Reinforcement Learning-Enhanced Large Language Models for Automated Modeling of Nuclear Thermal-Hydraulic Systems: A Plan-and-Act Agent Framework

Automating system-level nuclear thermal-hydraulic (T-H) model construction remains challenging because platform-specific API syntax, graph connectivity, parameter dependency ordering, and solver admissibility must be satisfied simultaneously. This study develops a closed-loop modeling framework on the SAFRI platform by combining supervised fine-tuning (SFT), a Plan-and-Act agent with retrieval-grounded parameter completion, and reinforcement learning based on group relative policy optimization (GRPO). The SFT stage uses a 6003-record domain corpus derived from expert-authored or expert-verified SAFRI modeling exemplars, while system-level generalization is evaluated on a held-out 50-case in-house evaluation set separated at the case-template level. At the component level, LoRA-adapted Qwen3-8B achieves 100% code accuracy, compared with 50% for zero-shot and 74% for one-shot prompting. At the system level, the SFT agent attains a 100% syntax success rate (SSR), 90% topology success rate (TSR), and 72.4% physical convergence rate (PCR), showing that local API correctness is insufficient for solver-valid model assembly. After GRPO training with schema, topology, physics, and sequence rewards, the full SAFRI-SFT-RL agent reaches a 100% SSR, 100% TSR, and 88.8% PCR on the in-house evaluation set, while an error self-healing loop resolves execution-time failures in an average of 2.3 corrective iterations. These results show that solver-grounded reinforcement learning is effective for closing the gap between syntactically correct script generation and physically convergent nuclear T-H model construction.