Effect of type of processing on the microstructural features and mechanical properties of Al-Cu/SiC metal matrix composites

In this study, an aluminum based metallic matrix (Al-2wt.% Cu) was reinforced with SiC particulates using a conventional casting technique and a new disintegrated melt deposition technique. Microstructural characterization studies conducted on the samples taken from disintegrated melt deposition technique revealed a more uniform distribution of SiC particulates and good interfacial integrity between SiC particulates and metallic matrix when compared to the conventionally cast composite samples. Results of ambient temperature mechanical tests demonstrate an increase in 0.2% YS and ultimate tensile strength of samples taken from disintegrated melt deposition technique when compared with the unreinforced and conventionally cast composite samples. The results of microstructural characterization and mechanical testing were finally rationalized in terms of the nature of processing technique employed to reinforce Al-2wt.% Cu metallic matrix with SiC particulates.     

Asymptotic stabilization of some equilibria of an underactuated underwater vehicle

The stabilization problem for selected relative equilibria of an underactuated rigid body, modelling a simple underwater vehicle, moving in an ideal fluid is addressed. State feedback control laws achieving local asymptotic stability of a forward motion and of a diving/rising with forward/reverse motion are proposed. The control design exploits the Hamiltonian nature of the system to be controlled and it is based on the so-called interconnection and damping assignment (IDA) procedure. Simulation results complete the work.     

Application of the blind source separation method to feature extraction of machine sound signals

As the result of vibration emission in air, a machine sound signal carries important information about the working condition of machinery. But in practice, the sound signal is typically received with a very low signal-to-noise ratio. To obtain features of the original sound signal, uncorrelated sound signals must be removed and the wavelet coefficients related to fault condition must be retrieved. In this paper, the blind source separation technique is used to recover the wavelet coefficients of a monitored source from complex observed signals. Since in the proposed blind source separation (BSS) algorithms it is generally assumed that the number of sources is known, the Gerschgorin disk estimator method is introduced to determine the number of sound sources before applying the BSS method. This method can estimate the number of sound sources under non-Gaussian and non-white noise conditions. Then, the partial singular value analysis method is used to select these significant observations for BSS analysis. This method ensures that signals are separated with the smallest distortion. Afterwards, the time-frequency separation algorithm, converted to a suitable BSS algorithm for the separation of a non-stationary signal, is introduced. The transfer channel between observations and sources and the wavelet coefficients of the source signals can be blindly identified via this algorithm. The reconstructed wavelet coefficients can be used for diagnosis. Finally, the separation results obtained from the observed signals recorded in a semi-anechoic chamber demonstrate the effectiveness of the presented methods .     

Single-sided ventilation — Part II. further considerations

This paper extends an earlier report of measurements and computer predictions of flows in a cubical cavity exposed on one side to the scavenging effect of an external stream. Further measurements and photographs are presented; they are consistent with each other and with the predictions of the computational model. Specifically, comparisons are made between measured and predicted mean velocities and turbulent shear stresses, and photographs are used to elucidate features of the shear layers generated by accelerating and decelerating external flows. The computer model is used to deduce approximate relations between internal turbulent activity (measured by mean velocity, turbulence energy and shear stress) and external velocity, percentage ‘window’ opening and relative position of the plane walls upstream and downstream of the cavity.     

电厂电气运行中常见故障分析及其应对措施探讨

为了保证电厂的生产效率,必须要分析电气的运行特征,确定电气故障发生的原因,综合考虑多方面因素,选择切实可行的措施解决问题。简要分析了电厂电气运行常见故障发生原因,并提出了相应的解决措施。     

Important issues in a molecular dynamics simulation for characterising the mechanical properties of carbon nanotubes

This paper discusses several important issues in a molecular dynamics simulation for analysing carbon nanotubes and their mechanical properties. In particular, the paper addresses the problems in selecting appropriate inter-atomic potentials, number of thermostat atoms, thermostat techniques, time and displacement steps and number of relaxation steps to reach the dynamic equilibrium. Based on these, the structural changes of armchair and zigzag nanotubes and their mechanical properties are investigated. The Young's modulus and Poisson's ratio of the armchair tube are 3.96 and 0.15 TPa, respectively, and those of the zigzag tube are 4.88 and 0.19 TPa, respectively. The best simulation technique identified in this study predicts that the ultimate tensile strain of a carbon nanotube is around 40% before atomic bond breakage.     

Ag-Cu-In-Sn合金的结构特性

借助金相、X光衍射及SEM方法研究Ag-Cu-In-Sn合金的结构特性及合金各组元在合金相中的分布规律,还讨论了某些相结构对合金力学性能的影响。四元合金的化学成分是(wt％)57～63Ag,17～23Cu,(20—x)In,xSn,其中:Ag+Cu=80wt％,In+Sn=20wt％,2≤x≤16。     

化工设备故障诊断仿真模型设计及计算分析

通过化工设备的结构模型、功能模型建立仿真模型,在对仿真模型分析的基础上得到症状与故障之间的数量关系并进行分析计算,推断出潜在的故障模式,为设计化工设备故障诊断专家系统奠定基础。     

Structure damage diagnosis using neural network and feature fusion

A structure damage diagnosis method combining the wavelet packet decomposition, multi-sensor feature fusion theory and neural network pattern classification was presented. Firstly, vibration signals gathered from sensors were decomposed using orthogonal wavelet. Secondly, the relative energy of decomposed frequency band was calculated. Thirdly, the input feature vectors of neural network classifier were built by fusing wavelet packet relative energy distribution of these sensors. Finally, with the trained classifier, damage diagnosis and assessment was realized. The result indicates that, a much more precise and reliable diagnosis information is obtained and the diagnosis accuracy is improved as well.