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Erschienen in: Structural and Multidisciplinary Optimization 6/2019

04.01.2019 | Research Paper

A multi-objective optimization framework for robust axial compressor airfoil design

verfasst von: Ivo Martin, Lennard Hartwig, Dieter Bestle

Erschienen in: Structural and Multidisciplinary Optimization | Ausgabe 6/2019

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Abstract

Airfoil design for stationary gas turbines is a challenging task involving both aerodynamic and structural aspects. The paper describes a multidisciplinary optimization process for axial compressor airfoils which is able to find optimal designs w.r.t. multiple objectives and constraints starting from a reference design and very few specifications of the new compressor. The process allows to simultaneously execute arbitrarily many instances of design evaluation processes independently from each other, which speeds it up, not just due to parallelization, but also because fast-running low-fidelity evaluation may take the design lead at an early design stage, whereas high-fidelity evaluation processes simultaneously contribute with more reliable results on the actual performance. For consistency of aerodynamic and structural analysis, an innovative method for direct loaded-to-unloaded design transformation is incorporated. Additionally, the process accounts for design robustness by utilizing production tolerances as an optimization objective. Therefore, a procedure is developed which allows to find the production tolerance which may be allowed without violating any constraints. An application example demonstrates that the proposed optimization process incorporating automatic detection of failure-critical eigenmode bands is able to shift them such that structurally reliable, robust, and simultaneously aerodynamically efficient designs are obtained.

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Metadaten
Titel
A multi-objective optimization framework for robust axial compressor airfoil design
verfasst von
Ivo Martin
Lennard Hartwig
Dieter Bestle
Publikationsdatum
04.01.2019
Verlag
Springer Berlin Heidelberg
Erschienen in
Structural and Multidisciplinary Optimization / Ausgabe 6/2019
Print ISSN: 1615-147X
Elektronische ISSN: 1615-1488
DOI
https://doi.org/10.1007/s00158-018-2164-3

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