Dose estimation for complex urban environments using RUGUD, SWORD, ADVANTG and Denovo

This paper proposes a novel dose estimation methodology using a combination of RUGUD, SWORD, ADVANTG, and Denovo to estimate radiation doses to populations residing in complex urban environments. Previous methodologies use MCNP in combination with the Hazus dataset and in-house developed tools to represent the urban environment with highly detailed structures for Monte Carlo calculations. In comparison, the proposed method uses a combination of software and datasets that are being actively developed for governmental users to mesh the urban geometry for discrete ordinates calculations. In the proposed method, the urban geometry is represented by RUGUD-generated CTDB, SWORD is used to add scenario-specific information to the geometry, and subsequently ADVANTG and Denovo are used to perform a set of discrete ordinates calculations to determine a flux map resulting from the source and geometry. Subsequent processing tools can be used to convert the relevant flux maps to doses and other quantities of interest. The proposed methodology requires minimal user input and a relatively short computation time (∼15 hours wall time); it represents an alternative way to compute flux and dose in urban environments. In this paper, the RUGUD-SWORD-ADVANTG-Denovo toolchain is first described. Then, a use-case scenario is presented to examine the dose to the outdoor population associated with a hypothetical nuclear detonation in Washington, D.C. To gauge the accuracy of the discrete ordinates results, the dose distribution predicted by Denovo is compared to the distribution estimated using continuous-energy MCNP simulations. Finally, the limitations and the future directions of this work are discussed.