Retrieval-Augmented System Architecture Generation for Model-Based Systems Engineering Using Task-Oriented Functional Semantics

Automated system architecture generation is a key task in Model-Based Systems Engineering (MBSE), where requirement and functional models need to be transformed into a physical architecture composed of selected components and port-level connections. However, this task remains challenging because functional information is often semi-structured and distributed, and is represented at a different level of abstraction from physical architectures. Existing knowledge-driven methods provide systematic and traceable reasoning but require labor-intensive formal modeling and rule definition, whereas direct Large Language Model (LLM)-based generation improves automation but may suffer from component hallucination and low interpretability. To address these limitations, this study proposes a retrieval-augmented system architecture generation approach using Task-Oriented Functional Semantics (TOFS). TOFS is constructed from SysML requirements and functional models through an LLM-based process, and represents system goals, functions, logical entities, ports, and engineering constraints in a unified representation. Based on TOFS, a hybrid component retrieval method is proposed, which integrates interface matching, parameter constraint matching, and semantic matching to retrieve candidate components from an engineering component library. TOFS and the retrieved component sets are then jointly organized as the generation context to guide LLM-based architecture generation. A hydronic heating control system is used as a case study, and scenario-based comparative experiments demonstrate improved component retrieval performance, functional completeness, and engineering validity.