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SummaryHigh-level radioactive waste from nuclear fuels processing is stored in underground waste storage tanks located in the tank farms on the Hanford Site. Waste in tank storage contains low concentrations of fissile isotopes, primarily U-23 5 and Pu-239. The composition and the distribution of the waste components within the storage environment is highly complex and not subject to easy investigation. An important safety concern is the preclusion of a self-sustaining neutron chain reaction, also known as a nuclear criticality. A thorough technical evaluation of processes, phenomena, and conditions is required to make sure that subcriticality will be ensured for both current and future tank operations. Subcriticality limits must be based on considerations of tank processes and take into account all chemical and geometrical phenomena that are occurring in the tanks. The important chemical and physical phenomena are those capable of influencing the mixing of fissile material and neutron absorbers such that the degree of subcriticality could be adversely impacted.This report describes a logical approach to resolving the criticality safety issues in the Hanford waste tanks. The approach uses a structured logic diagram (SLD) to identify the characterization needed to quantify risk. The scope of this section of the report is limited to those branches of logic needed to quantify the risk associated with a criticality event occurring. The process is linked to a conceptual model that depicts key modes of failure which are linked to the SLD. Data that are needed include adequate knowledge of the chemical and geometric form of the materials of interest. This information is used to determine how much energy the waste would release in the various domains of the tank, the toxicity of the region associated with a criticality event, and the probability of the initiating criticality event. Different characterization options are identified, each providing a different uncertainty in the risk calculation. Recommendations include processing existing data through the SLD to estimate risk, developing models needed to link characterization information to risk, and examini...
