Augmenting simulation predictions of wind around buildings using measurements
Event details
| Date | 22.04.2015 |
| Hour | 16:00 › 17:00 |
| Speaker | Didier Vernay, Applied Computing and Mechanics Laboratory (IMAC), ENAC, EPFL |
| Location | |
| Category | Conferences - Seminars |
Wind behavior in urban areas is receiving an increasing amount of interest from city planners and architects. In the context of Singapore, knowledge of wind behavior helps to improve building ventilation and, on a larger scale, urban ventilation. Computational fluid dynamics (CFD) simulation is often employed to assess the wind behavior around buildings. However, the accuracy of CFD simulations is often unknown. Measurements can be used to help understand wind behavior around buildings more accurately.
A model-based data interpretation framework will be presented to integrate information obtained from measurements with simulation results. The information provided by measurements is used to estimate the parameter values of the simulation, including those for inlet wind conditions, through solutions of an inverse problem. The information content of measurement data depends on levels of measurement and modelling uncertainties at sensor locations. Strategies will be presented to evaluate important sources of modelling uncertainties in CFD simulations of wind around buildings, such as uncertainties associated with turbulence and uncertainties associated with thermal processes such as convection. Results show that uncertainties, including their biases, depend on the location and the time of day.
The model-based data interpretation framework is applied to several full-scale case studies. The framework successfully includes modelling and measurement uncertainties in order to provide ranges of predictions at unmeasured locations. It is concluded that the framework has the potential to identify time-dependent sets of parameter values as well as predict time-dependent ranges of predictions at unmeasured locations. Prediction ranges at unmeasured locations are reduced after measurements.
A model-based data interpretation framework will be presented to integrate information obtained from measurements with simulation results. The information provided by measurements is used to estimate the parameter values of the simulation, including those for inlet wind conditions, through solutions of an inverse problem. The information content of measurement data depends on levels of measurement and modelling uncertainties at sensor locations. Strategies will be presented to evaluate important sources of modelling uncertainties in CFD simulations of wind around buildings, such as uncertainties associated with turbulence and uncertainties associated with thermal processes such as convection. Results show that uncertainties, including their biases, depend on the location and the time of day.
The model-based data interpretation framework is applied to several full-scale case studies. The framework successfully includes modelling and measurement uncertainties in order to provide ranges of predictions at unmeasured locations. It is concluded that the framework has the potential to identify time-dependent sets of parameter values as well as predict time-dependent ranges of predictions at unmeasured locations. Prediction ranges at unmeasured locations are reduced after measurements.
Practical information
- General public
- Free
Organizer
- IMAC
Contact
- Gaudenz Moser