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首页> 外文期刊>Journal of Mathematics Research >Domain Decomposition Modified with Characteristic Mixed Finite Element and Numerical Analysis for Three-Dimensional Slightly Compressible Oil-Water Seepage Displacement
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Domain Decomposition Modified with Characteristic Mixed Finite Element and Numerical Analysis for Three-Dimensional Slightly Compressible Oil-Water Seepage Displacement

机译:特征混合有限元修正的区域分解及三维轻度可压缩油水渗流位移的数值分析

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A parallel algorithm is presented to solve three-dimensional slightly compressible seepage displacement where domain decomposition and characteristics-mixed finite element are combined. Decomposing the computational domain into several subdomains, we define a special function to approximate the derivative at interior boundary explicitly and obtain numerical solutions of the saturation implicitly on subdomains in parallel. The method of characteristics can confirm strong stability at the fronts, and can avoid numerical dispersion and nonphysical oscillation. It can adopt large-time step but can obtain small time truncation error. So a characteristic domain decomposition finite element scheme is put forward to compute the saturation. The flow equation is computed by the method of mixed finite element and numerical accuracy of Darcy velocity is improved one order. For a model problem we apply some techniques such as variation form, domain decomposition, the method of characteristics, the principle of energy, negative norm estimates, induction hypothesis, and the theory of priori estimates of differential equations to derive optimal error estimate in $l^2$ norm. Numerical example is given to testify theoretical analysis and numerical data show that this method is effective in solving actual applications. Then it can solve the well-known problem.
机译:提出了一种并行算法来解决三维微压缩渗流位移问题,其中域分解和特征混合有限元相结合。将计算域分解为几个子域,我们定义了一个特殊函数来显式近似内部边界处的导数,并隐式地在子域上并行获取饱和度的数值解。该特性方法可以确认前部的强稳定性,并且可以避免数值离散和非物理振荡。它可以采用较大的时间步长,但可以获得较小的时间截断误差。因此提出了一种特征域分解有限元方案来计算饱和度。用混合有限元法计算了流动方程,达西速度的数值精度提高了一级。对于模型问题,我们应用了一些技术,例如变化形式,域分解,特征方法,能量原理,负范数估计,归纳假设和微分方程先验估计理论,以得出$ l的最优误差估计^ 2 $规范。数值算例验证了理论分析的正确性,数值数据表明该方法在解决实际应用中是有效的。这样就可以解决众所周知的问题。

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