  This study explores the application of large language models (LLMs) with
callable tools in energy and power engineering domain, focusing on gas path
analysis of gas turbines. We developed a dual-agent tool-calling process to
integrate expert knowledge, predefined tools, and LLM reasoning. We evaluated
various LLMs, including LLama3, Qwen1.5 and GPT. Smaller models struggled with
tool usage and parameter extraction, while larger models demonstrated favorable
capabilities. All models faced challenges with complex, multi-component
problems. Based on the test results, we infer that LLMs with nearly 100 billion
parameters could meet professional scenario requirements with fine-tuning and
advanced prompt design. Continued development are likely to enhance their
accuracy and effectiveness, paving the way for more robust AI-driven solutions.
