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A New Mistuning Identification Method Based on the Subset of Nominal System Modes Method

机译:基于标称系统模式方法子集的失谐识别新方法

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摘要

The mistuning problem of quasi-periodic structures has been the subject of numerous scientific investigations for more than 50years. Researchers developed reduced-order models to reduce the computational costs of mistuning investigations including finite element models. One question which has also high practical relevance is the identification of mistuning based on modal properties. In this work, a new identification method based on the subset of nominal system modes method (SNM) is presented. Different to existing identification methods where usually the blade stiffness of each sector is scaled by a scalar value, N identification parameters are used to adapt the modal blade stiffness of each sector. The input data for the identification procedure consist solely of the mistuned natural frequencies of the investigated mode family as well as of the corresponding mistuned mode shapes in the form of one degree-of-freedom per sector. The reduction basis consists of the tuned mode shapes of the investigated mode family. Furthermore, the proposed identification method allows for the inclusion of centrifugal effects like stress stiffening and spin softening without additional computational effort. From this point of view, the presented method is also appropriate to handle centrifugal effects in reduced-order models using a minimum set of input data compared to existing methods. The power of the new identification method is demonstrated on the example of an axial compressor blisk. Finite element calculations including geometrical mistuning provide the database for the identification procedure. The correct functioning of the identification method including measurement noise is also validated to show the applicability to a case of application where real measurement data are available.
机译:准周期结构的模糊问题一直是50多年来众多科学研究的主题。研究人员开发了降阶模型,以减少包括有限元模型在内的错误调查的计算成本。在实践中也有很高的相关性的一个问题是基于模态特性的雾化识别。在这项工作中,提出了一种基于名义系统模式方法(SNM)的子集的新识别方法。与通常通过标量值缩放每个扇区的叶片刚度的现有识别方法不同,N个识别参数用于调整每个扇区的模态叶片刚度。识别过程的输入数据仅包含被调查模式族的失谐固有频率以及每个扇区一个自由度形式的相应失谐模态。减少的基础包括所研究的模式族的已调整模式形状。此外,所提出的识别方法允许包括离心效应,例如应力硬化和自旋软化,而无需额外的计算工作。从这个角度来看,与现有方法相比,所提出的方法还适合使用最少的输入数据集处理降阶模型中的离心效应。在轴向压缩机叶盘的示例中演示了新识别方法的强大功能。包括几何模糊的有限元计算为识别过程提供了数据库。包括测量噪声在内的识别方法的正确功能也得到了验证,以显示适用于实际测量数据可用的情况。

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  • 来源
    《Journal of Engineering for Gas Turbines and Power》 |2020年第2期|021016.1-021016.10|共10页
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  • 作者单位

    Institute of Thermal Turbomachinery and Machinery Laboratory (ITSM) University of Stuttgart Stuttgart D-70569 Germany;

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