The Assessment of Noise Estimation and Noise Control Work

Abstract

Because high noise levels can lead to psychological and physiological discomfort, the assessment of noise estimation and noise control becomes crucial. Six researches that have contributed in the fields of computational acoustics, machinery noise prediction, sound muffler design, and applied acoustics in microseismic signals are included in this special issue.

Gear box noise is one of the major noise and vibrational sources of rotating machineries in industry [1 –4]. The prediction of gear noise is essential before accurate noise abatement can be performed. D. Guo and G. Sun in exploring the gearbox axle noise generation assess the vibroacoustic analysis of the complete axle structure using the boundary element method.

Heterogeneitic absorbent material will significantly affect the acoustic performance of a silencer [5 –8]. F. D. Denia et al. establish a finite element model in analyzing the acoustic performance of automotive dissipative silencers which include a perforated duct with uniform axial mean flow and an outer chamber with heterogeneitic absorbent material. It has been found that bulk density heterogeneities seem to have a considerable influence on the transmission loss of automotive silencers.

Experimental studies on the source of tonal noise from centrifugal fans show that the source region is concentrated in the vicinity of the volute tongue [9]. There are multiple blades in this type of fan and the structure is compact. Therefore, it is difficult to capture the fan's internal flow field information by testing. S. Zhou and J. Wang then predict multiblade centrifugal fan noise using an aerodynamic and aeroacoustic analysis. Moreover, a hybrid technique for combining flow field calculations and acoustic analysis is applied to solve the multiblade centrifugal fan aeroacoustic problem.

Cau et al. [10] obtain a fully detailed relative flow pattern of the impeller by measuring an industrial-type centrifugal fan. Based on the long-eared owl wing, Chen et al. [11] developed a bionic fan in conjunction with the Taguchi method to optimize the aerodynamic performance of the bionic blade. The owl wing which has excellent flight characteristics at a low Reynolds number is thus disclosed. Xiaomin Liu and Xiang Liu by assessing the aerodynamic performance and noise of a bionic airfoil based on an owl wing develop a bionic airfoil at a relatively low Reynolds number. First, a bionic airfoil run at a Reynolds number of 12,300 is constructed. Secondly, the Large Eddy Simulation (LES) using the Smagorinsky model is adopted to simulate unsteady flow fields around the bionic airfoil and the standard NACA0006 airfoil. The acoustic sources are thus extracted from the unsteady flow fields’ data. Then, based on Lighthill's acoustic theory, the propagation of these acoustic sources will be obtained by solving the Ffowcs Williams-Hawkings (FW-H) equation. Results show that the lift-to-drag ratio of a bionic airfoil is higher than that of the traditional NACA 0006 airfoil because of its deeply concaved lower surface geometry.

Nowadays, rail service has become the best solution for urban transportation: it is fast, it has a large carrying capacity, it is efficient, its energy consumption is low, and it does not pollute the environment [12]. Similarly, sound quality for rail vehicles is essential. K. Hu et al. present sound quality evaluation and optimization for the interior noise of rail vehicles. Here, some interior noises are measured on site while the train is running in tunnel at a speed of 60 km/h. The sound pressure levels (SPLs), loudness, and sharpness and roughness of the measured noise are analyzed. An acoustical simulation of the carriage is performed using a finite element model. Moreover, an optimization of a sound quality is performed by using an experimental orthogonal design. An improvement of sound quality that shows a decrease of a 4.81 dB in the SPL and a 1.07 sone reduction in loudness is achieved.

The monitoring of microseisms began in the 1990s. The history of monitoring mine microseisms only encompasses about 20 years [13, 14]. S. Tang et al. explore the coal rock rupture microseismic signal using the wavelet transform, which is an important method in microseismic signal processing. The current study found that Coiflet and Symlet wavelets are suitable for analyzing coal rock microseismic signals. Sym 8 and Coif 2 wavelets were found to be suitable for analyzing and denoising coal rock microseismic signals. After Sym 8 wavelet denoising, the signal-to-noise ratio (SNR) and the root mean square error were 30.4184 and 1.3109E − 07, respectively. After Coif 2 wavelet denoising, the SNR and the root mean square error values were 35.2176 and 1.0312E − 07, respectively. The results will aid in the analysis and extraction of coal rock microseismic signals.

Consequently, we hope the overview will inform the reader of the innovative work that is now being done in the noise estimation and abatement.

Min-Chie ChiuM. G. PrasadK. T. ChenChao-Nan WangWojciech P. Rdzanek

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