« Simplified Urban Heat Island Modelling » By Adil Rasheed

1. « Simplified Urban Heat Island Modelling » By Adil Rasheed

2. Some Questions • How does a city development affect the microclimate ? • How important is the effect of environment on buildings ? • How should building design respond to urban microclimate ? • Is it possible to minimise the energy consumption and improve pedestrian comfort by designing/modifying certain parameters in a city? • Is it possible to develop a strategy for city design ?

3. Overview of the presentation • Brief description of Urban Heat Island • State of Research • State of research conducted by the student • Future plan of research

4. Urban Heat Island Effect Temperature profile in a plain rural area

5. Urban Heat Island Effect -0.5 to 7 C Temperature profile of a UHI

6. Measured data: London

7. Measured data: London

8. Urban Heat Island Effect • Causes of UHI: • Radiometric • Albedo, emmisivity • Thermophysical • Cp, K… • Geometric • Change in drag and shear • Evaporative and evapotranspiration • Anthropogenic heat

9. Consequences of UHI • Changes heating and cooling loads. • Changes comfort level. • Influences pollutant dispersion. • Can severely affect the cloud formation and rain • mean monthly rainfall rates within 30-60 kilometers (18 to 36 miles) downwind of the cities are, on average, about 28% greater than the upwind region.(research conducted by NASA) http://www.gsfc.nasa.gov/topstory/20020613urbanrain.html Conclusion: Urban area changes the energy, momentum and humidity balance.

10. Mass Mom. Energy Humidity TKE UHI Modelling • Governing Equations: • Comments on the governing equations: • Highly non linear • Strongly coupled. • Mismatch between the number of unknowns and number of equations (problem of closure)

11. UHI modelling • All the existing models can be categorized into • Analytical Model • Physical Model • Numerical Model All these models are governed by the same set of conservation equations

12. Analytical Modelling • Solving the governing equation with assumptions which simplifies the equation. • Flow over a gaussian mountain • Plume from a constant area source • Advantages:  • Gives exact solution. • Reliable for validation. • Computationally inexpensive • Disadvantages:  • Doesn’t represent reality. • Very limited application. • Sample assumptions: • No pressure gradients in 2 directions • Free slip BC near the ground • No consideration for urban geometry • Radial Symmetry

13. Physical Modelling • Requirements: • Similarity criteria • 3-D model of the city • Hi-tech instrumentation • Advantages: • More close to reality. • Can handle the complexities of fluid flow. • Disadvantages: • Difficulties in satisfying the similarity criteria. • Measuring instruments can alter the flow. • Difficult to maintain the desired boundary conditions • Effects of radiation, cloud formation etc. can not be simulated Similarity Criteria

14. Coef. Of thermal expansion Surface heat flux Physical Modelling LU et al. Dept. Of marine, earth and atmospheric sciences, North Carolina State Univ.

15. Numerical Modelling • Involves solving the governing equations numerically. • Advantages: • Takes into account the key parameters that can affect UHI with (potentially) good spatio-temporal resolution. • Disadvantages: • Numerically very expensive. • The model is complex and needs experties to run the model. • Error analysis is not feasible at the moment.

16. Numerical Modelling: Scales Microscale (buildings resolved) • Microscale • Grid size is much smaller than the dimension of buildings to be resolved. • Mesoscale • Grid is very coarse (a few km) • Simulation should be run for sufficiently long duration so that the wind sweeps the whole domain • Macroscale Mesoscale (Buildings can’t be resolved) Present Challanges: Urban and Turbulence parametrisation Macroscale

17. Numerical Modelling: Urban Parametrization City to be modelled.

18. Numerical Modelling: Urban Parametrization Fine Grid: Can resolve the effects of buildings but is computationally intractable.

19. Numerical Modelling: Urban Parametrization Very coarse Grid: Can’t resolve the effects of buildings explicitly but is computationally feasible. Implicit modelling required(parametrization of urabn effects)

20. Mesoscale Grid Urban Grid Martilli’s Urban Parametrisation • Highlights: • Impact of horizontal and vertical wall (drag and shear) • Accounts for solar radiation • Accounts for building density, urban forms and different landuse • 1D heat conduction is solved in the soil, wall and roof to estimate the surface heat fluxes Source (Source)mesoscale = (urban fraction)*Σ(urban effect on urban grid)+(rural fraction)*(rural effect)+….. Surface temperature: Computed by solving 1D heat transfer equation

21. Numerical Modelling: Turbulence Big whorls have little whorls, Which feed on their velocity; And little whorls have lesser whorls, And so on to viscosity -Lewis Richardson in 1922

22. DNS 3D, unsteady RANS Steady / unsteady • LES • 3D, unsteady Numerical Modelling: Turbulence • Different approaches to make turbulence computationally tractable: • DNS: Direct Numerical Simulation. • RANS: Reynolds Averaged Navier Stokes (or time or ensemble) • LES: Large Eddy Simulation (Spatially average ) Resolved E(k) Modelled Resolved Modelled Κ (wave number) Filter

23. Turbulence: Direct Numerical Simulation (DNS) • Resolves all the turbulent length and time scales • Computationally expensive. • Can be applied only for low reynolds number flow.

24. Turbulence: RANS • Reynolds Averaged Navier Stokes • Predicts only the time averaged effects. • Based on the assumption that the instantaneous fluctuations are much smaller than the fluctuations in the mean flow. • Since all the turbulent scales are modelled the result may deviate significantly from reality. • Two additional equations are required to predict the length and velocity scales.

25. Large Eddy Simulation (LES) • Some applications need explicit computation of accurate unsteady fields. • Bluff body aerodynamics • Aerodynamically generated noise (sound) • Fluid-structure interaction • Mixing • Combustion • …

26. LES - Rationales e Eu,ET k,f • Large eddies: responsible for the transports of momentum, energy, and other scalars. anisotropic, subjected to history effects, are strongly dependent on boundary conditions, which makes their modeling difficult. Small eddies tend to be more isotropic and less flow-dependent (universal), mainly dissipative scales, which makes their modeling easier.

27. RANSmodel 2 LES RANS model1 RANS vs LES

28. Hypothesis Current situation Historic climate file for some (possibly distant) location Building Reality Due to large scale flow and topographical features, the climate bounding the city is different Urban albedo (sw, lw), evapotranspiration, anthropogenic gains, momentum transfer Historic climate file for some (possibly distant) location Building Possible solution Historic climate files for some (possibly distant) locations Interpolation ormacroscale flow model Mesoscale flow model with urban parameterisation Local microscale (simple CFD) flow model Building Unidirectional model nesting Pre-process

29. Approach Global Model + Measured data Mesoscale Model Building Simulation Program Microscale Model

30. Research done by the candidate • Co-development and Restructuring of the FVM code. • Development of the Microscale Model. • Test of various Convective Schemes for Atmospheric flow.

31. Advection Turbulence Surface Diffusion Pressure Co-development and restructuring of the FVM code This work was done along with Andrea Krpo from LPAS

32. Features: Based on SIMPLE algorithm. (Semi Implicit Method for Pressure Linked Equations) Explicit resolution of buildings. Solves for velocity and scalars in 3D Problems: Based on cartesian grid so can’t be adapted to the terrain Microscale Model

33. Convective Schemes • One dimensional fluid flow: 0 C ? 100 C Closer to 100 C Closer to 0C where Conclusion: Interpolation schemes should be sensitised to magnitude as well as the direction of flow

34. Future plan of research • Validation of the basic assumptions in Urban Parametrisation • Improvement in the Urban Parametrisation • Coupling with the Microscale Model • Application to real cities

35. Verification • Test the urban canopy model against experimental and LES results. • Finetune the RANS model to suite the common geometries occuring in the city by comparing the result against LES for isothermal cases. • Repeat the step 1 and 2 for non-isothermal cases.

36. Improvement of Urban Parametrization • Study the effects of changing the urban geometry. • Introduce the simplified radiosity algorithm to compute the radiative fluxes accurately. • Further Verifications.

37. Numerical Modelling: Urban Parametrization Can be approximated to a regular array of buildings

38. Numerical Modelling: Urban Parametrization May not be well represented by a regular array of buildings.

39. Numerical Modelling: Urban Parametrization Parametrization schemes can be defined which cover the entire set of urban characteristics

40. Mesoscale Model Microscale Model Coupling of Mesoscale and Microscale Models Supply the boundary conditions from the Mesoscale Model to the Microsclae Model

41. Application of the Model to real cities • Run the model on cities and investigate • Effects of variables (geometric, surface, sources, sinks) • Effects of latitude • Generate annual data sets. • Statistical Reduction of the annual data sets. • First contribution to urban planning guidelines to best control UHI

42. Schedule

43. Thank You 