Review of Urban Modeling Program at LLNL

CRTI-02-0093RD Project Review Meeting

Canadian Meteorological Centre

August 22-23, 2006

FEM3MP – An Urban Dispersion Model

• Massively parallelized CFD model based on solving 3D time- dependent Navier-Stokes equations for large-scale problems

• Finite element method for effective treatment of terrain, complex geometries and flows

• Simple and advanced turbulence closures

• Sub-models for canopies, aerosols, UV radiation decay, surface heating, etc.

• Validated against data from wind tunnel and urban field experiments

Governing Equations

• Plus Smagorinsky SGS turbulence model & Plus Smagorinsky SGS turbulence model & wall damping function by Piomelli, et al. (1987)wall damping function by Piomelli, et al. (1987)

)(

)(

0

cuu

u

uuuup

uu

jjxjxc

jtc

jjx

idjijxixjxiu

jit C

FEM3MP Applications

• Model flow and dispersion in urban areas• Perform simulations to optimize utilization of resources

in the design of field experiments• Generate realistic scenarios to support emergency

planners in planning of special events• Use model results to provide improved parameterization

in larger scale models• Source inversion for contaminant plume dispersion in

urban areas

AUDIM – LLNL’s Next-generation Urban Dispersion Modeling Capability

AUDIM

Adaptive Urban Dispersion Integrated Model

Adaptive mesh refinement for enhanced fidelity: release points, building entrances, etc.

Complex release scenarios: moving sources, etc.

CFD code for urban dispersion

FEM3MPParallel adaptive

mesh support

SAMRAIRapid geometry to

mesh capability

Overture

Automatic mesh construction from building datasets.

Geometrically complex buildings and cityscapes

Diverse urban environments: stadiums, arenas, subways, etc.

Immersed Boundary Formulation

• Ghost-cell method of Tseng and Ferziger (2003)– set values at “ghost points” inside boundary using interpolation from

outside neighbors

– interpolation to enforce conditions at boundary– Conditions applied: u = 0, dp/dn = 0

Immersed boundary

Ghost point

Nearest neighbors

Enforce zero velocities on the immersed boundary

normalBCs applied at boundary point

closest to ghost point

T. Chow

,)()1( uuzacft

udroof

,)()2( vvzacft

vdroof

,)()3( wwzacft

wdroof

Momentum Equations:

(froof: roof fraction, cd: urban drag coef., a(z): roof surface area density profile)

Urban Canopy Parameterization (UCP)

TKE Equation:

),()()(

)4(333wvuzacf

t

TKEdroof

Potential Temperature Equation:

zRff

Bz

qf

zRf

c ptNc

roofurburb

urbN

urb )[()1

1()1{(1

)5(1

]},)(C

qczbf

roof

roofproof

Roof Surface Energy Equation:

).()()1()6(_

4 TTVccTRRqroof roofroofdproofLWSW

Street Canyon

Roof-Top

Anthropogenic

furb= froof+ fcnyn

(Chin et al., 2005, MWR)

Key urban surface and building infrastructure parameters of UCP are derived from USGS land-use data using a table conversion method.

urban thermal properties

anthropogenic heating

canopy heating & cooling

drag

radiation attenuation

turbulence production

radiation trapping

Seamless Coupling Between Regional and Urban Scale Models

Urban scale models resolve small scale flows which must be parameterized in large scale models – considerable current scientific interest

Downtown

REGIONAL SCALE4km grid size

URBAN SCALE

1m grid size

Coupling will provide accurate boundary conditions for urban scale simulations

MESOSCALE

Possible release locations are identified to within a ~25m x 150m area including the actual source

Inflow wind Sensors ( )

Markov chain sampling

Possible source locations

Actual sourcelocation

Histogram shows simultaneous determination of release rate to within 10% of actual value

Actual release rate

Computational approach uses Green’s function methodology• 2560 pre-computed unit source simulations • Total CPU = 13,056hrs (12+ hrs on 1024 2.4 GHz Xeon processors)• Event reconstruction requires ~2 minutes (20000 Markov iterations)

Event Reconstruction - Computational framework will support multiple stochastic algorithms, models, and platforms

Output Handler

Input Handler

MCMC SMC

HYBRIDMULTI-RES.

Informed prior and proposal sampling

with nonlinearoptimization

Job Distributor

MODEL DRIVER

Model Handler

Input Handler

Output Handler

Urban Puff Model

Output Handler

Input Handler

3D Particle Model

Output Handler

Input Handler

2D Puff Model Urban CFD Model

STOCHASTIC TOOLS

...

SYSTEM HARDWARE

PC workstation

Massively parallel system