The nucleation and growth of Si nanoparticle produced by pulsed laser ablation in helium gas ambient is investigated via direct simulation Monte Carlo method with a real physical scale of target-substrate configuration. The nucleation area is important for the formation of Si nanoparticles, and the average size and size distribution of Si nanoparticles formed in this region depend on its range. The narrower the nucleation area and, therefore, the less the maximum times of collisions between Si atoms in the region, the smaller and the more uniform the Si nanoparticles. A nucleation and growth process is clearly observed. It is shown that the nucleation region and the nucleation growth internal is changing with time. The ambient gas pressure is important to nucleation region. The suitable pressure range under certain conditions is given and our simulated results are approximately in agreement with the previous experimental data.
An effective scheme of parameter identification based on wavelet neural network is presented for improving dynamic
performance of direct torque control system. The wavelet transform is localized in time-frequency domains, yielding
wavelet coefficients at different scales. This gives the wavelet transform much greater compact support for analysis of
signals with localized transient components. The input nodes of wavelet neural network are current error and change in
the current error and the output node is the stator resistance error. To fulfill the network structure parameter, the
improved least squares algorithm is used for initialization. The stator flux vector and electromagnetic torque are acquired
accurately by the parameter estimator once the instants are detected. This function can make induction motor operate
well in low region and can optimize the inverter control strategy. The simulation results show that the proposed method
can efficiently reduce the torque ripple and current ripple.
By combining wavelet analysis and neural network, a new approach for condition monitoring is presented for rotating
machine fault. The wavelet analysis can accurately localize the features of transient signal in time-frequency domains.
The wavelet transform technology is appropriate for processing of fault signals consisting of short-lived, high-frequency
components closely located in time as well as long duration components closely spaced in frequency. In a view of the
inter relationship of wavelet decomposition theory, the crucial components as features are inputted into radial basis
function for fault pattern recognition. In order to acquire the network parameters, the improved Levenberg-Marquardt
optimization technique is used for training process. By choosing enough samples to train wavelet network, the fault
pattern can be determined according to the output results. Also, the robustness of wavelet network for fault diagnosis is
discussed. The applied results show that the proposed method can improve the performance for real-time monitoring of
vibration fault.
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