DEVELOPING UNIVERSAL AI/ML BENCHMARKS FOR NUCLEAR APPLICATIONS
Creators: name: Richards, William Stephen
Publisher: name: Purdue University Graduate School
Year: 2023
Subjects: classificationCode: 401906
subject: Nuclear engineering (incl. fuel enrichment and waste processing and storage)
classificationCode: 460299
subject: Artificial intelligence not elsewhere classified
classificationCode: 461199
subject: Machine learning not elsewhere classified
classificationCode: 460207
subject: Modelling and simulation
classificationCode: 461105
subject: Reinforcement learning

description: Recent developments in Artificial Intelligence (AI) and Machine Learning (ML) have not only revolutionized engineering but also the way humanity foresees the future with machines. From self-driving cars to large language models and ChatGPT, AI and ML will continue to redefine the boundaries of innovations and reshape the way we interact with the world. The anticipated benefits are transformative, enabling enhanced productivity, improved decision-making, and the potential for significant cost savings. These developments in AI/ML and the promise for improved reliability, anomaly detection, efficient operation, etc., have unavoidably caught the attention of nuclear engineers. Advancing nuclear predictive models and providing real-time support with regard to operation and maintenance are just a few of the potential tasks AI/ML could provide assistance. Microreactors is just one example of future nuclear systems where semi-autonomous operation and fully digital instrumentation and control with AI/ML-based decision support would be required for cost-effective deployment in remote areas.However, the world of nuclear engineering is skeptical of the direct application of AI/ML at nuclear facilities mostly due to limited past experience, potential high risk for false negatives, and limited amount of available data to demonstrate widespread applicability with high confidence. In order to curb these worries and take advantage of recent public interest in AI/ML, publicly available, real-time datasets need to be created. In this thesis, a universal AI/ML dataset is developed takes advantage of the recent digitization of Purdue University Reactor One (PUR-1) and using real-time data directly from PUR-1. The expectation is to follow the paradigm of the AI/ML community where open datasets (e.g., Kaggle, ImageNet, etc.) were the stepping stone towards new algorithms, facilitating collaborative problem-solving, and driving breakthroughs in the field of AI/ML through open competitions and knowledge sharing.PUR-1 is capable of providing real-time research data to the second for over 2000 different parameters ranging from physical components such as neutron flux and control rod positions to calculated signals such as the system change rate. The proposed Purdue Reactor Integrated Machine Learning dataset (PRIMaL), as described in the thesis herein, includes ten signals handpicked to create simple and of various degree of complexity AI/ML benchmarks related directly to the nuclear field, with the goal of kickstarting both a new-founded interest in the nuclear field by AI/ML professionals and building faith in AI/ML amongst nuclear engineers. To the best of our knowledge, PRIMaL is the first curated AI/ML benchmark based on real reactor data and focused on nuclear applications, aiming to advance safety, efficiency, and innovation in the nuclear industry while promoting the responsible and secure use of AI/ML technologies.To confirm the validity of the dataset and provide a simple example on how to use the dataset for AI/ML benchmarking, an example problem of classifying shutdown data as gang lowers or SCRAM was performed using three ML algorithms: support vector machine, random forest, and logistic regression. This binary classification problem was repeated 288 times for each algorithm, varying the balance ratio of the SCRAMs to gang lowers, the time prior to the shutdown, and the time after the shutdown the algorithms have access to. The sample problem was a success, as the algorithms were able to distinguish SCRAMs and gang lowers with reasonable accuracy in all cases. Future work would include gathering more data from PUR-1 for the database, as further testing with different sized balanced datasets lead to unusually high accuracy due to the smaller sample size.