One of the major uncertainties in dispersion-based simulations at the local scale is the representation of terrain effects. The aim of the current study is to quantify this type of uncertainty for dose-rate predictions over a homogeneous forest cover. At the investigated nuclear reactor, situated in a forested environment, ambient gamma-dose-rate data from routine Ar-41 releases are available in the first 300 m from the release point. We develop a forest parameterization that meets the site-specific needs, and integrate it in different dispersion models. Using different terrain-roughness parameterizations, we compare three types of models: a dispersion model driven by a Langevin equation, an advection-diffusion model, and a Gaussian plume model as a special case of the latter one. We find that all models are biased up to a factor of four, partly due to an uncertain source strength. The dose-rate uncertainty due to the model choice is a factor of 2.2 for a stack release and a factor of 14 for a ground release.
Kernel smoothers are often used in Lagrangian particle dispersion simulations to estimate the concentration distribution of tracer gasses, pollutants etc. Their main disadvantage is that they suffer from the curse of dimensionality, i.e., they converge at a rate of 4/(d+4) with d the number of dimensions. Under the assumption of horizontally homogeneous meteorological conditions, we present a kernel density estimator that estimates a 3D concentration field with the faster convergence rate of a 1D kernel smoother, i.e., 4/5. This density estimator has been derived from the Langevin equation using path integral theory and simply consists of the product between a Gaussian kernel and a 1D kernel smoother. Its numerical convergence rate and efficiency are compared with that of a 3D kernel smoother. The convergence study shows that the path integral-based estimator has a superior convergence rate with efficiency, in mean integrated squared error sense, comparable with the one of the optimal 3D Epanechnikov kernel. Horizontally homogeneous meteorological conditions are often assumed in near-field range dispersion studies. Therefore, we illustrate the performance of our method by simulating experiments from the Project Prairie Grass data set.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.