Turbulence parameters, in particular integral length scale (ILS) and turbulence intensity (Tu), are key input parameters for various applications in aerodynamics and aeroacoustics. The estimation of these parameters is typically performed using data obtained via hot-wire measurements. On the one hand, hot-wire measurements are affected by external disturbances resulting in increased measurement noise. On the other hand, commonly applied turbulence parameter estimators lack in robustness. If not addressed correctly, both issues may impede the accuracy of the turbulence parameter estimation. In this article, a procedure consisting of several signal processing steps is presented to filter non-turbulence related disturbances from the unsteady velocity data. The signal processing techniques comprise time- and frequency-domain approaches. For the turbulence parameter estimation, two different models of the turbulence spectra—the von Kármán model and the Bullen model—are fitted to match the spectrum of the measured data. The results of several parameter estimation techniques are compared. Computational Fluid Dynamics (CFD) data are used to validate the estimation techniques and also to assess the influence of the variation in window size on the estimated parameters. Additionally, hot-wire data from a high-speed fan rig are analyzed. ILS and Tu are assessed at several radial positions for two fan speeds. It is found that most techniques yield similar values for ILS and Tu. The comparison of the fitted spectra with the spectra of the measured data shows a good agreement in most cases provided that a sufficiently fine frequency resolution is applied. The ratio of ILS and Tu of the velocity components in longitudinal and transverse direction allows the assessment of flow-isotropy. Results indicate that the turbulence is anisotropic for the investigated flow fields.

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The separation of turbulence parameters such as turbulence intensity and integral length scale of fan rotor wakes from background flow from unsteady veloci