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Building Simulation 2016, 9(2): 213-220 https://doi.org/10.1007/s12273-015-0260-5
Research Article | Issue | Published: 24 November 2015

Improving predictions of heat transfer in indoor environments with eddy viscosity turbulence models

Show Author's Information Christian Heschl1,  Yao Tao2,  Kiao Inthavong2( ),  Jiyuan Tu2
Fachhochschulstudiengaenge Burgenland, University of Applied Science, Steinamangerstraße 21, Pinkafeld 7423, Austria
School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, PO Box 71, Plenty Road, Bundoora, Victoria 3083, Australia
Keywords:
natural convection, convection heat transfer, v2-f model, turbulent
Cite this article:
Heschl C, Tao Y, Inthavong K, et al. Improving predictions of heat transfer in indoor environments with eddy viscosity turbulence models. Building Simulation, 2016, 9(2): 213-220. https://doi.org/10.1007/s12273-015-0260-5
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Abstract Full text About this article

Heat transfer modelling in indoor environments requires an accurate prediction of the convective heat transfer phenomenon. Because of the lower computational cost and numerical stability, eddy viscosity turbulence models are often used. These models allow modification to turbulent Prandtl number, and near wall correction which influences stagnation points, entrainment, and velocity and time scales. A modified v2-f model was made to correct the entrainment behaviour in the near wall and at the stagnation point. This new model was evaluated on six cases involving free and forced convection and room airflow scenarios and compared with the standard k-ε, and k-ω-SST models. The results showed that the modification to the v2-f model provided better predictions of the buoyant heat transfer flows while the standard k-ε failed to reproduce and underestimate the convective heat transfer. The k-ω-SST model was able to predict the flow field well only for a 2D square cavity room, and 3D partitioned room case, while it was poor for the other four cases.


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About this article

Improving predictions of heat transfer in indoor environments with eddy viscosity turbulence models

Show Author's information Christian Heschl1, Yao Tao2, Kiao Inthavong2( ), Jiyuan Tu2
Fachhochschulstudiengaenge Burgenland, University of Applied Science, Steinamangerstraße 21, Pinkafeld 7423, Austria
School of Aerospace, Mechanical and Manufacturing Engineering, RMIT University, PO Box 71, Plenty Road, Bundoora, Victoria 3083, Australia

Abstract

Heat transfer modelling in indoor environments requires an accurate prediction of the convective heat transfer phenomenon. Because of the lower computational cost and numerical stability, eddy viscosity turbulence models are often used. These models allow modification to turbulent Prandtl number, and near wall correction which influences stagnation points, entrainment, and velocity and time scales. A modified v2-f model was made to correct the entrainment behaviour in the near wall and at the stagnation point. This new model was evaluated on six cases involving free and forced convection and room airflow scenarios and compared with the standard k-ε, and k-ω-SST models. The results showed that the modification to the v2-f model provided better predictions of the buoyant heat transfer flows while the standard k-ε failed to reproduce and underestimate the convective heat transfer. The k-ω-SST model was able to predict the flow field well only for a 2D square cavity room, and 3D partitioned room case, while it was poor for the other four cases.

Keywords: natural convection, convection heat transfer, v2-f model, turbulent

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Publication history
Copyright
Acknowledgements

Publication history

Received: 30 August 2015
Revised: 20 October 2015
Accepted: 02 November 2015
Published: 24 November 2015
Issue date: April 2016

Copyright

© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2015

Acknowledgements

The authors would like to acknowledge the financial support provided by the Australian Research Council (Project ID: DP120103958), and the resources provided at the NCI National Facility Systems at the Australian National University through the National Computational Merit Allocation Scheme supported by the Australian Government. Also we acknowledge the Austrian Research Program "Josef-Ressel Centre" funded by the BMWFJ.

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