考虑静水压力的压电弹性层合圆柱壳动力响应的控制模型

基于经典的层合板理论和Navier解法,对静水压作用下压电弹性层合圆柱壳的动力问题的主动控制进行了研究。首先由Hamilton原理导出压电弹性层合壳的非线性动力基本方程。利用压电材料的正、逆压电效应,通过闭环方式,采用速度反馈控制方法得到了任意形式动载作用下带压电感测层/激励层的简支层合圆柱壳动力响应的主动控制模型。数值算例中对于三种不同的外载条件下该控制模型对圆柱壳的动力响应的控制效果进行了研究。结果表明本文中提出的控制模型能够有效抑制动载作用下结构的振动。

静水压力下压电弹性圆柱振动的主动控制

对静水压力下压电弹性层合壳的振动控制进行了研究· 首先利用Hamiltion原理推导出压电弹性层合壳的非线性动力基本方程,进一步得到了静水压力作用下封闭压电弹性层合壳的动力方程· 对两端简支条件下的压电弹性圆柱壳的振动问题进行了求解,并基于速度反馈控制法得到了带压电感测层/激励层的层合圆柱壳的主动控制模型· 相应的数值结果表明在静载的情况下,压电层上施加合适大小、方向的电压可以改变圆柱壳的静变形;对于系统的动力响应问题,速度反馈的增益越大,越能抑制系统在共振区的振动。

Delineation of bed boundaries of array induction logging curves using deep learning

Delineation of bed boundaries based on resistivity logging curves is important prior information for the inversion and interpretation of resistivity logging data.Traditionally,the layering algorithm mainly use the derivatives of resistivity curves or other logging methods as reference.However,measurement error or resolution mismatch may lead to misjudgment of the boundary.In view of the shortcomings of traditional methods,this paper presents an automatic layering algorithm of array induction logging curves based on deep learning.In this algorithm,a locally connected convolution neural network is used,and the generalization ability of the network is improved by enlarging the training set,optimizing the window length and threshold,and strengthening the layering effect.Simulation and field data show the eff ectiveness of the proposed algorithm.

Design of capacitance sensor system for void fraction measurement

Simulation and optimization were applied to a capacitive sensor system based on electrical tomography technology. Sensors, consisting of Morgantown Energy Technology Center (METC) axial synchro driving guard electrodes and two sets of detecting electrodes, make it possible to obtain simultaneously two groups of signals of the void fraction in oil-gas two-phase flow. The computational and experimental results showed that available sensors, charactered by high resolution and fast real-time response can be used for real-time liquid-gas two-phase flow pattern determination.

Short-and Long-Term Response of Phytoplankton to ENSO in Prydz Bay, Antarctica: Evidences from Field Measurements, Remote Sensing Data and Stratigraphic Biomarker Records

The study provides one of the first lines of evidence showing linkages between Antarctic phytoplankton abundance and composition in response to ENSO, based on historical reconstruction of sediment biomarkers. In addition to sediment biomarkers, field measured and remote sensing data of phytoplankton abundance were also recorded from Prydz Bay, Eastern Antarctica. Com-munity structure of field measured phytoplankton showed significant El Ni？o/La Ni？a-related succession during 1990 to 2002. In general, the number of algae species decreased during El Ni？o and La Ni？a years compared to normal years. Austral summer monthly variation of remotely sensed chlorophyll-a （Chl-a）, particulate organic carbon （POC）, and sea surface temperature （SST） indicated that ENSO impacted the timing of phytoplankton blooms during 2007 to 2011. Phytoplankton blooms （indicated by Chl-a and POC） preceded the increases in SST during El Ni？o years, and lagged behind the SST increases during La Ni？a years. Stratigraphic record of marine sedimentary lipid （brassicasterol, dinosterol and alkenones） biomarkers inferred that the proportions of different algae （diatoms, dinoflagellates and haptophytes） changed significantly between El Ni？o and La Ni？a events. The relative proportion of diatoms increased, with that of dinoflagellates being decreased during El Ni？o years, while it was reversed during La Ni？a years.

Solubility prediction of disperse dyes in supercritical carbon dioxide and ethanol as co-solvent using neural network

Nowadays artificial neural networkS （ANNs） with strong ability have been applied widely for prediction of non- linear phenomenon. In this work an optimized ANN with 7 inputs that consist of temperature, pressure, critical temperature, critical pressure, density, molecular weight and acentric factor has been used for solubility predic- tion of three disperse dyes in supercritical carbon dioxide （SC-C02） and ethanol as co-solvent. It was shown how a multi-layer perceptron network can be trained to represent the solubility of disperse dyes in SC-C02. Numeric Sensitivity Analysis and Garson equation were utilized to find out the degree of effectiveness of different input variables on the efficiency of the proposed model. Results showed that our proposed ANN model has correlation coefficient, Nash-Sutcliffe model efficiency coefficient and discrepancy ratio about 0.998, 0.992, and 1.053 respectively.

Transition prediction of the supersonic boundary layer on a cone under the consideration of receptivity to slow acoustic waves

Transition prediction of the supersonic boundary layer on a cone with small angle of attack and Mach number 3.5 is investi-gated under the consideration of receptivity to slow acoustic waves, as the acoustic waves are the main environmental distur-bances in a conventional, i.e. non-quiet, wind tunnel. It is shown that the e-N method can still yield fairly satisfactory results incomparison with those obtained in wind tunnel experiments, provided that the boundary layer receptivity to slow acousticwaves is properly taken into account, including the dependence of the amplitude of disturbances on the frequency andstream-wise location. Neither the conventional e-N method nor the improved e-N method can yield correct result of transitionprediction, because the receptivity mechanisms considered there are not in accord with the real situation in the wind tunnel.

Study on Sensitivity Improving of Fiber Bragg Grating Based pH Sensor

The key factor of the sensitivity in the FBG-based pH sensor is analyzed in detail. A multi-thin-layer structure of the gel coated cover was proposed and implemented with a special process. The sensors with the coated thickness of 420 ~m, 500 ~m, and 580 ~m were built up, respectively. The corresponding spectral shifts of 0.08nm, 0.13nm, and 0.22nm were detected when the pH sensors were soaked in the pH value of 3-9. Meanwhile, the sensor with the gel layer thickness of 580 ~tm was measured in the optimum measurement time period with the pH value changing from 3-12, in which the detected sensitivity of 52pm/pH was achieved in the pH range of 6-12.

Fiber-Optic Surface Plasmon Resonance Sensor With Multi-Alternating Metal Layers for Biological Measurement

Optical fiber based SPR sensors have attracted more and more attention due to their unique advantages over the prism-based SPR sensors. A novel fiber-optic SPR sensor with multi-alternating metal layers for biochemical analysis is presented in this paper. Based on the fundamental SPR theory of the fiber optic sensing technology, we theoretically investigated the effects of the existence of alternating layers deposited on sensing region SPR wavelength changes. The emphasis was placed on the numerical simulation of the fiber-optic SPR sensor＇s sensitivity which could be affected by its technical parameters such as the metal thickness, number of alternating layers. Results showed that, compared to the normal SPR sensor with the single metal layer, the proposed sensor had a wider detecting range of the refractive index and higher sensitivity, which can find applications in biological analysis.