Uncertainties and Design Margins A robust design approach for jet engine component design

Abstract

Aerospace industry is driven by the need to develop new concepts and methods to handle the constraints of weight and performance efficiency, reliability, regulatory safety compliance, and cost-effectiveness. In parallel to these demands, engineers have to manage increasing design complexity by using Multi Disciplinary models and accelerate the product development cycles to be able to fulfil the market demands. To achieve high performing, robust and sustainable product design, a more informed management of design margins is required that minimizes or reduces the uncertainty. First part of study has employed a qualitative methodology where interviews have been conducted with experienced engineers. The interviews have focused the understanding and practical experience on use of design margins, uncertainty quantification. Furthermore, feasibility of adopting probabilistic tools in their day-to-day engineering workflows have been asked. The data gathered through the interview study has been analyzed and presented in AIM diagrams to establish the case for a computational study. It has been observed that the decisions on design margins are implicit and not in detail recorded, but are following previous design practices connected to the area. These challenges are addressed by in the second part of the study, where a generalized probabilistic framework are used into the existing designanalysis environment at GKN. In this work it is realized by using the workbench of ANSYS OptiSLang, which contains a workflow including sensitivity analysis to be able to assess parameters influence to the response, a deterministic design optimization to find the optimal combination of values for parameters. This can be finalized by robustness and reliability analysis to ensure the considered design satisfies the user defined robustness criteria expressed in terms of six sigma. The scope of the thesis further extents to show that robustness analysis can also be studied by integration of the mathematical framework of Probabilistic VMEA (variation mode and effect analysis) into the workflow of ANSYS OptiSLang. This integration is a challenge as well as an opportunity to make a more efficient algorithm for robustness analysis. In this study, a steel hook and a simplified steel lug (used in aerospace engines) have been used to illustrate the methodology with comparative results as well as showing the opportunities by using Probabilistic VMEA.