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

Bridging the Gap Between Statistics and Engineering

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Bridging the Gap Between Statistics and Engineering.  Statistical calibration of CFD simulations in Urban street canyons with Experimental data. Liora Malki-Epshtein and Serge Guillas With Nina Glover, Stella Karra. Outline. Background: - PowerPoint PPT Presentation

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Bridging the Gap between Statistics and Engineering

Bridging the Gap Between Statistics and EngineeringStatistical calibration of CFD simulations in Urban street canyons with Experimental dataLiora Malki-Epshtein and Serge GuillasWith Nina Glover, Stella KarraOur collaboration was made possible by the Bridging the gaps program, initially meeting at one the BTG events, then being supported in our existing research activities and enabling us to develop expertise in a difficult field, test new ideas and develop them. In preparing todays presentation I looked back at the application forms we put in for the various stages of the program, the ideas we had at every stage and the work we did, and it was great to see how our ideas and techniques have matured in that time, and the new contacts we have made and supplementary projects we became involved with. This presentation describes the various activities we undertook since the previous BTG event and how we are starting now to put it all together as a proof of concept. The project is currently in the final phase and we anticipate having research results by the end of the summer.

1OutlineBackground: The challenges measuring and modelling urban airflow and pollution dispersionSimple Urban streetsComplex Urban streetsOur studyOur methodsWhat we can achieve

2Challenges 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 backgroundNumerical models produce detailed three dimensional outputs that can be explored in depth. The art of environmental monitoring of airborne pollution or particles, or variables such as heat, and water vapour is increasingly being put into question. What are we really monitoring? Can we rely on the data? How representative are the data? It is obvious that if the monitoring is not accurate, all the interpretation, prediction and modelling of the current state and future change will not be accurate

Traditional monitoring techniques, frequently using regular grids to locate monitors or just positioning them at convenient spots, are limited in the amount of information they gather. This is a particular problem in the urban environment, where a large and dense population is combined with many sources of pollution in a relatively small geographical area.

As an example, typically, pollution sensors are placed high above the urban rooftops to capture background pollution levels. These are quite inadequate at capturing the variability that exist in poorly ventilated urban canyons or under stagnant meteorological conditions, and therefore do not measure the exposure to pollutants that is experienced by humans on the ground.

3*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- 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

Direct Numerical Simulation of turbulence is still impossible at this scale. Simplifications are needed turbulence modelsTwo-equation turbulence models, especially the standard k- model, are now most commonly used to model urban flow and dispersion These models harshly criticised by turbulence experts as calibrated surrogates for turbulence, due to lack of universality, being unreliable for flows with different geometry than those used to develop them. Poor performance compared with more complex models such as LES (Large Eddy Simulation), but much cheaper and faster to runThe default parameters of the model are based on best fit to a wide range of applications in mechanical engineering, not necessarily suitable for urban flowsPerformance is much improved by adjusting the default model parameters based on experimental dataEven the most basic urban flows are a challenge

4Urban Airflow and Dispersion

Previous 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)

Flow inside streets is affected by the wind flow profile and direction above the urban surface layer.

Wind direction can be perpendicular, parallel and oblique to the street canyon. The situation of interest in street canyons is the perpendicular flow. These flows have traditionally been modelled as a boundary layer meteorology problem. The complexity of urban geometry means that it is not possible to model it with the simplified assumptions that are used at the Meteorological scale, which largely treats the ground and everything on it as a flat surface applying friction on the two dimensional flow of air above. But we are now more interested in street scale effects and in understanding ground level exposure to pollution by humans, historical buildings

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But: Real Streets are More Complex

LondonNicosia

CO data at 1.5 , 2.5 m height higher exposure on the groundWind speed profilesThese real streets were studied in field studies carried out by my PhD students Stella and Nina. Stella measured CO levels inside a street in Nicosia. The street in Nicosia would ordinarily be modelled as a symmetrical street canyon, and is the closest thing to such a canyon existing in a real city. What we find from field measurements of pollution, and from tests on a laboratory model, is that the airflow and pollution dispersion in this real street are significantly, qualitatively and quantitatively, different from those for the simple, symmetrical model, as I will show later.

We show that the distribution of the pollution in the street is so sensitive as to depend also on the exact geometry of the street, and the position of the traffic lane within the street, whether in the middle or to one side of the street. Exposure at human height at various locations within the street canyon is much higher than that which would be measured by typical rooftop monitors.

Nina is measuring airflow around trees in a street in South London, and her first tests show very different airflows for a tree-lined street compared with an empty street.

Preliminary lab experiments and field measurements have shown high spatial variability of pollution levels within a street and around trees. High quality and well calibrated models are a necessary step towards better design of well-ventilated and thermally balanced streets. 6Our Project

To develop a technique to improve models of air flow throughout complex urban 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.

Initially the research output would be the technique that would be used by modellers to help in the simulation and understanding of different type of flows and situations in the urban setting. Ultimately we would help to develop monitoring guidelines that could be used by non-experts such as local planners, engineers and local councils to improve urban observations of pollution and meteorological variables, to better capture the exposure by people on the ground.

To the best of our knowledge, Bayesian statistics have never been used to calibrate CFD models before.

7A Day in the Life - CFD Research

ANSYS CFXsoftwareNinas research tools using ANSYS CFX, the popular commercial CFD code in use by industry and by Built Environment researchers and designers8

Field MeasurementsNina on the roof of a church in South London2-D and 3-D sonic anemometers to measure wind speed and directionCO monitors to measure pollution levels, as a passive (chemically inert) tracer following the airflowTo allow her to escape the computer once in a while we bought some expensive equipment and planned for her a field study campaign. Here she is on a very high church rooftop with our Fluids technician, Les, installing a weather station.9Experimental Setup

Stella setting up her experiment

PIV and PLIF measure velocity fields and dye concentrationsLow turbulence flume in CEGE Fluids lab

Laser systemStella spends much of her time in a noisy lab, fiddling with a dangerous Laser system.

Velocity and concentration fields were measured simultaneously in a low turbulence flume in models of a symmetrical canyon, non symmetrical canyons and a scaled down model of the real street canyon studied in Cyprus. This is done by simultaneous measurements with a Laser system using both PIV (obtaining velocity information in the flow) and PLIF (obtaining pollutant concentrations). Emission line source is used for visualisation and to simulate traffic related pollutants and dye is released from the centre of the street.

Several street canyon geometries have been tested a few are presented here.

Measurements were taken in the centre of the canyons

10Comparing Different Street Geometries

Symmetrical street canyonCross section of the streetThe arrows show you the direction and speed of the airflow and the colour scheme shows how much pollution remains in the street canyon, with light blue and green having higher values than dark blue.

Zooming in on the inside of the street, for the symmetrical street canyon, the most simple geometry and extensively studied in previous research. A vortex forms within the symmetrical canyon and most of the pollution accumulates on the upwind building.

This is the worst case for pollution dispersion, due to the vortex formed inside the street, very little pollution escapes above the rooftops.

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Comparing Different Street GeometriesStep-down street canyonCross section of the streetIn the step-down canyon, the roofs of the buildings on one side are lower than on the other. The main vortex is weaker and its centre is moved upwards towards the level of the roof and is also shifted towards the downwind building. Less pollution accumulates in the street canyon.

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Comparing Different Street GeometriesReal street canyonCross section of the streetIn the laboratory we replaced the simple street canyons with a complex scaled down model of a street canyon in Nicosia, including the full detail of the gaps between the buildings and the variations in roof height.

No vortex is created in this position and pollution levels are generally lower, and are distributed more evenly within the street. The structure of the flow changes due to the flow entering into that section from further down the street canyon due to the gaps and the non-uniform building heights along the canyon.

13Airflow 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 laserHere we can see a video of the real street model. The fluid flow is visualised with a fluorescent dye illuminated by the Laser system. The flow varies with time and in space and we do not see the regular vortex that we could see in the simple model.

This is really the situation we are trying to model successfully, and it is a challenge to do this by CFD. 14CFD Model Testing and ValidationDifferent turbulence models and boundary conditions yield different results Difficult to match model outputs to experiments even for a simple flowDifficult to reproduce turbulence patterns within street canyons

This work is very much on-going. Initially we are trying to develop a model that works for an empty water flume without street models in it, as we are trying to see how the Atmospheric boundary layer above the city can be modelled accurately. This is crucial if we are to successfully model complex urban streets in CFX.

Nina has been running several different variations for the inlet boundary conditions, with the standard k-epsilon model and testing them against accurate vertical velocity profiles obtained in the lab by Stella.

Two model outputs are shown here, the standard k-epsilon model with a simple boundary condition at the inlet (top), and with a complex boundary layer profile for the inlet , sand grain roughness for the floor and a symmetry boundary at the top instead of a wall boundary (bottom)15Model Calibration

Identify the parameters that give the best model outputsKnown parameters of the experiment set up: geometry and typical length of the street canyonUnknown calibration parameters: turbulent kinetic energy, velocity profiles tested in the pilot study last yearSerge Guillas, Department ofStatistical ScienceThe next step: Calibration of the model coefficients - the parameters that are the building blocks of the numerical model

An iterative process between the collaborators 16Evaluation of Model ErrorsThe statistical calibration results in estimates of uncertainties of the model and of the calibration parameters.

Histogram showing posterior distribution for calibration parameter Turbulence intensity. The histogram indicates a value between 0.3 and 0.4 would be the optimal value for this parameter. This specific result was obtained in a pilot study carried out last year using a more simple CFD model and experimental data. Current calibration is underway for the experiments described before. 17Where 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 . The Urban environment requires a different approach than that adopted by the Meteorology community. We are integrating a variety of modelling and measuring techniques, in order to represent accurately the Urban micro-climate. The ultimate aim of air flow and pollution dispersion modelling in urban streets is to lead to better design of urban spaces so that they are well-ventilated, providing both clear health benefits to the population and enhanced energy efficiency - due to better removal of contaminants and heat from the urban environment, there would be required less intensive investment in mechanical ventilation, air conditioning and air filtration systems in public spaces and office buildings.

The enhanced transport and dispersion modelling capabilities that we are developing with our method will also lead to, and integrate with, much better observations of air pollution and other environmental variables (such as heat, aerosols and particulates) in the urban space.

The urban space requires a different approach to monitoring than that adopted by the Earth sciences community, and the engineering community must begin to develop the role it must play now in monitoring and modelling the Urban environment. We aim to create fundamental building blocks towards achieving this.

18ConclusionUltimately, 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. 19