Using Generative AI to Implement the Discrepancy Checker for a Nearly Autonomous Management and Control System for Advanced Reactors

Developments related to generative artificial intelligence (AI) have brought a major breakthrough
in AI. These developments are rapidly accelerating developments in different science and
engineering applications. Nearly Autonomous Management and Control (NAMAC) system
provides recommendations to the operator for maintaining the safety and performance of the reactor.
The discrepancy checker (DC) is an important component of the NAMAC system, whose goal is to
determine if the plant is moving toward the expected system state after the control actions are
injected. In this work, we explore generative AI methods, particularly, a generative pretrained
transformer (GPT) for implementing the DC function in NAMAC. The GPT-based DC aims to alert
the operator in situations outside NAMAC’s scope and act as a chatbot that the operator can use to
retrieve relevant information.
This study involves two versions of GPT developed by OpenAI: GPT-3.5 and GPT-4. These GPTs
are trained on huge amounts of undisclosed general domain datasets. We explored two methods to
adapt GPTs for DC implementation in NAMAC: fine-tuning and retrieval augmented generation. A
small knowledge base (information file) that encompasses rules for DC implementation and some
general information related to NAMAC has been created to support DC implementation using GPT.
In this work, the GPT-based DC implementations have been tested for their reasoning abilities,
comprehension, information retrieval, and extraction abilities. It should be noted that this paper only
presents a preliminary study to test the feasibility of DC implementation using generative AI
technology. Given the potential risks and severe consequences associated with nuclear reactor
applications, combined with the black-box nature of AI, extensive offline and online testing and
reliability analyses of GPT-based DCs are needed for further developing such capabilities.