This work examines novel impinging injector designs enabled by additive manufacturing that reduce forward pressure loss while maintaining high relative back-flow resistance (diodicity). A steady, non-reacting computational fluid dynamics (CFD) model is used to assess the hydraulic characteristics of fluidic diode features in a liquid bi-propellant impinging doublet-type injector configuration relevant to rocket propulsion applications. A design trade study is conducted to determine an effective fluidic diode feature to be implemented within the injector elements, constrained by practical considerations for additive manufacturing. Noteworthy increases in diodicity are achieved within the constraints of producibility relative to conventional designs. A complimentary transient, multiphase CFD model is used to evaluate propellant backflow behavior when subject to a high-pressure impulse within a downstream chamber. Preliminary results suggest that the diodicity is a relevant predictor of transient performance as injector stiffness decreases.

Unmanned aerial systems, commonly known as drones, present new opportunities to perform autonomous tasks. Handling qualities requirements for manned vertical lift aircraft have been well defined and documented. The need to define handling qualities requirements for vertical take-off and landing (VTOL) unmanned aerial systems (UAS) to meet mission demands is of paramount importance for all potential operators and procurement agencies. One way to relate handling qualities specifications of large-scale manned and subscale unmanned aircraft is through Froude dynamic scaling. Froude dynamic scaling based on hub-to-hub distance has shown great promise in relating the natural frequencies of scaled multicopters. There have been recent efforts to develop a VTOL-UAS handling qualities standard by scaling mission task elements and rating their performance through a Trajectory, Tracking, and Aggression (TTA) score. This paper proposes a new performance standard adapted from the TTA scoring method, along with a modified Cooper–Harper scale as a VTOL-UAS handling qualities framework that is consistent with the spirit of Aeronautical Design Standard 33 (ADS-33). These newly proposed performance standards were then validated through simulation and flight testing on a small hexacopter UAS, flown at the University of Portland. A key outcome of this work is the flight verification of a key dynamic response metric, the disturbance rejection bandwidth, and associated validation of Froude scaling for predicted handling qualities metrics.

Unmanned aerial systems, commonly known as drones, present new opportunities to perform autonomous tasks. Handling qualities requirements for manned vertical lift aircraft have been well defined and documented. The need to define handling qualities requirements for Vertical Takeoff and Landing (VTOL) Unmanned Aerial Systems (UAS) to meet mission demands is of paramount importance for all potential operators and procurement agencies. One way to relate handling qualities specifications of large-scale manned and sub-scale unmanned aircraft is through Froude dynamic scaling. Froude dynamic scaling based on hub-to-hub distance has shown great promise in relating the natural frequencies of scaled multicopters. There have been recent efforts to develop a VTOL-UAS handling qualities standard by scaling Mission Task Elements and rating their performance through a Trajectory, Tracking and Aggression (TTA) score. This paper proposes a new performance standard adapted from the TTA scoring method, along with a modified Cooper-Harper scale as a VTOL-UAS handling qualities framework that is consistent with the spirit of ADS-33. These newly proposed performance standards were then validated through simulation and flight testing on a small hexacopter UAS, flown at the University of Portland. A key outcome of this work is the flight verification of a key dynamic response metric, the disturbance rejection bandwidth, and associated validation of Froude scaling for predicted handling qualities metrics.