Carsten Spehr

Armin Goudarzi, Carsten Spehr, Steffen Herbold

This paper presents an Expert Decision Support System for the identification of time-invariant, aeroacoustic source types. The system comprises two steps: first, acoustic properties are calculated based on spectral and spatial information. Second, clustering is performed based on these properties. The clustering aims at helping and guiding an expert for quick identification of different source types, providing an understanding of how sources differ. This supports the expert in determining similar or atypical behavior. A variety of features are proposed for capturing the characteristics of the sources. These features represent aeroacoustic properties that can be interpreted by both the machine and by experts. The features are independent of the absolute Mach number which enables the proposed method to cluster data measured at different flow configurations. The method is evaluated on deconvolved beamforming data from two scaled airframe half-model measurements. For this exemplary data, the proposed support system method results in clusters that mostly correspond to the source types identified by the authors. The clustering also provides the mean feature values and the cluster hierarchy for each cluster and for each cluster member a clustering confidence. This additional information makes the results transparent and allows the expert to understand the clustering choices.

Armin Goudarzi, Carsten Spehr, Steffen Herbold

Beamforming is an imaging tool for the investigation of aeroacoustic phenomena and results in high dimensional data that is broken down to spectra by integrating spatial Regions Of Interest. This paper presents two methods that enable the automated identification of aeroacoustic sources in sparse beamforming maps and the extraction of their corresponding spectra to overcome the manual definition of Regions Of Interest. The methods are evaluated on two scaled airframe half-model wind-tunnel measurements and on a generic monopole source. The first relies on the spatial normal distribution of aeroacoustic broadband sources in sparse beamforming maps. The second uses hierarchical clustering methods. Both methods are robust to statistical noise and predict the existence, location, and spatial probability estimation for sources based on which Regions Of Interest are automatically determined.

Marius Lehmann, Daniel Ernst, Marc Schneider et al.

Beamforming methods for sound source localization are usually based on free-field Green's functions to model the sound propagation between source and microphone. This assumption is known to be incorrect for many industrial applications and the beamforming results can suffer from this inconsistency regarding both, accuracy of source power estimation, and accuracy of source localisation. The aim of this paper is to investigate whether the use of numerically calculated Green's functions can improve the results of beamforming measurements. The current test cases of numerical and experimental investigations consists of sources placed in a short rectangular duct. The measurement is performed outside the duct in a semi-anechoic chamber. A typical example for this kind of installation is a fan with a heat exchanger. The Green's functions for this test case are calculated numerically using the boundary element method. These tailored Green's functions are used to calculate the corresponding beamforming steering vectors. The weighting of the Green's functions in the steering vectors has a decisive influence on the beamforming results. A generalization of the common steering vector formulations is given based on two scalars. It is shown that arbitrary differentiable Green's functions can be used to find the correct source position or source power level by using the appropriate steering vector formulations. Beamforming measurements are performed using a loudspeaker as a reference source at different positions in the heat exchanger duct. The measurements are evaluated in the frequency domain and by means of different validation criteria it can be shown that the results with the numerical calculated Green's function are improved compared to free field beamforming especially at low frequencies.

Hans-Georg Raumer, Carsten Spehr, Thorsten Hohage et al.

This article discusses aeroacoustic imaging methods based on correlation measurements in the frequency domain. Standard methods in this field assume that the estimated correlation matrix is superimposed with additive white noise. In this paper we present a mathematical model for the measurement process covering arbitrarily correlated noise. The covariance matrix of correlation data is given in terms of fourth order moments. The aim of this paper is to explore the use of such additional information on the measurement data in imaging methods. For this purpose a class of weighted data spaces is introduced, where each data space naturally defines an associated beamforming method with a corresponding point spread function. This generic class of beamformers contains many well-known methods such as Conventional Beamforming, (Robust) Adaptive Beamforming or beamforming with shading. This article examines in particular weightings that depend on the noise (co)variances. In a theoretical analysis we prove that the beamformer, weighted by the full noise covariance matrix, has minimal variance among all beamformers from the described class. Application of the (co)variance weighted methods on synthetic and experimental data show that the resolution of the results is improved and noise effects are reduced.