USA As Represented by Secretary of the Navy

Aurora Flight Sciences Corporation

Carl G Schaefer, Jr

David J Haas

Kelly M McCool

Francesco Giannini

Daniel B Cottrell

Martin Gomez

Dan Cottrell

Jean-Charles Lede

Herbert E Hunter

Tom Roberts

Timothy Nuhfer

Jonathan Grohs

Thomas Roberts

Steve Petullo

Dorian Colas

Brian Whipple

Tim Nuhfer

Jonathon Grohs

Herb Hunter

A hybrid propulsion aircraft is described having a distributed electric propulsion system. The distributed electric propulsion system includes a turbo shaft engine that drives one or more generators through a gearbox. The generator provides AC power to a plurality of ducted fans (each being driven by an electric motor). The ducted fans may be integrated with the hybrid propulsion aircraft's wings. The wings can be pivotally attached to the fuselage, thereby allowing for vertical take-off and landing. The design of the hybrid propulsion aircraft mitigates undesirable transient behavior traditionally encountered during a transition from vertical flight to horizontal flight. Moreover, the hybrid propulsion aircraft offers a fast, constant-altitude transition, without requiring a climb or dive to transition. It also offers increased efficiency in both hover and forward flight versus other VTOL aircraft and a higher forward max speed than traditional rotorcraft.

Hybrid propulsion vertical take-off and landing aircraft

The invention is directed to means, utilizing a neural network, for estimating helicopter airspeed at speeds below about 50 knots using only fixed system parameters as inputs to the neural network. The system includes: means for entering at least one initial parameter; means for measuring, in a nonrotating reference frame associated with the helicopter, a plurality of variable state parameters generated during flight of the helicopter; means for determining a plurality of input parameters based on the at least one initial parameter and the plurality of variable state parameters and for generating successive signals representing the input parameters; at least one equation representing a nonlinear input-output relationship between the input parameters and airspeed; memory means for storing the at least one equation and for successively receiving and storing signals from the determining means; and processing means responsive to signals received from the memory means for generating airspeed information based on the input parameters and the at least one equation.

Neural network based helicopter low airspeed indicator

The invention is directed to a system, utilizing a neural network, for estimating helicopter airspeed in the low airspeed flight range of below about 50 knots using only fixed system parameters as inputs to the neural network. The method includes the steps of: (a) defining input parameters derivable from variable state parameters generated during flight of the helicopter and measurable in a nonrotating reference frame associated with the helicopter; (b) determining the input parameters and a corresponding helicopter airspeed at a plurality of flight conditions representing a predetermined low airspeed flight domain of the helicopter; (c) establishing a learned relationship between the determined input parameters and the corresponding helicopter airspeed wherein the relationship is represented by at least one nonlinear equation; (d) storing the at least one nonlinear equation in a memory onboard the helicopter; (e) measuring real time values of the variable state parameters during low airspeed flight of the helicopter; (f) calculating real time values of the input parameters; (g) storing the real time values of the input parameters in the memory; (h) processing the real time values of the input parameters in accordance with the at least one nonlinear equation to determine real time airspeed; and (i) displaying the real time airspeed.

Neural network based method for estimating helicopter low airspeed

The invention is directed to a method, utilizing a neural network, for estimating helicopter airspeed in the low airspeed flight range of below about 50 knots using only fixed system parameters as inputs to the neural network. The method includes the steps of: (a) defining input parameters derivable from variable state parameters generated during flight of the helicopter and measurable in a nonrotating reference frame associated with the helicopter; (b) determining the input parameters and a corresponding helicopter airspeed at a plurality of flight conditions representing a predetermined low airspeed flight domain of the helicopter; (c) establishing a learned relationship between the determined input parameters and the corresponding helicopter airspeed wherein the relationship is represented by at least one nonlinear equation; (d) storing the at least one nonlinear equation in a memory onboard the helicopter; (e) measuring real time values of the variable state parameters during low airspeed flight of the helicopter; (f) calculating real time values of the input parameters; (g) storing the real time values of the input parameters in the memory; (h) processing the real time values of the input parameters in accordance with the at least one nonlinear equation to determine real time airspeed; and (i) displaying the real time airspeed.

Growing community of inventors

Woodbridge, VA, United States of America

Carl G Schaefer, Jr