19,901 research outputs found
Air pollution modelling using a graphics processing unit with CUDA
The Graphics Processing Unit (GPU) is a powerful tool for parallel computing.
In the past years the performance and capabilities of GPUs have increased, and
the Compute Unified Device Architecture (CUDA) - a parallel computing
architecture - has been developed by NVIDIA to utilize this performance in
general purpose computations. Here we show for the first time a possible
application of GPU for environmental studies serving as a basement for decision
making strategies. A stochastic Lagrangian particle model has been developed on
CUDA to estimate the transport and the transformation of the radionuclides from
a single point source during an accidental release. Our results show that
parallel implementation achieves typical acceleration values in the order of
80-120 times compared to CPU using a single-threaded implementation on a 2.33
GHz desktop computer. Only very small differences have been found between the
results obtained from GPU and CPU simulations, which are comparable with the
effect of stochastic transport phenomena in atmosphere. The relatively high
speedup with no additional costs to maintain this parallel architecture could
result in a wide usage of GPU for diversified environmental applications in the
near future.Comment: 5 figure
An inverse Gaussian plume approach for estimating atmospheric pollutant emissions from multiple point sources
A method is developed for estimating the emission rates of contaminants into
the atmosphere from multiple point sources using measurements of particulate
material deposited at ground level. The approach is based on a Gaussian plume
type solution for the advection-diffusion equation with ground-level deposition
and given emission sources. This solution to the forward problem is
incorporated into an inverse algorithm for estimating the emission rates by
means of a linear least squares approach. The results are validated using
measured deposition and meteorological data from a large lead-zinc smelting
operation in Trail, British Columbia. The algorithm is demonstrated to be
robust and capable of generating reasonably accurate estimates of total
contaminant emissions over the relatively short distances of interest in this
study
A Comparison of Two Shallow Water Models with Non-Conforming Adaptive Grids: classical tests
In an effort to study the applicability of adaptive mesh refinement (AMR)
techniques to atmospheric models an interpolation-based spectral element
shallow water model on a cubed-sphere grid is compared to a block-structured
finite volume method in latitude-longitude geometry. Both models utilize a
non-conforming adaptation approach which doubles the resolution at fine-coarse
mesh interfaces. The underlying AMR libraries are quad-tree based and ensure
that neighboring regions can only differ by one refinement level.
The models are compared via selected test cases from a standard test suite
for the shallow water equations. They include the advection of a cosine bell, a
steady-state geostrophic flow, a flow over an idealized mountain and a
Rossby-Haurwitz wave. Both static and dynamics adaptations are evaluated which
reveal the strengths and weaknesses of the AMR techniques. Overall, the AMR
simulations show that both models successfully place static and dynamic
adaptations in local regions without requiring a fine grid in the global
domain. The adaptive grids reliably track features of interests without visible
distortions or noise at mesh interfaces. Simple threshold adaptation criteria
for the geopotential height and the relative vorticity are assessed.Comment: 25 pages, 11 figures, preprin
- …