<p dir="ltr">The estimation of local irradiation conditions is a complex and crucial task with significant implications for reactor safety, operation, and spent nuclear fuel management. This study aims to investigate the feasibility of using measurements of a limited number of nuclides taken at the time of discharge to infer local irradiation conditions. Specifically, the focus is on determining the local operating power, void fraction, and burnup. These factors are required to calculate the isotopic composition of discharged reactor assemblies. Existing methods often struggle with substantial uncertainties when estimating these local conditions, leading to inaccuracies in isotopic calculations. Therefore, markedly different, this research aims to establish a relationship between local conditions and isotopic measurements, benefiting from the low uncertainty associated with experimental isotopic measurements. To achieve this goal, a two-step approach is employed. First, a mathematical inference procedure is developed to correlate the isotopic composition of discharged fuel with the local irradiation conditions. Second, given a certain prediction accuracy, efforts are made to minimize the number of isotopic measurements required at the time of discharge. To do so, this work develops an inference algorithm employing a simplified depletion model of a single pin in a BWR assembly using SCALE Polaris module. Polaris module generates the virtual measurement of 29 nuclides including actinides and fission products with assumed power and void fraction histories provided to SCALE Polaris as inputs. Employing these virtual measurements, a similarity measure metric is employed to minimize the number of nuclides to estimate irradiation conditions, and the inference method used to estimate the irradiation conditions is the ordinary least squares method.</p>
| Identifer | oai:union.ndltd.org:purdue.edu/oai:figshare.com:article/24291280 |
| Date | 12 October 2023 |
| Creators | Tarikul Islam (17131093) |
| Source Sets | Purdue University |
| Detected Language | English |
| Type | Text, Thesis |
| Rights | CC BY 4.0 |
| Relation | https://figshare.com/articles/thesis/LOCAL_IRRADIATION_CONDITION_INFERENCE_ANALYZING_SPENT_FUEL_ISOTOPICS/24291280 |
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