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Fast computation of inverse transient analysis for pipeline condition assessment via surrogate modeling with sparse sampling strategy

机译：稀疏抽样策略代理建模的流水线状况评估逆瞬态分析的快速计算

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摘要

Inverse transient analysis (ITA) is a powerful tool for pipeline condition assessment, where the concerned parameters are decided by matching the measured transient signal with its physical model. This scheme is computationally expensive because, in the optimization procedure, the model has to be evaluated many times via time-domain full-wave numerical simulations. To address this problem, a fast algorithm of ITA is proposed, where the role of the numerical model is replaced by an easy-to-evaluate surrogate model. The Kriging method is employed to build the surrogate model of transient wave, given a series of samples of concerned parameters and the associated numerical model outputs. Considering the sparse nature of the high-dimensional pipe condition parameters, a sparse sampling strategy is proposed to optimize the samples of parameters. Two application examples, i.e., pipe viscoelastic parameter estimation and leakage identification, are introduced to demonstrate that the proposed method is as accurate as the traditional ITA but has a much lower computational cost.
机译：逆瞬态分析（ITA）是一种强大的管道条件评估工具，其中通过将测量的瞬态信号与其物理模型匹配来决定相关参数。该方案是计算昂贵的，因为在优化过程中，必须通过时域全波数模拟来评估模型。为了解决这个问题，提出了一种快速算法的ITA，其中数值模型的作用被易于评估的代理模型所取代。考虑到一系列相关参数和相关的数控输出，采用Kriging方法来构建瞬态波的替代模型。考虑到高维管道条件参数的稀疏性，提出了一种稀疏的采样策略，以优化参数的样本。引入了两个应用示例，即管粘弹性参数估计和泄漏识别，以证明该方法与传统ITA一样准确，但计算成本更低。

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