Airflow rates are directly affected by the amount of open area and consequently by the inhabitant behavior with respect to window opening. In this paper, a stochastic model using Markov chains, developed at the LESO to generate time series of single-window opening angle is modified to generate multiple window openings. It is based on data measured by the TNO Delfton 80 identical, 16 openings dwellings located at Schiedam (NL). The model is then validated by a comparison of the real andgenerated data.
In order to monitor the use of movable air intake grilles by the inhabitants a measurement system is set up in 9 apartments with anatural ventilation system in a building in Namur. The apartments to be monitored are part of an apartment building in Namur wherein a measurement campaign is going on in theframework of a CEC-DGXVII energy demonstration project. A description of the building is given in annex.
The scope of the task 2.2 is to provide an algorithm simulating the behaviour of the inhabitants with respect to the openings of doors and windows. The purpose of this report is to present the status of the work carried out by Scartezzini and his colleagues at the LESO-EPFL in this task on May 15th 1989.
Inhabitants may influence the indoor air quality and air flow patterns within buildings in several ways: a) through windows and doors openings; b) through action on mechanical ventilation systems (fans on and off, dampers closed and open, etc); c) creating extra-flow patterns through their own heat, when moving or using portable fans; d) through their activity, inhabitants are an important (but not the only one) source of pollutants. Some of these influences are of great importance (e.g. a and b) or might be negligible, as c) in some cases.
In this note we discuss the problem (concealed by the latter statement) of calculating the inside air temperature which varies with time and is, when not measured directly, in general not known. The inside air temperature (Tin), which is in betwe
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