Aerodynamic Drag Reduction Strategies for Box-Shaped Payloads in Delivery Drones: A Multimodal Experimental Study

View at publisher
Authors: Manuel González
Cited by: 0
References: 3

Abstract

This work presents a multimodal aerodynamic evaluation methodology integrating computational fluid dynamics (CFD), wind-tunnel testing, and full-scale flight experiments to characterize the aerodynamic behavior of standardized box-shaped payloads carried by multirotor unmanned aerial vehicles (UAVs). Three representative configurations—a baseline parcel, a front-fairing modification, and a combined fairing–boat-tail arrangement—were examined to demonstrate the methodology. Across all phases, environment-specific corrective procedures were implemented to address the limitations of each evaluation mode, including turbulence-model verification in CFD, moving-average force filtering in wind-tunnel testing at reduced Reynolds number, and atmospheric-density correction, stabilizer-tail implementation, and force-vector alignment correction with independent measurement of UAV and payload pitch angles during flight. These corrective steps minimized the influence of environmental variability, scale effects, and dynamic flight behavior, allowing the aerodynamic characteristics of each configuration to emerge consistently across the three platforms. Cross-validation across CFD, wind-tunnel, and flight testing showed close agreement in configuration-dependent aerodynamic trends, with all three phases reproducing similar variations in drag coefficient C_D and comparable drag-reduction performance C_(D,RED). The results demonstrate that the proposed multimodal methodology provides a robust and physically consistent framework for assessing UAV payload aerodynamics and establishes a foundation for future studies evaluating additional payload configurations and aerodynamic devices.

Fields of Study & Subjects

Aerospace and Aviation Technology Aerodynamics and Fluid Dynamics Research Fluid dynamics and aerodynamics studies

Publication details

Journal / source
AIAA SciTech Forum
Publication date
8 January 2026