Advertisement

Validation of a Zonal Model to Capture the Detailed Indoor Thermal Environment of a Room Heated by a Stove

  • Martin ThalfeldtEmail author
  • Laurent Georges
  • Øyvind Skreiberg
Conference paper
Part of the Springer Proceedings in Energy book series (SPE)

Abstract

Using wood stoves is a common space-heating strategy in the Nordic countries. Currently, the lowest available nominal power of wood stoves is significantly oversized compared to the design space-heating load of highly-insulated houses. This oversizing might deteriorate the indoor thermal environment by causing overheating and a large vertical temperature stratification. Modelling the indoor thermal environment of rooms heated with a wood stove with acceptable computational time and accuracy, however, is a complex task. The purpose of this study is to analyze the accuracy of a new IDA-ICE zonal model currently under development and to calibrate it against measurements. For this, several experiments were conducted in a test cell, which was heated by an electric stove mimicking a wood stove with a nominal power of 4 kW. Room air temperatures in various positions were measured, while the stove that was placed in the middle of the room was run in cycles with different durations and surface temperature profiles, leading to a thermal stratification of 0.5–2.2 K/m. The zonal model could reproduce the temperatures at the bottom and top layers of the room with good accuracy. However, the model still needs further development and validation to reach good agreement with measurements in the middle layers of the zone. Nevertheless, already at this stage, the model could be used to roughly assess thermal stratification in rooms heated by wood stoves.

Keywords

Building simulation Zonal model Thermal indoor environment Wood stoves Space heating 

Notes

Acknowledgements

The authors acknowledge the financial support by the Research Council of Norway and a number of industrial partners through the research project WoodCFD (“Clean and efficient wood stoves through improved batch combustion models and CFD modelling approaches”, 243752/E20), and good collaboration with EQUA Simulation AB.

References

  1. 1.
    L. Georges, Ø. Skreiberg, Simple modelling procedure for the indoor thermal environment of highly insulated buildings heated by wood stoves. J. Build. Perform. Simul., 1–17 (2016)Google Scholar
  2. 2.
    EQUA AB home page, IDA Indoor Climate and Energy. http://equa.se/en/ida-ice
  3. 3.
    S. Togari, Y. Arai, K. Miura, A simplified model for predicting vertical temperature distribution in a large space. Trans. ASHRAE, 84–99 (1993)Google Scholar
  4. 4.
    L. Eriksson, G. Grozman, P. Grozman, P. Sahlin, M.H. Vorre, L. Ålenius, in CFD-Free, Efficient, Micro Indoor Climate Prediction in Buildings. Proceedings of the First Building Simulation and Optimization Conference (Loughborough, UK, 2012), pp. 149–156Google Scholar
  5. 5.
    L. De Backer, J. Laverge, A. Janssens, M. De Paepe, in The Use of a Zonal Model to Calculate the Stratification in a Large Building. Proceedings of 35th AIVC Conference (Poznan, Poland, 2014), pp. 522–531Google Scholar
  6. 6.
    L. De Backer, J. Laverge, A. Janssens, M. De Paepe, in On the Coupling of a Zonal Model with a Multizone Building Energy Simulation Model. Proceedings of 10th Nordic Symposium on Building Physics (Lund, Sweden, 2014), pp. 71–78Google Scholar
  7. 7.
    EN ISO 7730: 2005. Ergonomics of the thermal environment Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria. ISO (2005)Google Scholar
  8. 8.
    L. Georges, Ø. Skreiberg, V. Novakovic, On the proper integration of wood stoves in passive houses under cold climates. Energy Build. 72, 87–95 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Department of Energy and Process EngineeringNorwegian University of Science and TechnologyTrondheimNorway
  2. 2.Sintef Energy ResearchTrondheimNorway

Personalised recommendations