用于零维降阶模型规划的多智能体框架
A Multi-Agent Framework for Zero-Dimensional Reduced-Order Model Planning
- Institute of Engineering Thermophysics, Chinese Academy of Sciences（中国科学院工程热物理研究所）
- National Key Laboratory of Science and Technology on Advanced Light-duty Gas-turbine（先进轻型燃气轮机技术重点实验室）
- University of Chinese Academy of Sciences（中国科学院大学）
- Qingdao Institute of Aeronautical Technology（青岛航空技术研究院）
- Nanjing Future Energy System Research Institute（南京未来能源系统研究院）
机构由 AI 辅助整理，请以论文原文为准。
AI总结：
本文针对复杂零维降阶模型规划中依赖人工、传统方法局限等问题，提出多智能体架构Z-COPA，其核心是专用图表示法，将规划转化为图结构优化问题，经多基准测试验证，该框架性能卓越且打破传统范式，提供新规划技术方法。
AI中文摘要：
零维降阶模型（0D ROMs）是高端复杂设备多维设计工作流程的核心。然而，目前的规划过程依赖人工专业知识，限制了拓扑探索并延长了迭代时间。即使是遗传算法等传统优化方法也通常局限于局部参数调整。虽然大语言模型智能体在探索大样本空间方面有前景，一些框架也提高了推理可靠性，但单个智能体仍不足以应对复杂0D ROM规划的长期和高度耦合特性。本文提出了零维降阶模型协同规划框架（Z-COPA），这是一种具有符号动作图引擎（SAGE）和MILP引导导航（MGN）优化器的多智能体架构。其核心创新是一种专用图表示方法，将经验规划过程转化为严格的图结构优化问题。我们在两个真实飞机发动机二次空气系统、两个IEEE配电重新配置基准和两个配水管网基准上验证了Z-COPA的正向和反向设计能力及泛化性能。结果显示出卓越的任务完成质量，在空气系统的正向和反向设计中均获得最佳性能。Z-COPA打破了传统0D模型规划范式，为探索更广泛的拓扑空间和实现高度自动化、全局最优的空气系统架构提供了新的技术方法。
英文摘要：
Zero-dimensional reduced-order models (0D ROMs) are central to multi-dimensional design workflows for high-end complex equipment. However, the planning process currently relies on manual expertise, limiting topological exploration and prolonging iterations. Even traditional optimization methods such as Genetic Algorithms (GA) are typically confined to local parameter tuning. Although Large Language Model (LLM) agents have shown promise in exploring large sample spaces, and frameworks such as Chain of Thought (CoT) and Reason and Act (ReAct) improve reasoning reliability, while Retrieval-Augmented Generation (RAG) overcomes domain knowledge barriers, a single agent still falls short for the long-horizon and highly coupled nature of complex 0D ROM planning. This paper proposes the Zero-dimensional reduced-order model CO-Planning framework (Z-COPA), a multi-agent architecture featuring a Symbolic Action Graph Engine (SAGE) and a MILP-Guided Navigation (MGN) optimizer. Its core innovation is a dedicated graph representation method that accurately encodes the 0D flow network topology, converting the empirical planning process into a rigorous graph structure optimization problem. We validate the forward and inverse design capabilities and generalization performance of Z-COPA on two real aircraft engine secondary-air systems, two IEEE power-distribution reconfiguration benchmarks, and two water-distribution network benchmarks. The results show superior task completion quality, obtaining the best performance in both forward and reverse design of air systems. Z-COPA disrupts the traditional 0D model planning paradigm, providing a new technical approach for exploring broader topological space and achieving highly automated, globally optimal air system architectures.
