Bayesian inversion in resin transfer molding
Bayesian inversion in resin transfer molding
复制标题
树脂传递模塑中的贝叶斯反演
DOI：
10.1088/1361-6420/aad1cc
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发表时间：
2018
期刊：
影响因子：
2.1
通讯作者：
Iglesias M
中科院分区：
文献类型：
--
作者：
Iglesias M
我们研究了贝叶斯逆问题的上下文中出现的树脂传递模塑（RTM），这是一个过程中常用的纤维增强复合材料的制造。正演模型用多孔介质中的移动边界问题来描述。在RTM树脂的注射过程中，我们的目标是更新，在飞行中，我们的概率知识的渗透性的材料，只要压力测量和观察的树脂移动域变得可用。通过这些测量/观察的反演对材料渗透率进行概率性动态表征对于最佳实时控制至关重要，目的是最大限度地减少RTM内的工艺持续时间和缺陷形成风险。我们考虑RTM的一维（1D）和二维（2D）正演模型。基于1D情况下的解析解，我们证明了存在的序列后验序列，所产生的序列贝叶斯制定的无限维框架内。对于一维情形下贝叶斯后验的数值特征，我们研究了全贝叶斯序贯蒙特卡罗方法（SMC）在高维反问题中的应用。通过SMC，我们构建了一个基准，对我们比较性能的一种新的正则化集合卡尔曼算法（REnKA），我们建议近似后验在实际情况下的计算效率的方式。我们调查的鲁棒性建议REnKA可调参数和计算成本。我们证明了REnKA的优势与SMC相比，具有少量的颗粒。我们进一步调查，在1D和2D的设置，REnKA相关的RTM，其中包括压力传感器配置的影响和观测噪声水平的不确定性，通过贝叶斯后验序列量化的日志渗透率的实际方面。这项工作的结果也是有用的其他应用程序比RTM，这可以模拟一个随机移动边界问题。
We study a Bayesian inverse problem arising in the context of resin transfer molding (RTM), which is a process commonly used for the manufacturing of fiber-reinforced composite materials. The forward model is described by a moving boundary problem in a porous medium. During the injection of resin in RTM, our aim is to update, on the fly, our probabilistic knowledge of the permeability of the material as soon as pressure measurements and observations of the resin moving domain become available. A probabilistic on-the-fly characterisation of the material permeability via the inversion of those measurements/observations is crucial for optimal real-time control aimed at minimising both process duration and the risk of defects formation within RTM. We consider both one-dimensional (1D) and two-dimensional (2D) forward models for RTM. Based on the analytical solution for the 1D case, we prove existence of the sequence of posteriors that arise from a sequential Bayesian formulation within the infinite-dimensional framework. For the numerical characterisation of the Bayesian posteriors in the 1D case, we investigate the application of a fully-Bayesian sequential Monte Carlo method (SMC) for high-dimensional inverse problems. By means of SMC we construct a benchmark against which we compare performance of a novel regularizing ensemble Kalman algorithm (REnKA) that we propose to approximate the posteriors in a computationally efficient manner under practical scenarios. We investigate the robustness of the proposed REnKA with respect to tuneable parameters and computational cost. We demonstrate advantages of REnKA compared with SMC with a small number of particles. We further investigate, in both the 1D and 2D settings, practical aspects of REnKA relevant to RTM, which include the effect of pressure sensors configuration and the observational noise level in the uncertainty in the log-permeability quantified via the sequence of Bayesian posteriors. The results of this work are also useful for other applications than RTM, which can be modelled by a random moving boundary problem.
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影响因子：
5.2
作者：
D. R. Nielsen;R. Pitchumani
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DOI：
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发表时间：
2007-01-01
影响因子：
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作者：
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影响因子：
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资料同化
DOI：
10.11499/sicejl.56.656
发表时间：
2017
期刊：
Journal of The Society of Instrument and Control Engineers
影响因子：
--
作者：
河本高文;二木厚吉;吉岡 信和;福元 豊，大塚 悟;上野 玄太
通讯作者：
上野 玄太