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Evaluation of a Micro-scale Wind Model's Performance over Realistic Building Clusters Using Wind Tunnel Experiments

doi: 10.1007/s00376-016-5273-1

  • The simulation performance over complex building clusters of a wind simulation model (Wind Information Field Fast Analysis model, WIFFA) in a micro-scale air pollutant dispersion model system (Urban Microscale Air Pollution dispersion Simulation model, UMAPS) is evaluated using various wind tunnel experimental data including the CEDVAL (Compilation of Experimental Data for Validation of Micro-Scale Dispersion Models) wind tunnel experiment data and the NJU-FZ experiment data (Nanjing University-Fang Zhuang neighborhood wind tunnel experiment data). The results show that the wind model can reproduce the vortexes triggered by urban buildings well, and the flow patterns in urban street canyons and building clusters can also be represented. Due to the complex shapes of buildings and their distributions, the simulation deviations/discrepancies from the measurements are usually caused by the simplification of the building shapes and the determination of the key zone sizes. The computational efficiencies of different cases are also discussed in this paper. The model has a high computational efficiency compared to traditional numerical models that solve the Navier-Stokes equations, and can produce very high-resolution (1-5 m) wind fields of a complex neighborhood scale urban building canopy (∼ 1 km × 1 km) in less than 3 min when run on a personal computer.
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  • Ahmad K., M. Khare, and K. K. Chaudhry, 2005: Wind tunnel simulation studies on dispersion at urban street canyons and intersections- review. Journal of Wind Engineering and Industrial Aerodynamics, 93, 697- 717.
    Arnfield A. J., 2003: Two decades of urban climate research: A review of turbulence, exchanges of energy and water, and the urban heat island. Int. J. Climatol., 23, 1- 26.10.1002/ in urban climatology over the two decades since the first publication of the International Journal of Climatology is reviewed. It is emphasized that urban climatology during this period has benefited from conceptual advances made in microclimatology and boundary-layer climatology in general. The role of scale, heterogeneity, dynamic source areas for turbulent fluxes and the complexity introduced by the roughness sublayer over the tall, rigid roughness elements of cities is described. The diversity of urban heat islands, depending on the medium sensed and the sensing technique, is explained. The review focuses on two areas within urban climatology. First, it assesses advances in the study of selected urban climatic processes relating to urban atmospheric turbulence (including surface roughness) and exchange processes for energy and water, at scales of consideration ranging from individual facets of the urban environment, through streets and city blocks to neighbourhoods. Second, it explores the literature on the urban temperature field. The state of knowledge about urban heat islands around 1980 is described and work since then is assessed in terms of similarities to and contrasts with that situation. Finally, the main advances are summarized and recommendations for urban climate work in the future are made.
    Ashie Y., T. Kono, 2011: Urban-scale CFD analysis in support of a climate-sensitive design for the Tokyo Bay area. Int. J. Climatol., 31, 174- 188.10.1002/ Available
    Chang C. H., J. S. Lin, C. M. Cheng, and Y. S. Hong, 2013: Numerical simulations and wind tunnel studies of pollutant dispersion in the urban street canyons with different height arrangements. Journal of Marine Science and Technology, 21, 119- 126.10.6119/ pollution in big city areas resulting from exhaust emissions is a major urban problem. Often traffic pollution excess controls air pollution management decisions. There are a number of elaborate predictive models of pollutant dispersion and diffusion that address the effects of variable shapes of city buildings on pollutant concentrations, but few are fully validated. This study presents ventilation behavior in different street canyon configurations. To evaluate dispersion in a model urban street canyon, a series of tests with various street canyons with different height in upwind and downwind of street canyon are presented. These buildings were arranged in 2-D configurations with different height in upwind and downwind of street canyon. The results showed that a higher concentration of pollutants accumulates under the leeward of the street canyon due to the occurrence of a clockwise vortex inside the street canyon when the street canyon aspect ratio (B/H) is 2. On the contrary, over the windward of the street canyon, a lower concentration of pollutants accumulates due to the occurrence of an anti-clockwise vortex. The flow and dispersion of gases emitted by a line source located between two buildings inside of the urban street canyons were also determined by numerical model. Calculations were compared against CFD prediction in an Environmental Wind Tunnel of Wind Engineering Center at Tamkang University.
    Claus J., P. . Krogstad, and I. P. Castro, 2012: Some measurements of surface drag in urban-type boundary layers at various wind angles. Bound.-Layer Meteor., 145, 407- 422.10.1007/ experimental data obtained in naturally grown boundary layers over a generic urban-type roughness (height h) it is shown that the surface drag is strongly dependent on the flow direction with respect to the roughness orientation. The variations with wind direction are accompanied by corresponding changes in the parameters contained in the usual logarithmic description of the flow in the near-wall inertial layer, {U/u_tau=1/kappaln[(z-d)/z_o]}, principally the roughness length z , which can vary by a factor of around three. The maximum surface drag (and roughness length) occur when the flow direction is at an angle around 45° to the faces of the cubical roughness elements, consistent with the known fact that the drag of an isolated cube in a thick boundary layer is much larger at that orientation than for flow directions normal to the faces. An accurate electronic balance was used to determine the surface drag (and hence friction velocity u ) and pressure-tapped roughness elements allowed estimation of the zero plane displacement d. It is shown that the best logarithmic-law fits then generally require values of the von Kármán `constant' κ significantly lower than its classical value of around 0.41. For a factor of six increase in the Reynolds number (from {U_refh/ν≈ 3,500}), Reynolds number effects are shown to be very weak and, coupled with the form drag and total drag data, the results thus suggest that frictional contributions to the total surface drag are relatively small.
    Eliasson I., B. Offerle, C. S. B. Grimmond, and S. Lindqvist, 2006: Wind fields and turbulence statistics in an urban street canyon. Atmos. Environ., 40, 1- 16.10.1007/ is the first paper of a long-term measurement campaign to explore wind, temperature, radiation and energy fields within an urban canyon. A canyon and a rooftop mast were installed in a canyon with an aspect ratio (Height/Width) of 锝2.1 in Goteborg, Sweden. A number of instruments including sonic anemometers, radiometers and thermocouples were mounted in vertical profiles and across the width of the canyon. The experimental set-up, the characteristics of the canyon flow pattern and mean and turbulence statistics with respect to above canyon flow are examined using data collected under clear-sky conditions in summer and autumn 2003. Results show that under cross-canyon (within 60° of orthogonal) flow, a single helical vortwr exists. High temporal resolution analysis suggests that eddies frequently penetrate the shear stress layer at the canyon top disrupting established flow patterns. A combination of complex building roof shapes and local topography may contribute to this effect by maintaining a high degree of turbulence. The profile of mean wind speed within the canyon and the relation with that above canyon depends on the ambient flow direction in relation to the canyon long axis. Turbulence statistics show results similar to other field studies, with turbulence kinetic energy and vertical mixing greatest toward the windward wall.
    Fujiwara C., K. Yamashita, M. Nakanishi, and Y. Fujiyoshi, 2011: Dust devil-like vortices in an urban area detected by a 3D scanning Doppler Lidar. J. Appl. Meteor. Climatol. , 50, 534- 547.10.1175/ boundary layer (ABL) observations were conducted in an urban area (Sapporo, Japan) from April 2005 to July 2007 using a three-dimensional scanning coherent Doppler lidar. During this period, 50 dust devil--like vortices were detected in the area; they occurred during the daytime and were located at vertices or in the branches of convective cells (''fishnet'' patterns of wind field). The diameters of the vortex cores ranged from 30 to 120 m, and maximum vorticity ranged from 0.15 to 0.26 s--1. More than 60%% of the vortices were cyclonic; the rest were anticyclonic. The tangential velocity component of the strongest vortex varied from --5.4 to ++1.4 m s--1, and the signal-to-noise ratio was weak in the core. Temporal changes were observed in the three-dimensional structures of two vortices from 1330 to 1354 (Japan standard time) 14 April 2005, and the temporal evolution of the stronger vortex was studied. The vortex initially formed along a low-level convergence line in a fishnet and developed vertically. Its vorticity increased with time in association with shrinkage in the core diameter.
    Hertwig D., G. C. Efthimiou, J. G. Bartzis, and B. Leitl, 2012: CFD-RANS model validation of turbulent flow in a semi-idealized urban canopy. Journal of Wind Engineering and Industrial Aerodynamics, 111, 61- 72.10.1016/ flow fields computed by two steady Computational Fluid Dynamics models based on the Reynolds-averaged Navier Stokes equations (CFD-RANS) are compared to validation data measured in a boundary-layer wind-tunnel experiment. The numerical simulations were performed with the research code ADREA and the commercial code STAR-CD. Turbulent flow within and above a 1:225-scale wind-tunnel model representing a novel semi-idealized urban complexity represents the test case. In a systematic study the quality of the numerical predictions of mean wind fields is evaluated with a focus on the identification of model strengths and limitations. State-of-the-art validation metrics for numerical models were used to quantify the agreement between the data sets. Based on detailed spatial identification of locations of good or bad comparison the study showed how unsteady flow effects within street canyons are a major cause for discrepancies between numerical and experimental results.
    Hu X.-M., M. Xue, P. M. Klein, B. G. Illston, and S. Chen, 2016: Analysis of urban effects in Oklahoma city using a dense surface observing network. J. Appl. Meteor. Climatol.,55, 723-741, doi: 10.1175/JAMC-D-15-0206.1.10.1175/ Available Not Available
    Kochanski A. K., E. R. Pardyjak, R. Stoll, A. Gowardhan, M. J. Brown, and W. J. Steenburgh, 2015: One-way coupling of the WRF-QUIC urban dispersion modeling system. J. Appl. Meteor. Climatol., 54, 2119- 2139.10.1175/ Simulations of local weather and air quality in urban areas must account for processes spanning from meso- to microscales, including turbulence and transport within the urban canopy layer. Here, we investigate the performance of the building resolving Quick Urban Industrial Complex (QUIC) Dispersion Modeling System driven with mean wind profiles from the mesoscale Weather Research and Forecasting (WRF) model. Dispersion simulations are performed for intensive observation periods (IOPs) 2 and 8 of the Joint Urban 2003 field experiment conducted in Oklahoma City, using an ensemble of expert-derived wind profiles from observational data, as well as profiles derived from WRF runs. The results suggest that WRF can be used successfully as a source of inflow boundary conditions for urban simulations, without the collection and processing of intensive field observations needed to produce expert derived wind profiles. Detailed statistical analysis of tracer concentration fields suggests that for the purpose of the urban dispersion, WRF simulations provide wind forcing as good as individual or ensemble expert-derived profiles. Despite problems capturing the strength and the elevation of the Great Plains low-level jet, the WRF-simulated near-surface wind speed and direction were close to observations, thus assuring realistic forcing for urban dispersion estimates. Tests performed with multi-layer and bulk urban parameterizations embedded in WRF did not provide any conclusive evidence of the superiority of one scheme over the other, although the dispersion simulations driven by the latter showed slightly better results.
    Li X. X., R. E. Britter, T. Y. Koh, L. K. Norford, C. H. Liu, D. Entekhabi, and D. Y. C. Leung, 2010: Large-eddy simulation of flow and pollutant transport in urban street canyons with ground heating. Bound.-Layer Meteor., 137, 187- 204.10.1007/ study employed large-eddy simulation (LES) based on a one-equation subgrid-scale model to investigate the flow field and pollutant dispersion characteristics inside urban street canyons. Unstable thermal stratification was produced by heating the ground of the street canyon. Using the Boussinesq approximation, thermal buoyancy forces were taken into account in both the Navier-Stokes equations and the transport equation for subgrid-scale turbulent kinetic energy (TKE). The LESs were validated against experimental data obtained in wind-tunnel studies before the model was applied to study the detailed turbulence, temperature, and pollutant dispersion characteristics in the street canyon of aspect ratio 1. The effects of different Richardson numbers ( Ri) were investigated. The ground heating significantly enhanced mean flow, turbulence, and pollutant flux inside the street canyon, but weakened the shear at the roof level. The mean flow was observed to be no longer isolated from the free stream and fresh air could be entrained into the street canyon at the roof-level leeward corner. Weighed against higher temperature, the ground heating facilitated pollutant removal from the street canyon.
    Marciotto E. R., G. Fisch, 2013: Wind tunnel study of turbulent flow past an urban canyon model. Environmental Fluid Mechanics, 13, 403- 416.10.1007/ dispersion in urban area requires appropriate input parameters, in particular aerodynamic roughness parameters. A low-speed wind tunnel was deployed to study flow patterns over an urban canyon model with three aspect ratios and three flow speeds of 2, 5, and 10聽m/s with the objective of obtaining these parameters. Flow speed, standard deviation, and turbulence intensity profiles were determined with a single directional hot-wire anemometer at several positions across the urban canyon model. The aerodynamic parameters $u_*$ , $z_0$ , and $d_0$ were obtained from flow speed profile via a non-linear fit after a suitable choice of the initial value of $d_0$ for which all aerodynamic parameters converge. Flow speed and standard deviation profiles do not change significantly with the position across the canyon, but are much affected by the free flow speed. The regular way they respond to the free flow speed suggested a normalization for which all profiles collapse onto a single profile, which depends only on the canyon aspect ratio. The normalization criterion revealed to be important for obtaining convergent dimensionless profiles. To describe the general profiles characteristics a simple new parameterization is proposed, in which a single-valued function (Gaussian curve) describing the flow speed profile is used in a flux-gradient relationship for describing the standard deviation profiles. This parameterization works well down to $z/h \sim $ 0.25 -0.50.
    Michioka T., A. Sato, and K. Sada, 2013: Large-eddy simulation coupled to mesoscale meteorological model for gas dispersion in an urban district. Atmos. Environ., 75, 153- 162.10.1016/ microscale large-eddy simulation (LES) model coupled to a mesoscale LES model is implemented to estimate a ground concentration considering the meteorological influence in an actual urban district. The microscale LES model is based on a finite volume method with an unstructured grid system to resolve the flow structure in a complex geometry. The Advanced Regional Prediction System (ARPS) is used for mesoscale meteorological simulation. To evaluate the performance of the LES model, 1-h averaged concentrations are compared with those obtained by field measurements, which were conducted for tracer gas dispersion from a point source on the roof of a tall building in Tokyo. The concentrations obtained by the LES model without combing the mesoscale LES model are in quite good agreement with the wind-tunnel experimental data, but overestimates the 1 h averaged ground concentration in the field measurements. On the other hand, the ground concentrations using the microscale LES model coupled to the mesoscale LES are widely distributed owing to large-scale turbulent motions generated by the mesoscale LES, and the concentrations are nearly equal to the concentrations from the field measurements.
    Offerle B., I. Eliasson, C. S. B. Grimmond, and B. Holmer, 2007: Surface heating in relation to air temperature, wind and turbulence in an urban street canyon. Bound.-Layer Meteor., 122, 273- 292.10.1007/ and temperature measurements from within and above a deep urban canyon (height/width = 2.1) were used to examine the thermal structure of air within the canyon, exchange of heat with the overlying atmosphere, and the possible impacts of surface heating on within-canyon air flow. Measurements were made over a range of seasons and primarily analysed for sunny days. This allowed the study of temperature differences between opposing canyon walls and between wall and air of more than 15°C in summer. The wall temperature patterns follow those of incoming solar radiation loading with a secondary daytime effect from the longwave exchange between the walls. In winter, the canyon walls receive little direct solar radiation, and temperature differences are largely due to anthropogenic heating of the building interiors. Cool air from aloft and heated air from canyon walls is shown to circulate within the canyon under cross-canyon flow. Roofs and some portions of walls heat up rapidly on clear days and have a large influence on heat fluxes and the temperature field. The magnitude and direction of the measured turbulent heat flux also depend strongly on the direction of flow relative to surface heating. However, these spatial differences are smoothed by the shear layer at the canyon top. Buoyancy effects from the heated walls were not seen to have as large an impact on the measured flow field as has been shown in numerical experiments. At night canyon walls are shown to be the source of positive sensible heat fluxes. The measurements show that materials and their location, as well as geometry, play a role in regulating the heat exchange between the urban surface and atmosphere.
    Oke T. R., 1988: Street design and urban canopy layer climate. Energy and Buildings, 11, 103- 113.10.1016/0378-7788(88) dilemma is investigated by reviewing the results of recent urban canyon field studies and of scale and mathematical modelling. By concentrating on quantifiable relations it appears that it may be possible to find a range of canyon geometries that are compatible with the apparently conflicting design objectives of mid-latitude cities. If this is correct, traditional European urban forms are climatically more favourable than more modern, especially North American, ones.
    Ouyang Y., W. M. Jiang, F. Hu, S. G. Miao, and N. Zhang, 2003: Experimental study in wind tunnel in the field of air flows and pollutant dispersion in the urban sub-domain. Journal of Nanjing University (Natural Sciences), 39, 770- 780. (in Chinese with English abstract)10.1016/S0955-2219(02) experimental study in wind tunnel on rules of the distribution of streamlines and dispersion of pollutants in the urban sub_domain is described in the paper. In this experiment we set up a model with the ratio 1 to 250 for Fangzhuang residential area in the southeast of Beijing in wind tunnel. This experiment measured air flows and air pollution concentrations in the urban sub_domain as well as air flows and air pollution concentrations around buildings. The results indicate that horizontal field of air flows is affected by buildings that are of various heights in the urban sub_domain. However, the distribution of vertical wind speed is in accordance with power law. The distribution of air pollution concentration in the urban sub_domain is also affected by buildings and wind speed. The results on streamlines around a single building indicate that air flows lift in front of the building and then get across it, and effects of ventilation can raise wind speed 2 or 3 times faster. Around a single building pollution concentrations will decrease as the distance from measuring point to source increases. Vertical distribution of pollution concentrations is in accordance with the rules of source on the ground. The experiment is well compatible with observations. Comparisons between numerical model and wind tunnel experiment indicate that numerical model results on air dreams and pollution concentrations tally with wind tunnel experiment as a whole, except several differences in detail.
    Rotach M. W., Coauthors, 2005: BUBBLE-an urban boundary layer meteorology project. Theor. Appl.Climatol, 81, 231- 261.10.1007/ Basel UrBan Boundary Layer Experiment (BUBBLE) was a year-long experimental effort to investigate in detail the boundary layer structure in the City of Basel, Switzerland. At several sites over different surface types (urban, sub-urban and rural reference) towers up to at least twice the main obstacle height provided turbulence observations at many levels. In addition, a Wind Profiler and a Lidar near the city center were profiling the entire lower troposphere. During an intensive observation period (IOP) of one month duration, several sub-studies on street canyon energetics and satellite ground truth, as well as on urban turbulence and profiling (sodar, RASS, tethered balloon) were performed. Also tracer experiments with near-roof-level release and sampling were performed. In parallel to the experimental activities within BUBBLE, a meso-scale numerical atmospheric model, which contains a surface exchange parameterization, especially designed for urban areas was evaluated and further developed. Finally, the area of the full-scale tracer experiment which also contains several sites of other special projects during the IOP (street canyon energetics, satellite ground truth) is modeled using a very detailed physical scale-model in a wind tunnel. In the present paper details of all these activities are presented together with first results.
    Salem N. B., V. Garbero, P. Salizzoni, G. Lamaison, and L. Soulhac, 2015: Modelling pollutant dispersion in a street network. Bound.-Layer Meteor., 155, 157- 187.10.1007/ study constitutes a further step in the analysis of the performances of a street network model to simulate atmospheric pollutant dispersion in urban areas. The model, named SIRANE, is based on th
    Singh B., B. S. Hansen, M. J. Brown, and E. R. Pardyjak, 2008: Evaluation of the QUIC-URB fast response urban wind model for a cubical building array and wide building street canyon. Environmental Fluid Mechanics, 8, 281- 312.10.1007/ paper describes the QUIC-URB fast response urban wind modeling tool and evaluates it against wind tunnel data for a 702×0211 cubical building array and wide building street canyon. QUIC-URB is based on the R02ckle diagnostic wind modeling strategy that rapidly produces spatially resolved wind fields in urban areas and can be used to drive urban dispersion models. R02ckle-type models do not solve transport equations for momentum or energy; rather, they rely heavily on empirical parameterizations and mass conservation. In the model-experiment comparisons, we test two empirical building flow parameterizations within the QUIC-URB model: our implementation of the standard R02ckle (SR) algorithms and a set of modified R02ckle (MR) algorithms. The MR model attempts to build on the strengths of the SR model and introduces additional physically based, but simple parameterizations that significantly improve the results in most regions of the flow for both test cases. The MR model produces vortices in front of buildings, on rooftops and within street canyons that have velocities that compare much more favorably to the experimental results. We expect that these improvements in the wind field will result in improved dispersion calculations in built environments.
    Vardoulakis S., B. E. A. Fisher, K. Pericleous, and N. Gonzalez-Flesca, 2003: Modelling air quality in street canyons: A review. Atmos. Environ., 37( 2), 155- 182.10.1016/S1352-2310(02) pollution levels have been often observed in urban street canyons due to the increased traffic emissions and reduced natural ventilation. Microscale dispersion models with different levels of complexity may be used to assess urban air quality and support decision-making for pollution control strategies and traffic planning. Mathematical models calculate pollutant concentrations by solving either analytically a simplified set of parametric equations or numerically a set of differential equations that describe in detail wind flow and pollutant dispersion. Street canyon models, which might also include simplified photochemistry and particle deposition09恪皉esuspension algorithms, are often nested within larger-scale urban dispersion codes. Reduced-scale physical models in wind tunnels may also be used for investigating atmospheric processes within urban canyons and validating mathematical models.
    Wood C. R., L. Pauscher, H. C. Ward, S. Kotthaus, J. F. Barlow, M. Gouvea, S. E. Lane, and C. S. B. Grimmond, 2013: Wind observations above an urban river using a new lidar technique, scintillometry and anemometry. Science of the Total Environment, 442, 527- 533.10.1016/ along rivers might provide a key mechanism for ventilation in cities: important for air quality and thermal comfort. Airflow varies in space and time in the vicinity of rivers. Consequently, there is limited utility in point measurements. Ground-based remote sensing offers the opportunity to study 3D airflow in locations which are difficult to observe with conventional approaches. For three months in the winter and spring of 2011, the airflow above the River Thames in central London was observed using a scanning Doppler lidar, a scintillometer and sonic anemometers. First, an inter-comparison showed that lidar-derived mean wind-speed estimates compare almost as well to sonic anemometers (root-mean-square error (rmse) 0.65–0.6802m02s 61021 ) as comparisons between sonic anemometers (0.35–0.7302m02s 61021 ). Second, the lidar duo-beam operating strategy provided horizontal transects of wind vectors (comparison with scintillometer rmse 1.12–1.6302m02s 61021 ) which revealed mean and turbulent airflow across the river and surrounds; in particular, channelled airflow along the river and changes in turbulence quantities consistent with the roughness changes between built and river environments. The results have important consequences for air quality and dispersion around urban rivers, especially given that many cities have high traffic rates on roads located on riverbanks.
    Wyszogrodzki A. A., S. G. Miao, and F. Chen, 2012: Evaluation of the coupling between mesoscale-WRF and LES-EULAG models for simulating fine-scale urban dispersion. Atmos. Res., 118, 324- 345.10.1016/ investigate small-scale transport and dispersion (T&D) within urban areas, we couple a large-eddy-simulation (LES)-based urban-scale fluid solver (EULAG), with the mesoscale Weather Research and Forecasting (WRF) system. The WRF model uses two different urban canopy models (UCM) to parameterize urban effects: a single-layer parameterization (SLUCM) and a multilayer building-effect parameterization (BEP) model coupled to Bougeault and Lacarr猫re planetary boundary-layer scheme. In contrast, EULAG uses the immersed-boundary (IMB) approach to explicitly resolve complex building structures. Here we present details of the downscaling transfer approach where the mesoscale conditions are used to supply initial and lateral boundary conditions for EULAG. We demonstrate its benefits and applicability to solve dispersion problems in the complex urban environment of Oklahoma City. The coupled modeling system is evaluated with data obtained from two intensive observation periods (IOP) of the Joint Urban 2003 experiment, representative for daytime convective (IOP6) and nighttime stable (IOP8) conditions. We assess the sensitivity of urban dispersion simulations to accuracy of the WRF-generated mesoscale conditions. The results show that WRF-BEP reproduces the observed mean near-surface and boundary-layer wind and temperature fields during daytime conditions, and provides accurate statistics during the nighttime more accurately than WRF-SLUCM. The EULAG model performance is exhibited with time-averaged and instantaneous peak concentration statistics. The improved statistics during IOP6 are achieved by using WRF-BEP indicating how important the proper meteorological conditions are to the accuracy of small-scale urban T&D modeling.
    Xie Z. T., O. Coceal, and I. P. Castro, 2008: Large-eddy simulation of flows over random urban-like obstacles. Bound.-Layer Meteor., 129, 1- 23.10.1007/ to our previous large-eddy simulation (LES) of flow over a staggered array of uniform cubes, a simulation of flow over random urban-like obstacles is presented. To gain a deeper insight into the effects of randomness in the obstacle topology, the current results, e.g. spatially-averaged mean velocity, Reynolds stresses, turbulence kinetic energy and dispersive stresses, are compared with our previous LES data and direct numerical simulation data of flow over uniform cubes. Significantly different features in the turbulence statistics are observed within and immediately above the canopy, although there are some similarities in the spatially-averaged statistics. It is also found that the relatively high pressures on the tallest buildings generate contributions to the total surface drag that are far in excess of their proportionate frontal area within the array. Details of the turbulence characteristics (like the stress anisotropy) are compared with those in regular roughness arrays and attempts to find some generality in the turbulence statistics within the canopy region are discussed.
    Zhang N., W. M. Jiang, and S. G. Miao, 2006: A large eddy simulation on the effect of buildings on urban flows. Wind and Structures, 9, 23- 35.10.12989/ effect of buildings on flow in urban canopy is one of the most important problems in local/micro-scale meteorology. A large eddy simulation model is used to simulate the flow structure in an urban neighborhood and the bulk effect of the buildings on surrounding flows is analyzed. The results demonstrate that: (a) The inflow conditions affect the detailed flow characteristics much in the building group, including: the distortion or disappearance of the wake vortexes, the change of funneling effect area and the change of location, size of the static-wind area. (b) The bulk effect of the buildings leads to a loss of wind speed in the low layer where height is less than four times of the average building height, and this loss effect changes little when the inflow direction changes. (c) In the bulk effect to environmental fields, the change of inflow direction affects the vertical distribution Of turbulence greatly. The peak value of the turbulence energy appears at the height of the average building height. The attribution of fluctuations of different components to turbulence changes greatly at different height levels, in the low levels the horizontal speed fluctuation attribute mostly, while the vertical speed fluctuation does in high levels.
    Zhang N., Y. S. Du, and S. G. Miao, 2016: A microscale model for air pollutant dispersion simulation in urban areas: Presentation of the model and performance over a single building. Adv. Atmos. Sci.,33, 184-192, doi: 10.1007/s00376-015-5152-1.10.1007/ microscale air pollutant dispersion model system is developed for emergency response purposes. The model includes a diagnostic wind field model to simulate the wind field and a random-walk air pollutant dispersion model to simulate the pollutant concentration through consideration of the influence of urban buildings. Numerical experiments are designed to evaluate the model- performance, using CEDVAL (Compilation of Experimental Data for Validation of Microscale Dispersion Models) wind tunnel experiment data, including wind fields and air pollutant dispersion around a single building. The results show that the wind model can reproduce the vortexes triggered by urban buildings and the dispersion model simulates the pollutant concentration around buildings well. Typically, the simulation errors come from the determination of the key zones around a building or building cluster. This model has the potential for multiple applications; for example, the prediction of air pollutant dispersion and the evaluation of environmental impacts in emergency situations; urban planning scenarios; and the assessment of microscale air quality in urban areas.
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Manuscript History

Manuscript received: 07 January 2016
Manuscript revised: 16 April 2016
Manuscript accepted: 18 April 2016
通讯作者: 陈斌,
  • 1. 

    沈阳化工大学材料科学与工程学院 沈阳 110142

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Evaluation of a Micro-scale Wind Model's Performance over Realistic Building Clusters Using Wind Tunnel Experiments

  • 1. Institute for Climate and Global Change Research and School of Atmospheric Sciences, Nanjing University, Nanjing 210093
  • 2. Sichuan Environmental Monitoring Center, Chengdu 610091
  • 3. Institute of Urban Meteorology, China Meteorological Administration, Beijing 100089
  • 4. Beijing Municipal Climate Center, Beijing 100089

Abstract: The simulation performance over complex building clusters of a wind simulation model (Wind Information Field Fast Analysis model, WIFFA) in a micro-scale air pollutant dispersion model system (Urban Microscale Air Pollution dispersion Simulation model, UMAPS) is evaluated using various wind tunnel experimental data including the CEDVAL (Compilation of Experimental Data for Validation of Micro-Scale Dispersion Models) wind tunnel experiment data and the NJU-FZ experiment data (Nanjing University-Fang Zhuang neighborhood wind tunnel experiment data). The results show that the wind model can reproduce the vortexes triggered by urban buildings well, and the flow patterns in urban street canyons and building clusters can also be represented. Due to the complex shapes of buildings and their distributions, the simulation deviations/discrepancies from the measurements are usually caused by the simplification of the building shapes and the determination of the key zone sizes. The computational efficiencies of different cases are also discussed in this paper. The model has a high computational efficiency compared to traditional numerical models that solve the Navier-Stokes equations, and can produce very high-resolution (1-5 m) wind fields of a complex neighborhood scale urban building canopy (∼ 1 km × 1 km) in less than 3 min when run on a personal computer.

1. Introduction
  • Cities are usually composed of complex geometric units such as street canyons, roads and buildings, and the urban climate is strongly affected by the geometries and materials making up urban canyons (Arnfield, 2003). Knowledge of the meteorological processes of urban canyons is important to understand the microscale climate/environment within urban canopies, as well as to understand the full-scale urban climate. The wind field is a key property not only affecting the energy and moisture advection and the exchange of energy/water between the urban surface and the atmosphere, but also linking environmental issues such as energy consumption, ventilation in buildings and dispersion of air pollutants, as well as human comfort and safety (Vardoulakis et al., 2003).

    Wind fields and concentrations of air pollutants in urban street canyons or urban canopies have been widely investi-gated using wind tunnel experiments and numerical simulations; several field experiments have also been carried out for this purpose (Rotach et al., 2005; Hu et al., 2016). A significant amount of research has focused on the complicated flow characteristics around buildings and within urban street canyons, due to its critical role in the dispersion of air pollutants in urban environments.

    Figure 1.  The wind tunnel physical model for the CEDVAL B1-4 experiment (from the CEDVAL database).

    The wind field around a building can be characterized by several key zones, including the upwind cavity, lee-side cavity, and rooftop recirculation zone. The wind patterns become more complicated between two rows of buildings (street canyons); (Oke, 1988) classified the wind flow in a street canyon into three types: isolated roughness flow, wake interface flow, and skimming flow. More complex structures have been found using numerical simulations (Xie et al., 2008; Li et al., 2010; Hertwig et al., 2012; Michioka et al., 2013), physical experiments (Ahmad et al., 2005; Chang et al., 2013; Marciotto and Fisch, 2013), and in-situ observations (Eliasson et al., 2006; Offerle et al., 2007; Fujiwara et al., 2011; Claus et al., 2012; Wood et al., 2013).

    Computational fluid dynamical (CFD) models, including large-eddy simulation (LES) models and Reynolds-Averaged Navier-Stokes simulations, are widely used to simulate urban wind fields (Xie et al., 2008; Li et al., 2010; Ashie and Kono, 2011; Hertwig et al., 2012; Michioka et al., 2013). (Wyszogrodzki et al., 2012) coupled an LES model with the mesoscale Weather Research and Forecasting model to obtain fine-scale wind field predictions over an urban area. However, such models are usually expensive and require significant CPU time for city-scale simulations (Ashie and Kono, 2011). Several fast models (Singh et al., 2008; Kochanski et al., 2015; Salem et al., 2015; Zhang et al., 2016) have been developed to simulate the wind flow/air dispersion in building clusters, with relatively lower accuracy and less CPU time, for emergency response conditions on the urban neighborhood scale. Some of these models have been implemented with weather forecasting models for neighborhood-scale urban environment services (Kochanski et al., 2015).

    (Zhang et al., 2016) introduced the Urban Microscale Air Pollution dispersion Simulation model (UMAPS), which includes a diagnostic model (Wind Information Field Fast Analysis model, WIFFA) to simulate wind fields around urban buildings and a Random Walk air pollutant dispersion Model (Nanjing University random walk dispersion model, NJU-RWM) to simulate the pollutant transport in urban canopies. The wind field model (WIFFA) is composed of two parts: an interpolation model to obtain the first-guess fields of different zones around a building or street canyon, and a mass conservation wind model to obtain the detailed wind field over the entire simulation domain. The performance of WIFFA is fundamental to the simulation performance of the entire system, and it is important to know how well such a simplified model can represent the wind fields over complex and real building clusters.

    This paper expands upon the work of (Zhang et al., 2016) to evaluate the performance of the wind model (WIFFA) against wind tunnel experimental data, as the simulation of wind fields is fundamental for air pollution dispersion simulation in urban areas.

2. Wind tunnel and numerical experiment settings
  • Two wind tunnel datasets were used for the evaluations in this paper. The first was the B1-4 experiment in the CEDVAL database. CEDVAL is a wind tunnel dataset for model evaluations of flow patterns and air pollutant dispersion around buildings (Compilation of Experimental Data for Validation of Microscale Dispersion Models, The B1-4 experiment in the CEDVAL database measures the wind fields around four square-ring buildings, two of them with slanted roofs. The building model height (H s) is 0.06 m, and the length and width are both 4.17H s (without the roofs). The physical scale factor is 1:200; therefore, the real building height would be 12 m. The distribution of the buildings is shown in Fig. 1. The measurements were taken under neutral conditions, and wind fields in six sections were observed, including two horizontal sections at z=0.5H s and z=0.83H s, and four vertical sections at y=-2.58H s, y=-1.0H s, y=2.0H s and y=3.58H s, marked as A, B, C and D in Fig. 1. The inlet flow in the experiment was set as a power-law profile, u(z)=U ref×(z/100.0)0.22; the reference height was 100.0 m, the reference wind speed (U ref) was 6.0 m s-1, and the power coefficient was 0.22.

    In the numerical simulations, the building shape was calculated using the physical scale factor, the building length/width/height (without a roof) was 50 m/50 m/12 m, the roof top height was 18 m, and the street canyon width was 12 m. The simulation domain was set to X× Y× Z=400\; m:200\; m:100\; m, and both the horizontal and vertical resolutions were 1 m. The inlet flow was set following the x-coordinate, and the wind profile was the same as in the wind tunnel experimental setting.

  • The second database used in this paper was the Nanjing University-Fang Zhuang neighborhood (NJU-FZ) wind tunnel experiment dataset. The wind tunnel experiments were conducted in the Nanjing University Environmental Wind Tunnel to simulate wind fields and air pollutant dispersion in a real neighborhood in Beijing (Ouyang et al., 2003). The neighborhood size was 500\; m× 700\; m and included 30 buildings; the average building height was 32.4 m and ranged from 4 m to 78 m. The model physical scale factor was 1:250. The wind fields were measured under two wind directions and two reference wind speeds, and the inlet flow was set as a power profile, as shown in Table 1. Three wind speed profiles were observed for different wind direction experiments at different locations, as shown in Fig. 2.

    Figure 2.  The building distribution and measurement locations for the FZ wind tunnel experiments (triangles represent the vertical profile measurement locations; the numbers 1-3 indicate the southwest experiments; and 4, 2 and 5 the northwest experiments).

    Locations 1, 2 and 3 are for the southwest wind experiments and locations 2, 4 and 5 are for the northwest wind experiments. The wind profiles were measured at 14 points and the vertical spatial distances varied with height. The numerical simulation domain for the real neighborhood experiments was set to 1000\; m× 1000\; m× 200\; m. The horizontal and vertical resolutions were 5 m and 2 m, respectively. The inlet wind flows were set to be same as the wind tunnel experiments.

    The same evaluation methods as used in (Zhang et al., 2016) are used in the present study, and the following statistical parameters are employed: the mean value (MN); the mean error between simulations and observations (E), the relative simulation error (RE); the root-mean-square error (RMSE); the normalized RMSE (NMSE), the correlation coefficient (R), and the factor of two of observations (FAC2). The definitions are as follows: \begin{eqnarray*} {MN}&=&\overline {X_i}(i={o,p}) ;\\ E&=&X_{o}-X_{p} ;\\ {RE}&=&|\overline {X_{o}}-\overline {X_{p}}|/\overline {X_{o}} ;\\ {RMSE}&=&\sqrt{\overline {(X_{o}-X_{p})^2}} ;\\ {NMSE}&=&\dfrac{\overline {(X_{o}-X_{p})^2}}{\overline {X_{o}}\overline {X_{p}}} ;\\ R&=&\dfrac{\overline {(X_{o}-\overline {X_{o}})(X_{p}-\overline {X_{p}})}}{\sigma_{X_{o}}\sigma_{X_{p}}} ;\\ {FAC2}&=&\dfrac{{N}(0.5\le X_{p}/X_{o}\le 2.0)}{N} , \end{eqnarray*} where X o is the wind tunnel-measured value and X p is the respective numerically simulated one.

    Figure 3.  The horizontal wind fields in different sections in the CEVAL B1-4 experiments: (a) observed and (b) simulated results at $z/H=0.50$; and (c) observed and (d) simulated results at $z/H=0.83$.

3. Results
  • The numerical simulation results were bilinearly interpolated at the wind tunnel measurement locations to evaluate the model performance. Figure 3 compares the horizontal wind fields in the z/H=0 and z/H=0.83 sections for the wind tunnel observations and the numerical simulations. The results show that complex flow patterns occurred due to the building shapes and distributions. Additionally, vortex patterns appeared at both the exits of the y-direction street canyon and the cross sections of the canyons. The model can capture the flow patterns well; the REs in the z/H=0.5 and z/H=0.83 sections are 1.8% and 27.8%, respectively, and the FAC2s are greater than 85%. The model overestimated the wind speed at z/H=0.83, and the mean bias is 0.51 m s-1. This error results primarily from the overestimation of the wind speed near the y-direction orientated street canyon; this also causes the low R between the observations and simulations, as shown in Table 2.

    Figure 4.  Wind speeds and velocities along $x/H=0.0$ in the $z/H=0.5$ and $z/H=0.83$ sections.

    Figure 5.  The (a-d) observed and (e-h) simulated wind fields in different vertical sections (sections A, B, C and D, as shown in Fig. 1) in the CEDVAL B1-4 experiment.

    The horizontal profiles of normalized wind velocities (u/U ref,v/U ref) and normalized wind speed (\(U/U_ref,U=\sqrt{u^2+v^2}\)) at x/H=0.0 at different levels were compared, as shown in Fig. 4. The simulation error at z/H=0.50 results from the overestimation of u between y/H=0.0 and 1.0; the overestimations of the u value between y/H=-1.0 and 1.0 and the overestimations of the v value at y/H>2.0 and y/H<-2.0 contribute to the overestimation of the total wind speed in the z/H=0.83 section. As shown in (Zhang et al., 2016), the first-guess part of WIFFA usually overestimates the horizontal wind speed in the lateral wall zone around a bulk building. In this experiment, the horizontal wind speed in the street canyon between the downward buildings is overestimated in the first-guess part as well, and then the following mass conversation formula (MCF) model calculates a higher wind speed in the y-orientated street canyon to meet the restriction of the equation of continuity.

    The observed and simulated vertical sections are shown in Fig. 5. A clockwise vortex appears in vertical sections A, C and D in both the wind tunnel experiment and the numerical simulations. The vortex circulation center appears at 1.25H, 0.6H and 0.8H in sections A, C and D; while the respective simulated location is at approximately 0.9H. The clockwise vortex also appears in section B in the numerical simulations, because the wind field in this section is also calculated as the "skimming flow type"; while the vortex is not measured in the wind tunnel experiments. Because the wind speed in the street canyon is relatively weak, the R values between the experiment and simulations can also be above 0.75 for all sections. The slanted roofs also trigger cavity vortexes in section A and D; however, this is not represented well in the model simulations. The model overestimated the wind speed in all vertical sections. The largest simulation error occurred in section A, with RE=100%, NMSE=49.4%, and FAC2=39.9%.

  • Figure 6 illustrates the simulated wind fields at a height of 5 m under different wind directions. Due to blocking by buildings, the wind speed is weak before the upwind walls of buildings and vortexes appear behind the buildings. The channeling effect is also captured by the model; the wind speed between tall buildings may be greater than the incoming wind speed. Longer buildings triggered larger cavities and caused larger weak wind areas.

    The simulated vertical wind speed profiles were compared with the observations, as shown in Figs. 7 and 8, revealing the simulations agreed well with the observations. Vertical profiles at different locations show different characteristics under different inlet wind directions, and the profiles at the same positions are similar with different inlet wind speeds. In the southwest wind experiments, the wind speeds at location 1 show a power-law profile above 50 m; while for levels under 50 m, the wind speed first increases with height from the ground, reaches a peak at a height of approximately 35 m, and then decreases with increasing height. Location 2 is in the open-space of the neighborhood, and the wind profiles at this position resemble a power-law. Location 3 is in a cavity behind a building, and therefore the wind speed under 40 m is very low but increases dramatically above 40 m. In the northwest experiments, location 4 is located before the building clusters, and the wind profiles are not affected by the buildings at this position. Locations 2 and 5 are now on the downwind side of the buildings, and the wind speed is low at the lower levels and increases dramatically at the upper levels. The largest RMSE occurred at location 5 with a value of 0.56 m s-1, due to the simulation of the skimming flow patterns above the building roofs.

    Figure 6.  The simulated wind fields in a real neighborhood at a height of 5 m under different wind directions with an incoming wind speed of 1.22 m s$^-1$: (a) southwest wind; (b) northwest wind.

  • Computational efficiency is important for emergency response models. In this paper, the model was compiled using an Intel Fortran Compiler (version 11.), and all numerical simulations were run on a Linux PC with an AMD (Advanced Micro Devices company) 2.66 GHz CPU and 8 G of memory. The CPU time-cost is listed in Table 3. All simulations cost less than 4 min; and low-resolution runs can save additional CPU time. Another factor affecting the computational efficiency is the convergence of the MCF model; all experiments in this paper used the maximum step number of the iteration in the MCF model. The CPU time-cost reduces significantly when the first-guess wind field has good convergent characteristics. The current experiments show that the number of buildings included in the simulation does not influence the computational efficiency.

    Figure 7.  Observed and simulated vertical profiles of the wind speed in different locations and with different incoming wind speeds with a southwest inlet: (a-c) profiles at locations 1, 2 and 3 for experiments with an incoming wind speed of 1.22 m s$^-1$; (d, e) for experiments with an incoming wind speed of 2 m s$^-1$. Measurements in the wind tunnel experiments are taken at the heights of 2 m, 5 m, 10 m, 15 m, 20 m, 30 m, 40 m, 50 m, 62.5 m, 75 m, 87.5 m, 100 m, 117.5 m and 135 m.

    Figure 8.  As in Fig. 7 but for the northwest experiments and the profiles observed at locations 4, 2 and 5.

    The computational efficiency of the current model is much higher than previous simulations by (Zhang et al., 2006) with an LES model, which cost 3-5 h for a 30-min FZ-A/FZ-B simulation on the same PC platform. The simulation accuracy of WIFFA is less than that of the LES model due to the lack of physical progress description (details are not discussed in this paper), but the comparisons between the numerical simulations and wind tunnel experiments can also prove that UMAPS is able to output relatively accurate wind fields, even when only using an empirical diagnosis first-guess method and a simple MCF model. At the same time, the computational efficiency is highly improved compared to the CFD method, because it does solve the full complex Navier-Stokes equation. WIFFA is a good trade-off tool for urgent urban air pollution episodes, when both computational efficiency and the simulation accuracy should be considered.

4. Summary
  • The diagnostic wind field model (WIFFA) is an important part of the micro-scale air pollutant model system (UMAPS) because it supplies wind field information to the subsequent air pollutant dispersion model. WIFFA is composed of two parts: an interpolation model to obtain the first-guess fields of different zones around a building or street canyon, and a mass conservation wind model to obtain a detailed wind field over the entire simulation domain. The model simulates the wind fields around a complex building with a very simple and fast method; therefore, it is important to understand how well the model works for further development.

    The simulation performance over complex building clusters with WIFFA was evaluated using different wind tunnel experimental data, including CEDVAL wind tunnel experiment data and FZ wind tunnel experiment data. The results show that the wind model can reproduce the vortices triggered by urban buildings well, and that the flow patterns in urban street canyons and building clusters can also be represented. Due to the complex shapes of buildings and their distributions, the simulation deviations/discrepancies from the measurements are usually caused by the simplification of building shapes and the determination of the key zone sizes and the lack of physical description, because the determination of the key zones is only based on several empirical coefficients mostly estimated from previous wind tunnel studies on regular buildings. The model typically overestimates the horizontal wind speed near street canyon exits, and may also miss the skimming flow vortex behind slanted roofs.

    The computational efficiencies of different cases were also discussed in this paper. The model produced very high-resolution (several to ten meters) wind fields on the neighborhood scale (several to ten km) in just minutes. The computational efficiency is affected by the simulation size, model resolution and the iteration loop number in the mass conservation wind model; the building numbers and building shapes have little influence. Given the computational skill, the model could be a strong candidate for prediction and environmental impact evaluation in urban emergency response conditions.




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