Artificial Neural Networks as Surrogate Models for Uncertainty Quantification and Data Assimilation in 2-D/3-D Fuel Performance Studies

Abstract

This paper preliminarily investigates the use of data-driven surrogates for fuel performance codes. The objective is to develop fast-running models that can be used in the frame of uncertainty quantification and data assimilation studies. In particular, data assimilation techniques based on Monte Carlo sampling often require running several thousand, or tens of thousands of calculations. In these cases, the computational requirements can quickly become prohibitive, notably for 2-D and 3-D codes. The paper analyses the capability of artificial neural networks to model the steady-state thermal-mechanics of the nuclear fuel, assuming given released fission gases, swelling, densification and creep. An optimized and trained neural network is then employed on a data assimilation case based on the end of the first ramp of the IFPE Instrumented Fuel Assemblies 432.