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Improvement of generalized finite difference method for stochastic subsurface flow modeling
Journal of Computational Physics ( IF 4.1 ) Pub Date : 2020-11-17 , DOI: 10.1016/j.jcp.2020.110002
Shang-Ying Chen , Kuo-Chin Hsu , Chia-Ming Fan

Uncertainty is embedded in groundwater flow modeling because of the heterogeneity of hydraulic conductivity and the scarcity of measurements. To quantify the uncertainty of the modeled hydraulic head, this study proposes an improved version of the meshless generalized finite difference method (GFDM) for solving the statistical moment equation (ME). The proposed GFDM adopts a new support sub-domain for calculating the derivative of the head to improve accuracy. Synthetic fields are applied to validate the proposed method. The proposed GFDM outperforms the conventional GFDM in terms of accuracy based on a comparison with the results of the finite difference method. The ME-GFDM scheme is shown to be 2.6 times faster than Monte Carlo simulation with comparable accuracy. The ME-GFDM is versatile in that it easily handles irregular domains, allows the node location and number to be changed, and allows the sequential addition of new data without remeshing, which is required for traditional mesh-based methods.



中文翻译:

随机地下渗流模型广义有限差分法的改进

由于水力传导率的非均质性和测量的稀缺性,地下水流动模型中存在不确定性。为了量化建模的液压压头的不确定性,本研究提出了一种改进的无网格广义有限差分法(GFDM),用于求解统计矩方程(ME)。提出的GFDM采用新的支持子域来计算磁头的导数,以提高精度。综合领域被用来验证所提出的方法。根据与有限差分法结果的比较,提出的GFDM在准确性方面优于传统GFDM。事实证明,ME-GFDM方案比Monte Carlo模拟快2.6倍。ME-GFDM用途广泛,可以轻松处理不规则域,

更新日期:2020-11-17
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