed using the DBM approach.

AB - The research behind this article ultimately concerns control system robustness and optimisation for the regulation of temperatures in multiple buildings that are linked to a controllable external heating supply network. Lancaster University campus is being used as a case study, for which the building management system provides data. Nonetheless, situations arise when it is difficult or expensive to obtain suitable data for specific rooms or buildings and, in such cases, computational fluid dynamics (CFD) models are utilised to investigate relevant heat transfer phenomena. Such models can be limited by their complexity and they are inappropriate for model-based control design. Hence, the present article investigates a hybrid approach based on both CFD and data-based mechanistic (DBM) models. DBM models are obtained initially from statistical analysis of observational time-series but are only considered credible if they can be interpreted in physically meaningful terms. A laboratory forced ventilation chamber is used to investigate the modelling issues arising and to make recommendations relating to the wider project. The chamber is first discretised into finite volumes and the associated Navier--Stokes equations are solved to determine the physical properties of each zone. The model responses are compared with experimental data and analysed using the DBM approach.

KW - Computational fluid dynamics (CFD)

KW - data--based mechanistic (DBM)

KW - heating, ventilation and air conditioning (HVAC)

KW - micro-climate

U2 - 10.1016/j.ifacol.2018.09.145

DO - 10.1016/j.ifacol.2018.09.145

M3 - Journal article

VL - 51

SP - 263

EP - 268

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

SN - 2405-8963

IS - 15

T2 - 18th IFAC Symposium on System Identification

Y2 - 9 July 2018 through 11 July 2018

ER -