Computational implementation of sampling-based approaches to the calculation of expected dose in performance assessments for the proposed high-level radioactive waste repository at Yucca Mountain, Nevada

A deep geologic repository for high-level radioactive waste is under development by the US Department of Energy (DOE) at Yucca Mountain (YM), Nevada. As mandated in the Energy Policy Act of 1992, the US Environmental Protection has promulgated public health and safety standards (i.e., 40 CFR Part 197) for the YM repository, and the US Nuclear Regulatory Commission has promulgated licensing standards (i.e., 10 CFR Parts 2, 19, 20, etc.) consistent with 40 CFR Part 197 that the DOE must establish are met in order for the YM repository to be licensed for operation. Important requirements in 40 CFR Part 197 and 10 CFR Parts 2, 19, 20, etc. relate to the determination of expected (i.e., mean) dose to a reasonably maximally exposed individual (RMEI) and the incorporation of uncertainty into this determination. This paper is the second part of a two-part presentation on the determination of expected dose to the RMEI in the context of 40 CFR Part 197 and 10 CFR Parts 2, 19, 20, etc. The first part of this presentation is contained in the preceding paper, “Conceptual Basis for the Definition and Calculation of Expected Dose in Performance Assessments for the Proposed High-Level Radioactive Waste Repository at Yucca Mountain, Nevada”, and describes how general and typically nonquantitative statements in 40 CFR Part 197 and 10 CFR Parts 2, 19, 20, etc. can be given a formal mathematical structure that facilitates both the calculation of expected dose to the RMEI and the appropriate separation in this calculation of aleatory uncertainty (i.e., randomness in the properties of future occurrences such as igneous and seismic events) and epistemic uncertainty (i.e., lack of knowledge about quantities that are poorly known but assumed to have constant values in the calculation of expected dose to the RMEI). The present paper describes and illustrates sampling-based procedures for the estimation of expected dose and the determination of the uncertainty in estimates for expected dose.