TY - GEN

T1 - Optimal Sensor Placement and Model Updating of Axial Compressor Casing Components

AU - Amer, Mona

AU - Schmid, Simon

AU - Paehr, Martin

AU - Panning-von Scheidt, Lars

AU - Seume, Joerg R.

N1 - Funding Information: Acknowledgments The authors kindly thank the Forschungsvereinigung Verbrennungskraftmaschinen e.V. (FVV) for funding this research project.

PY - 2023

Y1 - 2023

N2 - Experimental modal analysis of complex structures requires a good sensor concept to capture the component’s vibrational behavior ideally. Especially, when the geometry is big or consists of many attached parts and the number of available sensors is limited, the sensor placement in order to maximize the gained output information is relevant. For this purpose, several methods and algorithms are implemented and tested on the stator housing of an axial compressor test rig. These procedures include among others a genetic algorithm, an artificial bee algorithm, a method based on the QR-decomposition, as well as the effective independence method. This work contributes to the provision of more validation data for real structures, which are rarely found in the literature. The resulting measurement data are analyzed in order to evaluate the qualification of the methods presented for modal parameter extraction. The positioning techniques are compared among each other in order to choose the most suitable method for the modal identification of a complex mechanical structure. Additionally, the experimental results are compared to results from a finite element model. In order to improve the fit between experimental data and numerical results, a model updating procedure is carried out.

AB - Experimental modal analysis of complex structures requires a good sensor concept to capture the component’s vibrational behavior ideally. Especially, when the geometry is big or consists of many attached parts and the number of available sensors is limited, the sensor placement in order to maximize the gained output information is relevant. For this purpose, several methods and algorithms are implemented and tested on the stator housing of an axial compressor test rig. These procedures include among others a genetic algorithm, an artificial bee algorithm, a method based on the QR-decomposition, as well as the effective independence method. This work contributes to the provision of more validation data for real structures, which are rarely found in the literature. The resulting measurement data are analyzed in order to evaluate the qualification of the methods presented for modal parameter extraction. The positioning techniques are compared among each other in order to choose the most suitable method for the modal identification of a complex mechanical structure. Additionally, the experimental results are compared to results from a finite element model. In order to improve the fit between experimental data and numerical results, a model updating procedure is carried out.

KW - Axial compressor

KW - Experimental modal analysis

KW - Model update

KW - Model validation

KW - Sensor positioning

UR - http://www.scopus.com/inward/record.url?scp=85135813139&partnerID=8YFLogxK

U2 - 10.1007/978-3-031-05445-7_1

DO - 10.1007/978-3-031-05445-7_1

M3 - Conference contribution

AN - SCOPUS:85135813139

SN - 9783031054440

T3 - Conference Proceedings of the Society for Experimental Mechanics Series

SP - 1

EP - 10

BT - Topics in Modal Analysis & Parameter Identification, Volume 8

A2 - Dilworth, Brandon J.

A2 - Marinone, Timothy

A2 - Mains, Michael

CY - Cham

T2 - 40th IMAC, A Conference and Exposition on Structural Dynamics, 2022

Y2 - 7 February 2022 through 10 February 2022

ER -