Ph.D. Proposal

Aaron Crawford

(Advisor: Prof. Marilyn Smith)

"Fidelity Assessment in Computational Predictions of Unsteady Loads for Multi-Rotor Vehicles"

Friday, July 26th 

2:00 p.m.

Montgomery Knight Room 317

Abstract

The advent of smaller, lighter, Urban/Advanced Air Mobility (U/AAM) and Unmanned Aerial Vehicles (UAV) focus on missions that are performed primarily in urban areas where safety is of the utmost importance. These vehicles can experience larger transients and gusts during normal operation due to a variety of factors. The flight environment can be impacted by prevailing winds and weather, the atmospheric boundary layer, and the influence of large structures and buildings. Studies done by researchers in Canada have shown across metropolitan cities, various street and building configurations produce highly variable wind speeds and turbulence levels.  As AAMs take flight in these unknown and unsteady conditions, the aerodynamics of the vehicle must be understood both under ideal flight conditions as well as in significant transient and gust conditions. Understanding the aerodynamic behavior of these vehicles is nontrivial as most concepts and designs do not conform to typical known helicopter configurations. Additionally, the understanding of aerodynamic effects in non-ideal conditions has implications for vehicle lifecycles, material safety, controls, pilot workload, and handling qualities. With AAMs, the conditions experienced may change radically between two vehicles operating in the same location and thus a wide range of flight conditions must be studied and understood. Understanding the aerodynamic impact of unsteady flight conditions, and the impact of these conditions on lifecycle analysis will allow AAM operators and original equipment manufacturers (OEM) to better design, build and operate these new vehicles with the highest safety.  This research proposed the development and implementation of Hybrid Navier-Stokes-Potential CFD methods to form a multi-fidelity study of AAM aerodynamics. To test transient conditions, velocity perturbations will be added to the freestream velocity experienced by the vehicle through the Field Velocity or Split Velocity Method. Finally, a simple neural network will be developed to predict the unsteady air loads across the vehicle for future work in predicting fatigue and component lifecycle. 

 Committee

• Prof. Marilyn Smith – Daniel Guggenheim School of Aerospace Engineering (advisor)
• Prof. Juergen Rauleder – Daniel Guggenheim School of Aerospace Engineering
• Prof. Graeme Kennedy – Daniel Guggenheim School of Aerospace Engineering