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Review of Urban Modeling Program at LLNL

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

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Review of Urban Modeling Program at LLNL

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  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)

  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. Overture FEM3MP SAMRAI Rapid geometry to mesh capability Parallel adaptive mesh support CFD code for urban dispersion AUDIM Adaptive Urban Dispersion Integrated Model • Adaptive mesh refinement for enhanced fidelity: release points, building entrances, etc. • Complex release scenarios: moving sources, etc. • Automatic mesh construction from building datasets. • Geometrically complex buildings and cityscapes • Diverse urban environments: stadiums, arenas, subways, etc. AUDIM – LLNL’s Next-generation Urban Dispersion Modeling Capability

  6. Enforce zero velocities on the immersed boundary Nearest neighbors Immersed boundary normal Ghost point 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 BCs applied at boundary point closest to ghost point T. Chow

  7. Urban Canopy Parameterization (UCP) turbulence production Momentum Equations: radiation attenuation drag canopy heating & cooling anthropogenic heating radiation trapping urban thermal properties TKE Equation: Potential Temperature Equation: furb= froof+ fcnyn Street Canyon Anthropogenic Roof-Top Roof Surface Energy Equation: (froof: roof fraction, cd: urban drag coef., a(z): roof surface area density profile) Key urban surface and building infrastructure parameters of UCP are derived from USGS land-use data using a table conversion method. (Chin et al., 2005, MWR)

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

  9. Actual source location Possible source locations Possible release locations are identified to within a ~25m x 150m area including the actual source Markov chain sampling Inflow wind Sensors ( )

  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. Job Distributor Urban Puff Model Input Handler Input Handler Input Handler Input Handler Model Handler Output Handler Output Handler Output Handler Output Handler Event Reconstruction - Computational framework will support multiple stochastic algorithms, models, and platforms MODEL DRIVER STOCHASTIC TOOLS MCMC SMC HYBRID MULTI-RES. Informed prior and proposal sampling withnonlinear optimization 3D Particle Model Urban CFD Model 2D Puff Model ... PC workstation SYSTEM HARDWARE Massively parallel system

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