Multi-Agent AI Framework Streamlines Aerodynamic Design of NACA Airfoils

Global: Multi-Agent AI Framework Streamlines Aerodynamic Design of NACA Airfoils

A team of researchers introduced a multi‑agent artificial intelligence framework aimed at improving the engineering design process, particularly for aerodynamic optimization of four‑digit NACA airfoils. The study, posted on arXiv in November 2025, seeks to reduce the resource intensity and inefficiencies commonly associated with traditional, multi‑disciplinary design collaborations.

Framework Architecture

The proposed system comprises three specialized AI agents: a Graph Ontologist, a Design Engineer, and a Systems Engineer. Each agent fulfills a distinct role, leveraging large language models and domain‑specific knowledge representations to coordinate design activities.

Knowledge Graph Construction

The Graph Ontologist employs a large language model to extract and organize information from airfoil design literature into two separate knowledge graphs. These graphs capture both the theoretical foundations and practical design considerations, providing a structured knowledge base for downstream agents.

Requirement Definition and Management

Guided by a human manager, the Systems Engineer translates high‑level objectives into concrete technical requirements. These requirements steer the Design Engineer’s generation of candidate airfoil geometries.

Design Generation and Evaluation

Using the design knowledge graph and computational simulation tools, the Design Engineer proposes airfoil candidates that satisfy the stipulated requirements. Performance metrics, such as lift‑to‑drag ratio, are computed to assess each candidate.

Iterative Feedback Loop

The Systems Engineer reviews each design, providing qualitative and quantitative feedback through its own knowledge graph. This feedback loop repeats until the manager validates a design that meets all criteria, after which final optimization fine‑tunes the airfoil for maximum performance.

Outcomes and Implications

The authors report that the collaborative AI approach enhances efficiency, consistency, and overall quality of the design process. By formalizing interactions among knowledge‑driven agents, the framework demonstrates a scalable pathway for complex engineering tasks beyond aerodynamics.

This report is based on information from arXiv, licensed under Academic Preprint / Open Access. Based on the abstract of the research paper. Full text available via ArXiv.