United States Nuclear Regulatory Commission - Protecting People and the Environment

MACCS Best Practices as Applied in the State-of-the-Art Reactor Consequence Analyses (SOARCA) Project (NUREG/CR-7009)

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Publication Information

Manuscript Completed: June 2014
Date Published:
August 2014

Prepared by:
Nathan Bixler, Joseph Jones, Doug Osborn, and Scott Weber

Sandia National Laboratories
Albuquerque, New Mexico 87185
Operated for the U.S. Department of Energy

Jonathan Barr, NRC Project Manager

Prepared for:
Office of Nuclear Regulatory Research
U.S. Nuclear Regulatory Commission
Washington, DC 20555-0001

NRC Job Code N6306

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Abstract

The evaluation of accident phenomena and the offsite consequences of severe reactor accidents has been the subject of considerable research by the U.S. Nuclear Regulatory Commission (NRC) over the last several decades. By applying modern analysis tools and techniques, a body of knowledge regarding the realistic outcomes of severe reactor accidents has been developed. The integrated modeling of accident progression and offsite consequences in the State-of-the-Art Reactor Consequence Analyses (SOARCA) project has created best modeling practices drawn from the collective wisdom of the severe accident analysis community. The objective of this document is to describe the consequence model improvements, modeling approach, parameter selection, and consequence analyses that support the SOARCA project and also explain the significance of the modeling improvements and approaches. This document presents a compilation of experience from using the MELCOR Accident Consequence Code System (MACCS) to model the offsite consequences, in terms of health effect risk, for NUREG-1935, “State-of-the-Art Reactor Consequence Analyses (SOARCA) Report.” This provides a description of how MACCS modeling capabilities were used to represent important aspects of radionuclide atmospheric transport, emergency response, and dose response to radiation exposure. Additionally, a description of choices among certain alternative modeling options and input parameters is provided.

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