Introduction

A new dispersion modeling system based on the well-used FORTRAN-based QUIC (Quick Urban and Industrial Complex) dispersion modeling system originally developed by the University of Utah and Los Alamos National Laboratory [1], has been under development as collaboration between the University of Utah, the University of Minnesota, Duluth and Pukyong National University. Quick Environmental Simulation (QES) is a microclimate simulation platform for computing 3D environmental scalars in urban areas and over complex terrain. The modules are the following:

  • QES-Winds is the new wind model computing divergence-free steady-state 3D wind field in complex domain.

  • QES-TURB is a stand-alone turbulence model that computes turbulence fields from the calculated wind field in QES-Winds.

  • QES-Plume is a stand-alone Lagrangian dispersion model with the ability to calculate spatially and temporally varying scalar concentrations.

  • QES-Fire is a fire-spread model.

  • QES-Transport is a transport model (to be implemented).

Figure below shows a schematic of QES system and how different elements of the system interact with each other.

_images/QES_chart.png

Schematic of the QUIC EnvSim system and the relationship between different elements of the system including data flow from one element to the other

The QES code is a low-computational-cost framework designed to compute high-resolution wind and concentration fields in complex atmospheric-boundary-layer environments. QES is written in C++ and NVIDIA’s CUDA for Graphics Processing Unit (GPU) acceleration. The code uses NVIDIA’s dynamic parallelism API to substantially accelerate simulations. QES requires a NVIDIA GPU with Compute Capability of 7.0 (or higher).

QES-Winds

QES-Winds is a fast-response 3D diagnostic urban wind model using a mass-conserving wind-field solver [2]. QES-Winds uses a variational analysis technique to ensure the conservation of mass rather than slower yet more physics-based solvers that include the conservation of momentum. QES-Winds minimizes the difference between an initial wind field that is specified using empirical parameterizations and the final wind field. This method requires the solution of a Poisson equation for Lagrange multipliers. The Poisson equation is solved using the Successive Over-Relaxation (SOR) method (an iterative solver), which is a variant of the Gauss-Seidel method with more rapid convergence.

QES-Turb

QES-Turb is a turbulence model based on Prandtl’s mixing-length and Boussinesq eddy-viscosity hypotheses. QES-Turb computes the stress tensor using local velocity gradients and some emprical non-local parameterizations.

QES-Plume

QES-Plume is a stochastic Lagrangian dispersion model using QES-Winds mean wind field and QES-Turb turbulence fields. QES-Plume solves the generalized Langevin equations to compute the fluctuations of the particle in the turbulent flow fluid. A time-implicit integration scheme is used to solve the Langevin equation, eliminating ’rogue’ trajectories. The particles are advanced using a forward Euler scheme. QES-Plume is also a stand-alone dispersion model that can run using fields from diverses sources such as RANS or LES models.

QES-Fire

QES-Fire is a microscale wildfire model coupling the fire front to microscale winds. The model consists of a simplified physics rate of spread model, a kinematic plume-rise model, and a mass-consistent wind solver. The QES-Fire module is currently not publicly available.

Publications

  • QES-Winds: Dynamic Parallelism Solver

    Bozorgmehr, B., Willemsen, P., Gibbs, J.A., Stoll, R., Kim, J.-J., Pardyjak, E.R., 2021. Utilizing dynamic parallelism in CUDA to accelerate a 3D red-black successive over relaxation wind-field solver. Environ Modell Softw 137, 104958. https://doi.org/10.1016/j.envsoft.2021.104958

  • QES-Winds: Isolated Tree Model

    Margairaz, F., Eshagh, H., Hayati, A.N., Pardyjak, E.R., Stoll, R., 2022. Development and evaluation of an isolated-tree flow model for neutral-stability conditions. Urban Clim 42, 101083. https://doi.org/10.1016/j.uclim.2022.101083

  • QES-Winds: Raw-Oriented Canopy Model

    Ulmer, L., Margairaz, F., Bailey, B.N., Mahaffee, W.F., Pardyjak, E.R., Stoll, R., 2022. A fast-response, wind angle-sensitive model for predicting mean winds in row-organized canopies. Agric. For. Meteorol. 329, 109273. https://doi.org/10.1016/j.agrformet.2022.109273

  • QES-Plume: Lagrangian Dispersion Model

    Margairaz, F., Singh, B., Gibbs, J.A., Atwood, L., Pardyjak, E.R., Stoll, R., 2023. QES-Plume v1.0: a Lagrangian dispersion model. Geosci Model Dev 16:5729–5754. https://doi.org/10.5194/gmd-16-5729-2023

  • QES-Fire: wildfire model

    Moody, M.J., Gibbs, J.A., Krueger, S., Mallia, D., Pardyjak, E.R., Kochanski, A.K., Bailey, B.N., Stoll, R., 2022. QES-Fire: a dynamically coupled fast-response wildfire model. Int J Wildland Fire 31, 306–325. https://doi.org/10.1071/wf21057

  • QES: Turbulence and Passive scalar transport in Raw-Oriented Canopy

    Ulmer L., Margairaz F., Mahaffee W.F., Stoll R., 2024. A fast-response model of turbulence and passive scalar transport in row-organized canopies. Agric For Meteorol 349:109919. https://doi.org/10.1016/j.agrformet.2024.109919

Acknowledgements

This work was partly supported by grants from:

  • The National Institute of Environment Research (NIER), funded by the Ministry of Environment (MOE) of the Republic of Korea (NIER-SP2019-312). In addition, we would like to acknowledge Dr. Jae-Jin Kim from Department of Environmental Atmospheric Sciences, Pukyong National University, Republic of Korea, as the main Principal Investigator (PI) on the grant from the National Institute of Environment Research (NIER).

  • The United States Department of Agriculture National Institute for Food and Agriculture Specialty Crop Research Initiative Award No. 2018-03375.

  • The United States Department of Agriculture Agricultural Research Service through Research Support Agreement 58-2072-0-036.