Automating Monte Carlo simulations in nuclear engineering with domain knowledge-embedded large language model agents - Zavier Ndum Ndum; Jian Tao; John Ford; Yang Liu (Energy and AI)
Next-generation nuclear reactor technologies, such as molten salt and fast reactors present complex analytical challenges that require advanced modeling and simulation tools. Yet, traditional workflows for Monte Carlo simulations like FLUKA are labor-intensive and error-prone, relying on manual input file generation and post-processing. This limits scalability and efficiency. In this work, we present AutoFLUKA, a novel framework that leverages domain knowledge-embedded large language models (LLMs) and AI agents to automate the entire FLUKA simulation workflow from input file creation to execution management, and data analysis. AutoFLUKA also integrates Retrieval-Augmented Generation (RAG) and a web-based user-friendly graphical interface, enabling users to interact with the system in real time. Benchmarking against manual FLUKA simulations, AutoFLUKA demonstrated substantial improvements in resolving FLUKA error-related queries, particularly those arising from input file creation and execution. Traditionally, such issues are addressed through expert support on the FLUKA user forum, often resulting in significant delays. The resolution time for these queries was also reduced from several days to under one minute. Additionally, human-induced simulation errors were mitigated, and a high accuracy in key simulation metrics, such as neutron fluence and microdosimetric quantities, was achieved, with uncertainties below 0.001 % for large sample sizes. The flexibility of AutoFLUKA was demonstrated through successful application to both general and specialized nuclear scenarios, and its design allows for straightforward extension to other simulation platforms. These results highlight AutoFLUKA’s potential to transform nuclear engineering analysis by enhancing productivity, reliability, and accessibility through AI-driven automation.