表面活性剂强化含水层修复模拟的尺度提升研究

为了寻找到能够在保证模拟精度的前提下，大大提高表面活性剂强化含水层修复（Surfactant-enhanced aquifer remediation，SEAR）模型计算效率的合适的渗透系数尺度提升方法，在概略介绍拉普拉斯-外壳法的基础上，利用算术平均尺度提升法与拉普拉斯-外壳法建立大尺度模型，与小尺度模型进行对比.结果表明，拉普拉斯-外壳法所建模型对含水层污染物残余质量的最大计算误差在所有情况下均优于算术平均尺度提升法，含水层非均质性越强，拉普拉斯-外壳法优越性越明显；并且拉普拉斯-外壳法对于污染羽质心位置与形状的刻画效果也更好.大尺度模型能大幅度减小SEAR的计算成本，应用算术平均尺度提升法可减少至原计算时间的6.5%左右，应用拉普拉斯-外壳法可减少至4.5%左右.

Upscaling of reactive transport modeling for surfactant-enhanced aquifer remediation

In order to find an appropriate permeability upscaling method which can greatly improve the computational efficiency of surfactant-enhanced aquifer remediation (SEAR) model while ensuring the accuracy of the simulation, two coarse-scale models based on Laplacian with skin method and the arithmetic mean upscaling method were applied in this study, and their performance was also compared with fine-scale models. The results indicated that the maximum calculation error of the residual mass of aquifer pollutants by the model based on Laplacian with skin was better than the model built by the arithmetic mean upscaling method in all cases. The superiority of Laplacian with skin became more significant when the aquifer heterogeneity was stronger. In addition, Laplacian with skin achieved a better simulation effect on the centroid location and the shape of the pollutant. Particularly, the use of coarse-scale model can greatly reduce the computational cost of SEAR, leading to the significant computational cost-savings, e.g., about 6.5% of the original runtime by using the arithmetic mean upscaling method and 4.5% by Laplacian with skin.