United Technologies Corporation

Raytheon Technologies Corporation

Other

Rtx Corporation

Timothy J Crowley

Richard P Meisner

Chaohong Cai

David Lei Ma

Joshua Adams

Martin Richard Amari

Sorin Bengea

Manuj Dhingra

Tyler J Selstad

David Gelwan

Timothy J Gaudet

Mark E Lacour

Kevin Hendricks

Coy Bruce Wood

James W Fuller

Ramesh Rajagopalan

Vincent B Pizziconi

Robert H Luppold

Andrew B Thompson

Michael H Levesque

Ding Xibei

A system for neural network compensated aero-thermodynamic gas turbine engine parameter/inlet condition synthesis. The system includes an aero-thermodynamic engine model configured to produce a real-time model-based estimate of engine parameters, a machine learning model configured to generate model correction errors indicating the difference between the real-time model-based estimate of engine parameters and sensed values of the engine parameters, and a comparator configured to produce residuals indicating a difference between the real-time model-based estimate of engine parameters and the sensed values of the engine parameters. The system also includes an inlet condition estimator configured to iteratively adjust an estimate of inlet conditions based on the residuals and adaptive control laws configured to produce engine control parameters for control of gas turbine engine actuators based on the inlet conditions.

Machine learned aero-thermodynamic engine inlet condition synthesis

A hybrid electric propulsion system includes a gas turbine engine having at least one compressor section and at least one turbine section operably coupled to a shaft. The hybrid electric propulsion system includes an electric motor configured to augment rotational power of the shaft of the gas turbine engine. A controller is operable to determine hybrid electric propulsion system parameters based on a composite system model and sensor data, determine a prediction based on the hybrid electric propulsion system parameters and the composite system model, determine a model predictive control optimization for a plurality of hybrid electric system control effectors based on the prediction using a plurality of reduced-order partitions of the composite system model, and actuate the hybrid electric system control effectors based on the model predictive control optimization.

Adaptive model predictive control for hybrid electric propulsion

A control system for limiting power turbine torque (QPT) of a gas turbine engine includes a controller including a processor and memory configured to control the gas turbine engine, the controller including an engine control module that provides an effector command signal to a gas generator of the gas turbine engine; a power turbine governor module that outputs a preliminary torque request (QPT_req_pre); and a power turbine torque (QPT) optimal limiter module that outputs a maximum torque topper (QPT_max) to limit a power turbine speed overshoot of the gas turbine engine; wherein the controller outputs a minimum value between the preliminary torque request (QPT_req_pre) and the maximum torque topper (QPT_max) to the engine control module.

Systems and methods for optimal speed protection for power turbine governing

A hybrid electric propulsion system includes a gas turbine engine having at least one compressor section and at least one turbine section operably coupled to a shaft. The hybrid electric propulsion system includes an electric motor configured to augment rotational power of the shaft of the gas turbine engine. A controller is operable to determine an estimate of hybrid electric propulsion system parameters based on a composite system model and sensor data, determine a model predictive control state and a prediction based on the hybrid electric propulsion system parameters and the composite system model, determine a model predictive control optimization for a plurality of hybrid electric system control effectors based on the model predictive control state and the prediction using a plurality of reduced-order partitions of the composite system model, and actuate the hybrid electric system control effectors based on the model predictive control optimization.

A control system for limiting a power turbine torque of a gas turbine engine is disclosed. In various embodiments, the control system includes an engine control module configured to output an effector command signal to a gas generator of the gas turbine engine; a power turbine governor module configured to output to the engine control module a power turbine torque request signal; and a power turbine torque limiter module configured to output to the power turbine governor module a power turbine speed rate signal to limit a power turbine speed overshoot of the gas turbine engine.

Speed limiting for power turbine governing and protection in a turboshaft engine

A power turbine control system for a gas turbine engine may comprise a controller comprising one or more processors in communication with the gas turbine engine. The processors may comprise an engine control module configured to receive a torque request signal and generate a torque achieved signal. A rate of change of power turbine speed estimation module may generate an estimated rate of change of power turbine speed signal. A dynamic inversion power turbine governor module may generate the torque request signal based on the torque achieved signal and estimated rate of change of power turbine speed signal.

Systems and methods for power turbine governing

A system for controlling a plurality of hydraulic effectors operably connected to an engine to control engine parameters. The system also includes a plurality of sensors operably connected to measure a state or parameter of each effector, a pump configured to supply fluid to the plurality of effectors, and a controller operably connected to the plurality of sensors, the plurality of effectors, and the pump. The controller executes a method for an adaptive model-based control for controlling each effector, The method includes receiving a request indicative of a desired state for each effector, receiving a weighting associated each request, obtaining information about a current state of each effector, and updating an adaptive model based control (MBC) based upon the information. The method also includes generating a control command for an effector based upon the adaptive MBC and commanding the effector based upon the control command.

Model predictive control sub-system hydraulic flow management

A control system for a gas turbine engine, a method for controlling a gas turbine engine, and a gas turbine engine are disclosed. The control system may include a nozzle scheduler for determining an exhaust nozzle position goal based on a nozzle schedule of exhaust nozzle positions related to flight conditions. The control system may further include a control module for determining a control command for the gas turbine engine. The control command may include, at least, a fuel flow command and an exhaust nozzle position command and the control command may be based on, at least, the exhaust nozzle position goal and an estimated thrust value. The control system may further include an actuator for controlling the gas turbine engine based on the control command.

Optimal thrust control of an aircraft engine

A system for controlling a plurality of electromechanical effectors operably connected to an engine to control engine parameters. The system also includes a plurality of sensors operably connected to measure a state or parameter of each effector, a power supply configured to supply power to the plurality of effectors, and a controller operably connected to the plurality of sensors, the plurality of effectors, and the power supply. The controller executes a method for an adaptive model-based control for controlling each effector, The method includes receiving a request indicative of a desired state for each effector, receiving a weighting associated each request, obtaining information about a current state of each effector, and updating an adaptive model based control (MBC) based upon the information. The method also includes generating a control command for an effector based upon the adaptive MBC and commanding the effector based upon the control command.

Model predictive control sub-system power management

A method for controlling a gas turbine engine having a constrained model based control (CMBC) system. The method including obtaining information about a current and previous states of the engine, updating model data information in the CMBC and a parameter estimation system based on the obtained information, and identifying trends in the data based on the information. The method also includes diagnosing the engine, based on the identified trends, determining at least one of a new constraint, objective, initial condition, model characteristic, prediction horizon, and control horizon for the control system based on the diagnosing step if the diagnosing step identified a fault condition, and adapting the CMBC system based on the at least one new constraint, objective, initial condition, model characteristic, prediction and control horizon. The method further includes generating at least on control command based on the adapting and commanding an actuator based on the control command.

Fault-accommodating, constrained model-based control using on-board methods for detection of and adaption to actuation subsystem faults

According to an aspect, a method includes generating, by a computer processor, thermo-fluid parameter estimates of a thermal management system (TMS) of an engine based on sensed parameters and monitoring for TMS component failures based on the thermo-fluid parameter estimates and the sensed parameters. Thermo-mechanical parameter estimates are generated based on selected thermo-fluid parameters. Life usage estimates and life usage rate estimates are generated based on the selected thermo-fluid parameters and the thermo-mechanical parameter estimates. Life usage rate targets are generated based on external commands and the life usage estimates. Limits and goals are modified based on the life usage rate estimates, failure flags, and the life usage rate targets. A model predictive control is applied to command one or more TMS control components based on thermo-mechanical model parameters, the failure flags, and the limits and goals.

Thermal management system control and heat exchanger life extension

A system and methods are provided for controlling turboshaft engines. In one embodiment, a method includes receiving input signals for a collective lever angle (CLA) command and real-time power turbine speed (NP) of an engine, determining system data for engine effectors by the control unit based on the input signals for the collective lever angle (CLA) command and the real-time power turbine speed (NP) based on an integrated model for the turboshaft engine including a model of a gas generator section of the turboshaft engine and a model of a power turbine and rotor load section of the turboshaft engine. The method may also include determining control output based on model-based multi-variable control including optimization formulation and a constrained optimization solver. The method may also include outputting one or more control signals for control of the turboshaft engine.

Turboshaft engine control

A fuel system for a gas turbine engine includes, among other things, a plurality of components defining a plurality of localized nodes at distinct locations relative to a fuel flow path, each of the plurality of localized nodes characterized by a distinct set of failure parameters. One or more fuel sensors are configured to measure at least one fuel condition relating to flow through the fuel flow path. A fuel observation assembly is coupled to one or more engine sensors configured to measure at least one engine condition. The fuel observation assembly comprises an estimation module operable to calculate an expected condition of each of the plurality of localized nodes based upon the set of failure parameters, an observation module operable to calculate an observed condition of each of the plurality of localized nodes, the observed condition based on present values of the at least one fuel condition and the at least one engine condition, and a comparison module operable to determine an abnormality corresponding to a first one of the plurality of localized nodes based upon a comparison of the expected condition and the observed condition for each of the plurality of localized nodes.

Growing community of inventors

Tolland, CT, United States of America

Timothy J Crowley