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SUMMARY:Augmenting simulation predictions of wind around buildings using m
 easurements
DTSTART:20150422T160000
DTEND:20150422T170000
DTSTAMP:20260413T152938Z
UID:c761affbb09a8ab181ba51654280289ae0e4df1c7b60133d30b8687c
CATEGORIES:Conferences - Seminars
DESCRIPTION:Didier Vernay\, Applied Computing and Mechanics Laboratory (I
 MAC)\, ENAC\, EPFL\nWind behavior in urban areas is receiving an increasin
 g amount of interest from city planners and architects. In the context of 
 Singapore\, knowledge of wind behavior helps to improve building ventilati
 on and\, on a larger scale\, urban ventilation. Computational fluid dynami
 cs (CFD) simulation is often employed to assess the wind behavior around b
 uildings. However\, the accuracy of CFD simulations is often unknown. Meas
 urements can be used to help understand wind behavior around buildings mor
 e accurately.\nA model-based data interpretation framework will be present
 ed to integrate information obtained from measurements with simulation res
 ults. The information provided by measurements is used to estimate the par
 ameter values of the simulation\, including those for inlet wind condition
 s\, through solutions of an inverse problem. The information content of me
 asurement data depends on levels of measurement and modelling uncertaintie
 s at sensor locations. Strategies will be presented to evaluate important 
 sources of modelling uncertainties in CFD simulations of wind around build
 ings\, such as uncertainties associated with turbulence and uncertainties 
 associated with thermal processes such as convection. Results show that un
 certainties\, including their biases\, depend on the location and the time
  of day.\nThe model-based data interpretation framework is applied to seve
 ral full-scale case studies. The framework successfully includes modelling
  and measurement uncertainties in order to provide ranges of predictions a
 t unmeasured locations. It is concluded that the framework has the potenti
 al to identify time-dependent sets of parameter values as well as predict 
 time-dependent ranges of predictions at unmeasured locations. Prediction r
 anges at unmeasured locations are reduced after measurements.
LOCATION:GC G1 515 http://plan.epfl.ch/?lang=fr&zoom=19&recenter_y=5864267
 .48334&recenter_x=730958.60537&layerNodes=fonds\,batiments\,labels\,inform
 ation\,parkings_publics\,arrets_metro&floor=1&q=GC_G1%20515
STATUS:CONFIRMED
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