Matthew McCrink

Blade Element Momentum Modeling of Low-Re Small UAS Electric Propulsion Systems

A model for the propulsion system of a small-scale electric Unmanned Aircraft System (UAS) is presented. This model is based on a Blade Element Momentum (BEM) model of the propeller, with corrections for tip losses, Mach effects, three-dimensional flow components, and Reynolds scaling. Particular focus is placed on the estimation of scale effects not commonly encountered in the full-scale application of the BEM modeling method. Performance predictions are presented for geometries representative of several commercially available propellers. These predictions are then compared to experimental wind tunnel measurements of the propellers’ performance. The experimental data supports the predictions of the proposed BEM model and points to the importance of scale effects on prediction of the overall system performance.

Blade Element Momentum Modeling of Low-Reynolds Electric Propulsion Systems

A model for the propulsion system of a small-scale electric unmanned aerial system is presented. This model is based on a blade element momentum (BEM) model of the propeller, with corrections for tip losses, Mach effects, three-dimensional flow components, and Reynolds scaling. Particular focus is placed on the estimation of scale effects not commonly encountered in the full-scale application of the BEM modeling method. Performance predictions are presented for geometries representative of several commercially available propellers. These predictions are then compared with experimental wind tunnel measurements of the propellers’ performance. The experimental data support the predictions of the proposed BEM model and point to the importance of scale effects on prediction of the overall system performance.

Flight Test Protocol for Electric Powered Small Unmanned Aerial Systems

Flight testing procedures for a small scale Unmanned Aerial System are presented. The major performance parameters estimated include the zero-lift drag coefficient, span efficiency factor, lift-to-drag ratios, and excess power across a range of airspeeds. A ground based experimental method for measuring the thrust and power performance of a commercially available electric motor system is detailed. These parameters are compared to the first order design estimates in order to close the design loop. A detailed discussion of the onboard data acquisition and sensor suite is provided. Unique features found in the resulting flight test data were used to further correct sensors errors in the inertial navigation system, improving attitude estimation performance by an order of magnitude. Flight test results for a small scale aircraft are presented detailing the power available, power required, lift-todrag ratio, and rate of climb.

Accuracy of Smartphone-Based Barometry for Altitude Determination in Aircraft Flight Testing

Low-cost digital data acquisition systems have substantially transformed how aircraft flight testing is taught at The Ohio State University. With the advent of smartphones with built-in sensor suites, students now have access to modern data acquisition techniques and an expanded envelope of aircraft characteristics that can be measured in flight, and with much greater fidelity. A recent AIAA conference paper (Gregory and Jensen, AIAA-2012883) details the initial implementation of digital data acquisition techniques for flight testing education, with a specific focus on smartphone-based systems. However, that work showed that altitude measurement with smartphone-based GPS was highly erroneous. This work seeks to assess the accuracy of barometric pressure sensors on smartphones for measurement of altitude in flight, and how well cabin pressure may be used as a proxy for freestream static pressure. Results presented here indicate a measurement accuracy of ±40 ft, which improves to ±15 if the difference between freestream static and cabin pressures is calibrated and accounted for.