Building Simulation

, Volume 9, Issue 3, pp 335–346 | Cite as

Predicting thermal and energy performance of mixed-mode ventilation using an integrated simulation approach

Research Article Indoor/Outdoor Airflow and Air Quality

Abstract

Mixed-mode ventilation can effectively reduce energy consumption in buildings, as well as improve thermal comfort and productivity of occupants. This study predicts thermal and energy performance of mixed-mode ventilation by integrating computational fluid dynamics (CFD) with energy simulation. In the simulation of change-over mixed-mode ventilation, it is critical to determine whether outdoor conditions are suitable for natural ventilation at each time step. This study uses CFD simulations to search for the outdoor temperature thresholds when natural ventilation alone is adequate for thermal comfort. The temperature thresholds for wind-driven natural ventilation are identified by a heat balance model, in which air change rate (ACH) is explicitly computed by CFD considering the influence of the surrounding buildings. In buoyancy-driven natural ventilation, the outdoor temperature thresholds are obtained directly from CFD-based parametric analysis. The integrated approach takes advantage of both the CFD algorithm and energy simulation while maintaining low levels of complexity, enabling building designers to utilize this method for early-stage decisionmaking. This paper first describes the workflow of the proposed integrated approach, followed by two case studies, which are presented using a three-floor office building in an urban context. The results are compared with those using an energy simulation program with built-in multizone modules for natural ventilation. Additionally, adaptive thermal comfort models are applied in these case studies, which shows the possibility of further reducing the electricity used for cooling.

Keywords

mixed-mode ventilation CFD energy simulation adaptive thermal comfort model 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

Supplementary material

12273_2016_271_MOESM1_ESM.pdf (1.3 mb)
Supplementary material, approximately 1307 KB.

References

  1. ASHRAE (2013a). ASHRAE Standard 55-2013. Thermal Environmental Conditions for Human Occupancy. Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers.Google Scholar
  2. ASHRAE (2013b). ASHRAE Standard 62.1-2013. Ventilation for Acceptable Indoor Air Quality. Atlanta, American Society of Heating, Refrigerating and Air-Conditioning Engineers.Google Scholar
  3. Asfour OS, Gadi MB (2007). A comparison between CFD and network models for predicting wind-driven ventilation in buildings. Building and Environment, 42: 4079–4085.CrossRefGoogle Scholar
  4. Atkinson J, Chartier Y, Pessoa-Silva C, Jensen P, Li Y, Seto WH (2009). Natural Ventilation for Infection Control in Health-care Settings—WHO Guidelines. Geneva: World Health Organization.Google Scholar
  5. Bangalee MZI, Lin SY, Miau JJ (2012). Wind driven natural ventilation through multiple windows of a building: A computational approach. Energy and Buildings, 45: 317–325.CrossRefGoogle Scholar
  6. Beausoleil-Morrison I (2002). The adaptive conflation of computational fluid dynamics with whole-building thermal simulation. Energy and Buildings, 34: 857–8871.CrossRefGoogle Scholar
  7. Brager G, Borgeson S, Lee YS (2007). Summary Report: Control Strategies for Mixed-mode Buildings. Berkeley: Centre for the Built Environment, University of California.Google Scholar
  8. Cândido C, de Dear R, Lamberts R, Bittencourt L (2008). Natural ventilation and thermal comfort: Air movement acceptability inside naturally ventilated buildings in Brazilian hot humid zone. In: Proceedings of Air Conditioning and the Low Carbon Cooling Challenge Conference, Windsor, UK.Google Scholar
  9. Chen Q (2009). Ventilation performance prediction for buildings: A method overview and recent applications. Building and Environment, 44: 848–858.CrossRefGoogle Scholar
  10. Chen Q, Glicsman L, Lin J, Scott A (2007). Sustainable urban housing in China. Journal of Harbin Institute of Technology (New Series), 14s: 6–9.Google Scholar
  11. CMU (2004). Guidelines for High Performance Buildings. NSF/IUCRC Center for Building Performance and Diagnostics at Carnegie Mellon University, Advanced Building Systems Integration Consortium.Google Scholar
  12. de Dear RJ, Brager GS (1998). Developing an adaptive model of thermal comfort and preference. ASHRAE Transactions, 104(1): 145–167.Google Scholar
  13. de Dear, RJ, Brager, GS (2002). Thermal comfort in naturally ventilated buildings: Revisions to ASHRAE Standard 55. Energy and Buildings, 34: 549–561.CrossRefGoogle Scholar
  14. DOE (2014). EnergyPlus engineering reference: The reference to EnergyPlus calculations. US Department of Energy.Google Scholar
  15. Dutton S, Shao L (2010). Window opening behaviour in a naturally ventilated school. In: Proceedings of SimBuild, New York, USA, pp.260–268.Google Scholar
  16. Evans M (1980). Housing, Climate and Comfort. New York: John Wiley and Sons.Google Scholar
  17. Ezzeldin S, Rees SJ (2013). The potential for office buildings with mixed-mode ventilation and low energy cooling systems in arid climates. Energy and Buildings, 65: 368–381.CrossRefGoogle Scholar
  18. Gandhi P, Brager G, Dutton S (2014). Mixed mode simulation tools. Internal report, Center for the Built Environment (CBE).Google Scholar
  19. Haw LC, Saadatian O, Sulaiman MY, Mat S, Sopian K (2012). Empirical study of a wind-induced natural ventilation tower under hot and humid climatic conditions. Energy and Buildings, 52: 28–38.CrossRefGoogle Scholar
  20. Jiang Y, Chen Q (2003). Buoyancy-driven single-sided natural ventilation in buildings with large openings. International Journal of Heat and Mass Transfer, 46: 973–988.CrossRefGoogle Scholar
  21. Khan N, Su Y, Riffat SB (2008). A review on wind driven ventilation techniques. Energy and Buildings, 40: 1586–1604.CrossRefGoogle Scholar
  22. Launder BE, Spalding DB (1974). The numerical computation of turbulent flows. Computer Methods in Applied Mechanics and Engineering, 3: 269–289.CrossRefMATHGoogle Scholar
  23. Liu PC, Lin HT, Chou JH (2009). Evaluation of buoyancy-driven ventilation in atrium buildings using computational fluid dynamics and reduced-scale air model. Building and Environment, 44: 1970–1979.CrossRefGoogle Scholar
  24. Manz H, Frank T (2005). Thermal simulation of buildings with double-skin facades. Energy and Buildings, 37: 1114–1121.CrossRefGoogle Scholar
  25. Mentor Graphics (2014). FloVENT. Available at http://www.mentor.com/products/mechanical/flovent. Accessed 31 Jul 2014.Google Scholar
  26. MI Research (2014). FlowDesigner. Available at http://www.mi-research. com/MI_Research/FlowDesigner.html. Accessed 31 Jul 2014.Google Scholar
  27. Neofytou P, Venetsanos AG, Vlachogiannis D, Bartzis JG, Scaperdas A (2006). CFD simulations of the wind environment around an airport terminal building. Environmental Modelling & Software, 21: 520–524.CrossRefGoogle Scholar
  28. Pappas A, Zhai Z (2008). Numerical investigation on thermal performance and correlations of double skin facade with buoyancydriven airflow. Energy and Buildings, 40: 466–475.CrossRefGoogle Scholar
  29. Rijal HB, Tuohy P, Humphreys MA, Nicol JF, Samuel A, Clarke J (2007). Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings. Energy and Buildings, 39: 823–836.CrossRefGoogle Scholar
  30. Robert McNeel & Associates (2015). Rhinoceros. Available at https:// www.rhino3d.com. Accessed 22 Jun 2015Google Scholar
  31. Shen X, Zhang G, Bjerg B (2012). Comparison of different methods for estimating ventilation rates through wind driven ventilated buildings. Energy and Buildings, 54: 297–306.CrossRefGoogle Scholar
  32. Sherman MH (1990). Tracer-gas techniques for measuring ventilation in a single zone. Building and Environment, 25: 365–374.CrossRefGoogle Scholar
  33. Sherman MH, Walker IS, Lunden MM (2014). Uncertainties in air exchange using continuous-injection, long-term sampling tracer-gas methods. International Journal of Ventilation, 13: 13–27.Google Scholar
  34. Tan G, Glicksman L (2005). Application of integrating multi-zone model with CFD simulation to natural ventilation prediction. Energy and Buildings, 37: 1049–1057.CrossRefGoogle Scholar
  35. Tong Z, Wang YJ, Patel M, Kinney P, Chrillrud S, Zhang KM (2012). Modeling spatial variations of black carbon particles in an urban highway-building environment. Environmental Science & Technology, 46: 312–319.CrossRefGoogle Scholar
  36. Tong Z, Zhang KM (2015). The near-source impacts of diesel backup generators in urban environments. Atmospheric Environment, 109: 262–271.CrossRefGoogle Scholar
  37. Wang L, Chen Q (2005). On solution characteristics of coupling of multizone and CFD programs in building air distribution simulation. In: Proceedings of 9th International IBPSA Building Simulation Conference, Montreal, Canada.Google Scholar
  38. Wang L, Chen Q (2007). Theoretical and numerical studies of coupling multizone and CFD models for building air distribution simulations. Indoor Air, 17: 348–361.CrossRefGoogle Scholar
  39. Wang L, Chen Q (2008). Evaluation of some assumptions used in multizone airflow network models. Building and Environment, 43: 1671–1677.CrossRefGoogle Scholar
  40. Wang L, Wong NH (2007). The impacts of ventilation strategies and facade on indoor thermal environment for naturally ventilated residential buildings in Singapore. Building and Environment, 42: 4006–4015.CrossRefGoogle Scholar
  41. Wang L, Wong NH (2008). Coupled simulations for naturally ventilated residential buildings. Automation in Construction, 17: 386–398.CrossRefGoogle Scholar
  42. Wang L, Wong NH (2009). Coupled simulations for naturally ventilated rooms between building simulation (BS) and computational fluid dynamics (CFD) for better prediction of indoor thermal environment. Building and Environment, 44: 95–112.CrossRefGoogle Scholar
  43. Wang YJ, Nguyen MT, Steffens JT, Tong Z, Wang Y, Hopke PK, Zhang KM (2013). Modeling multi-scale aerosol dynamics and microenvironmental air quality near a large highway intersection using the CTAG model. Science of the Total Environment, 443: 375–386.CrossRefGoogle Scholar
  44. Xie Z, Castro IP (2006). LES and RANS for turbulent flow over arrays of wall-mounted obstacles. Flow, Turbulence and Combustion, 76: 291–312.CrossRefMATHGoogle Scholar
  45. Yi YK, Feng N (2013). Dynamic integration between building energy simulation (BES) and computational fluid dynamics (CFD) simulation for building exterior surface. Building Simulation, 6: 297–308.CrossRefGoogle Scholar
  46. Zhai ZJ, Chen QY (2005). Performance of coupled building energy and CFD simulations. Energy and Buildings, 37: 333–344.CrossRefGoogle Scholar
  47. Zhang R, Lam KP, Yao SC, Zhang Y (2013). Coupled EnergyPlus and computational fluid dynamics simulation for natural ventilation. Building and Environment, 68: 100–113.CrossRefGoogle Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag Berlin Heidelberg 2016

Authors and Affiliations

  • Ali Malkawi
    • 1
  • Bin Yan
    • 1
  • Yujiao Chen
    • 1
  • Zheming Tong
    • 1
  1. 1.Center for Green Buildings and Cities, Graduate School of DesignHarvard UniversityCambridgeUSA

Personalised recommendations