Rossi-Alpha distribution analysis of DDSI data for spent nuclear fuel investigation

Virgini Solans, Sophie Grape, Henrik Sjöstrand, Erik Branger, Peter Schillebeeckx, Alessandro Borella, Riccardo Rossa, Anders Sjöland

    Research outputpeer-review


    The differential die-away self-interrogation (DDSI) prototype instrument is a neutron coincidence instrument developed to investigate nuclear safeguards-relevant properties of spent nuclear fuel (SNF). The distribution of the time intervals between neutrons detected in coincidence creates the Rossi-Alpha distribution (RAD). The RAD depends on properties of the SNF such as the net multiplication. In this work, a new technique to analyze the RAD and infer SNF parameters is investigated. In previous analysis, the simulated RAD was fitted with a single exponential between a time window of 4 and 52 µs to determine the so-called early die-away time (tearly). The tearly has been found to be correlated to the net multiplication. However, tearly is sensitive to the choice of the time window, and an analysis of measurement data collected from SNF at the Central Interim Storage Facility for Spent Nuclear Fuel (Clab) in Sweden revealed that the optimal time window identified by simulations could not be used. In addition, tearly is not very sensitive to burnup (BU) and initial enrichment (IE). Therefore, an analysis technique that is not dependent on fixed time windows and is more sensitive to BU and IE is explored. In this work, a function consisting of two exponential functions is fitted to the whole RAD. The method is applied to both simulated and experimental data. Results from experiments and simulations are compared, and significant discrepancies in the RAD-parameters are found in line with previous work using a single exponential. The four parameters determined by the fit of the double exponential (two amplitudes and two time constant) are used as input parameters in a machine learning algorithm employing a k-nearest neighbours algorithm to infer BU and IE. In addition, a method to correct for discrepancies between predicted parameters and operator data was developed. It is found that BU and IE can be predicted with a RMSE of 2.69 GWd/tHM and 0.104 % on the simulated dataset.
    Original languageEnglish
    Title of host publicationInternational nuclear safeguards 2022
    PublisherIAEA - International Atomic Energy Agency
    Number of pages8
    StatePublished - 3 Nov 2022
    Event2022 - Symposium on International Safeguards 2022: Reflecting on the Past and Anticipating the Future - IAEA, Vienna
    Duration: 31 Oct 20224 Nov 2022


    Other2022 - Symposium on International Safeguards 2022
    Abbreviated titleIAEASG2022
    Internet address

    Cite this