Presentation from Liora Malki-Epshtein of Civil, Environmental and Geomatic Engineering at UCL. The presentation covers her work with Statistical Sciences to improve models of street canyon pollution by calibration with experimental data

1. Bridging the Gap Between Statistics and Engineering Statistical calibration of CFD simulations in Urban street canyons with Experimental data Liora Malki-Epshteinand Serge Guillas With Nina Glover, Stella Karra

3. The challenges – measuring and modelling urban airflow and pollution dispersion

4. Simple Urban streets

5. Complex Urban streets

6. Our study

7. Our methods

9. *Some* Challenges in CFD Modelling of Urban Airflows Direct Numerical Simulation of turbulence is still impossible at this scale. Simplifications are needed – turbulence models The 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 flows Weakness: 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 Dispersion Previous research: simple models for street canyons with a simplified geometry Street canyons classified by the ratio of Height to Width Deeper street canyons are poorly ventilated Accumulation of pollution and heat Airflow over building arrays with increasing H/W. (Oke, 1988)

11. But: Real Streets are More Complex Wind speed profiles Nicosia CO data at 1.5 , 2.5 m height – higher exposure on the ground London

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 Research ANSYS CFX software

15. CO monitors to measure pollution levels, as a passive (chemically inert) tracer following the airflowNina on the roof of a church in South London

16. Experimental Setup Laser system Stella setting up her experiment PIV and PLIF measure velocity fields and dye concentrations Low turbulence flume in CEGE Fluids lab

17. Comparing Different Street Geometries Cross section of the street Symmetrical street canyon

18. Comparing Different Street Geometries Cross section of the street Step-down street canyon

19. Comparing Different Street Geometries Cross section of the street “Real” street canyon

20. Airflow and Pollution Dispersion in a Complex, “Real” Street Canyon Dye concentration (in colour) and velocity arrows, calculated from PLIF and PIV Fluid flow visualised with fluorescent dye and laser

22. Difficult to match model outputs to experiments even for a simple flow

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 of Statistical Science

26. Evaluation of Model Errors The statistical calibration results in estimates of uncertainties of the model and of the calibration parameters.

28. The Urban environment requires a different approach than that adopted by the Meteorology community.