Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.725496
Title: Automatically idealising quasi-axisymmetric component models to 2D axisymmetric finite element models
Author: Camacho Casero, Jorge
ISNI:       0000 0004 6423 9002
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2017
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Abstract:
At present, simulation is at the core of the product development cycle. To accelerate the design and simulation phases, it is necessary to idealise the CAD model prior to simulation activities. The strategy for model idealisation to be used depends on the original geometry and the type of analysis to be run. In industry, many components are either axisymmetric or quasi-axisymmetric, (i.e. largely axisymmetric with some non-axisymmetric features). Using 3D models for analising these components is computationally expensive and is often unpractical, especially at early stages of the design process when the design is constantly changing. For this reason, there is an ambition to use 2D-axisymmetric models to represent these quasi-axisymmetric components, with the section properties altered for the elements in the non-axisymmetric regions„to account for the actual component shape. The current industrial process of creating the 2D equivalent representation of a 3D model is manual, time consuming and requires significant skill. This thesis describes an innovative methodology which automatically creates a 2D-axisymmetric finite element (FE) model of a quasi-axisymmetric component from its 3D CAD model. The process has been implemented using the API to a commercial FE package. It creates the FE models in the order of minutes (< 2.5 minutes) for realistic aerospace gas turbine components. In a series of thermal analyses of an industrial Turbine Rear Structure, the results from the resulting axisymmetric FE model are demonstrated to be within 9% of the 3D FE model.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.725496  DOI: Not available
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