SwePub - search: WFRF:(Qiao Baijie)

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1.	Li, Zepeng, et al. (author) Optimal microphone selection for aero-engine acoustic measurement 2024 In: Aerospace Science and Technology. - 1270-9638. ; 148 Journal article (peer-reviewed)abstract Acoustic testing is crucial in the aero-engine design process, providing valuable information about the sound field in terms of acoustic modes. The conventional use of a uniform sampling array limits the achievable resolution for a given number of sensors, necessitating the use of a large number of sensors to achieve high-resolution estimates. This limitation may be overcome using a non-uniform sensing array. In this work, we propose an optimal microphone selection scheme to determine which microphone placements that are preferable given the typically available a priori knowledge of the relevant acoustic modes. Using a weighted A-optimality criterion, we formulate the sensor selection problem as the one minimizing the expected variance of the resulting parameter estimates. Using both numerical and measured data sets, we illustrate how the proposed scheme allows for improved performance as compared to random and uniform sensor placement schemes.

Li, Zepeng, et al. (author)
Optimal microphone selection for aero-engine acoustic measurement
2024
In: Aerospace Science and Technology. - 1270-9638. ; 148
Journal article (peer-reviewed)abstract
- Acoustic testing is crucial in the aero-engine design process, providing valuable information about the sound field in terms of acoustic modes. The conventional use of a uniform sampling array limits the achievable resolution for a given number of sensors, necessitating the use of a large number of sensors to achieve high-resolution estimates. This limitation may be overcome using a non-uniform sensing array. In this work, we propose an optimal microphone selection scheme to determine which microphone placements that are preferable given the typically available a priori knowledge of the relevant acoustic modes. Using a weighted A-optimality criterion, we formulate the sensor selection problem as the one minimizing the expected variance of the resulting parameter estimates. Using both numerical and measured data sets, we illustrate how the proposed scheme allows for improved performance as compared to random and uniform sensor placement schemes.

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