Using Machine Learning Methods to Predict Bias in Nuclear Criticality Safety. Issue 4 (19th September 2018) - Grechanuk, Pavel; Rising, Michael E.; Palmer, Todd S.
Abstract: This paper describes the application of machine learning (ML) tools to the prediction of bias in the criticality safety analysis. In particular, a set of over 1000 experiments included in the Whisper package were utilized in a variety of ML algorithms (notably Random Forest and AdaBoost implemented in SciKit-Learn) using neutron multiplication ( keff ) sensitivities (with and without energy dependence) for individual nuclides, and optionally, the simulated keff as the training features. Ultimately, the ML model was used to predict the bias (simulated–experimental keff ). The use of energy-integrated sensitivity profiles with simulated keff as training features lead to the best predictions as quantified by root-mean-square and mean absolute errors. In particular, the best-case estimates came from AdaBoost, with a mean absolute error of 0.00174, which is less than the mean experimental uncertainty of 0.00328 for the experiments included.