Machine Learning model applied to Reactor Dynamics

Sammanfattning: This project’s idea revolved around utilizing the most recent techniques in MachineLearning, Neural Networks, and Data processing to construct a model to be used asa tool to determine stability during core design work. This goal will be achieved bycollecting distribution profiles describing the core state from different steady statesin five burn-up cycles in a reactor to serve as the dataset for training the model. Anadditional cycle will be reserved as a blind testing dataset for the trained model topredict. The variables that will be the target for the predictions are the decay ratioand the frequency since they describe the core stability.The distribution profiles extracted from the core simulator POLCA7 were subjectedto many different Data processing techniques to isolate the most relevant variablesto stability. The processed input variables were merged with the decay ratio andfrequency for those cases, as calculated with POLCA-T. Two different MachineLearning models, one for each output parameter, were designed with Pytorch toanalyze those labeled datasets. The goal of the project was to predict the outputvariables with an error lower than 0.1 for decay ratio and 0.05 for frequency. Themodels were able to predict the testing data with an RMSE of 0.0767 for decay ratioand 0.0354 for frequency.Finally, the trained models were saved and tasked with predicting the outputparameters for a completely unknown cycle. The RMSE was even better forthe unknown cycle, with 0.0615 for decay ratio and 0.0257 for frequency,respectively.