USA As Represented by Secretary of the Navy

David J Haas

Carl G Schaefer, Jr

Kelly McCool

Kelly M McCool

Eric J Silberg

Daniel P Everson

Miguel A Morales

Gene L Barndt

Example embodiments provide a propeller apparatus comprising at least one blade able to twist along a blade twist axis, a hub, a shaft connected to the hub and configured to rotate such that the at least one blade rotates about a shaft axis, and at least one offset propeller unit located in at least one of an upper surface and/or a lower surface of the at least one blade, the at least one offset propeller unit configured to provide thrust. In the propeller apparatus the thrust of the at least one offset propeller unit generates a force to cause the at least one blade to rotate about the shaft axis, and a moment about the blade twist axis of the at least one blade to cause the blade to have a blade pitch angle.

Offset propeller controlled rotor

According to typical inventive practice, a cylindrical or prolate spheroidal marine hull has two congruent contra-rotative propellers coaxially situated at or near its axial ends. Each propeller has plural blades mechanically and/or flexibly attributed with changeability of blade pitch angles and blade flap angles. A blade-pitch control system adjusts the individual blade pitch angles of both propellers. The blade-pitch control system may be electronically and/or mechanically actuated, and is capable of: (i) cyclically adjusting the blade pitch angles of the two propellers so as to select two respective blade-tip-path planes, each characterized by a direction of thrust that is associated with the blade flap angles and is generally perpendicular to the blade-tip-path plane; (ii) collectively adjusting the blade pitch angles of the two propellers so as to select two respective magnitudes of thrust. The cyclic and collective blade commands, algorithmically coordinated, determine the direction, orientation, and speed of the hull.

Marine propulsion-and-control system implementing articulated variable-pitch propellers

As the present invention's adaptation process is typically practiced, an observation made by one or more sensing element(s) is classified as being either recognized or unrecognized in the context of a knowledge base. If the observation is classified as being recognized and consistent, then the observation is assimilated into the knowledge base; otherwise, it is not assimilated. If the observation is classified as being unrecognized, then the observation is classified as being uncorroborated in the context of the knowledge base. Prior to being classified as being uncorroborated, the unrecognized observation is categorized in the context of the knowledge base and is associated with an outcome in terms of relationship between/among physical parameters. At the time that corroboration is determined, the observation (originally unrecognized) and its categorization-related and association-related information are assimilated into the knowledge base.

Smart sensor continuously adapting to a data stream in real time using both permanent and temporary knowledge bases to recognize sensor measurements

An algorithm housed within a rotorcraft, controls real time generation of signals reflecting sensor measurement of plural parameters in accordance with a programmed sequence operationally intermapped pursuant to a linear or non-linear equation with calculations therefrom of estimates to provide an accurate determination of flight usage from which to evaluate and detect excessive damage with respect to components of the aircraft.

Monitoring of aircraft usage

Input parameters which correspond to operational flight data of an aircraft within a predetermined flight domain, are defined through measured variable state parameters generated during aircraft flight, utilizing a neural network trained by exemplars corresponding to such variable state parameters and reference information on the aircraft for data processing of real time values of the variable state parameter measurement to calculate values of the input parameters and provide a corresponding output as a reliable estimate of aircraft flight data such as airspeed, sideslip and angle of attack.

Neural network system for estimation of aircraft flight data

The invention is directed to a helicopter health and usage monitoring system utilizing a neural network for estimating gross weight and center of gravity location from measured flight condition parameter inputs; and includes means for measuring a plurality of variable flight condition parameters during flight of the helicopter; memory means for successively receiving and storing parameter input signals as well as estimates of gross weight and center of gravity location; and processing means responsive to the signals received from the measurement means for generating the gross weight and center of gravity location estimates.

Neural network system for estimation of helicopter gross weight and

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.

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North Potomac, MD, United States of America

David J Haas