Abstract

Future IoT, smart-city, and 6G ecosystems will increasingly use UAVs as autonomous nodes, creating demand for rapid yet traceable mission-to-airframe design workflows. This study evaluates whether a large language model (LLM) with retrieval-augmented generation (RAG) can convert a cargo-UAV mission brief into source-grounded preliminary sweep-angle ranges and whether one instantiated geometry remains compatible with downstream physics-based checks. An LLM first proposed broad ranges; a RAG stage, over a curated aerospace corpus, narrowed the three sweep intervals from a combined width of 25° to 7° (72% aggregate reduction) before CAD instantiation. The resulting tailless blended-wing geometry was screened with a SolidWorks 2025 Flow Simulation database spanning speed, angle of attack, sideslip, and elevon deflections. In the neutral-elevon, near-zero-sideslip slice, lift crossed the CAD-derived weight between 4° and 8° at 100 m/s and between 0° and 4° at 200 m/s. A three-level mesh check preserved force and moment signs. A nominal 6-DoF mission at 120 m/s over approximately 32 km remained within the prescribed corridor, with late mean cross-track and altitude-path errors of 10.7 m and 7 m. The contribution is a traceable early-stage design workflow, not a claim of aerodynamic optimality, flight readiness, controller robustness, or certification-level validation.

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