Simultaneous Robust Design and Tolerancing of Compressor Blades

The manufacturing processes used to create compressor blades inevitably introduce geometric variability to the blade surface. In addition to increasing the performance variability, it has been observed that introducing geometric variability tends to reduce the mean performance of compressor blades....

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Main Authors: Dow, Eric A., Wang, Qiqi
Other Authors: Massachusetts Institute of Technology. Aerospace Computational Design Laboratory
Format: Article
Language:en_US
Published: American Society of Mechanical Engineers 2015
Online Access:http://hdl.handle.net/1721.1/97548
https://orcid.org/0000-0001-9669-2563
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author Dow, Eric A.
Wang, Qiqi
author2 Massachusetts Institute of Technology. Aerospace Computational Design Laboratory
author_facet Massachusetts Institute of Technology. Aerospace Computational Design Laboratory
Dow, Eric A.
Wang, Qiqi
author_sort Dow, Eric A.
collection MIT
description The manufacturing processes used to create compressor blades inevitably introduce geometric variability to the blade surface. In addition to increasing the performance variability, it has been observed that introducing geometric variability tends to reduce the mean performance of compressor blades. For example, the mean adiabatic efficiency observed in compressor blades with geometric variability is typically lower than the efficiency in the absence of variability. This “mean-shift” in performance leads to increased operating costs over the life of the compressor blade. These detrimental effects can be reduced by using robust optimization techniques to optimize the blade geometry. The impact of geometric variability can also be reduced by imposing stricter tolerances, thereby directly reducing the allowable level of variability. However, imposing stricter manufacturing tolerances increases the cost of manufacturing. Thus, the blade design and tolerances must be chosen with both performance and manufacturing cost in mind. This paper presents a computational framework for performing simultaneous robust design and tolerancing of compressor blades subject to manufacturing variability. The manufacturing variability is modelled as a Gaussian random field with non-stationary variance to simulate the effects of spatially varying manufacturing tolerances. The statistical performance of the compressor blade system is evaluated using the Monte Carlo method. A gradient based optimization scheme is used to determine the optimal blade geometry and distribution of manufacturing tolerances.
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spelling mit-1721.1/975482022-10-01T07:54:59Z Simultaneous Robust Design and Tolerancing of Compressor Blades Dow, Eric A. Wang, Qiqi Massachusetts Institute of Technology. Aerospace Computational Design Laboratory Massachusetts Institute of Technology. Department of Aeronautics and Astronautics Wang, Qiqi Dow, Eric A. Wang, Qiqi The manufacturing processes used to create compressor blades inevitably introduce geometric variability to the blade surface. In addition to increasing the performance variability, it has been observed that introducing geometric variability tends to reduce the mean performance of compressor blades. For example, the mean adiabatic efficiency observed in compressor blades with geometric variability is typically lower than the efficiency in the absence of variability. This “mean-shift” in performance leads to increased operating costs over the life of the compressor blade. These detrimental effects can be reduced by using robust optimization techniques to optimize the blade geometry. The impact of geometric variability can also be reduced by imposing stricter tolerances, thereby directly reducing the allowable level of variability. However, imposing stricter manufacturing tolerances increases the cost of manufacturing. Thus, the blade design and tolerances must be chosen with both performance and manufacturing cost in mind. This paper presents a computational framework for performing simultaneous robust design and tolerancing of compressor blades subject to manufacturing variability. The manufacturing variability is modelled as a Gaussian random field with non-stationary variance to simulate the effects of spatially varying manufacturing tolerances. The statistical performance of the compressor blade system is evaluated using the Monte Carlo method. A gradient based optimization scheme is used to determine the optimal blade geometry and distribution of manufacturing tolerances. 2015-06-29T15:16:42Z 2015-06-29T15:16:42Z 2014-06 Article http://purl.org/eprint/type/ConferencePaper 978-0-7918-4561-5 http://hdl.handle.net/1721.1/97548 Dow, Eric A., and Qiqi Wang. “Simultaneous Robust Design and Tolerancing of Compressor Blades.” ASME Turbo Expo 2014: Turbine Technical Conference and Exposition Volume 2B: Turbomachinery (June 16, 2014). https://orcid.org/0000-0001-9669-2563 en_US http://dx.doi.org/10.1115/GT2014-25795 ASME Turbo Expo 2014: Turbine Technical Conference and Exposition Volume 2B: Turbomachinery Creative Commons Attribution-Noncommercial-Share Alike http://creativecommons.org/licenses/by-nc-sa/4.0/ application/pdf American Society of Mechanical Engineers Dow
spellingShingle Dow, Eric A.
Wang, Qiqi
Simultaneous Robust Design and Tolerancing of Compressor Blades
title Simultaneous Robust Design and Tolerancing of Compressor Blades
title_full Simultaneous Robust Design and Tolerancing of Compressor Blades
title_fullStr Simultaneous Robust Design and Tolerancing of Compressor Blades
title_full_unstemmed Simultaneous Robust Design and Tolerancing of Compressor Blades
title_short Simultaneous Robust Design and Tolerancing of Compressor Blades
title_sort simultaneous robust design and tolerancing of compressor blades
url http://hdl.handle.net/1721.1/97548
https://orcid.org/0000-0001-9669-2563
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