Abstract: A system for establishing components of a gas turbine engine may include a constraints engine. The constraints engine may be operable to determine a set of constraints based on one or more input parameters and selected values for a group of interrelated design parameters that may be associated with respective elements of an array. The design parameters may correspond to respective components of a gas turbine engine. The constraints engine may be operable to communicate the constraints to a design tool. The design tool may be operable to select values for the elements of the array from respective ranges of selectable values within a design space of the respective components based on a design model associated with the design parameters. A method for establishing components of a system is also disclosed.

Abstract: A method of assessing the quality of a manufactured component includes the steps of manufacturing a component and generating data at each of a plurality of locations on the component. The generating data is passed to a machine learning branch. The generated data is compared to training data at each of the locations to determine whether the component is a functionally tolerant dimension at each of the plurality of locations. The manufactured component is accepted or rejected based upon a determined percentage chance the component is functionally tolerant or fails to be functionally tolerant, respectively, at each of the plurality of locations. A system is also disclosed.

Abstract: A method of assessing the quality of a manufactured component includes the steps of manufacturing a component and generating data at each of a plurality of locations on the component. The generated data is passed to a machine learning branch wherein the generated data is compared to training data at each of the plurality of locations and across a plurality of folds using K-fold validation to determine whether the component is of a functionally tolerant dimension at each of the plurality of sections. A system is also disclosed.

Abstract: A method of assessing the quality of a manufactured component includes the steps of manufacturing a component and generating data at each of a plurality locations through the component. The generated data is passed to a machine learning branch wherein the generated data is compared to training data at each of the plurality of locations to determine whether the component is of functionally tolerant dimensions at each of the plurality of locations. The manufactured component is accepted should the component be within functionally tolerant dimensions at each of the plurality of locations, and the component is rejected if the component fails to be within functionally tolerant dimensions respectively, at each of the plurality of locations. A system is also disclosed.

Abstract: A method of assessing the quality of a manufactured component includes the steps of manufacturing a component and generating data at each of a plurality of locations through the component. The generated data is passed to a machine learning branch. The generated data is compared to training data at each of the plurality of locations and across a plurality of folds using K-fold validation to determine whether the component is of a functionally tolerant dimension at each of the plurality of locations. The training data at each of the plurality of folds at each of the plurality of locations is from a common part. A system is also disclosed.

Abstract: A method includes the steps of manufacturing and generating data at each of a plurality of locations on the component. Passing the data to a testing branch which performs tests and reaches a conclusion as to whether the component is functionally tolerant at each of the plurality of locations. The data is also passed to a machine learning branch wherein it is compared to training data to determine whether the component is of a functionally tolerant dimension at each of the plurality of locations. The manufactured component accepts should both the testing branch and the machine learning branch determine the component is of functionally tolerant dimensions at the plurality of locations, and rejects the component if either of the testing branch or the machine learning branch determines the component fails to be functionally tolerant dimensions, respectively. A system is also disclosed.

Abstract: An example method of analyzing a structure includes generating initial simulation results for at least one finite element model, each of the at least one finite element models includes a plurality of nodes representing a structure. The initial simulation results simulate a response of the plurality of nodes to boundary conditions for a plurality of sets of input variable values. Each set of input variable values represents a different geometry of the structure or a different set of boundary conditions. The initial simulation results are decomposed into a plurality of patterns that indicate correlations between values in the initial simulation results. The plurality of patterns has a quantity that is less than the plurality of nodes. A respective emulator is created for each pattern. The initial simulation results are expanded by determining additional simulation results for the plurality of nodes using the emulators and additional sets of input variable values.

Abstract: An example method of analyzing a structure includes generating initial simulation results for at least one finite element model, each of the at least one finite element models includes a plurality of nodes representing a structure. The initial simulation results simulate a response of the plurality of nodes to boundary conditions for a plurality of sets of input variable values. Each set of input variable values represents a different geometry of the structure or a different set of boundary conditions. The initial simulation results are decomposed into a plurality of patterns that indicate correlations between values in the initial simulation results. The plurality of patterns has a quantity that is less than the plurality of nodes. A respective emulator is created for each pattern. The initial simulation results are expanded by determining additional simulation results for the plurality of nodes using the emulators and additional sets of input variable values.