An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy Simulations

An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy Simulations
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DOI:
10.1080/01621459.2017.1409123
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发表时间:
2016-11
影响因子:
3.7
通讯作者:
Simon Mak;Chih-Li Sung;Xingjian Wang;Shiang-Ting Yeh;Yu-Hung Chang;V. R. Joseph;V. Yang;C. F. J. Wu
Simon Mak;Chih-Li Sung;Xingjian Wang;Shiang-Ting Yeh;Yu-Hung Chang;V. R. Joseph;V. Yang;C. F. J. Wu
中科院分区:
数学1区
文献类型:
--
作者:
Simon Mak;Chih-Li Sung;Xingjian Wang;Shiang-Ting Yeh;Yu-Hung Chang;V. R. Joseph;V. Yang;C. F. J. Wu

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摘要在寻求先进的推进和发电系统时,高保真仿真的计算成本太高,无法测量所需的设计空间,因此需要一种新的设计方法,将工程物理、计算机仿真和统计建模结合起来。在这篇文章中,我们提出了一个新的代理模型,提供了有效的预测和不确定性量化的涡流喷嘴与不同的几何形状,在许多工程应用中常用的设备。所提出的方法的新奇在于将流体流动的已知物理特性作为统计模型的简化假设。考虑到手头上的大量模拟数据(大约为数百GB),这些假设允许在大约一个小时的计算时间内进行准确的流量预测。相比之下,放弃这种简化的现有流仿真器可能需要比进行仿真本身所需的更多的用于训练和预测的计算时间。此外,通过考虑流动变量之间的耦合机制,所提出的模型可以共同减少预测的不确定性,并提取有用的流动物理,然后可以用于指导进一步的研究。本文的补充材料,包括可用于复制作品的材料的标准化描述,可作为在线补充。
ABSTRACT In the quest for advanced propulsion and power-generation systems, high-fidelity simulations are too computationally expensive to survey the desired design space, and a new design methodology is needed that combines engineering physics, computer simulations, and statistical modeling. In this article, we propose a new surrogate model that provides efficient prediction and uncertainty quantification of turbulent flows in swirl injectors with varying geometries, devices commonly used in many engineering applications. The novelty of the proposed method lies in the incorporation of known physical properties of the fluid flow as simplifying assumptions for the statistical model. In view of the massive simulation data at hand, which is on the order of hundreds of gigabytes, these assumptions allow for accurate flow predictions in around an hour of computation time. To contrast, existing flow emulators which forgo such simplifications may require more computation time for training and prediction than is needed for conducting the simulation itself. Moreover, by accounting for coupling mechanisms between flow variables, the proposed model can jointly reduce prediction uncertainty and extract useful flow physics, which can then be used to guide further investigations. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.