Bipolar electric field induced degradation in [001]c poled Pb(Mg1/3Nb2/3)O3-0.29PbTiO3 (PMN-0.29PT) single crystals was investigated at megahertz frequencies. The electromechanical coupling factor kt , dielectric constant εr , dielectric loss D, and piezoelectric constant d33 were measured as a function of amplitude, frequency, and number of cycles of the applied electric field. Our results showed that samples degrade rapidly when the field amplitude is larger than a critical value due to the onset of domain switching. We define this critical value as the effective coercive field Ec at high frequencies, which increases drastically with frequency. We also demonstrate an effective counter-depoling method by using a dc bias, which could help the design of high field driven devices based on PMN-PT single crystals and operated at megahertz frequencies.
The frequency dependence of the coercive field Ec in [001]c poled 0.71Pb(Mg1/3Nb2/3)O3-0.29PbTiO3 single crystals was investigated as a function of frequency f from 0.01 Hz to 5 MHz. Ec was found to be proportional to [Formula: see text] as predicted by the Ishibashi and Orihara model, but our results showed two frequency regimes separated at around 1.0 MHz with different β values. This change of switching kinetics may be due to the presence of slower relaxation times for non-180° domain switching and heterogeneous nucleation of polar nanoregions, whose contribution to polarization reversal is frozen out beyond 1.0 MHz, leading to a larger β.
The realization of automatic frequency tracking is of great significance in ultrasonic transducer due to its wide application area in nowadays industrial manufacture. However, excessive overshoot current, serious overheating and long response time remain the urgent issue. To tackle these existing ultrasonic frequency tracking problem, this paper proposes a hybrid technique that integrates fuzzy control theory with PID control on the system. The technique involves coarse adjustment by fuzzy control and accurate adjustment by PID. Then, direct digital synthesizer (DDS) is used to generate required PWM wave with high accuracy. Initial driving frequency of the system and its corresponding phase difference of the feedback voltage and current can both be acquired during sweep frequency test before the tracking process, which corresponds to the parallel resonant point where the current is the minimum. This phase difference is also regarded as the target tracking value. During the tracking process, fuzzy control will be adopted when phase difference value error between feedback value and target value is greater than 10%, or PID will be used. Current difference AI and change rate AI/Af are used as two input values while driving frequency is the output value in fuzzy control when coarse adjustment is needed. Incremental PID arithmetic is used when accurate adjustment is needed. The result shows that the technique can reduce overshoot current of the ultrasonic transducer by approximately 8%, shorten the time for achieving stabilization of the system by 12%. In conclusion, this novel hybrid technique can quickly and stably track the frequency of parallel resonant point of the ultrasonic transducer system, meanwhile keep the whole system operating more efficiency and stable.
Traditional gas compressibility factor estimation methods such as AGA8-92DC and SGERG-88 usually use overly complex theoretical derivation and corresponding estimation model. This will cost most of the operating memory of the low-power gas flowmeter. Therefore, the previous models are not suitable for application on the flowmeter using the low-power embedded chips. To solve this problem, this paper proposed a novel efficient soft computing model for natural gas compressibility factor based on Group Method of Data Handling(GMDH) neural network. First, the signal of working conditions such as temperature, pressure and gas mole fraction of components are used to calculate pseudo-critical pressure and pseudo-critical temperature. Second, the soft computing model based on GMDH neural network with Corrected Akaike's Information Criterion (AICc) is utilized by using pseudo-critical pressure and pseudo-critical temperature as training sets. For the four common natural gas types, the estimated results show that the mean absolute percentage error is only 0.0168% and the computing time is effectively reduced. It also proved that the GMDH neural network can significantly reduce the computing time and improve the accuracy of the compressibility factor. Feasibility and effectiveness of this model was verified. Our work provides a very useful way and also make it possible to real-timely estimate the natural gas compressibility factor in low-power flowmeter under the premise of satisfying the accuracy.
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