Parameter Sensitivity studies for the Performance of an Electric BiCP-VTOL UAV

The objective of the presented study is to provide tools for the power plant selection that meet the performance requirements of a VTOL prototype. The tool is integrated in a Mission-Oriented Design Calculator MODC that receives information of the VTOL prototype through a General Block Data (GBD), and then process the information using two algorithms that work together to obtain sub-optimal power plant selections. The data fed to the MODC receives the information of the candidate prototype via four smaller structures that provide geometric, weights, aerodynamic, and propulsive properties that are updated during the iteration process using a series of update rules. This information is then fed to two algorithms: Sensitivity Analysis Algorithm (SAA) and the Fixed-MTOW Analysis Algorithm (FMAA) for the selection of sub-optimal configurations. The SAA generates desired target performance levels for varying aircraft speed (V), propellers' diameter (D), number of engines, and scaling factor SF. The sensitivity analysis provides families of plausible solutions of power plant selections (engine, propeller and batteries) that satisfy the performance target requirements for both axial and longitudinal flight, which are denoted Convergence Zones. The FMAA finds the sub-optimal configuration within the Convergence Zones in order to maximize the Range/Endurance of the UAV for all flight regimes, and extends the study for different combinations for payload and battery mass. After each cycle of iteration the MODC updates the VTOL prototype characteristics in the GBD, hence serving as new data to conduct new sensitivity analysis using the SAA and fine tuning of the FMAA. Results are presented for both algorithms, and conclusions are presented indicating interesting trends towards defining sub-optimal power plant combinations.