Optical fiber Bragg grating (FBG) displacement sensors play an important role in various areas due to the high sensitivity to displacement. However, it becomes a serious problem of FBG cross-sensitivity of temperature and displacement in applications with FBG displacement sensing. This paper presents a method of temperature insensitive measurement of displacement via using an appropriate layout of the sensor. A displacement sensor is constructed with two FBGs mounted on the opposite surface of a cantilever beam. The wavelengths of the FBGs shift with a horizontal direction displacement acting on the cantilever beam. Displacement measurement can be achieved by demodulating the wavelengths difference of the two FBGs. In this case, the difference of the two FBGs' wavelengths can be taken in order to compensate for the temperature effects. Four cantilever beams with different shapes are designed and the FBG strain distribution is quite different from each other. The deformation and strain distribution of cantilever beams are simulated by using finite element analysis, which is used to optimize the layout of the FBG displacement sensor. Experimental results show that an obvious increase in the sensitivity of this change on the displacement is obtained while temperature dependence greatly reduced. A change in the wavelength can be found with the increase of displacement from 0 to 10mm for a cantilever beam. The physical size of the FBG displacement sensor head can be adjusted to meet the need of different applications, such as structure health monitoring, smart material sensing, aerospace, etc.
The effect of optical fiber attenuation differences (AD) induced temperature error of Raman distributed temperature sensor (RDTS) is analyzed using the temperature demodulation algorithm. First of all, a novel method to address the effects caused by the AD between Stokes and anti-Stokes light is proposed. Furthermore, the temperature measurement error caused by additional AD of fiber temperature is also reduced by using a formula obtained by experimental data. The experimental results demonstrate that the RDTS system can measure different temperature zones more accurately.
The cause and influence factors of crack of manifold were analyzed at a sour natural gas gathering project.Improper welding processes were the root causes.The failure characteristic of cracking was the typical delay cracking.
At present,the proven reserves of kerogen cracked and oil cracked gases at high thermal evolution stage have accounted for about 26%of the total reserves in China,so it is important to carry out systematic analysis of the formation and distribution characteristics of natural gas.Based on a number of pyrolytic experiments on kerogen and oil at high thermal stage,,combined with the researches on cracked gas,this paper further analyzed the genetic characteristics of cracked gases from oil,marine-lacustrine mudstone and lacustrine coal measures,and proposed five gas accumulation modes.Crude oil cracking was generally thought to occur at temperature more than 150℃.Our experiment proves that it begins to form cracking gas at temperature more than 190℃ in some areas.Gas generation potential of marine mudstone would exhaust when ROapproaches 3%.However,lacustrine coal measures still have great methane generation potential when ROis near 2.5%-5%,and the gas amount generated at this stage accounts for more than 20% of the total generated gas.The cracking potential of lacustrine mudstone lies between the two formers,and the coal source rock has the greatest gas cracking potential.Cracking gas mainly has five accumulation modes,including ancient uplift crude oil cracked gas,ancient slope weathering karst cracking gas,dense sandstone coal source rock cracking gas,special reservoir cracking gas,and coal seam-shale source cracking gas.The ancient uplift and slope reservoir,the marine-terrestrial transitional basin dense sandstone reservoir and Meso-Cenozoic special reservoir are important potential exploration direction.
A real time system used to detect phase difference between two sinusoidal signals is proposed in this paper. The system is designed to process the phase signal of the far-infrared (FIR) hydrogen cyanide (HCN) interferometer on J-TEXT. It is based on zero-crossing detection and makes use of the digital circuit. Compared with a traditional zero-crossing phase detector, it doesn't need to sacrifice the time resolution to expand the phase range. The phase difference is divided into two parts, the integer part and the fraction part. In each detecting cycle, they are detected separately. It outputs digital signals that are more stable for transmission. A prototype was built on J-TEXT using discrete components. A practical method is proposed to deal with the counting error caused by the deviation of electronic components in manufacture. Reasonable results were obtained on the prototype. The phase resolution reaches 2π/64 in test, and can still be improved by raising the clock frequency.
Deep learning (DL)-based structural damage identification recently attracts significant attention in the area of structural health monitoring (SHM). These methods are usually characterized as black-box models. The reliability of the model relies on the quantity and quality of data used to train it, which is often not available in practice. To assist the model training, development of physics-guided neural network (PGNN), that is, combining physics laws and data in training the model, is becoming a more and more popular researched topic and approach. However, the existing studies on structural damage identification using PGNN face the problems of poor generalization ability and lack of application to the large-scale structures. In order to overcome these challenges, based on modal sensitivity analysis of large-scale structures and model reduction, a new physics-based loss function is proposed and incorporated into the plain DL model to form a novel physics-guided DL (PGDL) framework. Two large-scale structures, including a numerical continuous rigid frame bridge and the tested I-40 steel-concrete composite bridge, are adopted to verify the feasibility and effectiveness of the proposed PGDL framework. The effect of common interferences, including random noise and sparse measurement, is also investigated to examine the noise-robustness of the proposed approach. The results demonstrate that the proposed approach has a superior damage localization and quantification performance than the plain DL model under the effect of multiple interferences. The proposed framework not only provides a better solution for damage identification of large-scale structures but also enriches the research scope of applying PGNN for SHM.
As an alternative or complementary approach to the classical probability theory, the ability of the evidence theory in uncertainty quantification (UQ) analyses is subject of intense research in recent years. Two state-of-the-art numerical methods, the vertex method and the sampling method, are commonly used to calculate the resulting uncertainty based on the evidence theory. The vertex method is very effective for the monotonous system, but not for the non-monotonous one due to its high computational errors. The sampling method is applicable for both systems. But it always requires a high computational cost in UQ analyses, which makes it inefficient in most complex engineering systems. In this work, a computational intelligence approach is developed to reduce the computational cost and improve the practical utility of the evidence theory in UQ analyses. The method is demonstrated on two challenging problems proposed by Sandia National Laboratory. Simulation results show that the computational efficiency of the proposed method outperforms both the vertex method and the sampling method without decreasing the degree of accuracy. Especially, when the numbers of uncertain parameters and focal elements are large, and the system model is non-monotonic, the computational cost is five times less than that of the sampling method.
