Building Simulation

, Volume 12, Issue 2, pp 247–258 | Cite as

A simulation-based method for air loop balancing and fan sizing using uncertainty and sensitivity analysis

  • Qiujian Wang
  • Yiqun PanEmail author
  • Yangyang Fu
  • Zhizhong Huang
  • Wangda Zuo
  • Peng Xu
Research Article Building Systems and Components


Two main tasks of commissioning an air-side HVAC system are to verify the fan capacity and to balance the air loop. Simulations can be of assistance to these two tasks. However, the models used in the simulation will inevitably have some uncertainties, especially for the models of the pressure loss components. This paper proposes to use uncertainty analysis to obtain the adjustment instructions for tuning the dampers and the fan pressure value for sizing the fan on a virtual testbed implemented in Modelica. An air-side system with eight terminals, ten dampers and seven junctions is taken as the use case. 24 correction factors for the pressure loss coefficients (PLC) (10 for the dampers, 14 for the junctions) are taken as the inputs for the uncertainty analysis. 1000 samples of the correction factors are generated by using the Latin Hypercube Sampling method. The proportional balancing method is adopted to determine the positions of the damper so that the designed terminal flow rates could be met. The fan pressure value is also determined accordingly. The distributions of the dampers’ positions and fan pressure can be used to guide the balancing work and fan sizing in practice. In addition, the sensitivity analysis reveals that the position adjustments and fan pressure results are more sensitive to the uncertainties of the dampers’ PLCs. When the uncertainty level of the dampers’ PLC is reduced from ±40% to ±10%, the ranges of the damper’s positions will be significantly narrowed down to less than 15%, and the 95th percentiles of the fan pressure will drop from 116Pa to 38Pa, which shows the practicality and benefit of the proposed method.


commissioning air loop balancing fan sizing uncertainty analysis sensitivity analysis Modelica modeling 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.



This research is supported by the U.S. National Science Foundation (Award No. IIS-1802017).


  1. Ai ZT, Mak CM (2013). Pressure losses across multiple fittings in ventilation ducts. The Scientific World Journal, 2013: 195763.CrossRefGoogle Scholar
  2. Allard I, Olofsson T, Nair G (2018). Energy evaluation of residential buildings: Performance gap analysis incorporating uncertainties in the evaluation methods. Building Simulation, 11: 725–737.CrossRefGoogle Scholar
  3. ASHRAE (2005). ASHRAE Standard. The Commissioning Process. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.Google Scholar
  4. ASHRAE (2009). ASHRAE Handbook—Fundamentals. Chapter 21 Duct Design. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.Google Scholar
  5. ASHRAE (2017). ANSI/ASHRAE Standard 120–2017. Method of Testing to Determine Flow Resistance of HVAC Ducts and Fittings. Atlanta, GA, USA: American Society of Heating, Refrigerating and Air-Conditioning Engineers.Google Scholar
  6. Atkin SM, Shao L (2000). Effect on pressure loss of separation and orientation of closely HVAC duct fittings. Building Services Engineering Research and Technology, 21: 175–178.CrossRefGoogle Scholar
  7. Chen H, Cai W, Chen C (2016). Model-based method for testing, adjusting and balancing of HVAC duct system. Energy and Buildings, 126: 498–507.CrossRefGoogle Scholar
  8. Crozier B (2000). Enhancing the Performance of Oversized Plant. Application Guide AG 1/2000. BSRIA.Google Scholar
  9. Fritzson P (1998). Modelica—A language for equation-based physical modeling and high performance simulation. In: Kågström B, Dongarra J, Elmroth E, Wasniewski J (eds), Applied Parallel Computing Large Scale Scientific and Industrial Problems. PARA 1998. Lecture Notes in Computer Science, vol 1541. Berlin: Springer. pp. 149–160.CrossRefGoogle Scholar
  10. Gan G, Riffat SB (1995). k-factors for HVAC ducts: Numerical and experimental determination. Building Services Engineering Research and Technology, 16: 133–139.CrossRefGoogle Scholar
  11. Hyun S-H, Park C-S, Augenbroe G (2007). Uncertainty and sensitivity analysis of natural ventilation in high-rise apartment buildings. In: Proceedings of the 10th International IBPSA Building Simulation Conference, Beijing, China, pp. 1013–1020.Google Scholar
  12. Idelchik IE, Malyavskaya GR, Martynenko OG, Fried E (1994). Handbook of Hydraulic Resistance. Cleveland, USA: CRC Press.Google Scholar
  13. Lakshmiraju M, Cui J (2006). Laminar pressure loss coefficient in close coupled fittings. In: Proceedings of ASME 2006 International Mechanical Engineering Congress and Exposition, Chicago, USA, pp. 713–719.Google Scholar
  14. LBNL (2017). Modelica Buildings Library Version 4.0.0. Available at
  15. Li A, Chen X, Chen L, Gao R (2014). Study on local drag reduction effects of wedge-shaped components in elbow and T-junction close-coupled pipes. Building Simulation, 7: 175–184.CrossRefGoogle Scholar
  16. Modelica Association (2008). Modelica Standard Library. Mumma SA, Mahank TA, Ke Y-P (1997}). Close coupled ductwork fitting pressure drop. HVAC & R Research, 3 (2): 158–177Google Scholar
  17. Mylaram NK, Idem S (2005). Pressure loss coefficient measurements of two close-coupled HVAC elbows. HVAC & R Research, 11: 133–146.CrossRefGoogle Scholar
  18. NEBB (2015). Procedural Standard for Testing, Adjusting and Balancing of Environmental Systems, 8th end. Gaithersburg, MD, USA: National Environmental Balancing Bureau.Google Scholar
  19. Pedranzini F, Colombo LPM, Joppolo CM (2013). A non-iterative method for Testing, Adjusting and Balancing (TAB) air ducts systems: Theory, practical procedure and validation. Energy and Buildings, 65: 322–330.CrossRefGoogle Scholar
  20. Prada A, Pernigotto G, Baggio P, Gasparella A (2018). Uncertainty propagation of material properties in energy simulation of existing residential buildings: The role of buildings features. Building Simulation, 11: 449–464.CrossRefGoogle Scholar
  21. Salehi M, Idem S, Sleiti A (2017). Experimental determination and computational fluid dynamics predictions of pressure loss in close-coupled elbows (RP-1682). Science and Technology for the Built Environment, 23: 1132–1141.CrossRefGoogle Scholar
  22. Sami S, Cui J (2004). Numerical study of pressure losses in closecoupled fittings. HVAC&R Research, 10: 539–552.CrossRefGoogle Scholar
  23. Shan K, Wang S, Xiao F, Sun Y (2013). Sensitivity and uncertainty analysis of measurements in outdoor airflow control strategies. HVAC&R Research, 19: 423–434.Google Scholar
  24. Small M (2002). Non-iterative technique for balancing an air distribution system. Master Thesis, Virginia Polytechnic Institute and State University.Google Scholar
  25. Sun Y, Gu L, Wu CFJ, Augenbroe G (2014). Exploring HVAC system sizing under uncertainty. Energy and Buildings, 81: 243–252.CrossRefGoogle Scholar
  26. Vorechovský M (2012). Correlation control in small sample Monte Carlo type simulations II: Analysis of estimation formulas, random correlation and perfect uncorrelatedness. Probabilistic Engineering Mechanics, 29: 105–120.CrossRefGoogle Scholar
  27. Wang Q, Pan Y, Zhu M, Huang Z, Xu P (2018). A new local pressure loss coefficient model of a duct tee junction applied during transient simulation of a HVAC air-side system. Journal of Building Performance Simulation, 11: 113–127.CrossRefGoogle Scholar
  28. Wetter M, Zuo W, Nouidui TS, Pang X (2014). Modelica Buildings library. Journal of Building Performance Simulation, 7: 253–270.CrossRefGoogle Scholar

Copyright information

© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2018

Authors and Affiliations

  • Qiujian Wang
    • 1
  • Yiqun Pan
    • 1
    Email author
  • Yangyang Fu
    • 2
  • Zhizhong Huang
    • 3
  • Wangda Zuo
    • 2
  • Peng Xu
    • 1
  1. 1.School of Mechanical EngineeringTongji UniversityShanghaiChina
  2. 2.Department of Civil Environmental and Architectural EngineeringUniversity of Colorado BoulderBoulderUSA
  3. 3.Sino-German College of Applied SciencesTongji UniversityShanghaiChina

Personalised recommendations