分类： 材料科学 >> 复合材料 分类： 力学 >> 固体力学 分类： 航空、航天科学技术 >> 飞行器制造技术 提交时间： 2024-04-09
摘要： 单向纤维增强塑料（UD-FRP）的力学性能受到内部微缺陷的影响，如随机纤维排列、纤维错位和微空隙。本研究旨在通过计算微观力学研究这些多个微观缺陷如何相互作用，以及它们如何影响UD-FRP的强度和失效机制。通过对具有代表性的体积单元进行有限元分析，模拟了失效行为；对于不同的载荷及其组合，考虑了基体和界面的破坏。研究发现，这些微观缺陷显著削弱了UD-FRP沿纵向的抗压强度。特别是纤维波纹度放大了纤维排列的影响，而微空隙则减小了这种影响。此外，纤维排列和微空隙显著削弱了UD-FRP的横向拉伸和压缩强度。此外，横向和纵向剪切强度受微孔隙的显著影响，但只有纵向剪切受纤维排列的影响，这种影响也因微孔隙而减弱。最后，得到了纵向压缩和横向荷载共同作用下的损伤包络线，并与Tsai Wu失效准则进行了比较。结果表明，Tsai Wu准则可以有效地估计这种双轴加载条件下的失效包络线。
分类： 光学 >> 量子光学 提交时间： 2023-02-19
摘要： We propose and experimentally demonstrate a novel interference fading suppression method for phase-sensitive optical time domain reflectometry (Phi-OTDR) using space-division multiplexed (SDM) pulse probes in few-mode fiber. The SDM probes consist of multiple different modes, and three spatial modes (LP01, LP11a and LP11b) are used in this work for proof of concept. Firstly, the Rayleigh backscattering light of different modes is experimentally characterized, and it turns out that the waveforms of Phi-OTDR traces of distinct modes are all different from each other. Thanks to the spatial difference of fading positions of distinct modes, multiple probes from spatially multiplexed modes can be used to suppress the interference fading in Phi-OTDR. Then, the performances of the Phi-OTDR systems using single probe and multiple probes are evaluated and compared. Specifically, statistical analysis shows that both fading probabilities over fiber length and time are reduced significantly by using multiple SDM probes, which verifies the significant performance improvement on fading suppression. The proposed novel interference fading suppression method does not require complicated frequency or phase modulation, which has the advantages of simplicity, good effectiveness and high reliability.
分类： 光学 >> 量子光学 提交时间： 2023-02-19
摘要： In the field of Internet of Things, there is an urgent need for sensors with large-scale sensing capability for scenarios such as intelligent monitoring of production lines and urban infrastructure. Brillouin optical time domain analysis (BOTDA) sensors, which can monitor thousands of continuous points simultaneously, show great advantages in these applications. We propose a convolutional neural network (CNN) to process the data of conventional Brillouin optical time domain analysis (BOTDA) sensors, which achieves unprecedented performance improvement that allows to directly retrieve higher spatial resolution (SR) from the sensing system that use long pump pulses. By using the simulated Brillouin gain spectrums (BGSs) as the CNN input and the corresponding high SR BFS as the output target, the trained CNN is able to obtain a SR higher than the theoretical value determined by the pump pulse width. In the experiment, the CNN accurately retrieves 0.5-m hotspots from the measured BGS with pump pulses from 20 to 50 ns, and the acquired BFS is in great agreement with 45/40 ns differential pulse-width pair (DPP) measurement results. Compared with the DPP technique, the proposed CNN demonstrates a 2-fold improvement in BFS uncertainty with only half the measurement time. In addition, by changing the training datasets, the proposed CNN can obtain tunable high SR retrieval based on conventional BOTDA sensors that use long pulses without any requirement of hardware modifications. The proposed data post-processing approach paves the way to enable novel high spatial resolution BOTDA sensors, which brings substantial improvement over the state-of-the-art techniques in terms of system complexity, measurement time and reliability, etc.
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