Unified Interoperability Solution Set to Support CONOPS Framework Development for Municipal, Provincial and Federal Collaboration to CBRN Response” Final Report

Defence Research and Recherche et développement Development Canada pour la défense Canada

Urban Flow Model Development for “Unified Interoperability Solution Set to Support CONOPS Framework Development for Municipal, Provincial and Federal Collaboration to CBRN Response” Final Report

Fue-Sang Lien and Hua Ji, Waterloo CFD Engineering Consulting Inc.

Contract Scientific Authority: E. Yee, DRDC Suffield

The scientific or technical validity of this Contract Report is entirely the responsibility of the contractor and the contents do not necessarily have the approval or endorsement of Defence R&D Canada.

Defence R&D Canada Contract Report DRDC Suffield CR 2008-244 October 2008

Urban Flow Model Development for "Unified Interoperability Solution Set to Support CONOPS Framework Development for Municipal, Provincial and Federal Collaboration to CBRN Response" Final Report

Fue-Sang Lien and Hua Ji Waterloo CFD Engineering Consulting Inc. 534 Paradise Crescent Waterloo ON N2L 3G1

Contract Number: W7702-06R136/001/EDM

Contract Scientific Authority: E. Yee (403-544-4605)

Defence R&D Canada – Suffield Contract Report DRDC Suffield CR 2008-244 October 2008

Urban Flow Model Development for “Unified Interoperability Solution Set to Support CONOPS Framework Development for Municipal, Provincial and Federal Collaboration to CBRN Response”

Final Report

by Fue-Sang Lien, Hua Ji Email: [email protected] Web site: http://www.watcfd.com/

Waterloo CFD Engineering Consulting Inc. 534 Paradise Crescent, Waterloo Ontario, N2L 3G1

Prepared for Defence R&D Canada – Suffield

Contract Number: W7702-06R136/001/EDM Technical Authority: Eugene Yee

ABSTRACT

The release of chemical, biological, radiological, or nuclear (CBRN) agents by terrorists or rogue states in a North American city (densely populated urban center) and the subsequent exposure, deposition and contamination are emerging threats in an uncertain world. The realities of disaster management today place inter-organizational collaboration at the forefront of any response, highlighting the need for a coherent and interoperable approach. In order to advance interoperability assessment and development, a common integrating architecture, which describes the structure of the components (organizations), their interrelationships, and the principles and guidelines governing their design and evolution over time is required. In essence, a CBRN incident response constitute a System-of-System (S-of-S) paradigm when viewed across the various responder organizations, agencies and departments and therefore requires a structure to support communication between diverse stakeholders, an ability to effectively manage complexity and to implement incremental change as each organization within the System-to-System paradigm evolves.

To support the development of an Operational Architecture that captures and identifies municipal-provincial-federal organizations, capabilities, processes and command and control relationships focused on CBRN response, specific CBRN scenarios involving the release of a CBRN agent over the Vancouver-Whistler geographic area (requiring realistic predictions of transport, dispersion, deposition, and fate of a CBRN agent released into the complex flows of a cityscape) has been executed in order to provide the resulting urban flow and dispersion data sets for incorporation into the Operational Architecture developed under CRTI-05-0058TD project. In addition, enhancement of a flow model “urbanSTREAM” developed under CRTI-02-0093RD has been achieved by adding an energy equation into the system. Validation against a range of benchmark test problems in both forced and mixed convection conditions, using either the Reynolds- averaged Navier-Stokes (RANS) or Large Eddy Simulation (LES) approach, has been performed. Agreement between the present numerical predictions and experimental data (when available) is very good for most cases examined here. There is still a scope for further improvement, which includes, e.g., development of a model for heavy gas dispersion in a built-up environment, incorporation of radiative heat flux into the energy budget for determination of building surface temperature, and validation against field trials conducted in the Joint Urban 2003 (JU2003) experiment, for which thermal effects (e.g., positive and negative buoyancy) on the flow and dispersion in the urban environment are assessed to be important.

2 Table of Contents

ABSTRACT...... 2 List of Figures...... 4 1. INTRODUCTION ...... 7 2. NUMERICAL METHOD...... 8 3. ENHANCEMENT OF URBANSTREAM TO INCLUDE THERMAL EFFECTS...... 8 3.1. Conjugate Heat Transfer Around a Matrix of Cubes (Case 6.2) ...... 9 3.2. Mixed Convection for a Fully Developed Channel Flow...... 13 3.2.1. Case 1: Gr/Re2=0 ...... 15 3.2.2. Case 2: Gr/Re2=213 ...... 16 4. TWO CBRN RELEASE SCENARIOS IN THE VANCOUVER-WHISTLER GEOGRAPHIC REGION...... 17 4.1. E-Wind Scenario...... 18 4.2. E-ESE-SE Wind Scenario...... 20 5. CONCLUSIONS AND RECOMMENDATIONS ...... 21 5.1. Inclusion of Thermal Effect...... 21 5.2. Release of CBRN Material in Vancouver Island...... 22 REFERENCES ...... 23 APPENDIX: README for CDROM ...... 26

3 List of Figures

Figure 1: Case 6.2 – Schematic of a wall-mounted matrix of cubes...... 27 Figure 2: Case 6.2 – Illustration of boundary conditions for a conjugate heat transfer problem...... 27 Figure 3: Case 6.2 – Interface condition between an epoxy layer and adjacent fluid (air)...... 28 Figure 4: Case 6.2 – Computational mesh of 47×47×47 nodes on the x-y plane at z/h=0 with an epoxy layer of 1-cell thickness...... 28 Figure 5: Case 6.2 – Computational mesh of 47×47×47 nodes on the x-z plane at y/h=0.5 with an epoxy layer of 1-cell thickness...... 29 Figure 6: Case 6.2 – Wall temperature distribution at y/h=0.5 on a mesh of 47×47×47 3 nodes with kepoxy =×0.7 0.24 = 0.168 [kg ⋅ m/s ⋅ K]...... 29 Figure 7: Case 6.2 – Wall temperature distribution at z/h=0 on a mesh of 47×47×47 nodes 3 with kepoxy =×0.7 0.24 = 0.168 [kg ⋅ m/s ⋅ K]...... 30 Figure 8: Case 6.2 – Computational mesh of 51×51×51 nodes on the x-z plane at y/h=0.5 with an epoxy layer of 5-cell thickness...... 30 Figure 9: Case 6.2 – Wall temperature distribution at y/h=0.5 on a mesh of 51×51×51 3 nodes with kepoxy =⋅0.24 [kg m/s⋅ K]...... 31 Figure 10: Case 6.2 – Wall temperature distribution at y/h=0.5 reported by Rundstrom & Moshfegh (2006) using FLUENT...... 31 Figure 11: Case 6.2 – Contours of temperature at y/h=0.5 and z/h=0 cutting planes...... 32 Figure 12: Case 6.2 – Velocity vectors on a x-y plane at z/h=0...... 32 Figure 13: Case 6.2 – Velocity vectors on a x-z plane at y/h=0.5...... 33 Figure 14: Case 6.2 – Velocity profiles at x/h=-0.3 and 1.3 on a vertical x-y plane at z/h=0...... 33 Figure 15: Local Nusselt number distributions at Re=12,600 (Hsieh & Lien, 2005)...... 34 Figure 16: Illustration of how to improve prediction of velocity gradient du/dy in the buffer-layer region for the wall-function approach...... 34 Figure 17: Case 6.2 – Comparison between the present modified thermal wall-function approach with LES by Niceno et al. (2002) at y/h=0.5...... 35 Figure 18: Case 6.2 – Comparison between the present modified thermal wall-function approach with LES by Niceno et al. (2002) at z/h=0...... 35 Figure 19: Case 6.2 – Comparison between the present modified thermal wall-function approach with RSM by Rundstrom & Moshfegh (2006) at z/h=0...... 36 Figure 20: Channel Flow – Flow geometry and coordinate system...... 36 Figure 21: Channel Flow – Velocity profiles obtained with the standard RANS k-ε model in comparison with DNS data of Moser et al. (1999) at Re=590 and Gr/Re2=0...... 37 Figure 22: Channel Flow –Temperature profiles obtained with the standard RANS k-ε model at Re=590 and Gr/Re2=0...... 37 Figure 23: Channel Flow – Velocity profiles obtained with the standard RANS k-ε model and the present “coarse-grid” DNS (represented by hollow circle and diamond symbols) in comparison with DNS data of Moser et al. (1999) (identified as a dash- dot-dash line) at Re=590 and Gr/Re2=0...... 38

4 Figure 24: Channel Flow – Temperature profiles obtained with the standard RANS k-ε model in comparison with the present “coarse-grid” DNS (represented by hollow circle and diamond symbols) at Re=590 and Gr/Re2=0...... 38 Figure 25: Channel Flow – urms-profiles obtained with the standard RANS k-ε model and the present “coarse-grid” DNS (represented by hollow circle symbol) in comparison with DNS data of Moser et al. (1999) (identified as a solid line) at Re=590 and Gr/Re2=0...... 39 Figure 26: Resolved Reynolds-stress profiles predicted by LES at different Reynolds number (Sagaut, 2006)...... 39 Figure 27: Channel Flow – Velocity profiles obtained with the present “coarse-grid” DNS and LES at Re=590 and Gr/Re2=0...... 40 Figure 28: Channel Flow – Temperature profiles obtained with the present “coarse-grid” DNS and LES at Re=590 and Gr/Re2=0...... 40 Figure 29: Channel Flow – Velocity profiles obtained with the present LES and URANS approaches at Re=590 and Gr/Re2=213 [DNS data from Moser et al. (1999) at Gr=0 is included to server as a reference curve]...... 41 Figure 30: Channel Flow – Temperature profiles obtained with the present LES and URANS approaches at Re=590 and Gr/Re2=213...... 41 Figure 31: Channel Flow – Contours of time-averaged turbulence kinetic energy superimposed with streamlines obtained with the k-ε URANS model at Re=590 and Gr/Re2=213...... 42 Figure 32: Channel Flow – Contours of time-averaged temperature superimposed with streamlines obtained with the k-ε URANS model at Re=590 and Gr/Re2=213...... 42 Figure 33: Vancouver City – Release location of chlorine at 49 deg, 17.11’ N latitude and 123 deg, 6.513’ W longitude in the Vancouver-Whistler geographical region...... 43 Figure 34: Vancouver City – Computational domain consisting of building-aware “inner region” and “outer region”, in which effects of virtual buildings are represented by a distributed drag-force approach (Lien et al., 2005)...... 43 Figure 35: Vancouver City – Resolved and virtual buildings are highlighted by red and gray colors, respectively...... 44 Figure 36: Typical wind profiles over various terrain types...... 44 Figure 37: Wind speed and direction at the CWWA weather station shown in Figure 38 between 10/16/2008 and 10/17/2008...... 45 Figure 38: Location of the CWWA weather station at 49.35 deg N latitude and 123.18333 deg W longitude in Vancouver...... 45 Figure 39: Vancouver City – Profiles of velocity, k and ε at inlet plane...... 46 Figure 40: Vancouver City – Sampler and source locations...... 47 Figure 41: Vancouver City – Time history of concentration at the 1st sampler location obtained with the 1st-order, 2nd-order and hybrid 1st/2nd-order time-stepping schemes for the E-wind scenario...... 47 Figure 42: Vancouver City – Time history of total mass contained in the computational domain obtained with the 1st-order, 2nd-order and hybrid 1st/2nd-order time-stepping schemes for the E-wind scenario...... 48 Figure 43: Vancouver City – Time history of concentration at the 1st sampler location obtained with the hybrid 1st/2nd-order time-stepping schemes and Δt=2 and 10 sec for the E-wind scenario...... 48

5 Figure 44: Vancouver City – Time history of concentration at 3 sampler locations with C [kg/m3] presented on a linear scale for the E-wind scenario...... 49 Figure 45: Vancouver City – Time history of concentration at 3 sampler locations with C [kg/m3] presented on a logarithmic scale for the E-wind scenario...... 49 Figure 46: Vancouver City – Time history of total mass contained in the computational domain for the E-wind scenario...... 50 Figure 47: Vancouver City – Contours of concentration on a logarithmic scale at t=5 min for the E-wind scenario...... 50 Figure 48: Vancouver City – Contours of concentration on a logarithmic scale at t=10 min for the E-wind scenario...... 51 Figure 49: Vancouver City – Contours of concentration on a logarithmic scale at t=20 min for the E-wind scenario...... 51 Figure 50: Vancouver City – Contours of concentration on a logarithmic scale at t=60 min for the E-wind scenario...... 52 Figure 51: . Vancouver City – Contours of dosage on a logarithmic scale at t=1 min for the E-wind scenario...... 52 Figure 52: Vancouver City – Contours of dosage on a logarithmic scale at t=15 min for the E-wind scenario...... 53 Figure 53: Vancouver City – Contours of concentration on a logarithmic scale at t=4 min for the E-ESE-E wind scenario...... 53 Figure 54: Vancouver City – Contours of concentration on a logarithmic scale at t=10 min for the E-ESE-E wind scenario...... 54 Figure 55: Vancouver City – Contours of concentration on a logarithmic scale at t=30 min for the E-ESE-E wind scenario...... 54 Figure 56: Vancouver City – Contours of dosage on a logarithmic scale at t=4 min for the E-ESE-SE wind scenario...... 55 Figure 57: Vancouver City – Contours of dosage on a logarithmic scale at t=10 min for the E-ESE-SE wind scenario...... 55 Figure 58: Vancouver City – Contours of dosage on a logarithmic scale at t=30 min for the E-ESE-SE wind scenario...... 56

1. INTRODUCTION

Complex situations resulting from chemical, biological, radiological or nuclear (CBRN) threats demand a collaborative response from diverse municipal, provincial, and federal stakeholders. The lack of a formal structure within which to capture and exercise joint municipal-provincial-federal CBRN response compromises public safety in Canada. Multi-jurisdictional first responders require clear standards, processes, protocols and capabilities to ensure a shared awareness of authority, responsibility, and competency in order to achieve operational interoperability. To this end, a CRTI project entitled “Unified Interoperability Solution set to Support CONOPS Framework Development – Municipal-Provincial-Federal Collaboration to CBRN Response” (CRTI-05-0058TD) has been approved in April 2006 to specifically address CBRN interoperable response to a terrorist incident across agencies and departments (civilian and military) at the municipal, provincial, and federal levels. The objective of this project is to employ a Capability Engineering and Design Approach (CEDA) to develop an Operational Architecture focused on the CBRN aspects of municipal-provincial-federal interoperability. The Operational Architecture will then be instantiated within a simulation-based analysis environment that will establish a Common Operating Picture Environment (COPE) based on a project-developed standardized, shared geo-spatial dataset of the Vancouver- Whistler geographical area. Specific CBRN scenarios involving the release of a CBRN agent in this urban environment will be executed through a series of demonstrations designed to “test” municipal-provincial-federal interoperability with the outcomes used to update the Operational Architecture providing increased awareness of interoperability challenges and leading to an Interoperability Framework for inter-organizational CBRN response.

The objective of the present study is to simulate specific CBRN release scenarios over the Vancouver-Whistler geographical area using a prototype of a state-of-the-science, multiscale, multi-physics modeling system (developed under CRTI-02-0093RD to predict the transport and dispersion of CBRN materials into the urban environment), and to provide the resulting urban flow and dispersion data sets for incorporation into the operational architecture that is developed under CRTI-05-0058TD. In Section 2, the baseline numerical framework employed for this project will be described. In Section 3, inclusion of thermal effects into the present microscale computational fluid dynamics (CFD) code urbanSTREAM is addressed and validated against two benchmark test problems: (1) conjugate heat transfer around of a matrix of cubes using the Reynolds-averaged Navier- Stokes (RANS) approach, and (2) a fully-developed channel flow in both forced and mixed convection conditions using the Large Eddy Simulation (LES) approach. Two CBRN release scenarios, one in time-varying wind condition, in the Vancouver-Whistler geographic region are discussed in Section 4, in which urbanSTREAM is executed in a stand-alone mode with inflow conditions inferred from wind speed/wind direction information provided by a near-by weather station. Finally, conclusions drawn from the present study and recommendations made for future studies are given in Section 5.

7 2. NUMERICAL METHOD

There are 4 major codes within the urban microscale modeling system [see Yee et al. (2007) for details], namely, urbanGRID, urbanSTREAM, urbanEU and urbanPOST, developed under CRTI-02-0093RD, which formed the base codes to conduct the present research. In the simplest terms, urbanGRID imports building information encoded in Environmental Systems Research Institute (ESRI) Shapefiles and uses this data to generate a structured grid over a user-selected computational domain in a given cityscape. Furthermore, urbanGRID imports three-dimensional meteorological fields (e.g., mean wind, turbulence kinetic energy, etc.) provided by urban GEM LAM (Cote et al., 1998), and uses this information to provide inflow boundary conditions for the urban microscale flow model.

The structured grid and inflow boundary conditions provided by urbanGRID are used as input by urbanSTREAM which is a computational fluid dynamics (CFD) model for the numerical simulation of the flows around and within the complex geometries of buildings in the cityscape. The flow solver urbanSTREAM provides the high-resolution wind and turbulence fields used by the Eulerian dispersion model urbanEU (source-oriented) to simulate the dispersion of contaminants in the urban domain. This urban dispersion model is based on the numerical solution of a K-theory advection-diffusion equation. Finally, urbanPOST is used to process the primary output files from urbanSTREAM to provide an appropriate specification of wind statistics required as input by the Eulerian urban dispersion model, urbanEU.

3. ENHANCEMENT OF URBANSTREAM TO INCLUDE THERMAL EFFECTS

The buoyancy term, based on the Boussinesq approximation in the form of

−−gTTirβ ()ef (1)

where gi is the gravitational acceleration vector, β is the thermal expansion coefficient and Tref is a reference temperature, is introduced to the momentum equations in urbanSTREAM for mixed convection problems. The temperature transport equation written as

∂∂TT ∂⎡ ν ∂ ''⎤ +=()uTjj⎢ − uT⎥ (2) ∂∂txjj ∂ x⎣⎢Pr ∂ xj⎦⎥

'' ν t ∂T where uTj =− based on the simple gradient diffusion hypothesis, is discretized Prtj∂x using the finite-volume method [see, e.g., Ferziger & Peric (2002)] and added as a new subroutine CALCT to urbanSTREAM. The thermal boundary conditions for temperature at the walls, based on the wall-function approach, are implemented. The standard wall functions, which are based on logarithmic law of the wall, assume that the near-wall region consists of two layers:

8 Inner layer (y+ < 11.6 ):

++⎛⎞u ⎛⎞yuτ uy⎜⎟===⎜⎟ (3) ⎝⎠uτ ⎝⎠ν Outer layer (y+ ≥ 11.6 ):

ln(Ey+ ) u+ = (4) κ where κ=0.42 is the von Karman constant and E=9.8 is an integration constant for smooth walls. In Eqs. (3) and (4), uτ = τ w / ρ is the friction velocity and τ w is the wall shear stress. The wall functions for temperature can be expressed similarly as follows [see, e.g., Bejan (1984)]

Inner layer (y+ < 13.2 ):

++⎛⎞ρCupwτ () T− T Ty⎜⎟==Pr (5) ⎝⎠qw Outer layer (y+ ≥ 13.2 ):

Pr Ty++=+13.2Prt ln − 5.66 (6) κ where Pr and Prt (≈0.9) are molecular and turbulent Prandtl numbers, respectively, Cp is the specific heat at constant pressure, Tw is the wall temperature and qw is the heat flux at walls.

''∂ui For the k −ε turbulence model, the production term Puki=− uj is replaced by ∂x j

()PGkk+ to include the turbulence generated through buoyancy effects, where '' Ggki=− βuiT [see Hsieh & Lien (2004, 2005) for details].

3.1. Conjugate Heat Transfer Around a Matrix of Cubes (Case 6.2) The solution domain, depicted in Figure 1, consists of a single heated cube in an equidistantly spaced matrix of wall-mounted cubes placed on the floor of a rectangle high-aspect-ratio wind-tunnel section (Meinders and Hanjalic, 1999). The side length of a cubes is h, where h =15 mm, and the channel height is Dh= 3.4 . The distance between the cubes is 3h, giving a center-to-center distance of SSxy==4 h. The computational domain, also shown in Figure 1, consists of a sub-channel unit of 443hh××.4 h. On the channel floor and top wall, no-slip conditions for velocities are applied by the use of wall functions. For thermal fields, the temperature of the incoming fluid was fixed to o TCref = 20 . At the outlet, the normal derivative of temperature is set to zero. On the floor and upper walls, adiabatic boundary condition is assumed. The heated cube consists of a

9 constant temperature core covered with a thin epoxy mantle of thickness 0.1h with a thermal conductivity of 0.24 W/(m⋅ K) . This test problem will be referred as “Case 6.2” here as the experimental data has been used to assess the performance of different turbulence models in the 6th and 8th ERCOFTAC/IAHR/COST Workshops on refined flow and turbulence modeling (see, e.g., http://www.ercoftac.nl/ workshop8/ case6_2/ case6_2.html). Comparison of LES and RANS for this test case has been reported, e.g., by Cheng et al. (2003).

As the temperature on the surface of the epoxy layer covering a heated copper cube at 75° C is unknown a priori as illustrated in Figure 2, the capability of handling a conjugate heat transfer problem is implemented by introducing a new component C_X=256 (bit 9; i.e., 29-1=256) in the FLAG array in additional to C_O=64 (bit 7; i.e., 27- 1=64) used to represent an obstacle cell (details of the FLAG array can be found in Yee et al., 2007). As exemplified in Figure 2, bit 9 (or C_X) is switched on within the epoxy 3 layer, in which the thermal conductivity, k, is set to be kepoxy =⋅0.24 [kg m/s⋅ K]. Bit 7 (or C_O) is only switched on inside the copper cube, in which temperature is fixed to 75° C .

In order to determine the surface temperature on the epoxy layer (say, on the east face of the obstacle as shown in Figure 3), the heat fluxes from the solid and fluid sides should be identical at the interface between these two phases:

TTis− TTf − i qkss=−22 = q f =− k f (7) ΔΔxs x f where Ts (and ks) and Tf (and kf) represent temperature (and thermal conductivity) in the solid and fluid regions, respectively. Eq. (7) can be solved to obtain the interface temperature Ti,

kTs sfΔ+ x k ffs T Δ x Ti = (8) kxsfΔ+Δ k fs x which can be imposed as the Dirichlet boundary condition at the solid/fluid interface. Since Eq. (7) is equivalent to

TTis− TTfs− qkss=−22 = qk ii =− (9) ΔΔxs xxfs+Δ ki (thermal conductivity at the interface) can be derived by substituting Eq. (8) into Eq. (9), yielding

kkf sf()Δ+Δ x x s ki = (10) kxsΔ+Δffs k x or,

kfix=−(1 )ks +fxkf (11)

10 where

kxsfΔ fx = (12) kxsΔ+Δffs k x To facilitate implementation of the interface conditions [Eqs. (7) and (9)], the original geometrical interpolation parameter

Δxs fx = (13) Δ+Δx f xs in the energy equation only (or the CALCT subroutine in urbanSTREAM) is re-set to Eq. (12). Interface temperature Ti is then calculated using Eq. (8) in order to be compared to the experimental data. Note that computational studies for Case 6.2, including thermal results, have been reported by, e.g., Niceno et al. (2002) and Rundstrom & Moshfegh (2006). However, no details were given regarding how this conjugate heat transfer problem was implemented numerically.

A preliminary study using a mesh of 47×47×47 nodes with an epoxy layer of 1-cell thickness was conducted, and the mesh projected on the x-y and x-z planes are given in Figures 4 and 5, respectively. It was found that the wall temperature was generally over- predicted (not shown). Therefore, the thermal conductivity for the epoxy layer was 3 reduced to 70% of its original value (viz., kepoxy =×0.7 0.24 = 0.168 [kg ⋅ m/s ⋅ K]) in order to best match with the experimental data as show in Figure 6 at yh/0= .5, and in Figure 7 at zh/0= .

In order to further identify possible sources causing the discrepancy between the present numerical predictions and the experimental data, a finer non-uniform mesh of 51×51×51 nodes containing an epoxy layer of 5-cell thickness was used, and is shown in Figure 8 on a x-z plane at yh /= 0.5 . Figure 9 is the resulting prediction of wall temperature distribution obtained with the standard (high-Reynolds-number) k −ε model at yh/0= .5, which should be viewed in conjunction with Figure 10 reported by Rundstrom & Moshfegh (2006) using a “two-layer” Reynolds-stress model (RSM) in FLUENT (http://www.fluent.com/). Despite the fact that Rundstrom & Moshfegh’s used a much finer mesh of 661,572 cells, in which 63,000 cells were in the epoxy layer, both results given in Figures 9 and 10 are reasonably comparable, exhibiting the same discrepancy in comparison with the experimental data. The wall temperatures on the front, side and rear faces of the cube are over-predicted by the RSM RANS model. Contours of temperature from the present prediction at yh /= 0.5 and are zh/0= are shown in Figure 11, and the corresponding velocity fields on the x-y plane (at zh/0= ) and x-z plane (at yh/0= .5) are given in Figures 12 and 13, respectively. The predicted velocity profiles at x / h = -0.3 and 1.3 (i.e., at 0.3h ahead of the front face and behind the rear face of the cube) on a vertical x-y plane at zh/0= in comparison with the experimental data are shown in Figure 14. As seen, agreement between our predictions and measurement is generally good. More flow field results for this forced-convection flow have been reported in our early study (Cheng et al., 2003). Note that effects of

11 buoyancy were also included in the calculations. However, Gr/Re2=0.0185 (<<1) in this case so that the buoyancy effects can be neglected here.

The over-prediction of wall temperature in the present shown in Figure 9 is consistent with our early simulations for a fully-developed flow over a 2-D array of ribs in a channel (Hsieh & Lien, 2005) as shown in Figure 15, in which local Nusselt number distributions are over-predicted even after the inclusion of the Yap-correction term in the form of

2 ⎡ ⎛⎞kk3/2⎛ 3/2⎞ε 2 ⎤ Yap=max⎢ 0.83⎜⎟− 1⎜⎟ ,0⎥ (14) 2.5yy 2.5 k ⎣⎢ ⎝⎠εε⎝⎠⎦⎥ (without the Yap term, the over-prediction of local Nusselt number is even more severe [not shown in the paper]). The purpose of the Yap term is to restrict the turbulence length scales in flow regions in which the original high-Reynolds-number (HRN) k −ε model tends to under-predict the turbulence dissipation rate, ε, and hence over-predict the eddy viscosity. In the present study, we have taken a different approach in order to improve our early results shown in Figure 9, and no adjustment of the thermal conductivity for the epoxy layer is required here (also cf. Figures 6 and 7 in which thermal conductivity for the epoxy layer was reduced). Let us start with a simple idea regarding how to improve du the prediction of velocity gradient in the buffer-layer region (where 11.6<

+ if yP ≤11.6 , du u ≈ P (15) dy yP

+ if yP >11.6 ,

du uP ≈ + (16) dy yP ln( EyP ) It should be pointed out here that Eq. (16) is only correct in the log-law region where + 60≤≤yP 300 . In other words, the standard wall functions can be erroneous when the Reynolds number is too low as in the present Case 6.2 test problem. This is because the du correct expression for in the buffer layer should be in between Eqs. (15) and (16). dy Note that modeling errors associated with the adoption of a wrong expression for velocity gradient in buffer layer in the standard wall-function approach mainly affect the prediction of skin friction (or, equivalently, the friction coefficient). Their negative effects on the resulting velocity profiles are generally less obvious, in particular for a flow separated from a sharp corner.

The same idea is then extended to modify the temperature gradient in the buffer-layer region by first rewriting Eq. (6) as

Pr TF++= t ln y (17) κ where ⎡()13.2Pr− 5.66 κ ⎤ F = exp ⎢ ⎥ (18) ⎣ Prt ⎦

dT In the log-law region, the correct expression for should be dy

dT TTPw− ≈ + (19) dy yP ln( FyP ) where Tw is the wall temperature. Eq. (19) is modified by introducing a damping function

fµ in the denominator in order to be applicable also in the buffer-layer region:

dT TTPw− ≈ + (20) dy yPPln( Fy ) fµ where y+ f =−1 exp( − P ) (21) µ A and A ≈ 20. Preliminary results obtained with the present “modified thermal wall functions” in comparison with LES results obtained by Niceno et al. (2002) and RANS results (using the RSM model) obtained by Rundstrom & Moshfegh (2006) are given in Figures 17-18 and Figure 19, respectively. It can be observed from Figures 17-19 that the present modified thermal wall-function approach clearly shows improvement in the prediction of wall temperature distributions compared to another more advanced RANS model (namely, the RSM model), in which a two-layer approach was employed as the near-wall treatment. Although our results are slightly inferior to the wall-resolved LES predictions by Niceno et al. (2002), in which 427,680 cells were used, the computational cost associated with RANS is significantly lower than LES. RANS is estimated to be 30- 70 times faster than LES for the present case.

3.2. Mixed Convection for a Fully Developed Channel Flow The second test case selected here is to demonstrate the LES capability of urbanSTREAM for solving a fully-developed channel flow under mixed convection conditions (see, e.g., benchmark DNS datasets from http://murasun.me.noda.tus.ac.jp /turbulence/). The standard Smagorinsky’s subgrid scale (SGS) model (1963) is employed for SGS stresses. For SGS heat fluxes, the eddy-viscosity model proposed by Moin et al. (1991) is adopted. For both SGS stresses and fluxes, the Smagorinsky’s constant is assumed to be ≈0.1. A schematic of the computational domain is given in Figure 20, which has dimensions of 22πδ×× πδ in the streamwise, wall-normal and

13 spanwise directions, respectively. The buoyancy effect in the channel is quantified by the Grashof number defined as

gTβ ()− T(2δ )3 Gr = wh wc (22) ν 2 where g is the gravitational acceleration, β is the thermal expansion coefficient, δ is half channel height, ν is the dynamic viscosity and Twh and Twc are the hot and cold wall temperatures, respectively. The Reynolds number is defined as

u δ Re = τ (23) ν where uτ is the friction velocity. In order to take into account the buoyancy in the y- momentum equation, the following term in dimensionless form is introduced:

Gr θ (24) Re2 where the dimensionless temperature is defined as TT− TT+ θ =Δref , TT=− T, T=wh wc (25) ΔT wh wc ref 2 Two cases, corresponding to Gr/Re2=0 and 213 at Re=590 and Pr=0.72 (air), are simulated here using both RANS and LES approaches in conjunction with wall functions on a mesh of 32×32×32 nodes. It was mentioned in Smith (2004) that “Although a number of researchers are looking at LES wall function model for velocities, there does not appear to be anyone looking at LES wall function models for energy. This is an area needing future funding support.” In the present study, details of wall-function implementation in the energy equation are described as follows.

*1n− 1. Guess uuττ= where “*” and “ n −1” denote the guessed value and value from the previous [or (n − 1)th ] time step, respectively. 2. Calculate the y+ at node P adjacent to walls using

y u* y+ = P τ (26) P ν 3. Determine the T+ value by

For y+ < 13.2 , Ty++= Pr ; Pr For y+ > 13.2 , Ty++=+13.2Prt ln − 5.66 , and Pr≈ 0.9 . κ t ⎛⎞q w 4. Compute Tτ ⎜⎟= from ⎝⎠ρCuP τ TT− T = wP (27) τ T +

14 where Tw are known (TTww= h or TTww= c).

5. Compute qw from

* qTCwP= ττρ u (28) *** 6. Update uuττ⇐ using

λ u ** u** = mP (29) τ ρ where

+ µ For y ≤ 11.6 , λm = ; yP * + ρuτ κ For y > 11.6 , λm = + . ln EyP 7. Repeat steps 2-6 until convergence is reached for each time step before marching to the next time step [or at the (n + 1)th time step].

3.2.1. Case 1: Gr/Re2=0 The velocity and temperature profiles obtained with the standard k −ε RANS model, the former in comparison with the DNS data of Moser et al. (1999) computed on a mesh of 384×257×384 nodes, are shown in Figures 21 and 22, respectively. As seen from Figure 21, agreement between the predicted velocity profiles with the DNS data is generally good. No DNS data for temperature profiles are available from Moser et al. (1999). Figure 22 will be compared with our own “coarse-grid” DNS data at the same Reynolds number later, since no temperature results were reported in Moser et al’s DNS study. In this section, the central differencing scheme (CDS) is used for LES calculations, and the UMIST scheme (Lien & Leschziner, 1994) is employed for RANS calculations, unless stated otherwise.

Comparison between the present “coarse-grid” DNS and RANS results in terms of dimensionless velocity and temperature profiles in wall units are given in Figures 23 and 24, respectively. Agreement between the coarse-grid DNS and RANS results in both figures is reasonably good, except that there is a “kink” (or “jump”) at the 2nd node of LES results at y+ ≈ 60 in both velocity and temperature profiles. This kink becomes even more obvious in the u ' value (or rms value of the streamwise Reynolds-stress) at the same location (i.e., y /δ ≈ 0.1) shown in Figure 25. As mentioned by Sagaut (2006), “…spurious bumps are observed in the turbulence intensities just near the first grid point. These unphysical overshoots are associated to the existence of large spurious streaky structures, whose size can be governed by either the mesh size or the numerical and subgrid model dissipation…No general cure for this problem is known…” Therefore, results referred in Sagaut’s book at different Reynolds numbers, shown in Figure 26, are consistent with the present predictions, except that no temperature profiles are reported in

15 the book. Also observed in Figure 25 is that the coarse-grid DNS with wall functions over-predicts urms-profile for y /δ ≤ 0.5, while RANS under-predicts urms-profile as a result of the linear stress-strain relationship employed in the standard k −ε eddy- viscosity model.

Finally, difference between the present LES and coarse-grid DNS results, both obtained with wall functions as near-wall models, are depicted in Figures 27 and 28 for velocity and temperature profiles, respectively. As clearly indicated in these figures, both u+ and T+ values predicted by LES are too high relative to the log-law [described by Eqs. (4) and (6)] on a mesh of 32×32×32 nodes. We did not attempt a finer mesh because the y+ value for the first grid point next to walls is ≈20, which is already within the buffer-layer region. It should be mentioned here that the velocity shift observed in Figure 27 has been observed in other LES and hybrid LES/RANS results reported by, e.g., Cabot & Moin (1999) and Benarafa et al. (2006). The velocity and temperature shifts seen in Figures 27 and 28 can be even more severe (not shown) if an upwind-biased scheme, such as the UMIST scheme, is used instead of the CDS scheme.

3.2.2. Case 2: Gr/Re2=213 In this section, LES results are compared with URANS predictions at Re=590 and Gr/Re2=213, as shown in Figures 29 and 30 for time-averaged velocity and temperature profiles, respectively. It can be seen from these figures that both u+ and T+ profiles are lower than the corresponding log-law profiles for y+ > 60 , being uy++=+ 2.5ln 5.5 and Ty++=+2.195ln 13.2Pr− 5.66, respectively. Generally speaking, URANS predictions for both u+ and T+ profiles are higher than those obtained with LES, although the over- prediction between URANS and LES calculations for T+ profiles seen in Figure 30 is significantly less than that observed in u+ profiles exhibited in Figure 29. There is currently no DNS data for the same test case to be included for comparison purpose. However, Wang et al. (2008) has conducted a similar LES study using a range of SGS stress and heat-flux models for a fully-developed unstably stratified horizontal channel flow at Re=150 and Gr/Re2 =58 and 213. Wang et al. employed the wall-resolved LES approach with the Smagorinsky’s constant determined by a dynamic procedure similar to that proposed by Lilly (1992). Their calculations were restricted to a low Reynolds number of Re=150, because only a relatively coarse mesh of 48×32×48 nodes was used on a computational domain, which has dimensions of 52πδ××2 πδ in the streamwise, wall-normal and spanwise directions, respectively. In contrast, Re=590 for the same Gr/Re2 =213 is used in the present study on a similar coarse grid of 32×32×32 nodes, owing to the use of wall functions as the near-wall treatment. Note that the present computational domain is 5 times smaller (i.e. 22πδ×× πδ vs. 52πδ××2 πδ ), than that used in Wang et al’s study.

Although there is a difference in Reynolds number between the present and Wang et al’s studies, at the same Gr/Re2 =213, Wang et al.’s results also show that both predicted u+ and T+ profiles are lower than the corresponding log-law profiles at Gr=0. Moreover, their LES results are generally under-predicted in comparison with the DNS data of Iida

16 & Kasagi (1997); a more significant under-prediction on the u+ profiles than on the T+ profiles is observed, which is consistent with our early observations in Figures 29 and 30. No URANS calculations were reported in Wang et al.’s study. From Figure 23, we hypothesize that URANS predictions can be viewed as reasonably good surrogates for DNS results in terms of velocity and temperature profiles when Gr/Re2>>0 at Re=590, in which no “true” DNS data is currently available. If so, the trend of our LES predictions in comparison with “DNS” (or URANS here) are qualitatively similar to Wang et al’s results for the same Gr/Re2=213 at different Reynolds numbers.

Contours of time-averaged turbulence kinetic energy (TKE) and temperature superimposed on streamlines, both obtained with the URANS k −ε model, are shown in Figures 31 and 32, respectively. The streamlines on the cross-sectional y-z plane exhibit two large longitudinal vortex rolls with axes aligned along the streamwise direction. The πδ lateral width of the middle vortex is about , oriented in the clockwise direction. These 2 longitudinal vortex rolls are a consequence of combined effects of buoyancy and the streamwise pressure gradient, giving rise to a characteristic pattern of organized secondary flow structures that are consistent with the experimental observations of Fukui et al. (1991). As seen in Figure 32, parcels of cold fluid at z /2δ ≈ .1at the upper wall and parcels of hot fluid at z /0δ ≈ .5 at the lower wall are entrained into the middle vortex roll in the downdraft and updraft regions, respectively.

4. TWO CBRN RELEASE SCENARIOS IN THE VANCOUVER-WHISTLER GEOGRAPHIC REGION

The release location of 830 kg of chlorine is at 49 deg, 17.11’ N latitude and 123 deg, 6.513’ W longitude in the Vancouver-Whistler geographical region as shown in Figure 33. The computational domain consists of “inner region” and “outer region” as indicated in Figure 34. In the inner region, all buildings are resolved as highlighted by the red color in Figure 35. In the outer region, effects of the “virtual buildings” highlighted by the gray color are represented by a distributed drag-force approach [see, e.g., Lien et al. (2005)] -1 with drag coefficient of CAd ≈ 0.155 m as suggested by Lien et al. (2008) based on the Joint Urban 2003 (JU2003) experiment conducted in Oklahoma City, Oklahoma during the period from June 28 to July 31, 2003 (see https://ju2003-dpg.dpg.army.mil/).

The velocity profiles at inlet are approximated by the following power law:

For z ≤ 400 m , p uz() ⎛⎞ z ≈ ⎜⎟ (30) u10 ⎝⎠10 For z > 400 m , uz( )= 6.76 m/s

17 based on the assumption that p ≈ 0.3, and u10 ≈ 2.235 m/s (or u10 ≈ 5 mi/h ), where u10 is the wind speed at 10 m above the ground. The exponent p ≈ 0.3 in Eq. (30) is a good approximation for wind speeds in suburbs (see Figure 36). The wind speed u10 ≈ 5 mi/h is chosen because it is reasonably close to the nominal speed (see Figure 37) at CWWA weather station indicated in Figure 38. More information can be found from http://www.met.utah.edu/mesowest/. The profile of turbulence kinetic energy at inlet is expressed by using the following two-layer model:

For zh≤ c , z kk≈ max () (31) hc

For zh> c ⎛⎞kk−−⎛ khkh ⎞ kz≈+⎜⎟max min⎜ mincB max L ⎟ (32) ⎝⎠hhcB−−L⎝ hh cBL ⎠ where hc ≈ 41 m is the average building height in the inner region shown in Figure 35, and hBL ≈1000 m is the atmospheric boundary-layer thickness. The maximum turbulence 22 kinetic energy kmax ≈ 0.4 m / s is estimated from the IOP9 test problem (Allwine et al., 2 2004) in the JU2003 experiment described earlier, and kumin≈ 0.01 10 at zh≈ BL . Similarly, the turbulence dissipation rate, ε, can be approximated by

k 3/2 ε = −3/4 (33) Czµ min(κ ,hBL / 3)

where κ=0.42 is the von Karman constant, and Cµ = 0.09 for the standard k −ε model. The profiles of velocity, k and ε at the inlet plane are depicted in Figure 39 for both scenarios described below.

4.1. E-Wind Scenario Wind direction in meteorology is reported by the direction from which it originates. For example, an east wind blows from the east to the west. In the east-wind (or E-wind) scenario, 3 samplers are placed at 630 m, 2766 m and 7200 m downstream of the source location as shown in Figure 40, and 830 kg chlorine is released from the source location (see also Figure 33) within 10 second. Different time-stepping schemes and time step Δt are attempted to solve the following K-theory advection-diffusion equation for concentration, C:

∂∂∂CC∂uCj ⎡⎤ +=⎢⎥()KK +t + Q (34) ∂∂txjj ∂ x⎣⎦⎢⎥ ∂ x j

18 using the CFD code “urbanEU”, in order to examine their effects on the predicted g concentration profiles at the 1st sampler location. Q [in ] in Eq. (34) is the source sm⋅ 3 density distribution for the contaminant. The turbulent diffusivity Kt is obtained from the turbulent viscosity ν t (predicted by urbanSTREAM) in combination with a turbulent

Schmidt number Sct ≈ 0.63 in the following manner:

ν t Kt = (35) Sct In the case of implicit (IM) time-stepping scheme, both the 1st-order and 2nd-order schemes given below are used,

1st-order scheme: ∂−CCnn+1 C ≈ (36) ∂Δtt

2nd-order scheme: ∂−CC34nn+−11 CC+n ≈ (37) ∂Δtt2 The predicted time history of concentration at the 1st sampler location and the total mass contained in the computational domain, using both the 1st-order and 2nd-order time- stepping schemes in conjunction with Δ=t 2 sec and the UMIST convection schemes, are given in Figures 41 and 42, respectively. It is very noticeable from Figure 42 that the 2nd- order time-stepping scheme over-predicts the total mass ≈910 kg for t > 10 sec. Note that 910 kg is 80 kg greater than 830 kg, the latter being the total amount of mass released from the source location for 01≤≤t 0 sec while the source is turned on. This is physically impossible. As a result, the peak value of the concentration time history predicted by the 2nd-order time-stepping scheme is ≈ 15% higher than that obtained with the 1st-order time-stepping scheme. This non-physical “overshoot” was speculated as a result of the source term Q in Eq. (34) being suddenly turned off at t =10 sec by a discontinuous step function. Similar to the basic idea behind the TVD scheme (see, e.g., Leveque, 2002) when applied to the Riemann problem, in which a “limiter” was introduced to switch a 2nd-order convection scheme to a 1st-order convection in the vicinity of shock wave (a discontinuity in space), we also employed the 1st-order time- stepping scheme only in the vicinity of t =10 sec, and used the 2nd-order time-stepping scheme for tt<−10 Δ and tt>+10 Δ sec. This is referred to here as the hybrid 1st/2nd- order time-stepping scheme. As seen from Figure 42 that the “overshoot” problem observed earlier is prevented when the hybrid time-stepping scheme is adopted. The resulting time history of concentration at the 1st sampler location shown in Figure 41 is in between results predicted by the 1st-order and 2nd-order time-stepping schemes. Therefore, for the rest of the calculations only the hybrid time-stepping scheme will be considered, unless stated otherwise.

19 Sensitivity studies of choosing Δ=t 2 and 10 sec to the predicted time history of concentration at the 1st sampler location are performed, and results are given in Figure 43. Note that, in the case of “ Δ=t 10 sec” as denoted in Figure 43, Δt is first set to 2 sec when t ≤10 sec, and Δt is then set to 10 sec when t >10 sec in our calculations. As seen, both results are very close to each other, suggesting that results obtained with Δ=t 10 sec are accurate enough. Therefore, Δ=t 10 sec will be used for the rest scenarios presented in Section 4. It should be mentioned here that a 1st-order explicit (EX) time-stepping scheme has also been implemented and tested for this E-wind scenario (not shown). It was found that in order to satisfy the Scarborough stability condition (Scarborough, 1958), Δ≤t 0.07 sec is required, which is about 140 times smaller than Δ=t 10 sec. For a 2-hour simulation after the chlorine gas is released from the source location, this means that ≈130,000 time steps are needed if Δ=t 0.07 sec is used. As a good compromise between solution accuracy and computational efficiency, the hybrid 1st-/2nd-order IM scheme in combination with Δ=t 10 sec will be used for all results presented hereafter.

The time history of concentration at 3 sampler locations exhibited in Figure 40 are shown in Figures 44 and 45 with C [in kg/m3] presented in linear and logarithmic scales, respectively. As suggested in Figure 45, the “center” of the cloud, where the level of concentration is the highest, is located at the 1st sampler location at t ≈ 6 min, the 2nd sampler location at t ≈18 min and the 3rd sampler location at t ≈ 36 min, respectively. The total mass contained in the present computational domain (see Figure 44) starts to decrease drastically for t > 20 min as shown in Figure 46, implying that significant portion of the cloud has left from the west end of the computational domain for the present E-wind scenario. This is consistent with contours of concentration, which are presented in 3-D view in Figures 47 to 50 at t = 5, 10, 20, 60 min, respectively. Note that the concentration levels in Figures 47 to 50 are in logarithmic scale. Furthermore, it can be seen from Figures 46 and 50 is that ≈7% of the total mass is left inside the computational domain at t = 60 min.

kg⋅ s For a nearly instantaneous (transient) release, let us defined the “dosage” in [ ] at m3 time t as t Dt()= Ct ()ˆˆ dt (38) ∫0

The time evolution of D(t) at t = 1 and 15 min is shown in Figures 51 and 52, in which the contours of dosage are in logarithmic scale with 3 iso-surfaces corresponding to log(dosage)=-4, -8, -12. The background dosage is 1.E-30 (or log(1.E-30)=-69.077) required to avoid singularity. Animations of time evolution of 3-D iso-surfaces of concentration for the present scenario are available in the appended CD-ROM

4.2. E-ESE-SE Wind Scenario In the E-wind scenario described in Section 4.1, the wind speed and wind direction are fixed during the simulations. In contrast, in this subsection wind direction changes

20 linearly from an east wind to an east-south-east (ESE) wind for 01≤≤t 5 min, and from an ESE to a south-east (SE) wind for 15≤≤t 30 min. This scenario will be referred to as “E-ESE-SE” wind scenario. The wind speed and profiles of turbulence kinetic energy and turbulence dissipation rate at the inflow plane are the same as those described in Eqs. (30)-(33) in Section 4.1.

Figures 53-55 depict 3-D contours of concentration on a logarithmic scale at t = 4, 10 and 30 min after 830 kg of the chlorine gas are released at the source location indicated in Figure 33. This is a nearly instantaneous (transient) release in the sense that the chlorine gas is turned off after 10 sec, as in the case of the E-wind scenario. The corresponding time evolution of dosage, also on a logarithmic scale, at the same times is shown in Figures 56-58. Animations of time evolution of 3-D contours of concentration and dosage for the present scenario are available in the appended CD-ROM.

5. CONCLUSIONS AND RECOMMENDATIONS

5.1. Inclusion of Thermal Effect The inclusion of thermal effect into urbanSTREAM is implemented and validated against two benchmark test problems; namely conjugate heat transfer around a matrix of cubes under the fully-developed flow conditions, in which periodic boundary conditions in the streamwise and spanwise directions are imposed. The capability of handling conjugate heat transfer problems is introduced into urbanSTREAM, and its detailed numerical implementation is described in the present report, which was omitted by earlier and similar studies [e.g., Niceno et al. (2002), Rundstrom & Moshfegh (2006)]. Consistent with the results reported by Rundstrom & Moshfegh (2006) using a Reynolds-stress RANS model, our (high-Reynolds-number) k −ε model combined with wall functions as a near-wall treatment over-predicts surface temperature. Since the y+ values for the first grid node adjacent to walls are ≈30 (well into the buffer-layer region) in our calculations, a simple damping function, similar to van Driest’s damping function (1956), is introduced to the standard 2-layer thermal wall functions for y+ > 13.2 so that the dT original temperature gradient (which is only correct y+ > 60 in the log-law region) dy is modified in such a way that the improved version should perform better when 13.2<

Preliminary results obtained with the present low-Reynolds-number thermal wall functions in comparison with the experimental data are encouraging. Our predicted wall- temperature distributions are fairly comparable to Niceno et al.’s LES results at only a fraction (estimated to be less than 5%) of their computational cost. It should be mentioned here that no changes were introduced to the standard wall functions for the momentum and turbulence (i.e., k- and ε-) equations in all calculations presented in Section 3.1. Therefore, it is recommended that the low-Reynolds-number modifications described in Eqs. (20)-(21) will need to be consistently incorporated into wall functions for momentum and turbulence equations. It is anticipated that more than one damping

21 function might be required, and the following relationships under the local equilibrium condition:

u3 kC==−1/2 u 2, ε τ (39) µτ κ y will need to be modified as well.

After testing the implementation of energy equation into urbanSTREAM for a forced convection (i.e., momentum and energy equations are decoupled) conjugate heat transfer problem using a RANS k −ε model, the Large Eddy Simulation (LES) capability is also introduced into urbanSTREAM and validated against a fully-developed channel flow in mixed convection conditions, in which Re=590 and Gr/Re2=213. Since urbanSTREAM is designed to simulate wind flows in an urban environment, where all buildings need to be explicitly resolved in the building-aware “inner region” (see Figure 35), the simple wall- function approach, traditionally developed for (U)RANS models, are implemented in both momentum and energy equations in the present LES framework in order to enhance computational efficiency when the nominal Reynolds numbers considered in our simulations are sufficiently high so that “wall-resolved” LES become prohibitively expensive.

Since there is no DNS data currently available for Re=590 and Gr/Re2=213 (mixed convection case discussed in Section 3.2.2), our LES results are compared to URANS solutions obtained with the same urbanSTREAM code. In the case of Re=590 and Gr/Re2=0 (forced convection case presented in Section 3.2.1), we observe that our URANS solutions, in terms of mean velocity profiles, are in fairly good agreement with the DNS data of Moser et al. (1999). We then hypothesize that mean velocity and temperature profiles obtained by the present URANS k −ε model can be viewed as surrogates of “DNS” data for Re=590 and Gr/Re2=213, to which our LES results are compare. Recall that Wang et al. (2008) computed a very similar fully-developed channel flow at the same Gr/Re2=213, but at Re=150. They compared their LES results with the DNS data of Iida & Kasagi (1997). No URANS results were reported by Wang et al. (2008). In both our current and Wang et al’s studies (although Reynolds number are different), mean velocity and temperature (or u+ − and T + − ) profiles predicted by LES are consistently lower than “DNS” data (note that URANS is interpreted as “DNS” at Re=590 in the present study). Moreover, the difference between u+ − profiles returned by LES and “DNS” are consistently greater than that between T + − profiles from both studies, which is very encouraging. We will revisit this test case when the true DNS data for Re=590 and Gr/Re2=213 become available.

5.2.Release of CBRN Material in Vancouver-Whistler Area The major deficiency in simulating 2 CBRN release scenarios in Vancouver-Whistler area is the specification of inflow conditions. In the present study, urbanSTREAM is executed in a stand-alone mode using the inflow conditions described by Eqs. (30)-(33) without being coupled directly to a mesocale GEM/LAM model developed by Environment Canada. Although the energy equation has been successfully implemented

22 into urbanSTREAM and validated against benchmark test problems, including in mixed convection conditions where effects of buoyancy are important, specification of thermal boundary conditions at building surfaces remains challenging. This will require the development of a microscale energy budget scheme, in which long-wave and short-wave radiative heat fluxes, convective heat flux, conductive heat flux and latent heat of evaporation are balanced at building surfaces, from which wall temperatures on the surfaces of each building can be computed (see, e.g., Huang et al., 2005), provided that convective heat flux information is supplied by urbanSTREAM.

As shown in Figure 35, flat-terrain assumption was made in the Vancouver-Whistler geographic region in our simulations. In order to further improve the accuracy of our numerical predictions, a digital elevation model (DEM) database, with a good resolution of 50 m or better, is required as an input file to urbanGRID in order to properly represent ground surface topography. The urbanGRID code is capable of generating a body-fitted multi-block mesh in a curvilinear coordinate system.

In the present study, the chlorine gas is considered as a passive scalar governed by an advection-diffusion equation as described by Eq. (34) in the context of URANS approach. However, chlorine should be considered as a dense (or heavy) gas, in which effects of compressibility should be taken into account via the equation of state (EOS) for an ideal gas. It is recommend that the dense-gas capability be included in urbanSTREAM using URANS (e.g., Chan et al., 1987) and/or LES (e.g., Qin et al., 2007) methodologies. The Thorney Island experiment (e.g., McQuaid, 1985) are well documented and can be used as the benchmark test cases to validate the implementation of dense gas (URANS and/or LES) models.

Finally, it is recommend that the transport equation for the concentration variance c2

2 ∂∂cc22∂uc2 ⎡⎤∂⎛⎞∂C +=j ⎢⎥()KK + +2 K⎜⎟ −ε (40) tx xttc x⎜⎟ x ∂∂jj ∂⎣⎦⎢⎥ ∂ j⎝⎠ ∂ j be solved in urbanEU, in addition to Eq. (34), so that a probabilistic model for concentration fluctuations using the clipped-gamma probability density function (PDF) distributions (Yee & Chan, 1997) can be implemented, and the exclusion zone information required by first responders can be quantified more realistically based on a best estimate for this quantity (derived from mean concentration) and the uncertainty in this estimate.

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24 Special Issue of Journal of Wind Engineering & Industrial Aerodynamics 96 (2008) 1832-1842. 19. D.K. Lilly, A Proposed Modification of the Germano Subgrid Scale Closure Method, Physics of Fluids A 4 (1992) 633-635. 20. J. McQuaid, Objectives and Design of the Phase I Heavy Gas Dispersion Trial, Journal of Hazardous Materials 11 (1985) 1-33. 21. E.R. Meinders and K. Hanjalic, Vortex Structure and Heat Transfer in Turbulent Flow Over a Wall-Mounted Matrix of Cubes, International Journal of Heat and Fluid Flow 20 (1999) 255-267. 22. P. Moin, K. Squires, W. Cabot and S. Lee, A Dynamic Subgrid Scale Model for Compressible Turbulence and Scalar Transport, Physics of Fluids A 3 (1991) 2746- 2757. 23. R.D. Moser, J. Kim and N.N. Mansour, Direct Numerical Simulation of Turbulent Channel Flow Up to Reτ = 590, Physics of Fluids 11 (1999) 943-945. 24. B. Niceno, A.D.T. Dronkers and K. Hanjalic, Turbulent Heat Transfer from a Multi- Layered Wall-Mounted Cube Matrix: a Large Eddy Simulation, International Journal of Numerical Methods for Heat and Fluid Flow 23 (2002) 173-185. 25. T.X. Qin, Y.C. Guo and W.Y. Lin, Large Eddy Simulation of Heavy Gas Dispersion Around an Obstacle, Proceeding of 5th International Conference on Fluid Mechanics, Tsinghua University Press & Springer (2007). 26. D. Rundstrom and B. Moshfegh, Investigation of Flow and Heat Transfer of an Impinging Jet in a Cross-Flow for Cooling of a Heated Cube, ASME Journal of Electronic Packaging 128 (2006) 150-156. 27. P. Sagaut, Large Eddy Simulation for Incompressible Flows, Springer-Verlag (2006). 28. J.B. Scarborough, Numerical Mathematical Analysis, Johns Hopkins University Press (1958). 29. J. Smagorinsky, General Circulation Experiments with the Primitive Equations, Monthly Weather Review 91 (1963) 99-165. 30. C.E. Smith, LES Software for the Design of Low Emission Combustion Systems for Vision 21 Plant, CFDRC Report 8321/14 (2004). 31. E.R. van Driest, On the Turbulent Flow Near a Wall, Journal of Aeronautical Science 23 (1956) 1007-1011. 32. B.C. Wang, E. Yee, D. Bergstrom and O. Iida, New Dynamic Subgrid Scale Heat Flux Models for LES of Thermal Convection Based on the General Gradient Diffusion Hypothesis, Journal of Fluid Mechanics 604 (2008) 125-163. 33. E. Yee, F.S. Lien and H. Ji, Technical Description of Urban Microscale Modeling System: Component 1 of CRTI Project 02-0093RD, Technical Report, DRDC Suffield TR 2007-067 (2007). 34. E. Yee and R. Chan, A Simple Model for the Probability Density Function of Concentration Fluctuations in Atmospheric Plumes, Atmospheric Environment 31 (1997) 991-1002.

APPENDIX: README for CDROM

1. Four animation files are stored in the “Animation” directory. 2. Two powerpoint files are stored in the “Powerpoint” directory. 3. The datasets for the E-wind scenario every 30 sec for 60 min are stored in “E- wind (every 30 sec)” directory. 4. The datasets for the E-wind scenario every 10 sec for 30 min are stored in “E- wind (every 10 sec)” directory. 5. The datasets for the E-ESE-SE wind scenario every 30 sec for 60 min are stored in “E- ESE-SE wind (every 30 sec)” directory. 6. The datasets can be read in using either “read_data.f” or “read_data_tecplot.f” Fortran code in the main directory. The code “read_data_tecplot.f” will output data in tecplot format.

26 Sub-channel unit

Y -Z

Channel walls X

Figure 1: Case 6.2 – Schematic of a wall-mounted matrix of cubes.

T=20° C at inlet dT/dx=0 at outlet Epoxy layer (0.1H thickness) dT/dy=0 at top and bottom walls FLAG=C_X

Copper at 75° C FLAG=C_O

Figure 2: Case 6.2 – Illustration of boundary conditions for a conjugate heat transfer problem.

27 TT TTis− fi− qkss=−22 = q f =− k f ΔΔxxsf kTΔ+ x k T Δ x ss f f f s T at interface ⇒ Ti = kxsfΔ+Δ k fs x

Δx f

q Ts Tf Ti

Solid (epoxy) Δx s Fluid (air)

Figure 3: Case 6.2 – Interface condition between an epoxy layer and adjacent fluid (air).

47x47x47 cells: 1 cell in epoxy layer 2

epoxy layer 1.5 copper

1 Y

0.5

0 floor 0 1 X

Figure 4: Case 6.2 – Computational mesh of 47×47×47 nodes on the x-y plane at z/h=0 with an epoxy layer of 1-cell thickness.

28 1

0.5

Z 0

epoxy layer copper

-0.5

-1 -0.5 0 0.5 1 1.5 X

Figure 5: Case 6.2 – Computational mesh of 47×47×47 nodes on the x-z plane at y/h=0.5 with an epoxy layer of 1-cell thickness.

0.7kepoxy

50 T

40 RANS Expt

30 1.5 2 2.5 3 3.5 S

Figure 6: Case 6.2 – Wall temperature distribution at y/h=0.5 on a mesh of 3 47×47×47 nodes with kepoxy =×0.7 0.24 = 0.168 [kg ⋅ m/s ⋅ K].

29 0.7kepoxy

50 T

RANS 40 Expt

30 0 1 2 3 S

Figure 7: Case 6.2 – Wall temperature distribution at z/h=0 on a mesh of 47×47×47 3 nodes with kepoxy =×0.7 0.24 = 0.168 [kg ⋅ m/s ⋅ K].

0.5

Z 0

epoxy layer copper

-0.5

-1 -0.5 0 0.5 1 1.5 X

Figure 8: Case 6.2 – Computational mesh of 51×51×51 nodes on the x-z plane at y/h=0.5 with an epoxy layer of 5-cell thickness.

1.0kepoxy

50 T

40 RANS Expt

30 1.5 2 2.5 3 3.5 S

Figure 9: Case 6.2 – Wall temperature distribution at y/h=0.5 on a mesh of 3 51×51×51 nodes with kepoxy =⋅0.24 [kg m/s⋅ K].

RSM, Rundstrom & Moshfegh (2006) using FLUENT 661,572 cells, in which 63,000 cells in epoxy layer

Figure 10: Case 6.2 – Wall temperature distribution at y/h=0.5 reported by Rundstrom & Moshfegh (2006) using FLUENT.

Figure 11: Case 6.2 – Contours of temperature at y/h=0.5 and z/h=0 cutting planes.

51x51x51 (=132,651) cells: 5 epoxy layer

Z=0 mm

1.5 Y 1

0.5

-1 0 1 2 X

Figure 12: Case 6.2 – Velocity vectors on a x-y plane at z/h=0.

32 Y=7.5 mm

1.5

0.5

0 Z

-0.5

-1

-1.5

-1 -0.5 0 0.5 1 1.5 2 2.5 X

Figure 13: Case 6.2 – Velocity vectors on a x-z plane at y/h=0.5.

Figure 14: Case 6.2 – Velocity profiles at x/h=-0.3 and 1.3 on a vertical x-y plane at z/h=0.

Figure 15: Local Nusselt number distributions at Re=12,600 (Hsieh & Lien, 2005).

+ yEPPln(y )

Figure 16: Illustration of how to improve prediction of velocity gradient du/dy in the buffer-layer region for the wall-function approach.

34 LES (Niceno, Dronkers and Hanjalic, 2002) 427,680 cells

Present, k-ε+WF, 103,823 cells

Figure 17: Case 6.2 – Comparison between the present modified thermal wall- function approach with LES by Niceno et al. (2002) at y/h=0.5.

LES (Niceno, Dronkers and Hanjalic, 2002) 427,680 cells

Present at Z/H=0

Figure 18: Case 6.2 – Comparison between the present modified thermal wall- function approach with LES by Niceno et al. (2002) at z/h=0.

35 Rundstrom & Moshfegh (2006)

Modified WF

Figure 19: Case 6.2 – Comparison between the present modified thermal wall- function approach with RSM by Rundstrom & Moshfegh (2006) at z/h=0.

2πδ

Twc 2δ y

x T wh πδ z

Figure 20: Channel Flow – Flow geometry and coordinate system.

36 25

Gr/Re2=0 20

15 + u

10 DNS hot wall cold wall viscous sublayer 5 log-law layer

100 101 102 y+

Figure 21: Channel Flow – Velocity profiles obtained with the standard RANS k-ε model in comparison with DNS data of Moser et al. (1999) at Re=590 and Gr/Re2=0.

2 20 Gr/Re =0

15 + T

viscous sublayer log-law layer 5 hot wall cold wall

0 100 101 102 y+

Figure 22: Channel Flow –Temperature profiles obtained with the standard RANS k-ε model at Re=590 and Gr/Re2=0.

37 25

Gr/Re2=0 20

15 + u

10 DNS (Gr=0) hot wall (DNS) cold wall (DNS) viscous sublayer (Gr=0) 5 log-law layer (Gr=0) hot wall (URANS)

100 101 102 y+

Figure 23: Channel Flow – Velocity profiles obtained with the standard RANS k-ε model and the present “coarse-grid” DNS (represented by hollow circle and diamond symbols) in comparison with DNS data of Moser et al. (1999) (identified as a dash-dot-dash line) at Re=590 and Gr/Re2=0.

2 20 Gr/Re =0

15 + T

viscous sublayer log-law layer 5 hot wall (DNS) cold wall (DNS) hot wall (URANS) cold wall (URANS) 0 100 101 102 y+

Figure 24: Channel Flow – Temperature profiles obtained with the standard RANS k-ε model in comparison with the present “coarse-grid” DNS (represented by hollow circle and diamond symbols) at Re=590 and Gr/Re2=0.

4 Gr/Re2=0 3.5

2.5 τ 2 u'/u

1.5

1 DNS (Gr=0) DNS 0.5 URANS

0 0 0.2 0.4 0.6 0.8 1 y/δ

Figure 25: Channel Flow – urms-profiles obtained with the standard RANS k-ε model and the present “coarse-grid” DNS (represented by hollow circle symbol) in comparison with DNS data of Moser et al. (1999) (identified as a solid line) at Re=590 and Gr/Re2=0.

Page 352, Sagaut’s book, 3rd ed.

Figure 26: Resolved Reynolds-stress profiles predicted by LES at different Reynolds number (Sagaut, 2006).

39 Sensitivity study: LES vs. DNS CDS for momentum and energy equations

Gr/Re2=0 20

15 + u

10 DNS (Gr=0) hot wall (LES) cold wall (LES) viscous sublayer (Gr=0) 5 log-law layer (Gr=0) hot wall (DNS) cold wall (DNS)

100 101 102 y+

Figure 27: Channel Flow – Velocity profiles obtained with the present “coarse-grid” DNS and LES at Re=590 and Gr/Re2=0.

Sensitivity study: LES vs. DNS CDS for momentum and energy equations

2 20 Gr/Re =0

15 + T

viscous sublayer log-law layer 5 hot wall (LES) cold wall (LES) hot wall (DNS) cold wall (DNS) 0 100 101 102 y+

Figure 28: Channel Flow – Temperature profiles obtained with the present “coarse- grid” DNS and LES at Re=590 and Gr/Re2=0.

40 25

Gr/Re2=213 20

15 + u

10 DNS (Gr=0) hot wall (LES) cold wall (LES) viscous sublayer (Gr=0) log-law layer (Gr=0) 5 hot wall (URANS) cold wall (URANS)

100 101 102 y+

Figure 29: Channel Flow – Velocity profiles obtained with the present LES and URANS approaches at Re=590 and Gr/Re2=213 [DNS data from Moser et al. (1999) at Gr=0 is included to server as a reference curve].

2 20 Gr/Re =213

15 + T

viscous sublayer log-law layer 5 hot wall (LES) cold wall (LES) hot wall (URANS) cold wall (URANS) 0 100 101 102 y+

Figure 30: Channel Flow – Temperature profiles obtained with the present LES and URANS approaches at Re=590 and Gr/Re2=213.

Figure 31: Channel Flow – Contours of time-averaged turbulence kinetic energy superimposed with streamlines obtained with the k-ε URANS model at Re=590 and Gr/Re2=213.

Figure 32: Channel Flow – Contours of time-averaged temperature superimposed with streamlines obtained with the k-ε URANS model at Re=590 and Gr/Re2=213.

Figure 33: Vancouver City – Release location of chlorine at 49 deg, 17.11’ N latitude and 123 deg, 6.513’ W longitude in the Vancouver-Whistler geographical region.

Outer region

Release point

Inner region

Figure 34: Vancouver City – Computational domain consisting of building-aware “inner region” and “outer region”, in which effects of virtual buildings are represented by a distributed drag-force approach (Lien et al., 2005).

-1 Drag coefficient: CdA=0.155 m

Figure 35: Vancouver City – Resolved and virtual buildings are highlighted by red and gray colors, respectively.

•V10≈5 mph=2.235 m/s is a reasonable wind speed near Vancouver-Whistler region •The exponent p is ≈0.3 in suburbs.

Figure 36: Typical wind profiles over various terrain types.

44 5 mi/h

Figure 37: Wind speed and direction at the CWWA weather station shown in Figure 38 between 10/16/2008 and 10/17/2008.

CWWA

Figure 38: Location of the CWWA weather station at 49.35 deg N latitude and 123.18333 deg W longitude in Vancouver.

45 800

600

400 Z[m]

200

0 0 0.5 1 1.5 2 2.5 3 3.5 4 Wind speed [m/s]

800

600

400 Z[m]

200

0 0 0.1 0.2 0.3 0.4 0.5 TKE [m2/s2]

800

600

400 Z[m]

200

0 0 0.0005 0.001 0.0015 0.002 0.0025 0.003 2 3 TED [m /s ] Figure 39: Vancouver City – Profiles of velocity, k and ε at inlet plane.

46 Sampler and source locations

Figure 40: Vancouver City – Sampler and source locations.

IM 1st-order vs. 2nd-order time-stepping schemes dt=2 sec, UMIST, at samp1

4.5E-05

4E-05

3.5E-05 1st-order 2nd-order 3E-05 2nd/1st-order ] 3 2.5E-05

2E-05 C[kg/m

1.5E-05

1E-05

5E-06

0 0 5 10 15 20 Time [min]

Figure 41: Vancouver City – Time history of concentration at the 1st sampler location obtained with the 1st-order, 2nd-order and hybrid 1st/2nd-order time- stepping schemes for the E-wind scenario.

47 IM 1st-order vs. 2nd-order time-stepping schemes dt=2 sec, UMIST, at samp1

1000

900 overshoot

800

700

600 1st-oder 500 2nd-order 2nd/1st-order 400 To tal Mas s [kg ] 300

200

100

0 0 5 10 15 20 Time [min]

Figure 42: Vancouver City – Time history of total mass contained in the computational domain obtained with the 1st-order, 2nd-order and hybrid 1st/2nd- order time-stepping schemes for the E-wind scenario.

IM 2nd/1st-order time-stepping schemes dt=2 &10 sec, UMIST, at samp1

4.5E-05

4E-05

3.5E-05 dt=2 sec dt=10 sec 3E-05 ] 3 2.5E-05

2E-05 C[kg/m

1.5E-05

1E-05

5E-06

0 0 5 10 15 20 Time [min]

Figure 43: Vancouver City – Time history of concentration at the 1st sampler location obtained with the hybrid 1st/2nd-order time-stepping schemes and Δt=2 and 10 sec for the E-wind scenario.

48 IM 2nd/1st-order time-stepping schemes dt=10 sec, UMIST, at samp1 to samp3 (linear scale)

4.5E-05

4E-05

3.5E-05

3E-05 samp1 ]

3 samp2 2.5E-05 samp3

2E-05 C[kg/m

1.5E-05

1E-05

5E-06

0 0 5 10 15 20 25 30 35 40 45 50 55 60 Time [min]

Figure 44: Vancouver City – Time history of concentration at 3 sampler locations with C [kg/m3] presented on a linear scale for the E-wind scenario.

IM 2nd/1st-order time-stepping schemes dt=10 sec, UMIST, at samp1 to samp3 (logarithmic scale)

10-5

samp1 samp2

] samp3 3

10-6 C[kg/m

10-7

5 1015202530354045505560 Time [min]

Figure 45: Vancouver City – Time history of concentration at 3 sampler locations with C [kg/m3] presented on a logarithmic scale for the E-wind scenario.

49 IM 2nd/1st-order time-stepping schemes dt=10 sec, UMIST

800

700

600

500

400

Total Mas s300 [kg]

200

100

0 0 5 10 15 20 25 30 35 40 45 50 55 60 Time [min]

Figure 46: Vancouver City – Time history of total mass contained in the computational domain for the E-wind scenario.

Figure 47: Vancouver City – Contours of concentration on a logarithmic scale at t=5 min for the E-wind scenario.

Figure 48: Vancouver City – Contours of concentration on a logarithmic scale at t=10 min for the E-wind scenario.

Figure 49: Vancouver City – Contours of concentration on a logarithmic scale at t=20 min for the E-wind scenario.

Figure 50: Vancouver City – Contours of concentration on a logarithmic scale at t=60 min for the E-wind scenario.

Figure 51: . Vancouver City – Contours of dosage on a logarithmic scale at t=1 min for the E-wind scenario.

Figure 52: Vancouver City – Contours of dosage on a logarithmic scale at t=15 min for the E-wind scenario.

Figure 53: Vancouver City – Contours of concentration on a logarithmic scale at t=4 min for the E-ESE-E wind scenario.

Figure 54: Vancouver City – Contours of concentration on a logarithmic scale at t=10 min for the E-ESE-E wind scenario.

Figure 55: Vancouver City – Contours of concentration on a logarithmic scale at t=30 min for the E-ESE-E wind scenario.

Figure 56: Vancouver City – Contours of dosage on a logarithmic scale at t=4 min for the E-ESE-SE wind scenario.

Figure 57: Vancouver City – Contours of dosage on a logarithmic scale at t=10 min for the E-ESE-SE wind scenario.

Figure 58: Vancouver City – Contours of dosage on a logarithmic scale at t=30 min for the E-ESE-SE wind scenario.

56 UNCLASSIFIED SECURITY CLASSIFICATION OF FORM (highest classification of Title, Abstract, Keywords)

DOCUMENT CONTROL DATA (Security classification of title, body of abstract and indexing annotation must be entered when the overall document is classified)

1. ORIGINATOR (the name and address of the organization 2. SECURITY CLASSIFICATION preparing the document. Organizations for who the document (overall security classification of the document, including special was prepared, e.g. Establishment sponsoring a contractor's warning terms if applicable) report, or tasking agency, are entered in Section 8.) Waterloo CFD Engineering Consulting Inc. Unclassified 534 Paradise Crescent Waterloo ON N2L 3G1

3. TITLE (the complete document title as indicated on the title page. Its classification should be indicated by the appropriate abbreviation (S, C or U) in parentheses after the title). Urban Flow Model Development for “Unified Interoperability Solution Set to Support CONOPS Framework Development for Municipal, Provincial and Federal Collaboration to CBRN Response”

4. AUTHORS (Last name, first name, middle initial. If military, show rank, e.g. Doe, Maj. John E.) Lien, F.-S. and Ji, H.

5. DATE OF PUBLICATION (month and year of publication of 6a. NO. OF PAGES (total containing 6b. NO. OF REFS (total document) information, include Annexes, cited in document) October 2008 Appendices, etc) 56 34

7. DESCRIPTIVE NOTES (the category of the document, e.g. technical report, technical note or memorandum. If appropriate, enter the type of report, e.g. interim, progress, summary, annual or final. Give the inclusive dates when a specific reporting period is covered.) Final contract report

8. SPONSORING ACTIVITY (the name of the department project office or laboratory sponsoring the research and development. Include the address.) Defence Research and Development Canada (CRTI)

9a. PROJECT OR GRANT NO. (If appropriate, the applicable 9b. CONTRACT NO. (If appropriate, the applicable number under research and development project or grant number under which the document was written.) which the document was written. Please specify whether project or grant.) W7702-06R136 CRTI-05-0058TD

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13. ABSTRACT (a brief and factual summary of the document. It may also appear elsewhere in the body of the document itself. It is highly desirable that the abstract of classified documents be unclassified. Each paragraph of the abstract shall begin with an indication of the security classification of the information in the paragraph (unless the document itself is unclassified) represented as (S), (C) or (U). It is not necessary to include here abstracts in both official languages unless the text is bilingual).

To support the development of an Operational Architecture that captures and identifies municipal- provincial-federal organizations, capabilities, processes and command and control relationships focused on CBRN response, specific CBRN scenarios involving the release of a CBRN agent over the Vancouver- Whistler geographic area (requiring realistic predictions of transport, dispersion, deposition, and fate of a CBRN agent released into the complex flows of a cityscape) has been executed in order to provide the resulting urban flow and dispersion data sets for incorporation into the Operational Architecture developed under CRTI-05-0058TD project. In addition, enhancement of a flow model “urbanSTREAM” developed under CRTI-02-0093RD has been achieved by adding an energy equation into the system. Validation against a range of benchmark test problems in both forced and mixed convection conditions, using either the Reynolds-averaged Navier-Stokes (RANS) or Large Eddy Simulation (LES) approach, has been performed. Agreement between the present numerical predictions and experimental data (when available) is very good for most cases examined here. There is still a scope for further improvement, which includes, e.g., development of a model for heavy gas dispersion in a built-up environment, incorporation of radiative heat flux into the energy budget for determination of building surface temperature, and validation against field trials conducted in the Joint Urban 2003 (JU2003) experiment, for which thermal effects (e.g., positive and negative buoyancy) on the flow and dispersion in the urban environment are assessed to be important.

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Computational Fluid Dynamics Urban flow Urban dispersion

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