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Data-assimilated computational fluid dynamics modeling of convection-diffusion-reaction problems

机译:对流扩散反应问题的数据辅助计算流体动力学建模

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This study focuses on the development and application of a data-assimilated multi-algorithm which combines a fourth-order finite-volume computational fluid dynamics (CFD) algorithm and the ensemble Kalman filter (EnKF) data assimilation algorithm for solving the problems involving important physics relevant to wave convection, molecular diffusion, and reaction of interest to engineering science. This data-assimilated CFD algorithm is applied to estimate an uncertain model parameter. By restricting the problems to one- and two-dimensional spatial space, this study allows us to develop a greater understanding of the integrated algorithm without the complexity or computational cost associated with three dimensions. Although scalar partial differential equations are used, fundamental issues in the algorithm development and application of EnKF to the physical processes occurring in a domain on engineering scales are sufficiently illuminated. Results of the one-dimensional convection-diffusion-reaction problem and the two-dimensional flame propagation demonstrate the validity of the data-assimilated CFD modeling system. (C) 2017 Elsevier B.V. All rights reserved.
机译:这项研究的重点是结合四阶有限体积计算流体动力学(CFD)算法和集合卡尔曼滤波(EnKF)数据同化算法的数据同化多算法的开发和应用,以解决涉及重要物理学的问题与波对流,分子扩散以及工程科学感兴趣的反应有关。这种数据辅助的CFD算法用于估计不确定的模型参数。通过将问题限制为一维和二维空间空间,本研究使我们能够对集成算法有更深入的了解,而无需考虑与三维相关的复杂性或计算成本。尽管使用了标量偏微分方程,但充分阐明了EnKF算法开发和将EnKF应用到工程规模领域中发生的物理过程中的基本问题。一维对流扩散反应问题和二维火焰传播的结果证明了数据辅助CFD建模系统的有效性。 (C)2017 Elsevier B.V.保留所有权利。

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