Implicit in any engineering design is an underlying optimization problem, although the exact objective function to be optimized is rarely stated explicitly. Nuclear systems optimization is as old as the discipline of nuclear engineering. Advanced manufacturing in the nuclear industry has opened the door for the re-examination of optimization in a way in which it was not possible before, namely, determining the optimal geometry for a given objective function. A trivial example is the sphere as the shape that minimizes the volume (or mass) of bare fissile material in a critical configuration. However, the problem becomes less trivial under even the simplest of multiphysics considerations. In this work, we develop the solution methodology for finding the minimum volume geometric configurations under the multiphysics constraints of 1,500 pcm excess reactivity and maximum fuel temperature of 618°C under forced-flow cooling conditions. Constraining the solution geometry only to right cylinders, surprisingly yields two disjoint solution regions. Flat, wide (disk-like) cylinders and tall, narrow (rod-like) cylinders both satisfy the constraints and yield very similar minimal volumes. However, the ultimate pursuit of this work is truly arbitrary geometry.
We present a data-centric deep learning (DL) approach using neural networks (NNs) to predict the thermodynamics of ternary solid solutions. We explore how NNs can be trained with a dataset of Gibbs free energies computed from a CALPHAD database to predict ternary systems as a function of composition and temperature. We have chosen the energetics of the FCC solid solution phase in 226 binaries consisting of 23 elements at 11 different temperatures to demonstrate the feasibility. The number of binary data points included in the present study is 102,000. We select six ternaries to augment the binary dataset to investigate their influence on the NN prediction accuracy. We examine the sensitivity of data sampling on the prediction accuracy of NNs over selected ternary systems. It is anticipated that the current DL workflow can be further elevated by integrating advanced descriptors beyond the elemental composition and more curated training datasets to improve prediction accuracy and applicability.
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