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Shape reconstruction of composite structures with monitoring of modeling changes using Brillouin-scattering based distributed optical fiber strain sensor network

机译:基于布里渊散射的分布式光纤应变传感器网络,通过监视建模变化来重构复合结构的形状

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摘要

This research proposes a novel approach for the shape reconstruction of composite structures using one of Brillouin-scattering based distributed optical fiber strain sensors, PPP-BOTDA system. We have constructed a displacement reconstruction algorithm using the finite element model of the target structure. The remarkable point is that, not only using raw distributed strain data, but using an index of the non-uniformity of strain distribution profiles, which is the normalized Laplacian value (NLV), the algorithm can detect modeling changes, such as changes in boundary conditions of the structures. In the verification, the algorithm was applied to the deflection identification of a composite laminate specimen with an embedded optical fiber network. From the NLV distribution, the change of the fixed end condition of the specimen was evaluated, and using the updated FE model, the deflection was able to be reconstructed with high accuracy. From the result, we show the validity of the proposing shape reconstruction algorithm, which has robustness against FE model changes.
机译:这项研究提出了一种新的方法,该方法使用基于布里渊散射的分布式光纤应变传感器PPP-BOTDA系统之一来重建复合结构的形状。我们使用目标结构的有限元模型构造了位移重建算法。值得注意的是,该算法不仅使用原始分布的应变数据,而且还使用应变分布曲线的非均匀性指标(即归一化的拉普拉斯值(NLV)),可以检测建模变化,例如边界变化结构的条件。在验证中,该算法被应用于带有嵌入式光纤网络的复合层压板试样的挠度识别。根据NLV分布,评估样品固定端条件的变化,并使用更新的FE模型,可以高精度地重建挠度。从结果可以看出,提出的形状重构算法的有效性,对有限元模型的变化具有鲁棒性。

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