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Review of Urban Modeling Program at LLNL CRTI-02-0093RD Project Review Meeting Canadian Meteorological Centre August 22-23, 2006.

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Presentation on theme: "Review of Urban Modeling Program at LLNL CRTI-02-0093RD Project Review Meeting Canadian Meteorological Centre August 22-23, 2006."— Presentation transcript:

1 Review of Urban Modeling Program at LLNL CRTI-02-0093RD Project Review Meeting Canadian Meteorological Centre August 22-23, 2006

2 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

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

4 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

5 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 FEM3MP Parallel adaptive mesh support SAMRAI Rapid geometry to mesh capability Overture  Automatic mesh construction from building datasets.  Geometrically complex buildings and cityscapes  Diverse urban environments: stadiums, arenas, subways, etc.

6 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 normal BCs applied at boundary point closest to ghost point T. Chow

7 Momentum Equations: (f roof : roof fraction, c d : urban drag coef., a(z): roof surface area density profile) Urban Canopy Parameterization (UCP) TKE Equation: Potential Temperature Equation: Roof Surface Energy Equation: Street Canyon Roof-Top Anthropogenic f urb = f roof + f cnyn (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

8 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 SCALE 4km grid size URBAN SCALE 1m grid size Coupling will provide accurate boundary conditions for urban scale simulations MESOSCALE

9 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 source location

10 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)

11 Event Reconstruction - Computational framework will support multiple stochastic algorithms, models, and platforms Output Handler Input Handler MCMC SMC HYBRID MULTI-RES. Informed prior and proposal sampling with nonlinear optimization 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 PCworkstation Massively parallel system


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