A Combined Finite Element-Multiple Criteria Optimization Approach for Materials Selection of Gas Turbine Components
Author(s) -
Ali Shanian,
Abbas S. Milani,
Natasha Vermaak,
Katia Bertoldi,
T. Scarinci,
M. Gerendás
Publication year - 2012
Publication title -
journal of applied mechanics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.69
H-Index - 97
eISSN - 1528-9036
pISSN - 0021-8936
DOI - 10.1115/1.4006461
Subject(s) - finite element method , design for manufacturability , operability , robustness (evolution) , computer science , material selection , combustor , mechanical engineering , weighting , aerospace , mathematical optimization , process engineering , materials science , structural engineering , engineering , mathematics , combustion , medicine , biochemistry , chemistry , organic chemistry , radiology , composite material , gene , software engineering , aerospace engineering
The design of critical components for aerospace applications involves a number of conflicting functional requirements: reducing fuel consumption, cost, and weight, while enhancing performance, operability and robustness. As several materials systems and concepts remain competitive, a new approach that couples finite element analysis (FEA) and established multicriteria optimization protocols is developed in this paper. To demonstrate the approach, a prototypical materials selection problem for gas turbine combustor liners is chosen. A set of high temperature materials systems consisting of superalloys and thermal barrier coatings is considered as candidates. A thermomechanical FEA model of the combustor liner is used to numerically predict the response of each material system candidate. The performance of each case is then characterized by considering the material cost, manufacturability, oxidation resistance, damping behavior, thermomechanical properties, and the FEA postprocessed parameters relating to fatigue and creep. Using the obtained performance values as design criteria, an ELECTRE multiple attribute decision-making (MADM) model is employed to rank and classify the alternatives. The optimization model is enhanced by incorporating the relative importance (weighting factors) of the selection criteria, which is determined by multiple designers via a group decision-making process. [DOI: 10.1115/1.4006461]
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