Bridging the Gaps Final Event: Statistical calibration of CFD simulations in Urban street canyons with Experimental data
- 1. Bridging the Gap Between Statistics and Engineering Statistical calibration of CFD simulations in Urban street canyons with Experimental dataLiora Malki-Epshteinand Serge GuillasWith Nina Glover, Stella Karra
- 3. The challenges – measuring and modelling urban airflow and pollution dispersion
- 6. Our study
- 7. Our methods
- 8. What we can achieveChallenges of Measuring Urban Air FlowsAirflow, meteorological variables and pollution are difficult and expensive to measure.Few monitoring stations, equipment is normally installed on rooftops high above the groundUrban geometry is very complexLarge and dense population combined with many sources of pollution in a relatively small geographical area. Result: Low resolution measurements in the urban environment, capturing mainly the background Numerical models produce detailed three dimensional outputs that can be explored in depth.
- 9. *Some* Challenges in CFD Modelling of Urban AirflowsDirect Numerical Simulation of turbulence is still impossible at this scale. Simplifications are needed – turbulence modelsThe standard k-ε model most commonly used for urban flow and dispersion, cheap and fast to run The default parameters of the model are based on best fit to a wide range of applications in mechanical engineering, not necessarily suitable for urban flowsWeakness: lack of universality - unreliable for flows with different geometry than those used to develop the model. Poor performance compared with more complex models such as LES (Large Eddy Simulation)Performance improved by adjusting the default model parameters Even the most basic, idealised urban streets are a challenge to model
- 10. Urban Airflow and DispersionPrevious research: simple models for street canyons with a simplified geometryStreet canyons classified by the ratio of Height to WidthDeeper street canyons are poorly ventilatedAccumulation of pollution and heatAirflow over building arrays with increasing H/W. (Oke, 1988)
- 11. But: Real Streets are More ComplexWind speed profilesNicosiaCO data at 1.5 , 2.5 m height – higher exposure on the groundLondon
- 12. Our Project To develop a technique to improve models of air flow throughout complexurban spaces, based on a combination of CFD simulation and field and laboratory observations, integrated using Bayesian statistical methods . Calibration of the numerical model parameters in CFD by data from lab and field measurements. Better understanding of where to position monitoring equipment in the field based on laboratory models.
- 13. A Day in the Life - CFD ResearchANSYS CFXsoftware
- 14. Field Measurements2-D and 3-D sonic anemometers to measure wind speed and direction
- 15. CO monitors to measure pollution levels, as a passive (chemically inert) tracer following the airflowNina on the roof of a churchin South London
- 16. Experimental SetupLaser systemStella setting up her experimentPIV and PLIF measure velocity fields and dye concentrationsLow turbulence flume in CEGE Fluids lab
- 17. Comparing Different Street GeometriesCross section of the streetSymmetrical street canyon
- 18. Comparing Different Street GeometriesCross section of the streetStep-down street canyon
- 19. Comparing Different Street GeometriesCross section of the street“Real” street canyon
- 20. Airflow and Pollution Dispersion in a Complex, “Real” Street CanyonDye concentration (in colour) and velocity arrows, calculated from PLIF and PIV Fluid flow visualised with fluorescent dye and laser
- 21. CFD Model Testing and ValidationDifferent turbulence models and boundary conditions yield different results
- 22. Difficult to match model outputs to experiments even for a simple flow
- 23. Difficult to reproduce turbulence patterns within street canyonsModel CalibrationIdentify the parameters that give the best model outputs
- 24. Known parameters of the experiment set up: geometry and typical length of the street canyon
- 25. Unknown calibration parameters: turbulent kinetic energy, velocity profiles – tested in the pilot study last yearThe next step: Calibration of the model coefficients - the parameters that are the building blocks of the numerical model An iterative process between the collaborators …Serge Guillas, Department ofStatistical Science
- 26. Evaluation of Model ErrorsThe statistical calibration results in estimates of uncertainties of the model and of the calibration parameters.
- 27. Where is all this going?Our immediate goal: to help end users make informed choices about which numerical CFD model to use in which situation and where more accurate models, at greater cost, need to be embedded .
- 28. The Urban environment requires a different approach than that adopted by the Meteorology community.
- 29. We are integrating a variety of modelling and measuring techniques, in order to represent accurately the Urban micro-climate. ConclusionUltimately, modelling air flow and pollution dispersion should lead to better design of urban spaces – to be better ventilated, accumulate less heat, use energy more efficiently and be better observed and monitored on a regular basis. We aim to develop fundamental building blocks towards achieving this.