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.

翻译：本研究探索了大型语言模型（LLMs）结合可调用工具在能源与动力工程领域的应用，重点聚焦于燃气轮机的气路分析。我们开发了一种双智能体工具调用流程，将专家知识、预定义工具与LLM推理能力相融合。对包括LLama3、Qwen1.5和GPT在内的多种LLM进行了评估。较小规模的模型在工具使用和参数提取方面表现困难，而较大规模的模型则展现出良好的能力。所有模型在处理复杂多组件问题时均面临挑战。基于测试结果，我们推断：通过微调与先进提示设计，参数规模接近千亿级的LLM能够满足专业场景需求。持续的技术发展有望进一步提升其准确性与有效性，为构建更稳健的AI驱动解决方案铺平道路。