An Efficient Surrogate Model for Emulation and Physics Extraction of Large Eddy Simulations
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大涡模拟的高效代理模型与物理机制提取
- 研究问题
- 高保真湍流模拟成本过高且输出规模巨大时，如何对不同几何结构的旋流喷注器高效预测流场、量化不确定性并提取物理信息？
- 方法与思路
- 作者将已知流体物理性质作为统计模型的简化假设，并纳入流动变量之间的耦合机制，构建预测与不确定性量化代理模型。
- 材料陈述的贡献
- 摘要称，物理简化使数百 GB 模拟数据的准确预测可在约一小时计算内完成，变量耦合还能降低预测不确定性并提取流动物理信息。
- 阅读关注点
- 应核对物理简化假设及适用几何范围、变量耦合的建模方式，以及准确性指标、计算时间口径和预测不确定性的验证方法。
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原文摘要
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.