Bridging the Gaps Final Event: Statistical calibration of CFD simulations in Urban street canyons with Experimental data
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Bridging the Gaps Final Event: Statistical calibration of CFD simulations in Urban street canyons with Experimental data

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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 ...

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

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Bridging the Gaps Final Event: Statistical calibration of CFD simulations in Urban street canyons with Experimental data Presentation Transcript

  • 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
  • 2. Outline
    • Background:
    • 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
    • 8. What we can achieve
  • Challenges of Measuring Urban Air Flows
    Airflow, meteorological variables and pollution are difficult and expensive to measure.
    Few monitoring stations, equipment is normally installed on rooftops high above the ground
    Urban geometry is very complex
    Large 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 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
  • 14. Field Measurements
    • 2-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 airflow
    Nina 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
  • 21. CFD Model Testing and Validation
    • Different 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 canyons
  • Model Calibration
    • Identify 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 year
    The 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.
  • 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.
  • Conclusion
    Ultimately, 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.