Integrated building energy computational fluid dynamics simulation for estimating the energy-saving effect of energy recovery ventilator with CO2 demand-controlled ventilation system in office space

As ventilation is one of the critical heat loads in an office space, the ventilation rate might be optimized to develop sustainable, low-energy buildings and a healthy indoor environment. To create comprehensive and optimized indoor environmental designs, a building energy simulation (BES)-computational fluid dynamics (CFD)-integrated simulation is used to provide accurate and informative prediction of the thermal and air-quality performance in buildings, especially in the design stage. With the aim of developing an optimization procedure for the ventilation rate, this paper presents simulations that integrates BES and CFD with CO2 demand-controlled ventilation (DCV) system, and applies them to a typical office space in Japan to optimize the ventilation rate through an energy recovery ventilator (ERV). The transient system control strategy is applied to two different airflow conditions in an office: a traditional ceiling supply system and an under-floor air distribution system. Compared with the fixed outdoor air intake rate, which is referred to as constant air volume ventilation, optimized ventilation systems associated with a CO2 DCV produces energy savings of 11.6% and 24.1%, respectively. The difference in the energy saving effects of the two ventilation systems is caused by the difference in the ventilation efficiency in the occupied zone. The ventilation rate and ventilation efficiency have a significant impact on the energy penalty of an ERV. Therefore, optimizing the ventilation rate according to a CO2 DCV system with an appropriate airflow pattern could contribute to both creating and maintaining a healthy, comfortable environment, in addition to saving energy.

[1]  Steven J. Emmerich,et al.  Literature review on CO{sub 2}-based demand-controlled ventilation , 1997 .

[2]  W. Fisk,et al.  Association of ventilation rates and CO2 concentrations with health and other responses in commercial and institutional buildings. , 1999, Indoor air.

[3]  Mglc Marcel Loomans,et al.  External coupling between CFD and energy simulation : implementation and validation , 2005 .

[4]  Qingyan Chen,et al.  On approaches to couple energy simulation and computational fluid dynamics programs , 2002 .

[5]  M J Mendell,et al.  Quantitative relationship of sick building syndrome symptoms with ventilation rates. , 2009, Indoor air.

[6]  Joseph Andrew Clarke,et al.  Simulation-assisted control in building energy management systems , 2002 .

[7]  S. Kato,et al.  New ventilation efficiency scales based on spatial distribution of contaminant concentration aided by numerical simulation , 1988 .

[8]  Chang Nyung Kim,et al.  Numerical Investigation of Indoor CO2 Concentration Distribution in an Apartment , 2011 .

[9]  Vitalijus Pavlovas,et al.  Demand controlled ventilation: A case study for existing Swedish multifamily buildings , 2004 .

[10]  Guohui Gan,et al.  Numerical simulation of the indoor environment , 1994 .

[11]  William J. Fisk,et al.  Changing ventilation rates in U.S. offices: Implications for health, work performance, energy, and associated economics , 2012 .

[12]  Zhiqiang John Zhai,et al.  Evaluation of Various Turbulence Models in Predicting Airflow and Turbulence in Enclosed Environments by CFD: Part 2—Comparison with Experimental Data from Literature , 2007 .

[13]  Ed Bas.  Indoor Air Quality: A Guide for Facility Managers , 2003 .

[14]  Zhiqiang John Zhai,et al.  Sensitivity analysis and application guides for integrated building energy and CFD simulation , 2006 .

[15]  M. B. Schell,et al.  Application of CO{sub 2}-based demand-controlled ventilation using ASHRAE Standard 62: Optimizing energy use and ventilation , 1998 .

[16]  James E. Braun,et al.  A methodology for estimating occupant CO2 source generation rates from measurements in small commercial buildings , 2007 .

[17]  O Seppänen,et al.  Ventilation and performance in office work. , 2006, Indoor air.

[18]  P. Nielsen,et al.  Velocity distribution in a room ventilated by displacement ventilation and wall-mounted air terminal devices , 2000 .

[19]  Jeong Tai Kim,et al.  Energy Benefit of the Underfloor Air Distribution System for Reducing Air-Conditioning and Heating Loads in Buildings , 2012 .

[20]  James E. Braun,et al.  Evaluation of simplified models for predicting CO2 concentrations in small commercial buildings , 2006 .

[21]  Joseph Andrew Clarke,et al.  The role of simulation in support of Internet-based energy services , 2004 .

[22]  Zs. Bakó-Birób,et al.  Ventilation rates in schools and pupils performance , 2011 .

[23]  P. J. O'Neill,et al.  Identification of flow and volume parameters in multizone systems using a single gas tracer technique , 1991 .

[24]  Cezar O.R. Negrão.  Integration of computational fluid dynamics with building thermal and mass flow simulation , 1998 .

[25]  Zhao Zhang,et al.  Evaluation of Various Turbulence Models in Predicting Airflow and 1 Turbulence in Enclosed Environments by CFD : Part-1 : 2 Summary of Prevalent Turbulence Models 3 4 , 2007 .

[26]  Kazuhide Ito,et al.  Energy consumption analysis intended for real office space with energy recovery ventilator by integrating BES and CFD approaches , 2012 .

[27]  Zhiqiang Zhai,et al.  Application of Computational Fluid Dynamics in Building Design: Aspects and Trends , 2006 .

[28]  W.K. Chow,et al.  Equations for a Ventilation Design Derived from Computational Fluid Dynamics , 1998, Indoor and Built Environment.

[29]  Syogo Ogasawara,et al.  Effect of energy conservation by controlled ventilation: Case study in a department store , 1979 .

[30]  Qingyan Chen,et al.  Floor-Supply Displacement Ventilation in a Small Office , 2003 .

[31]  Zhiqiang Zhai Ian D. Metzger.  Taguchi-Method-Based CFD Study and Optimisation of Personalised Ventilation Systems , 2012 .

[32]  Andrew K. Persily,et al.  State-ofthe-Art Review of CO 2 Demand Controlled Ventilation Technology and Application , 2003 .

[33]  Hazim B. Awbi,et al.  Computational Fluid Dynamics in Ventilation Design REHVA Guidebook No 10 , 2007 .

[34]  P. Nafstad,et al.  Occupancy density and benefits of demand-controlled ventilation in Norwegian primary schools , 2005 .