METHOD AND SYSTEM FOR MODELING THE PERFORMANCE OF A GAS TURBINE ENGINE
First Claim
1. A method for modeling the performance of a gas turbine engine, comprising the steps of:
- providing a processor;
inputting flight condition parameter data and engine output parameter data into a gas turbine engine model operating on the processor, which model includes a physics-based engine model that uses the flight condition parameter data to produce estimated engine output parameter data, and determines residual data from the engine output parameter data and the estimated engine output parameter data;
partitioning the flight condition parameter data and residual data into training data and testing data;
performing a correlation reduction on the training data, which analysis produces correlation adjusted training data;
performing an orientation reduction on the correlation adjusted training data, which reduction produces orientation adjusted training data;
reviewing the orientation adjusted training data relative to at least one predetermined criteria, and iteratively repeating the steps of performing a correlation reduction and an orientation reduction using the orientation adjusted training data, and reviewing if the criteria is not satisfied, and if the criteria is satisfied outputting the orientation adjusted training data;
producing estimated corrections to the orientation adjusted training data using one or more neural networks;
evaluating the neural adjusted data using the partitioned testing data; and
modeling the performance of the gas turbine using the estimated corrections to the orientation adjusted training data.
1 Assignment
0 Petitions
Accused Products
Abstract
A method for modeling the performance of a gas turbine engine is provided. The method includes the steps of: 1) providing a processor; 2) inputting flight condition parameter data and engine output parameter data into a gas turbine engine model operating on the processor, which model includes a physics-based engine model that uses the flight condition parameter data to produce estimated engine output parameter data, and determines residuals from the engine output parameter data and the estimated engine output parameter data; 3) partitioning the flight condition parameter data and residuals into training data and testing data; 4) performing a correlation reduction on the training data, which analysis produces correlation adjusted training data; 5) performing an orientation reduction on the correlation adjusted training data, which reduction produces orientation adjusted training data; 6) reviewing the orientation adjusted training data relative to at least one predetermined criteria, and iteratively repeating the steps of performing a correlation reduction and an orientation reduction using the orientation adjusted training data if the criteria is not satisfied, and if the criteria is satisfied outputting the orientation adjusted training data; 7) producing estimated corrections to the orientation adjusted training data using one or more neural networks; 8) evaluating the neural adjusted data using the partitioned testing data; and 9) modeling the performance of the gas turbine using the estimated corrections to the orientation adjusted training data.
82 Citations
13 Claims
-
1. A method for modeling the performance of a gas turbine engine, comprising the steps of:
-
providing a processor; inputting flight condition parameter data and engine output parameter data into a gas turbine engine model operating on the processor, which model includes a physics-based engine model that uses the flight condition parameter data to produce estimated engine output parameter data, and determines residual data from the engine output parameter data and the estimated engine output parameter data; partitioning the flight condition parameter data and residual data into training data and testing data; performing a correlation reduction on the training data, which analysis produces correlation adjusted training data; performing an orientation reduction on the correlation adjusted training data, which reduction produces orientation adjusted training data; reviewing the orientation adjusted training data relative to at least one predetermined criteria, and iteratively repeating the steps of performing a correlation reduction and an orientation reduction using the orientation adjusted training data, and reviewing if the criteria is not satisfied, and if the criteria is satisfied outputting the orientation adjusted training data; producing estimated corrections to the orientation adjusted training data using one or more neural networks; evaluating the neural adjusted data using the partitioned testing data; and modeling the performance of the gas turbine using the estimated corrections to the orientation adjusted training data. - View Dependent Claims (2, 3, 4, 5, 6, 7)
-
-
8. A system for modeling the performance of a gas turbine engine, comprising:
-
a processor adapted to receive flight condition parameter data and engine output parameter data, and adapted to have a physics based engine model that uses the flight condition parameter data to produce estimated engine output parameter data, and determines residual data from the engine output parameter data and the estimated engine output parameter data, wherein the processor includes; a partitioning module for partitioning the flight condition parameter data and residual data into training data and testing data; a training module having a correlation reduction module for reducing one or more correlations in the training data, which module produces correlation adjusted training data, an orientation reduction module for reducing one or more orientations in the correlation adjusted training data, which module produces orientation adjusted training data, and a review module for reviewing the orientation adjusted training data relative to at least one predetermined criteria, and directing the orientation adjusted training data iteratively within the training module if the criteria is not satisfied, and if the criteria is satisfied outputting the orientation adjusted training data; a neural network module for producing estimated corrections to the orientation adjusted training data using one or more neural networks; and an evaluation model for evaluating the neural adjusted data using the partitioned testing data; and wherein the processor is further adapted to model the performance of the gas turbine using the estimated corrections to the orientation adjusted training data. - View Dependent Claims (9, 10, 11, 12, 13)
-
Specification