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Title: On the characterization of measured geometry for the facilitation of uncertainty propagation and robust design
Author: Forrester, Jennifer A.
ISNI:       0000 0004 8510 4350
Awarding Body: University of Southampton
Current Institution: University of Southampton
Date of Award: 2019
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Political, environmental, and economic factors are driving an increased need to understand variability in aerospace product performance. Although there is a plethora of geometric inspection data gathered over many years, and an array of methods for informing design decisions based on statistically defined uncertainty, uncertainty in performance due to geometric variability is seldom assessed beyond the modification of geometric design parameters in existing analyses. The practical use of these approaches is limited by the cost of simulation, the lack of accurate statistical definition of geometric variation, and the complexity involved in modifying geometric definitions for their implementation in existing analysis. In this thesis, methods are developed to enable three sets of inspection measurement data from aero-engine components to further inform design. In the first case, turbine disc firtree slot flank geometry is characterized using existing parameters, fitted to point cloud measurement data using particle swarm optimization. In the second case, measurements of combustor features correspond directly to existing parameters. Implementation of changes to those parameters within the analysis mesh is achieved using a modified polar coordinate based mesh morphing algorithm with surrogate model based transformations. The final data sets are point cloud measurements along a blade firtree flank and have a form that differs from the design. Here, both defining the uncertainty and applying the geometric changes within the simulation require novel application of existing techniques. The complex shape of the variable geometry is modelled using a combined Kriging and principal component analysis based approach, and realized through radial basis function morphing of an existing mesh. The techniques enable the facilitation of design optimization in an uncertainty framework. This is achieved by providing an automated approach to producing a data-based reduced set of uncertain geometric variables, and associated probability distributions, from which appropriate designs of experiment can be sampled. The computer experiments can be executed using the free form deformation based mesh morphing methods for integration of the uncertainty with existing complex workflows.
Supervisor: Keane, Andrew Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID:  DOI: Not available