- Patent Title: Machine learned aero-thermodynamic engine inlet condition synthesis
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Application No.: US16265319Application Date: 2019-02-01
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Publication No.: US12180892B2Publication Date: 2024-12-31
- Inventor: Timothy J. Crowley , Ramesh Rajagopalan , Sorin Bengea
- Applicant: RTX Corporation
- Applicant Address: US CT Farmington
- Assignee: RTX Corporation
- Current Assignee: RTX Corporation
- Current Assignee Address: US CT Farmington
- Agency: CANTOR COLBURN LLP
- Main IPC: F02C7/057
- IPC: F02C7/057 ; G05B13/02 ; G06N3/08

Abstract:
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.
Public/Granted literature
- US20200248622A1 MACHINE LEARNED AERO-THERMODYNAMIC ENGINE INLET CONDITION SYNTHESIS Public/Granted day:2020-08-06
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