Modelling of Air Flow Analysis for Residential Homes Using Particle Image Velocimetry

  • Rajiv Pratap
  • Ramesh Rayudu
  • Manfred Plagmann
Conference paper
Part of the IFIP Advances in Information and Communication Technology book series (IFIPAICT, volume 448)


The purpose of this paper is to simulate the designed physical Particle Image Velocimetry (PIV) system, as a simulation platform to physically build and implement a system for residential building and housing research. The focus is on the angle filter; the filter used to process the images of a laser progressively scanning through a space by adjusting its angle.

An indicator of heat escape and ventilation in buildings is the airflow itself. Conventional airflow measurement techniques are typically intrusive, interfering with the data or the environment. For small flows such as that in residential housing, the error introduced can sometimes be large relative to the measured data. In contrast, PIV is a relatively a non-intrusive measurement tool that measures flows. However, there are a few problems with standard PIV techniques, for implementation in an attic space.

The proposed solution is to use dust particles, already present in the air, as tracers for the PIV system. In conclusion, our PIV system with a non-diverging laser beam produces a velocity field of similar quality to a velocity field of a standard PIV system.


Particle Image Velocimetry PIV air flow measurement building ventilation analysis Angle filtering 


  1. 1.
    Lloyd, C.R., Callau, M.F., Bishop, T., Smith, I.J.: The efficacy of an energy efficient upgrade program in New Zealand. Energy and Buildings 40(7), 1228–1239 (2008)CrossRefGoogle Scholar
  2. 2.
    Isaacs, N., et al.: Energy Use in New Zealand Households: Final Report on the Household Energy End-use Project (HEEP). BRANZ Study Report 221, BRANZ Ltd, Judgeford, New Zealand (2010)Google Scholar
  3. 3.
    Howden-Chapman, P., Crane, J., Matheson, A., Viggers, H., Cunningham, M., Blakely, T., O’Dea, D., Cunningham, C., Woodward, A., Saville-Smith, K., Baker, M., Waipara, N.: Retrofitting houses with insulation to reduce health inequalities: Aims and methods of a clustered, randomised community-based trial. Social Science & Medicine 61(12), 2600–2610 (2005)CrossRefGoogle Scholar
  4. 4.
    Raffel, M., Willert, C., Kompenhans, J.: Particle Image Velocimetry, A Practical Guide. Springer. New York (1998)Google Scholar
  5. 5.
    Cao, X., Liu, J., Jiang, N., Chen, Q.: Particle image velocimetry measurement of indoor airflow field: A review of the technologies and applications. Energy and Buildings 69, 367–380 (2014)CrossRefGoogle Scholar
  6. 6.
    Adrian, R.J.: Particle-imaging techniques for experimental fluid mechanics. Annual review of fluid mechanics 23(1), 261–304 (1991)CrossRefGoogle Scholar
  7. 7.
    Westerweel, J.: Fundamentals of digital particle image velocimetry. Measurement Science and Technology 8(12), 13–79 (1997)CrossRefGoogle Scholar
  8. 8.
    Bastiaans, R.J.: Cross-correlation PIV; theory, implementation and accuracy. Eindhoven University of Technology, Faculty of Mechanical Engineering (2000)Google Scholar
  9. 9.
    Roy, T.M.: Physically-Based Fluid Modeling using Smoothed Particle Hydrodynamics (1995), (accessed February 20, 2014)

Copyright information

© IFIP International Federation for Information Processing 2015

Authors and Affiliations

  • Rajiv Pratap
    • 1
  • Ramesh Rayudu
    • 1
  • Manfred Plagmann
    • 2
  1. 1.School of Engineering and Computer ScienceBRANZWellingtonNew Zealand
  2. 2.BRANZWellingtonNew Zealand

Personalised recommendations