The Application of a Linear Microphone Array in the Quantitative Evaluation of the Blade Trailing-Edge Noise Reduction

This paper concerns the application of a linear microphone array in the quantitative evaluation of blade trailing-edge (TE) noise reduction. The noise radiation from the blades with straight and serrated TEs is measured in an indoor open-jet wind tunnel. The array data are processed using the inverse method based on the Clean algorithm based on spatial source coherence (Clean-SC). In order to obtain correct application and achieve the best effect for the microphone array test, the computing software for array data reduction is firstly developed and assessed by Sarradj’s benchmark case. The assessment results show that the present array data processing method has a good accuracy with an error less than 0.5 dB in a wide frequency range. Then, a linear array with 32 microphones is designed to identify the noise source of a NACA65(12)-10 blade. The performance of the Clean-SC algorithm is compared with the Clean algorithm based on point spread functions (Clean-PSF) method for experimentally identifying the noise sources of the blade. The results show that there is about a 2 dB error when using the Clean-PSF algorithm due to the interference of different aerodynamic noise sources. Experimental studies are conducted to study the blade TE noise reduction using serrated TEs. The TE noise for the blade with and without sawtooth configurations is measured with the flow speeds from 20 m/s to 70 m/s, and the corresponding Reynolds numbers based on the chord are from 200,000 to 700,000. Parametric studies of the sawtooth amplitude and wavelength are conducted to understand the noise reduction law. It is observed that the TE noise reduction is sensitive to both the amplitude and wavelength. The flow speed also affects the noise reduction in the serrated TEs. To obtain the best noise suppression effect, the sawtooth configuration should be carefully designed according to the actual working conditions and airflow parameters.

Fused CLEAN deconvolution for compact and diffuse emission

Context. CLEAN algorithms are excellent deconvolution solvers that remove the sidelobes of the dirty beam to clean the dirty image. From the point of view of the scale, there are two types: scale-insensitive CLEAN algorithms, and scale-sensitive CLEAN algorithms. Scale-insensitive CLEAN algorithms perform excellently well for compact emission and perform poorly for diffuse emission, while scale-sensitive CLEAN algorithms are good for both point-like emission and diffuse emission but are often computationally expensive. However, observed images often contain both compact and diffuse emission. An algorithm that can simultaneously process compact and diffuse emission well is therefore required. Aims. We propose a new deconvolution algorithm by combining a scale-insensitive CLEAN algorithm and a scale-sensitive CLEAN algorithm. The new algorithm combines the advantages of scale-insensitive algorithms for compact emission and scale-sensitive algorithms for diffuse emission. At the same time, it avoids the poor performance of scale-insensitive algorithms for diffuse emission and the great computational load of scale-sensitive algorithms for compact emission in residuals. Methods. We propose a fuse mechanism to combine two algorithms: the Asp-Clean2016 algorithm, which solves the computationally expensive problem of convolution operation in the fitting procedure, and the classical Högbom CLEAN (Hg-Clean) algorithm, which is faster and works equally well for compact emission. It is called fused CLEAN (fused-Clean) in this paper. Results. We apply the fused-Clean algorithm to simulated EVLA data and compare it to widely used algorithms: the Hg-Clean algorithm, the multi-scale CLEAN (Ms-Clean), and the Asp-Clean2016 algorithm. The results show that it performs better and is computationally effective.