Local Area Forcing of Urban-to Regional-Scale Atmospheric Dispersion: Exchanging Fluxes in A Multiscale Environment

摘要

Urban areas are likely locations for release of toxic material into the atmosphere, whether by accident or terrorist act. Both the Department of Energy, through the Chemical and Biological National Security Program, and the Department of Defense, through the Defense Threat Reduction Agency, are supporting simulation and experimental efforts to develop urban modeling capabilities. These developed tools would be used in response to the release of toxic material in populated urban centers. We are developing a capability to predict the detailed flow and dispersion in and around population centers, to address issues of response to release of toxic agents into the atmosphere. Due to the complexity of these problems and their great demand on computing power, the scientific community has not had the ability to address the urban problem previously. LLNL's unique combination of modeling capability and access to terascale computing resources allows us to address such problems. However, in regions with such small scale features and with heterogeneous building configurations and complex terrain, classical approaches with simplifying assumptions are no longer valid. Turbulence closure approximations that are employed in models with 5km resolution are inappropriate when the resolution is 3 orders of magnitude finer. Also, closure assumptions based on vertical transfer are likely not appropriate when the nearest surface is a building face rather than the ground. The goal of the work described here is to advance the theoretical and empirical basis for transport and dispersion at urban and local scales. This would allow us to extend NARAC's capability for realistic dispersion prediction for smaller scales such as in urban areas. Our objectives within this goal and as part of the mid-year call for proposals are to: (1) test sub-grid scale (SGS) turbulence approximations in the high-resolution building-scale model FEM3MP, and determine sensitivity of dispersion predictions to these approximations; and (2) design procedures for information transfer from a regional scale model COAMPS to the FEM3MP model for prediction purposes, including parallel implementation. Circulation in the atmosphere is characterized by high Reynolds number, implying that many scales of motion exist; thus direct numerical simulation is not feasible and a Large Eddy Simulation (LES) approach is necessary. In the LES approach, the largest eddies are directly simulated but sub-grid scale motions are modeled. The size of the resolved eddies changes as the resolution of a host model changes, but often the SGS model remains unchanged. The resolved flow features and the turbulence intensity depend on that host model resolution. Empirical Monin-Obukhov similarity profiles for neutral conditions provide a good test for the sensitivity to host model resolution. We investigated resolution issues in the sub-grid scale modeling of turbulence and in the coupling and nesting of models from larger- to smaller-scale domains. These different but related issues are critical in accurately simulating circulation at small scales such as around buildings, street canyons and urban areas. Two numerical models were used in this study: a finite element model, FEM3MP, using a Smagorinsky eddy viscosity formulation for the SGS, and a mesoscale numerical weather prediction model, COAMPS. The coupling of COAMPS to FEM3MP is a one-way nesting, i.e. information passes only from COAMPS to FEM3MP. Time dependent profiles of velocity and turbulence kinetic energy from COAMPS are used to generate, and interpolate if necessary, to provide initial conditions and boundary conditions for FEM3MP. During the course of this project, we (1) developed protocols and procedures for coupling COAMPS to FEM3MP, (2) conducted mesh refinement studies, (3) tested the COAMPS-FEM3MP coupling for fidelity of the information transfer, and (4) compared the FEM3MP LES simulations with data from the Prairie Grass Field Experiment. Comparisons to that dataset included mean horizontal wind profiles, turbulence intensity, and concentration of released tracer as it disperses downwind from the source, as presented in the next section.