2016 Third International Conference on Mathematics and Computers in Sciences and in Industry
The Role of Computational Fluid Dynamics in Solving Wind Engineering Problems Dr Neihad Hussen Al-Khalidy CFD, Wind and Energy Technical Discipline Manager
[email protected]
SLR Consulting Australia Pty Ltd 2 Lincoln Street, Lane Cove NSW 2066, Australia http://www.linkedin.com/pub/neihad-al-khalidy/19/a5/341
Abstract - With advances in computational processing power and commercial software combined with customized user-defined functions, Computational Fluid Dynamics (CFD) tools are now routinely used to produce a combined real time internal-external flow and heat transfer analysis, solve a wide range of building design problems and address a wide range of environmental impacts associated with the building sector. Many building applications and modelling have been carried out to evaluate indoor and outdoor building environments under well controlled conditions. This paper presents case studies for a number of topics including the assessment of pedestrian comfort and safety and complex wind-driven rain ingress The paper also discusses some of the challenges facing CFD for modelling complex built environments.
standard allows the user to choose between wind-tunnel modelling or CFD to obtain the local urban design related contribution. The standard led to the specification of quality assurance requirements, both for CFD and for wind-tunnel testing [2]. A considerable number of CFD publications for the assessment of pedestrian wind comfort have been published in the Journal of Wind Engineering & Industrial Aerodynamics in the past two decades. It is widely accepted that the Navier-Stokes equations together with the continuity equation provide a valid description of laminar and turbulent flows. The most widely applied averaging procedure is Reynolds-averaging of the equations, resulting in the Reynolds-Averaged Navier-Stokes (RANS) equations.
Keywords - CFD, Building Design, Pedestrian Wind Comfort, Wind-Driven Rain
In the past decade, Scale-Resolving Simulation (SRS) models such as Large Eddy Simulation (LES) were incorporated in commercial CFD software to capture unsteady motions of large eddies in separated regions. All SRS methods require time-resolved simulations with relatively small time steps.
I. INTRODUCTION With advances in processing power and numerical turbulence models, the use of Computational Fluid Dynamics (CFD) is rapidly increasing for the assessment of pedestrian wind comfort and safety, wind-driven rain and wind-generated noise. CFD wind modelling offers the following advantages compared to wind tunnel testing: x
A number of best practice guidelines have been established for wind engineering applications including the assessment of pedestrian level wind environments [3, 4].
Small building features are better modelled in CFD versus wind tunnel testing, where model buildings are typically built to a scale of around 1:400. This is important when assessing the benefit of small canopies, porous screens and blades provided to mitigate adverse wind conditions.
x
Wind tunnel measurements are performed at a few selected points within the tested model while CFD can provide comprehensive output of the entire flow field.
x
CFD can have multiple “downstream” applications, for example the same model could be used to examine air quality (pollutant transport) issues, wind-driven rain ingress, fire simulations, etc., issues either difficult or impossible to examine via wind tunnel testing.
CFD predictions of wind flow around bluff bodies have been compared and validated against wind tunnel and full scale measurements in the open literature [5, 6, 7]. In general good agreement is obtained when best practice guidelines are used for the modelling. In recent years, CFD has been playing an important role in building design, particularly in optimising the design of sustainable buildings, diagnosing air flow problems during the concept design stage of building projects and promoting innovative engineering solutions [8]. This paper provides insight into using Computational Fluid Dynamics to predict pedestrian level wind conditions and solve other complex wind related problems such as wind-driven rain ingress and wind-induced noise.
On the other hand the turbulent (gust) wind flow environment in an urban environment is better modelled with wind tunnel when compared with CFD models employing low end turbulence models (eg the standard k-epsilon model).
II. PROBLEM FORMULATION CFD models solve the continuity, momentum, energy and species concentration (if required) equations. The equations for a steady state case can be written as follows:
In the Netherlands, a standard for the assessment of wind comfort and wind danger was published in 2006 [1]. The 978-1-5090-0973-2/16 $31.00 © 2016 IEEE DOI 10.1109/MCSI.2016.35
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w ( U ui ) 0 wxi w w ( U h) (u i U h ) wxi wxi w ( U ui u j ) wx j
x
w wT (k effective )S wxi wxi
wp wW ij ) U g i Fi wxi wx j
In the present instance, the proposed building was surrounded by a number of similar size buildings, located to the north, east and south; and
In the above, u is the velocity, p is the F are the gravitational body and external body forces, Lij is the stress tensor, h is the enthalpy, keffective is the effective thermal conductivity and S is the volumetric heat source.
x
Turbulence is predicted using one of the following methods: x
Direct Numerical Simulation (DNS)
x
Large Eddy Simulation (LES)
x
Reynolds-Averaged Navier-Stokes (RANS) Equations
Building Geometry: The shape of the building itself plays a significant role in determining local wind speeds, with some buildings having aerodynamic features which create accelerated windflow.
2) Geometry for CFD Modelling A detailed three dimensional model of the building and surrounding building blocks was created from AutoCAD files provided by the project Architect. The subject building incorporates low and high level louvres (Refer Fig 1). Additional blocks were added around the development site to an approximate radius of 450 m to accurately model the expected shielding available at the site.
For most real world building problems turbulence is, in principle, described by the Navier-Stokes equations [9, 11]. Commercial CFD codes provide a wide range of turbulence models including Spalart-Allmaras, k-H, k-w, v2f, Reynolds Stress, Scale Adaptive Simulation (SAS), Detached Eddy Simulation (DES), Large Eddy Simulation (LES) [11].
3) Boundary Conditions a) Wind Condition The CFD study was undertaken to estimate the velocity and pressure profile during moderate wind conditions. An estimate of average wind velocity was determined for two key prevailing project site wind conditions namely, north-westerly and south-easterly winds.
The quality of CFD simulation depends on the selected turbulence model. In practical problems the turbulence model should be as simple as the relevant physics will permit.
At the upwind free boundary inlet velocity profiles were derived from Bureau of Meteorology data and the Australian Wind Code AS1170.2. At the downwind and upper free boundaries constant pressure boundary conditions were applied.
III. CASE STUDIES A. Case Study 1: Wind-driven Rain The topic of this case study is the wind-driven rain assessment of thermal chimneys within a proposed educational building in Australia. The proposed building was designed to take advantage of natural ventilation where air is drawn into the building through low level louvres and hot air is removed via the thermal chimneys located in common areas, using thermal buoyancy effects. The impact of prevailing winds and rain on the proposed building was carried out via 3-dimensional CFD simulations. These simulations provided quantitative data useful in determining the primary direction of the local windflow and whether rain particles could penetrate through the proposed louvres incorporated in the building’s design.
Fig. 1. Proposed development
1) Points of Interest with Respect to Surrounding Built Environment The impact of wind and rain on buildings is a function of three basic elements: x
Local Wind Influences: The terrain, topography and built environment surrounding the site will play a role in the character of winds impacting upon the development site. If a development site is surrounded by many high-rise buildings, sheltering of the local wind occurs and the turbulence of the oncoming wind can increase significantly. On the other hand, buildings located on the coastline can have a very open exposure to onshore winds with minimal sheltering.
The study was focused on analysing average wind conditions (mean velocity magnitude of 3 m/s at 10 m height). b) Raindrop Size Rain drop size distribution (DSD) and its characteristics are based on Preston-Whyte and Tyson, 1988 publication [10].
Regional Wind Climate;
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A calculation domain of 900 m length, 900 m wide and 120 m high was used for the CFD analysis.
Table 1 shows that a typical rain droplet is approximately 1 mm in diameter with a fall velocity of 4 m/s. Fine rain with a 0.5 mm diameter has a fall speed of 2.8 m/s.
C. Results and Discussion Steady state simulations of airflow and rain particles distribution inside the proposed development were carried out using the geometric model described in Section 2.
c) Other Boundary Conditions The following additional boundary conditions were used: x
All low level louvres were assumed closed for the purpose of assessing high level chimneys louvre performance.
x
All high level louvres were assumed to be fully open.
x
Turbulence quantities (kinetic energy and dissipation rate) were calculated from empirical relationships [11].
x
A wall function data group was used to avoid using a very fine mesh near the wall and improve turbulent flow simulation.
Selected results of the southeast (SE) CFD simulations using average 3 m/s approach winds are presented below. 1) Air Flow Results Simulation results are shown in Fig 2 and Fig 3. Fig 2 shows wind speeds at a 2D horizontal section at 1.5 m above the ground (typical chest level). Velocity magnitudes are plotted on a colour coded scale between 0 m/s and 4 m/s (dark blue representing still conditions and red representing the wind speed at 4 m/s respectively. The following conclusions can be reached from Fig 2:
B. Discretization The software package utilised in the current CFD analysis is the commercially available code Fluent. The CFD model solves continuity and momentum equations in the computational domain to predict the steady state airflow inside and around the building. Based on a mesh sensitivity assessment, between 4,500,000 to 5,000,000 tetrahedral cells were used to cover the computational domain. Mesh distribution was optimised using the Solution Adaptation Technique, eg refining the mesh based on the numerical results for each prevailing wind direction. TABLE I.
Drop Type
Diameter (mm)
Fall Velocity (m/s)
5
8.9
Small rain drop
1
4.0
Fine rain
0.5
2.8
Drizzle
0.2
1.5
Large cloud drop
0.1
0.3
Medium cloud drop
0.05
0.076
Small cloud drop
0.01
0.003
Incipient drop
0.002
0.00012
Large Nucleus
0.001
0.00004
Wind approaches the site from the SE with a mean wind speed of 3 m/s as per the given boundary condition.
x
Wind is accelerated near the edges, channeled between adjacent roads and recirculated behind the buildings (stagnation zones) as expected.
Fig 3 shows wind speeds at a 2D vertical section at a selected chimney (2D section through the high level louvres). Velocity magnitudes in Fig 3 are plotted on a colour coded scale between 0 m/s and 4.5 m/s. The following conclusions can be reached from Fig 3:
TABLE 1: RAIN/CLOUD DROPLET SIZE AND FALL VELOCITIES [10]
Large rain drop
x
x Air enters through the southeast facing louvres and achieves a mean speed of 3.6 m/s close to the louvres on the inside of the chimney. Airflow then exits straight away from the leeward side creating a recirculation region at the base of the thermal chimney.
The following techniques were used for discretization: x
A second order numerical scheme numerical scheme was used for discretization of pressure and momentum to obtain improved accuracy.
x
An iterative procedure was used to compute air velocity in three orthogonal directions, pressure profile and turbulence parameters. For the pressure velocity coupling a global solver based on the SIMPLE algorithm was employed.
Fig. 2. Wind velocity vector (m/s) at 1.5 m above ground – Southeast wind
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The CFD results for rain drops of 1 mm diameter revealed that the majority of released particles were retained by the louvres. The CFD results were in a good qualitative agreement with the project site photo showing rain particles collecting on a damper, downstream of the analysed louvres (Fig 5). The CFD study proposed and modelled a number of design modification options including solid vertical screens in front of the louvres, dampers and motorised louvres to reduce or prevent rain ingress during persistent rain events with fine droplets combined with unfavourable wind directions. Fig 6 shows the impact of a dense screen placed at 300 mm offset to the both in-situ louvers. The proposed screen can significantly reduce number of fine particles reaching the base of the chimney.
Fig. 3. Contours of velocity magnitudes (m/s) at a 2D section through a selected chimney
2) Rain Particle Simulations Once the steady wind-flow field was obtained, raindrop trajectories were calculated using a Lagrangian particle tracking approach to simulate the trajectories of the rain drops. In the Lagrangian approach discrete particles are released into the flow and then tracked by integrating the particle equation of motion. Only the drag and the gravity-buoyancy forces are considered since the rain drops have a much higher density than the surrounding air flow. The particle equation of motion can be simplified as follows:
Fig. 4. Particle path lines for 0.2 mm diameter drizzle raindrops
In the above, d is the droplet diameter, Uf is the wind velocity, Up is the particle velocity, rf is the air density, Up is the rain droplet density, Pis the air viscosity; and g is the acceleration of gravity. For a given droplet diameter and initial droplet position and velocity, the trajectory of an individual droplet is computed by integrating the above equation. In order to predict the dispersion of rain particles due to turbulence, a stochastic method known as “Random Walk Model” is implemented to determine the instantaneous wind velocity [11]. The following rain droplets sizes were analysed: x
Typical 1 mm diameter rain drop particle size with a droplet velocity of 4 m/s.
x
0.5 mm diameter rain drop particle size with a droplet velocity of 2.8 m/s.
x
0.2 mm diameter drizzle rain drop particle size with a droplet velocity of 1.5 m/s.
Fig. 5. Site Photo for the Chimney
Fig 4 shows the computed trajectories for very fine rain drops of 0.2 mm diameter. One can see that most particles were able to penetrate through the louvres. Many particles followed the wind field, exiting from the leeward side. Approximately 12% of all released particles became trapped in the recirculation region collecing at the base of the thermal chimney.
Fig. 6. Wind Velocity Vector (m/s) for a Proposed Mitigation Option
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Further recommendations including operatable dampers were provided to control the size of the openings or to be fully shut during persistent rains with fine droplets combined with unfavourable wind directions.
developed for the project site by applying surface corrections to the weather station data and calibrating the resulting wind rose taking into account the local exposure factors by wind direction.
D. Case Study 2: Pedestrian Wind Comfort The topic of this case study concerns the pedestrian wind comfort and safety for two proposed commercial buildings in the United Kingdom.
3) Boundary Conditions Modelling was undertaken for eight compass wind directions and public locations then checked for any exacerbation of the current wind conditions caused by the proposed development.
Even medium rise buildings can create wind conditions that make the human experience at ground level unpleasant and private outdoor spaces unusable. Pedestrian wind comfort and safety studies are required by local governments to ensure that adverse wind conditions in streets and public open spaces are minimised.
At the upwind free boundary inlet, velocity profiles were derived from the “reference” wind rose and local roughness characteristics (ie urban-suburban terrain). At the downwind and upper free boundaries “constant pressure” boundary conditions were applied.
The choice of suitable criteria for evaluating the acceptability of particular ground level conditions has been the subject of extensive research. A number of criteria have been developed and applied in different countries. The criteria are based on the percent time certain wind speeds are exceeded for given periods (eg weekly, monthly, annually). The criteria can be based on gust speed [12], mean speed [13] or both peaks and gust speed [14] combined with frequency of occurrence statistics. A comparison of pedestrian wind acceptability criteria is detailed in [15].
4) Mesh for CFD Simulation The Finite Volume Method (FVM) was used to discretize the governing equations in section 2 to predict the steady state airflow in the computational domain.
Turbulence quantities (kinetic energy and dissipation rate) required for the upwind free boundary were calculated from empirical relationships.
The quality of the mesh is a critical aspect of the overall numerical simulation and it has a significant impact on the accuracy of the results and solver run time.
The CFD assessment in this case study was conducted in accordance with the following Lawson Comfort and safety Criteria. “Comfort” criteria relate a number of typical pedestrian activities such as purpose-walking, strolling, sitting, etc, in terms of the gust-equivalent mean (GEM) speed which is exceeded 5% of the time, on an annual basis. “Safety” criteria cover the circumstance where pedestrians might encounter difficulty in walking. They are defined by the incidence of GEM speeds occurring once or twice per year. Lawson criteria couple the probability of exceeding winds at given statistical levels with wind speed magnitudes originally related to the Beaufort Land Scale [16].
Fig. 7. Geometry for CFD modelling
1) Geometry for CFD Modelling A detailed 3-D model of two commercial buildings (8 and 11 storeys) connected by a linked bridge was created from AutoCAD files provided by the project Architect. Site topography and surrounding buildings to an approximate radius of 500 m were then incorporated in the CFD model.
For the current analysis a mesh sensitivity assessment was carried out and between almost 9 million mixed cells (tetrahedral, hexahedra and pyramids) were used to cover the computational domain. Mesh distribution was then optimised using a Solution Adaptation Technique, eg refining the mesh based on the numerical results for each prevailing wind direction.
The 3-D geometry of the proposed buildings and surrounding built environment for CFD Modelling is shown in Fig 7.
Mesh distribution on one of the proposed buildings is shown in Fig 8. One can see that fine structured meshes were used near the ground.
A calculation domain of 2,720 m length, 2,671 m wide and 350 m high was used for the CFD analysis.
5) Discretization The software package utilised in the current CFD analysis was the commercially available code Ansys-Fluent. The following schemes and algorithms were implemented in this study:
2) Wind Conditions for the Project Site Surface wind data was initially obtained from a nearby weather station which has a generally open exposure in all directions and hence representative of all such areas in the region. A local 10 m height “Reference” wind rose was then
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x
A Realizable k-H (rkH) turbulence model [11] was used for all analysed cases due to computational time advantages and model capability to capture flow separation and circulation.
x
A wall function data group was used to avoid using a very fine mesh near the wall and improve turbulent flow simulation.
x
A second order numerical scheme was used for discretization of pressure and momentum to obtain more accurate results.
x
An iterative procedure was used to estimate the air velocity in terms of three directions, pressure profile and turbulence parameters. For the pressure velocity coupling a global solver based on the SIMPLE algorithm was employed [17].
where the wind is approaching the site with Vref=1 as per the given boundary condition. Wind is then accelerated near the corners and a stagnation region is located immediately downstream of the proposed building and a number of neighbouring buildings. The wind impact underneath the proposed linked bridge for the prevailing south-westerly wind direction is shown in Fig 11. The mean wind velocity ratio near the ground is 1.05 affected by winds impacting on the west façade of the proposed building and deflecting towards the ground winds (Downwash Effect). The CFD results for all analysed wind directions were combined with the wind probability information from the local wind rose and assessment predictions developed in terms of Comfort and Safety using the Lawson Criteria. In areas of elevated wind speeds, wind mitigation treatments such as dense landscape, wind screens, etc, were recommended to satisfy the assessment criteria.
Fig 9 shows that the normalised residual of continuity was reduced by between five and six orders of magnitude while the normalised residuals of x-, y-, and z-velocity, k and H were reduced between six and eight orders of magnitude demonstrating a valid numerical solution.
The production of such models is challenging due to a combination of large dimensions and the non-orthogonal geometry of many small building features. In the last decade, SLR has undertaken extensive R&D on completed developments to confirm modelling accuracy against wind tunnel data.
Fig. 10. Mean velocity ratio (Vlocal/V10m) at 1.5 m above ground
Fig. 8. Sample mesh
Fig. 9. Scaled residual history
6) Sample Results and Wind Assessment Summary Selected results are shown in Fig 10 and Fig 11. Fig 10 shows mean airflow velocities ratios (V local/V reference at 10m) at pedestrian level (1.5 m above the ground) for the easterly wind direction. Velocity ratios are plotted on a colour coded scale between 0 and 1.82. One can see that overall flow characteristics are captured well by the developed CFD model
Fig. 11. Mean velocity ratio (Vlocal/V10m) at a 2D vertical section through the development
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[10] R. Preston-Whyte, and P. Tyson , The Atmosphere and Weather of Southern Africa, Oxford University Press, Cape Town, 1988. [11] Fluent Inc, Theory Manual, 2002. [12] H. Melbourne, Criteria for Environmental Wind Condition, International Journal of Wind Engineering and Industrial Aerodynamics, Vol 3, 1978, pp 241-249. [13] N. Isyumov and A. Davenport, The Ground Level Wind Environment in Built Up Areas, Fourth International Conference on Wind Effects on Buildings and Structure, Cambridge University Press, 1976, pp. 403422. [14] T. Lawson and A. Penwarden, The Effect of Wind on People in the Vicinity of Buildings, Forth International Conference on Wind Effects on Buildings and Structures, Cambridge University Press, 1976, pp. 605-622. [15] M. Ratcliff, J. Peterka, Comparison of Pedestrian Wind Acceptability Criteria, Journal of Wind Engineering and Industrial Aerodynamics, Vol 36, 1990, pp. 791-800. [16] S. Huler, Defining the Wind: The Beaufort Scale, and How a 19thCentury Admiral Turned Science into Poetry, Crown, 2004. [17] P. Vandoormaal and G. D. Raithby, Enhancements of the SIMPLE Method for Predicting Incompressible Fluid Flows, Numerical Heat Transfer, Vol. 7, 1984, pp.147–163. [18] N. Al-Khalidy, Shopping Sensation, Australian National Construction Review, (http://www.ancr.com.au/Pplaza.pdf), 2006, p. 49.
Fig. 12. Comparison between wind tunnel and CFD results
A comparison between CFD modelling and wind tunnel testing results using sound scouring technique for an iconic shopping centre in NSW Australia is shown in Fig 12 [18]. One can see that scoured regions underneath the footbridge in the wind tunnel test model are similar to that in the CFD model. The CFD model was also used to assess internal air flow though the retail levels of the proposed development including all building entrances and openings. IV. CONCLUSION This paper discussed the role of Computational Fluid Dynamics (CFD) in solving wind engineering problems. Case studies are provided for a number of topics including the assessment of pedestrian wind comfort and safety and complex wind-driven rain ingress. The paper also discusses some of the challenges facing CFD for modelling complex built environments. REFERENCES [1] [2]
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