Analysis of Aircraft Simulation Validity in Different Flight Conditions

Rapid growth in unmanned aircraft systems (UAS) applications has resulted in exponential increase in the number of new but inexpensive aircraft. Open-source or engineering-level analysis software supports most of these designs and their dynamic analyses. This work analyzes the validity of a perturbed non-linear six-degree-of-freedom simulation of a fixed-wing UAS under six flight conditions. The aircraft model is developed using a component build-up method. Simulations are compared to flight data under different flight conditions: straight flight, level turn, ascending and descending flight. We additionally assessed the dynamic model accuracy when the aircraft was forced into loss of control. In another flight test, the commanded flight speed was reduced to coerce the aircraft into a stall. Unsupervised learning algorithms are used to classify flight data into different flight phases and to select flight portions for analysis. Monte Carlo (MC) simulations are performed to assess dynamic model accuracy while taking simulation parameter uncertainties into account. Results qualify uncertainty levels in predicted states and show that the base dynamic model can only capture aircraft’s body rotation rate trends within some errors. The MC simulations mostly capture the flight rotation rates, however, in several instances, the flight data is not captured despite considering simulation parameter uncertainties.