Sound & Vibration

Experimental and Numerical Study of the key Non-Dimensional Geometrical Parameters on the Noise Level of Dry-Type Cast Resin Transformers

2020-05-21T08:03:00+08:00

Dry-Type Cast Resin Distribution Transformers (CRT) is the second-generation of air-cooled distribution transformers where oil is replaced by resin for electrical insulation. CRT transformers may installed indoor adjacent to or near residential areas since they are clean and safe comparing to the conventional transformers. But, as it is obvious, noise discrepancy is intrinsically accompanied with all types of transformers and is inevitable for CRT transformers too. Minimization of noise level caused by such these transformers has biological and ergonomic importance. As it is known the core of transformers is the main source of the noise generation. In this paper, experimental and numerical investigation is implemented for a large number of fabricated CRT transformers in ZDT Co (Zangan Distribution Transformer Company) to evaluate the effective geometrical parameters of the core on the overall sound level of transformers. Noise Level of each sample is measured according to criteria of IEC60651 and is reported in units of Decibel (dB). Numerical simulation is done using noncommercial version of ANSYS Workbench software to extract first six natural frequencies and mode shapes of CRT cores which is reported in units of Hz. Three novel non-dimensional variables for geometry of the transformer core are introduced. Both experimental and numerical results show approximately similar response to these variables. Correlation between natural frequencies and noise level is evaluated statistically. Pearson factor shows that there is a robust conjunction between first two natural frequencies and noise level of CRTs. Results show that noise level decreases as the two first natural frequencies increases and vice versa, noise level increases as the two natural frequencies of the core decreases. Finally the noise level decomposed to two parts.

A Novel Method for damping of complex mechanical systems

2020-05-21T08:03:01+08:00

Taking the complex mechanical systems as the research project, a theoretical model with 500 degree of freedom (DOF) was established on MATLAB. Based on the vibration characteristics analysis of this system, a novel method of damping was proposed, which can be applied to most of the complex mechanical systems. Through this method, limited grounding stiffness was made use of and added to certain DOF discretely. Thus, the root-mean-square of the systems amplitude can be reduced to ideal level. Provided a MATLAB code based on this method and test it on the theoretical model. Consider that complex mechanical systems are nonlinear and uncertain, theoretically the optimal solution of damping is inaccessible. However, this method can always provide a relatively effective solution.

Improvement of the sound absorption properties of flexible polyurethane (PU) foam using nanofibers and nanoparticles

2020-05-21T08:03:01+08:00

Polyurethane foam as the most well-known absorbent materials has a suitable absorption coefficient only in a limited frequency range. The aim of this study was to improve the sound absorption coefficient of flexible polyurethane (PU) foam in the range of various frequencies using clay nanoparticles, polyacrylonitrile nanofibers and polyvinylidine fluoride nanofibers. The response surface method was used to determine the effect of nanofibers of PAN and PVDF, clay nanoparticles, absorbent thickness and air gap on the sound absorption coefficient of flexible polyurethane foam (PU) in different frequency ranges. The absorption coefficient of the samples was measured using Impedance Tubes device. Nano clay at low thicknesses and polyacrylonitrile nanofibers and polyvinylfluoride nanofibers at higher thicknesses have a greater effect on absorption coefficient. The average sound absorption coefficient in composite with the highest absorption coefficient was respectively 0.798 and 0.75 at middle and high frequencies, respectively that, in comparison with pure polyurethane foam with the same thickness and air gap was respectively 2.22 times the middle frequencies and 1.47 times at high frequencies. Surface porosity increases with increasing nano clay, but decreases with increasing polyacrylonitrile nanofibers and polyvinyl fluoride nanofibers. The results showed that the absorption coefficient increased with increasing thickness and air gap. This study suggests that the use of a combination of nanoparticles and nanofibres can enhance the acoustic properties of flexible polyurethane foam.

Integrated Condition Monitoring of Large Captive Power Plants and Aluminum Smelters

2020-05-21T08:03:02+08:00

Condition monitoring is implementation of the advanced diagnostic techniques to reduce downtime and to increase the efficiency and reliability. The research is for determining the usage of advanced techniques like Vibration analysis, Oil analysis and Thermography to diagnose ensuing problems of the Plant and Machinery at an early stage and plan to take corrective and preventive actions to eliminate the forthcoming breakdown and enhancing the reliability of the system. Now days, the most of the industries have adopted the condition monitoring techniques as a part of support system to the basic maintenance strategies. Major technique that they follow is Vibration Spectrum Analysis, which can detect faults at a very early stage. But implementation of other techniques like Oil analysis or Ferrography, Thermography etc. can further enhance the data interpretation as they would detect the source of abnormality at much early stage thus providing us with a longer lead time to plan and take the corrective actions. In Large Captive Power Plants and Aluminium Smelters, Integrated Condition Monitoring techniques play an important role as stoppage of primary system and its auxiliaries (boiler, steam turbine, generator, coal and ash handling plants etc.) results into the stoppage of the entire plant, which in turn leads to loss of productivity. From economical and operational point of view, it is desirable to ensure optimum level of system availability.

Extrapolation for Aeroengine Gas Path Fault with SVM Bases on Genetic Algorithm

2020-05-21T08:03:03+08:00

Mining aeroengine operational data and developing pattern recognition model for the faults of aeroengines become the focus for engine fault diagnosis. Because of the complexity of working environment and fault types for aeroengines, it is unavoidable that the monitoring parameters vary widely and possess larger noise levels. This paper, for the first time, reports that the extrapolation of the air path fault model obtained from double-spool turbofan civil aviation engine is adopted for 20 kinds of air path fault diagnosis. This model is obtained with support vector machine (SVM) algorithm, together with genetic algorithm (GA), and based on the deviation of the monitoring parameters with the noise level of 10% for the training set. The SVM model (C = 24.7034; γ= 179.835) was extrapolated for the samples whose noise levels were larger than 10%. The accuracies of extrapolation for samples with the noise levels of 20% and 30% are 97% and 94%, respectively. Compared with the models reported on the same samples, the extrapolation results of the GASVM model are very satisfactory.