Efficient Parametric Analysis Framework for Rotor/Wing Hover Interference of DEP Unmanned Aerial Vehicles

During hover, DEP unmanned aerial vehicles (UAVs) suffer severe aerodynamic interference between rotor downwash and the wing, causing thrust loss. High-fidelity CFD can capture the interference physics but is too costly for iterative design. Existing reduced-order models (ROMs) are fast, but their predictions of wing download have not been adequately validated across variations in wing chord, nacelle offset, and rotor-to-wing height. To address this, we propose an integrated framework combining a validated overset-grid CFD model, a parametric reduced-order model that decomposes wing download into an equivalent coefficient multiplied by the slipstream-impinged area, and Latin hypercube sampling for design-space exploration. The equivalent coefficient is represented via Gaussian process regression over three geometric parameters using CFD-calibrated data. The reduced-order model predicts wing download in seconds (less than 10 s per evaluation on a standard desktop workstation), whereas the corresponding high-fidelity CFD simulation typically requires approximately 2 to 4 h per case on a 256 core HPC cluster. The reduced-order model predicts wing download in seconds with a maximum validation error of 7.6% and successfully classifies the design space into acceptable and high interference regimes. Nine geometrically distinct validation points across both interference regimes show correct categorization with no misclassification. The framework provides a lightweight screening tool for isolated rotor–wing interaction analysis, which is a fundamental building block for understanding more complex DEP configurations. The overall modeling framework is methodologically extendable to other configurations through recalibration, though such extensions are not pursued in the present work.