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RLOpenNeoMC

Reactor Optimization Benchmark by Reinforced Learning OpenMC is a community-developed Monte Carlo neutron and photon transport simulation code. This Benchmark within the OpenNeoMC framework is designed specifically for reinforcement learning. The benchmark involves a unit cell of a research reactor with two varying parameters; the fuel and water density. The goal is to maximize neutron flux while maintaining a reactor criticality of one. Minimizing the fitness function wil fulfill these both requirement. The fitness landscape is not trivial,their is a local minimum and a deep and steep global minimum at the edge of the parameter domain. Therefore, reinforce algorithm are more adapted in this case.

Slab Geometry

Top view of the system: the colors pink, yellow, blue, and green stand for enriched uranium, aluminum, water, and cadmium, respectively. The fuel and water density may vary.

Instructions

Requirments

First, clone the RLOpenNeoMC code provided here.

clone https://github.com/Scientific-Computing-Lab-NRCN/RLOpenNeoMC.git

Then, create a conda environment:

conda create --name openneomc python=3.7

Activate the new environment and install neorl:

activate openneomc
pip install neorl

Then, install OpenMC using this OpenMC installation

Citation

For more information about the measures and their means of the implementations, please refer to the paper. If you found these codes useful for your research, please consider citing: https://arxiv.org/abs/2403.14273

Running

Configuration

Set Cross-Sections

The simulation of OpenMC requires cross-sections files to run properly. We demonstrate in this paper a method for saving simulation runtime by moving these files to the RAM memory (tmp folder). This process is done by running the following code:

python copy_to_ram.py [path]

Note: you have to specify the correct path to endfb-vii.1-hdf5 files instead of [path]

Training Algorithms

There are several parameters for training JAYA and PPO-ES for our suggested benchmark in the main.py file.

opt_algorithm = 'JAYA'
ncores = 1 
verbose = True
os.environ['OPENMC_CROSS_SECTIONS'] = '/tmp/endfb-vii.1-hdf5/cross_sections.xml'
  • opt_algorithm - gets 'JAYA' or 'PPO-ES' correspondingly.
  • ncores - sets the number of parallel proccesses (note that we ran JAYA with 4 cores and PPO-ES with 1).
  • verbose - responsible for the amount of output information through the running.
  • os.environ['OPENMC_CROSS_SECTIONS'] - make sure that gets the correct tmp directory.

Finally, after these parameters are set. Run the training by the following command:

python main.py

To plot the best fit in each iteration make sure to set the correct results paths in read_and_plot_fitness_PPOES_JAYA.py and run:

python read_and_plot_fitness_PPOES_JAYA.py

Results

At the end of the simulation the best result and the rerunning time is printed in the terminal:

JAYA Results

x: [2.92546023, 24.9950639] y: 0.9986045239378821 running time: 3418.400379896164