Home
The Modularly Integrated Design Assistance Suite (MIDAS) is a robust optimization tool for solving nuclear
engineering optimization problems. It was first conceived in 2018 for LWR fuel lattice optimization. Afterward,
it grew in both optimization approaches and application space as it as it expanded to PWR loading pattern
optimization problems. It adopted its current name, MIDAS, and has since been continuously developed, with
new techniques and applications added regularly. Currently, it supports optimizations for LWR fuel lattices
and core designs. The optimization suite has several algorithms currently available including traditional meta
heuristic, and machine learning techniques. MIDAS can interface with widely used reactor design tools, such as
PARCS and Polaris, and due to the modular design of the code new interfaces can be easily implemented. More
information about MIDAS and its applications can be found in our references.
MIDAS aims at allowing a flexible management of optimization algorithms, optimization problems and codes.
All classes in MIDAS are modular allowing for easy implementation of new optimization algorithms and
applications.
[1] B. Andersen, G. Delipei, D. Kropaczek, and J. Hou, MOF: A Modular Framework for Rapid Application of Optimization Methodologies to General Engineering Design Problems, arXiv:2204.00141, 2022
[2] G. Delipei, J. Mikouchi-Lopez, P. Rouxelin and J. Hou, Reactor Core Loading Pattern Optimization with Reinforcement Learning, The International Conference on Mathematics and Computational Methods Applied to Nuclear Science and Engineering (Accepted), 2023.
[3] J Mikouchi-Lopez, G Delipei and J Hou, Development and evaluation of parallel simulated annealing algorithm for reactor core optimization problems, Nuclear Science and Technology Open Research, 2024.
[4] J C. Luque-Gutierrez, J Mikouchi-Lopez and J Hou, Investigation on Dimension Reduction Techniques for Visualization of Reactor Core Design Space, American Nuclear Society Advances in Nuclear Fuel Management conference (accepted), 2025.
[5] J Mikouchi-Lopez, J C. Luque-Gutierrez, M Edney and J Hou, A Parametric Study of Genetic Algorithm using IPWR Core Design, American Nuclear Society Advances in Nuclear Fuel Management conference (accepted), 2025.
[6] B Andersen, Development and assessment of multi-objective optimization utilizing genetic algorithms for nuclear fuel assembly design, M.S. thesis, North Carolina State University, Department of Nuclear Engineering, 2018.