Parallel Simulated Annealing with Embedded Machine Learning and Multifidelity Models for Reactor Core Design

This paper presents extensions to a penalty-free, parallel simulated annealing (SA) algorithm for multiconstrained combinatorial optimization with the aim of embedding multifidelity physics models into the annealing procedure. The method uses a low-fidelity, quickly executing model for rapid design space exploration and a high-fidelity model for detailed constraint resolution and on-the-fly bias correction. Machine learning models updated within the annealing procedure were used to bridge the gap between the multifidelity models, which led to accurate rapid exploration and efficient detailed constraint resolution. A software implementation of the new multifidelity optimization methods, called ML-PSA, was demonstrated on a continuous multifidelity optimization problem and a constrained combinatorial PWR lattice design problem. These problems demonstrate some of the features, parallel performance characteristics, and extensible nature of the multifidelity SA methods. This paper shows that the developed software and procedure are a general optimization tool that can be applied to a wide variety of scientific and engineering design optimization applications.