An efficient goal-based reduced order model approach for targeted adaptive observations
An efficient goal-based reduced order model approach for targeted adaptive observations
复制标题
一种有效的基于目标的降阶模型方法，用于有针对性的自适应观测
DOI：
10.1002/fld.4265
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发表时间：
2017
影响因子：
1.8
通讯作者：
Xiao D.
中科院分区：
文献类型：
--
作者：
Fang F.;Pain C. C.;Navon Ionel M.;Xiao D.
提出了一种有效的伴随灵敏度技术，用于最优地收集目标观测值。目标技术结合了来自数值模式预测的动态信息，以确定何时、何地以及何种类型的观测将在未来某个时间对特定模式的预测提供最大的改进。定义函数（目标）来衡量建模问题中被认为重要的内容。伴随灵敏度技术用于识别观测值对泛函预测精度的影响，然后将传感器放置在具有高影响的位置。本文提出的自适应（目标）观测技术具有以下特点：（1）与现有的目标观测技术相比，它的新奇在于将数值结果的插值误差引入到泛函（目标）中，保证了观测值之间的距离;（ii）使用适当的正交分解（POD）和用于前向和后向模拟的降阶建模，从而降低计算成本;以及（iii）非结构网格的使用。在非结构网格有限元模型（Fluidity）中开发了有针对性的自适应观测技术。在这项工作中，一个POD降阶建模是用来形成降阶正演模型从一个高维空间投影到一个降阶空间的原始复杂模型。然后直接从降阶正演模型构造降阶伴随模型。这种有效的自适应观测技术已验证了两个测试用例：海洋环流模型和二维城市街道峡谷流模型。版权所有© 2016约翰威利父子有限公司.
An efficient adjoint sensitivity technique for optimally collecting targeted observations is presented. The targeting technique incorporates dynamical information from the numerical model predictions to identify when, where and what types of observations would provide the greatest improvement to specific model forecasts at a future time. A functional (goal) is defined to measure what is considered important in modelling problems. The adjoint sensitivity technique is used to identify the impact of observations on the predictive accuracy of the functional, then placing the sensors at the locations with high impacts. The adaptive (goal) observation technique developed here has the following features: (i) over existing targeted observation techniques, its novelty lies in that the interpolation error of numerical results is introduced to the functional (goal), which ensures the measurements are a distance apart; (ii) the use of proper orthogonal decomposition (POD) and reduced order modelling for both the forward and backward simulations, thus reducing the computational cost; and (iii) the use of unstructured meshes.The targeted adaptive observation technique is developed here within an unstructured mesh finite element model (Fluidity). In this work, a POD reduced order modelling is used to form the reduced order forward model by projecting the original complex model from a high dimensional space onto a reduced order space. The reduced order adjoint model is then constructed directly from the reduced order forward model. This efficient adaptive observation technique has been validated with two test cases: a model of an ocean gyre and a model of 2D urban street canyon flows. Copyright © 2016 John Wiley & Sons, Ltd.