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.
Highlights
The EPFL and the Paul Scherrer Institute have been developing, in recent years, a multi-dimensional fuel performance tool named OFFBEAT [1,2,3]
In this paper the possibility has been investigated of using Artificial Neural Networks (ANN) as surrogate models for uncertainty quantification (UQ) and data assimilation (DA) in fuel performance studies
It has been shown that ANNs have the capability to accurately reproduce the results of the OFFBEAT fuel performance code
Summary
OFFBEAT is being developed for straightforward use in uncertainty quantification (UQ) and data assimilation (DA) This is achieved by exposing to user modification a large number of parameters, and by allowing to directly perturb the global effect of complex phenomena like creep, swelling, fission gas release, etc. This paper focuses on data-driven models, which are instead easy to set up, very general, and can benefit from the quick-paced developments in the field of machine learning As a drawback, they require data for training, which implies having to run the FOM for a possibly large set of cases. The behavior of the fuel at a given time, which limits the analysis to beginning-of-life (BOL) situations, or requires assumptions on the above mentioned time-dependent phenomena In this preliminary paper, only the second option is investigated. ANNs are not necessarily the best surrogate model for transient scenarios, where one may consider for instance the use of Gaussian Processes
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