High order sensitivity analysis of a mistuned blisk including intentional mistuning

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Pohle, L.; Tatzko, S.; von Scheidt, L.P.; Wallaschek, J.: High order sensitivity analysis of a mistuned blisk including intentional mistuning. In: Journal of Theoretical and Applied Mechanics (Poland) 55 (2017), Nr. 1, S. 353-368. DOI: https://doi.org/10.15632/jtam-pl.55.1.353

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Small deviations between turbine blades exist due to manufacturing tolerances or material inhomogeneities. This effect is called mistuning and usually causes increased vibration amplitudes and also a lower service life expectancy of bladed disks or so called blisks (bladed integrated disk). The major resulting problem is to estimate the maximum amplitude with respect to these deviations. Due to the probability distribution of these deviations, statistical methods are used to predict the maximum amplitude. State of the art is the Monte-Carlo simulation which is based on a high number of randomly re-arranged input parameters. The aim of this paper is to introduce a useful method to calculate the probability distribution of the maximum amplitude of a mistuned blisk with respect to the random input parameters. First, the applied reduction method is presented to initiate the sensitivity analysis. This reduction method enables the calculation of the frequency response function (FRF) of a Finite Element Model (FEM) in a reasonable calculation time. Based on the Taylor series approximation, the sensitivity of the vibration amplitude depending on normally distributed input parameters is calculated and therewith, it is possible to estimate the maximum amplitude. Calculating only a single frequency response function shows a good agreement with the results of over 1000 Monte-Carlo simulations.
License of this version: CC BY-NC 3.0 Unported
Document Type: Article
Publishing status: publishedVersion
Issue Date: 2017
Appears in Collections:Fakultät für Maschinenbau

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pos. country downloads
total perc.
1 image of flag of Germany Germany 100 52.63%
2 image of flag of China China 26 13.68%
3 image of flag of United States United States 22 11.58%
4 image of flag of Japan Japan 10 5.26%
5 image of flag of Italy Italy 4 2.11%
6 image of flag of United Kingdom United Kingdom 4 2.11%
7 image of flag of Norway Norway 2 1.05%
8 image of flag of Korea, Republic of Korea, Republic of 2 1.05%
9 image of flag of Iran, Islamic Republic of Iran, Islamic Republic of 2 1.05%
10 image of flag of France France 2 1.05%
    other countries 16 8.42%

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