Propellers are a vital component to achieve successful and reliable operation of drones. However, the drone developer faces many challenges while selecting a propeller and a common approach is to perform static thrust measurement. However, the selection of a propeller using a static thrust measurement system is time-consuming. To overcome a need for the static thrust system a virtual model has been developed for measuring both the static and dynamic thrust of a single and coaxial propeller. The virtual model is reliable enough to minimize the need for full-scale tests. The virtual model has been built using two open-source software Qblade and OpenFoam. Qblade is employed to obtain the lift and drag coefficients of the propeller’s airfoil section. OpenFoam is utilized to perform the flow simulations of propellers and for obtaining the thrust and torque data of the propeller. The developed virtual model is validated with experimental data and the experimental data are obtained by developing a multi-force balance system for measuring thrusts and torques of a single and a pair of coaxial contra-rotating propellers. The data obtained from the propeller virtual model are compared with the measurement data. For a single propeller, the virtual model shows that the estimated forces are close to the experiment at lower rotational speeds. For coaxial propellers, there are some deviations at the rear propeller due to the turbulence and flow disturbance caused by the front propeller. However, the computed thrust data are still accurate enough to be used in selecting the propeller. The studies indicate that in the future, these virtual models will minimize a need for experimental testing.

Predicting the aerodynamic performance of multirotor unmanned aerial vehicles (UAVs) remains challenging because of complex rotor–rotor and rotor–body interactions. Experimental characterisation of these interactions is often costly and time-consuming, making computational methods valuable for aerodynamic design and optimisation. This study presents a coupled computational fluid dynamics and blade element momentum theory (CFD-BEMT) framework for evaluating a multirotor UAV developed for power-line inspection. The airflow was simulated using OpenFOAM, while the propellers were represented as virtual rotors through distributed momentum-source terms calculated using BEMT. The CFD methodology was assessed by comparing predicted Ahmed-body drag coefficients at different yaw angles with experimental measurements. Steady-state Reynolds-averaged Navier–Stokes simulations provided satisfactory predictions at low yaw angles, while unsteady simulations generally improved the predictions at higher yaw angles, where large-scale flow separation and wake unsteadiness became significant. Standalone static-thrust measurements were also used as reference data to examine changes in rotor loading after integration with the UAV. The computational framework was subsequently applied to a baseline bluff-body configuration, V01, and a redesigned configuration, V02, incorporating smoother fuselage contours, improved payload integration, and modified rotor placement. A comprehensive simulation matrix was used to investigate the effects of forward velocity, propeller rotational speed, and nose-down angle of attack. The results show that propeller operation substantially modifies the aerodynamic response through nonuniform rotor-induced flow and rotor–body interactions. Compared with V01, the redesigned V02 configuration generally exhibits smaller variations in lift, drag, and lift-to-drag ratio across the investigated angles of attack. However, V02 does not provide uniformly lower drag or a consistently higher lift-to-drag ratio under all operating conditions. Its principal advantage is a more consistent aerodynamic response and reduced sensitivity to changes in nose-down angle of attack. The results demonstrate the capability of the coupled CFD-BEMT framework to assess installation effects, quantify rotor–body interactions, and support the aerodynamic development of multirotor inspection UAVs.