水下对转螺旋桨流致辐射噪声机理与预报方法*

水下对转螺旋桨流致辐射噪声的预报对于水下目标的特征提取和分类识别具有重要意义。由桨叶的旋转引起的湍流场是水下对转螺旋桨流致辐射噪声的源场。分述了水下对转螺旋桨湍流边界层脉动、旋转干涉效应和空化效应引发的水动力噪声机制和研究进展,比较了目前工程应用中的3种对转螺旋桨流致辐射噪声预报方法的特点。在分析对转螺旋桨流致辐射噪声数值预报难点的基础上,综述了对转螺旋桨流致辐射噪声计算方法的研究进展,指出间接数值模拟方法是工程中进行对转螺旋桨流致辐射噪声预报的有效方法。     

星载海洋激光雷达最佳工作波长分析

星载激光雷达是实现海洋垂直剖面探测的有效工具,也是目前迫切需求的海洋光学遥感手段。对星载海洋激光雷达的波长参数进行评估对保证探测有效性具有重要意义。本文从探测深度和信噪比两方面分析了星载海洋激光雷达探测全球海洋的最佳波长。利用MODIS 10个波段的水体光学特性数据,估算全球海水探测深度及相应的最优波长;并根据太阳夫琅禾费暗线特性,对信号信噪比进行优化。结果表明:在探测深度方面,最优探测波长在488 nm波段的海洋占全球海洋面积的70%左右,并且全球95%以上的海域在488 nm波段的探测深度优于0.8倍的真光层深度;在信噪比方面,相对于488 nm波段,486.134 nm夫琅禾费暗线处采用0.1 nm带宽的滤光片可以将背景光强度降低70%,相应地回波信噪比整体提升了约5.0%。就全球海洋探测来说,使用486.134 nm作为探测波长可以提高探测深度,有效抑制太阳背景光,提高信噪比,因此,486.134 nm是星载海洋激光雷达的最佳工作波长。     

光与原子纠缠存储寿命与磁场噪声关系的实验研究

在冷原子系综中,利用自发拉曼散射过程产生光与原子纠缠,测量了恢复效率随存储时间的关系。实验结果显示在没有施加轴向磁场时的存储寿命仅仅只有40μs。而在施加轴向磁场的情况下,存储时间在50μs以后甚至在400μs时都可以测量到明显的恢复信号,存储寿命明显高于100μs,远高于未施加轴向磁场时的情况。对这个实验现象进行分析认为:原子所处的环境中存在磁场噪声的影响,当没有轴向磁场时,噪声会扰乱自旋波信号的相位;当有轴向磁场时,磁场噪声对自旋波相位的影响便被抑制了。     

Frequencies Rotation at High Sound Pressure Levels Toward Low Frequencies

Today, analyzing of sound pressure level and frequency is considered as an important index in human society. Sound experts believe that analyzing of these parameters can help us to better understanding of work environments. Sound measurements and frequency analysis did to fix the harmful frequency in all sections in Shiraz gas power plant with sound analyzer model BSWA 308. The sound pressure levels (L_P) and the one and one-third octave band were continuously measured in A and C weighting networks and slow mode for time response. Excel 2013 and Minitab 18.1 software used for statistical calculations. Results analyzed by Minitab 18.1 software. The highest harmful frequency in Shiraz Gas Power Plant (SGPP) was 50 Hz with 115 dB. The sound pressure level (SPL) ranged from 45 dB to 120 dB in one-third octave band and weighting network C. The maximum sound pressure level was in Craft electricity generator with 105.3 dB and 67 Hz. Sound pressure level in surrounded environment was 120 dB. According to the results, in this industry the sound pressure level exceeded the Occupational Exposure Level of Iran (OEL). The value of sound pressure level were higher than the Standard of occupational health. SGPP consumes 47000 cubic meters of natural gas per hour to produce 100 MW (Mega Watt) of electricity. It is very high and it is not economical and cost effective. These numbers indicate that the power plant’s efficiency is low. It could be concluded that the noise pollution is an important issue in these industries. Moreover, SGPP produce noise with loss energy. Frequencies rotation at high sound pressure levels toward low frequencies were happened.     

Noise Reduction in Spur Gear Systems

This article lists some tips for reducing gear case noise. With this aim, a static analysis was carried out in order to describe how stresses resulting from meshing gears affect the acoustic emissions. Different parameters were taken into account, such as the friction, material, and lubrication, in order to validate ideas from the literature and to make several comparisons. Furthermore, a coupled Eulerian–Lagrangian (CEL) analysis was performed, which was an innovative way of evaluating the sound pressure level of the aforementioned gears. Different parameters were considered again, such as the friction, lubrication, material, and rotational speed, in order to make different research comparisons. The analytical results agreed with those in the literature, both for the static analysis and CEL analysis—for example, it was shown that changing the material from steel to ductile iron improved the gear noise, while increasing the rotational speed or the friction increased the acoustic emissions. Regarding the CEL analysis, air was considered a perfect gas, but its viscosity or another state equation could have also been taken into account. Therefore, the above allowed us to state that research into these scientific fields will bring about reliable results.     

Optimization of Transducer Location for Novel Non-Intrusive Methodologies of Diagnosis in Diesel Engines

The health monitoring has been studied to ensure integrity of design of engine structure by detection, quantification, and prediction of damages. Early detection of faults may allow the downtime of maintenance to be rescheduled, thus preventing sudden shutdown of machines. In cylinder pressure developed, vibrations and noise emissions data provide a rich source of information about condition of engines. Monitoring of vibrations and noise emissions are novel non-intrusive methodologies for which positioning of various transducers are important issue. The presented work shows applicability of these diagnosis methodologies adopted in case of diesel engines. The effects of changing various fuel injection parameters was analyzed. Scope of using non-intrusive technique has been analyzed by changing locations of microphone. Novelty of this worklies in exploring signal processing methods for various locations around the engine test set up. Various frequency ranges of contributing noise and vibration sources were identified. Time-Frequency analysis showed the onset of various cyclic. Based on the identification of various frequency bands, it is possible to device suitable filters in order to extract more information.     

Benefiting from information-rich multi-frequency AC voltammetry coupled with chemometrics on the example of on-line monitoring of leveler component of electroplating bath

Simultaneous application of multiple sinusoidal waveforms perturbations superimposed onto DC staircase step significantly enriches current response. The measured current is characterized by a matrix of data rather than a conventional voltammetric output in a form of a vector. This increase of the dimensionality of the current response and therefore the wealth of analytical information is achieved without compromising the time of analysis. The natural approach for compression of such data and extraction of relevant information is by utilizing multi-way chemometric decomposition techniques. An electroplating solution presents a very challenging analyte for electroanalysis as its constituents interact synergistically with each other during both the plating process and its simulation during electroanalysis. For some components the mechanism is not entirely understood. Therefore, the only way to benefit from the analytical data is by employing soft modeling. The electrode processes involving additives rely heavily on adsorption and, indirectly, on electron transfer kinetics for which AC voltammetry is an analytical technique capable of delivering informative signals. This paper presents a rigorous universal method for calculating and validating an exemplary multi-way calibration of a leveler component in a copper electroplating bath used in the semiconductor industry. The method presented employs comparatively Parallel Factor Analysis coupled with Inverse Least Squares Regression and multi-linear Partial Least Squares. The calibration training set consists of multi-frequency AC voltammetric data subjected to pretreatments aiming to select informative independent variables and exclude outliers.