Autonomous aerial robots must operate in cluttered, wind- disturbed environments where turbulence and gusts generated by wind-object and terrain interactions introduce significant aerodynamic risks, including orientation instability, sensor degradation, control drift, and increased power consumption, often leading to mission failure or crash. We present Graphlets-based Zero-Shot Planning Framework (GZS), a novel, non-parametric, fast computation, memory-efficient, zero-shot training-free onboard inference framework for real-time 3D spatial-aware aerodynamic risk perception that operates without prior scene knowledge. GZS dynamically classifies point clouds to extract local topology, incorporates physics-informed modeling of wind interactions, and applies attention-guided segment matching to generate onboard 3D representations of wind-induced aerodynamic risk. It transforms unstructured scene segments into structured graphlets topologies encoding aerodynamic risk-aware features, enabling UAVs to identify and navigate through regions of minimal aerodynamic hazard in real time and without prior training in any environment. Unlike computational fluid dynamics(CFD)-based, deep learning, or map-dependent approaches, GZS performs zero-shot aerodynamic risk estimation in previously unseen and dynamic conditions. Extensive experiments demonstrate 90-95% accurate aerodynamic risk zone identification compared to conventional methods of CFDs and wind tunnels, while substantially reducing computational and memory overhead, and a 100% success rate in creating onboard 3d spatial-aware risk perceptions. Our results establish GZS as a framework for a zero-shot, non-parametric, robust, aerodynamic risk perception for autonomous real-time trajectory planning in wind-affected aerial environments.