Visualizing Flow Effects on a Quadcopter to Optimize Atmospheric Sensor Placement

Accurate placement of atmospheric sensors on quadcopters is critical for collecting high-fidelity environmental data within the atmospheric boundary layer (ABL). As small uncrewed aerial systems (sUAS) become increasingly popular in scientific and industrial applications, especially in air quality monitoring and atmospheric research, understanding the aerodynamic environment surrounding rotary-wing platforms is essential. Rotor-induced disturbances such as turbulence, downwash, and wake interactions can significantly compromise sensor accuracy, particularly for sensitive instruments like optical particle counters (OPCs). This study investigates the optimal placement of an Alphasense OPC-N3 sensor on a DJI Matrice 100 quadcopter using computational fluid dynamics (CFD). CFD simulations were conducted in ANSYS Fluent using the Reynolds-Averaged Navier-Stokes (RANS) equations with an SST k-omega turbulence model. Simulations were performed under hover conditions with imposed 5 m/s headwinds and 2 m/s crosswinds, across a range of propeller speeds from 3000 to 7000 RPM. Pressure and velocity results revealed strong downwash, high-speed wake columns, and elevated turbulence beneath the rotors, while identifying a relatively undisturbed flow region approximately 0.2 meters above the airframe. To validate the CFD predictions, a 55%-scale 3D-printed model of the M100 was fabricated using PLA and epoxy finishing, with modified propeller designs to preserve geometric fidelity. Smoke flow visualization tests will be conducted in a subsonic wind tunnel to identify wake structures and regions of recirculation in future work. Together, the CFD and experimental findings support a sensor placement strategy that minimizes aerodynamic interference and enhances measurement stability. This work contributes to the growing field of sUAS-based atmospheric sensing by offering evidence-based guidance for integrating particulate matter sensors on rotary-wing platforms operating in real-world conditions.