We propose a novel Monte-Carlo based ab-initio algorithm for directly computing the statistics for quantities of interest in an immiscible two-phase compressible flow. Our algorithm samples the underlying probability space and evolves these samples with a sharp interface front-tracking scheme. Consequently, statistical information is generated without resorting to any closure assumptions and information about the underlying microstructure is implicitly included. The proposed algorithm is tested on a suite of numerical experiments and we observe that the ab-initio procedure can simulate a variety of flow regimes robustly and converges with respect of refinement of number of samples as well as number of bubbles per volume. The results are also compared with a state-of-the-art discrete equation method to reveal the inherent limitations of existing macroscopic models.
翻译:我们提出了一种新颖的蒙特卡罗基于从头计算的算法,用于直接计算不相溶的两相可压缩流体中感兴趣的数量的统计学信息。我们的算法采样概率空间并使用尖锐界面跟踪方案演化这些样本。因此,统计信息在没有诸如平衡状态等方案假设的情况下生成,并隐含包括有关基础微观结构的信息。我们在一组数值实验中测试了拟议算法,并观察到从头计算过程可以稳健地模拟各种流动状态,并且随着样本数量及每个体积中气泡数量的加倍而收敛。结果还与最先进的离散方程方法进行了比较,以揭示现有宏观模型的固有局限性。