Volume 42 Issue 4
Jul.  2023
Turn off MathJax
Article Contents
Dong Guiming, Wang Ying, Zhan Hongbin, Tian Juan, Li Jianing, Dai Lina. Numerical simulation of the water budget interval for unsteady two-dimensional confined flow[J]. Bulletin of Geological Science and Technology, 2023, 42(4): 75-82. doi: 10.19509/j.cnki.dzkq.tb20230028
Citation: Dong Guiming, Wang Ying, Zhan Hongbin, Tian Juan, Li Jianing, Dai Lina. Numerical simulation of the water budget interval for unsteady two-dimensional confined flow[J]. Bulletin of Geological Science and Technology, 2023, 42(4): 75-82. doi: 10.19509/j.cnki.dzkq.tb20230028

Numerical simulation of the water budget interval for unsteady two-dimensional confined flow

doi: 10.19509/j.cnki.dzkq.tb20230028
  • Received Date: 17 Jan 2023
  • Accepted Date: 07 Apr 2023
  • Rev Recd Date: 31 Mar 2023
  • Objective

    Groundwater numerical models often have uncertainties due to the complexity of the hydrogeological conditions and the economic and time constraints in collecting a sufficiently large dataset as inputs for conducting modelling exercises. In the past 50 years, stochastic methods have been one of the main methods of uncertainty analysis. The interval uncertainty is different from the stochastic uncertainty, and it considers the hydrogeological parameters as the intervals (ranges) without considering their stochastic properties.

    Methods

    From the perspective of interval uncertainty, a numerical simulation method based on first-order perturbation expansion was proposed for simulating unsteady two-dimensional confined flow with known hydrogeological parameters as intervals in this paper.The proposed method is implemented based on GFModel, a three-dimensional (3D) numerical simulation platform for groundwater flow and pollutant migration.

    Results

    The analysis shows that the relative error can be controlled within 10% when the parameter change rate is less than 0.1. The computational efficiency of the proposed method is obviously higher than that of the continuous sampling method with equal spacing.

    Conclusion

    This method allows the interval of the head or water budget to be calculated without the requirement for detailed statistical information (which is usually unavailable in advance) if the intervals of hydrogeological parameters are known.It provides a theoretical basis for decisions on the use and protection of groundwater resources.

     

  • loading
  • [1]
    Sreekanth J, Moore C. Novel patch modelling method for efficient simulation and prediction uncertainty analysis of multi-scale groundwater flow and transport processes[J]. Journal of Hydrology, 2018, 559: 122-135. doi: 10.1016/j.jhydrol.2018.02.028
    [2]
    薛佩佩, 文章, 梁杏. 地质统计学在含水层参数空间变异研究中的应用进展与发展趋势[J]. 地质科技通报, 2022, 41(1): 209-222. doi: 10.19509/j.cnki.dzkq.2022.0015

    Xue P P, Wen Z, Liang X. Application and development trend of geostatistics in the research of spatial variation of aquifer parameters[J]. Bulletin of Geological Science and Technology, 2022, 41(1): 209-222(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2022.0015
    [3]
    肖景红, 王敏, 王川, 等. 含优势渗流层边坡降雨入渗下的可靠度分析[J]. 地质科技通报, 2021, 40(6): 193-204. doi: 10.19509/j.cnki.dzkq.2021.0619

    Xiao J H, Wang M, Wang C, et al. Reliability analysis of slope with dominant seepage interlayer under rainfall infiltration[J]. Bulletin of Geological Science and Technology, 2021, 40(6): 193-204(in Chinese with English abstract). doi: 10.19509/j.cnki.dzkq.2021.0619
    [4]
    孙晓卓, 曾献奎, 吴吉春, 等. 一种改进的地下水模型结构不确定性分析方法[J]. 水文地质工程地质, 2021, 48(6): 24-33. https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG202106003.htm

    Sun X Z, Zeng X K, Wu J C, et al. An improved method for the structural uncertainty analysis of groundwater models[J]. Hydrogeology and Engineering Geology, 2021, 48(6): 24-33(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG202106003.htm
    [5]
    Niklas L, David G, James I, et al. On uncertainty quantification in hydrogeology and hydrogeophysics[J]. Advances in Water Resources, 2017, 110: 166-181. doi: 10.1016/j.advwatres.2017.10.014
    [6]
    钟乐乐, 曾献奎, 吴吉春. 基于高斯过程回归的地下水模型结构不确定性分析与控制[J]. 水文地质工程地质, 2019, 46(1): 1-10. https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG201901001.htm

    Zhong L L, Zeng X K, Wu J C. Analysis and control of structural uncertainty of groundwater model based on Gaussian process regression[J]. Hydrogeology and Engineering Geology, 2019, 46(1): 1-10(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-SWDG201901001.htm
    [7]
    Sreekanth J, Catherine M. Novel patch modelling method for efficient simulation and prediction uncertainty analysis of multi-scale groundwater flow and transport processes[J]. Journal of Hydrology, 2018, 559: 122-135. doi: 10.1016/j.jhydrol.2018.02.028
    [8]
    Ata J, Mohammad Z, Ali N Z, et al. Groundwater management under uncertainty using a stochastic multi-cell model[J]. Journal of Hydrology, 2017, 551(S1): 265-277.
    [9]
    Kifanyi G E, Ndambuki J M, Odai S N, et al. Quantitative management of groundwater resources in regional aquifers under uncertainty: A retrospective optimization approach[J]. Groundwater for Sustainable Development, 2019, 8: 530-540. doi: 10.1016/j.gsd.2019.02.005
    [10]
    Sreekanth J, Catherine M, Leif W. Pareto-based efficient stochastic simulation-optimization for robust and reliable groundwater management[J]. Journal of Hydrology, 2016, 533: 180-190. doi: 10.1016/j.jhydrol.2015.12.001
    [11]
    Chen M J, Izady A, Abdalla O A, et al. A surrogate-based sensitivity quantification and Bayesian inversion of a regional groundwater flow model[J]. Journal of Hydrology, 2018, 557: 826-837.
    [12]
    Tang Z C, Lu Z Z, Wang P, et al. Efficient numerical simulation method for evaluations of global sensitivity analysis with parameter uncertainty[J]. Applied Mathematical Modelling, 2016, 40(1): 597-611.
    [13]
    徐亚宁, 卢文喜, 王梓博, 等. 考虑参数和边界条件不确定性的地下水污染随机模拟[J]. 中国环境科学, 2022, 42(7): 3244-3253. https://www.cnki.com.cn/Article/CJFDTOTAL-ZGHJ202207028.htm

    Xu Y N, Lu W X, Wang Z B, et al. Stochastic simulation of groundwater pollution considering uncertainty of parameters and boundary conditions[J]. China Environmental Science, 2022, 42(7): 3244-3253(in Chinese with English abstract). https://www.cnki.com.cn/Article/CJFDTOTAL-ZGHJ202207028.htm
    [14]
    Han F, Zheng Y. Joint analysis of input and parametric uncertainties in watershed water quality modeling: A formal Bayesian approach[J]. Advances in Water Resources, 2018, 116: 77-94.
    [15]
    Joseph J, Ghosh S, Pathak A, et al. Hydrologic impacts of climate change: Comparisons between hydrological parameter uncertainty and climate model uncertainty[J]. Journal of Hydrology, 2018, 566: 1-22.
    [16]
    Marchant B, Mackay J, Bloomfield J. Quantifying uncertainty in predictions of groundwater levels using formal likelihood methods[J]. Journal of Hydrology, 2016, 540: 699-711.
    [17]
    Xie Y Q, Cook P G, Simmons C T, et al. Uncertainty of groundwater recharge estimated from a water and energy balance model[J]. Journal of Hydrology, 2018, 561: 1081-1093.
    [18]
    Albert C L, Merigó J M, Xu Y J. A coupled stochastic inverse sharp interface seawater intrusion approach for coastal aquifers under groundwater parameter uncertainty[J]. Journal of Hydrology, 2016, 540: 774-783.
    [19]
    Moore R E. Interval analysis[M]. Englewood Cliffs, New Jersey: Prentice-Hall, 1966.
    [20]
    Xie Y, Li Y, Huang G, et al. An interval fixed-mix stochastic programming method for greenhouse gas mitigation in energy systems under uncertainty[J]. Energy, 2010, 35(12): 4627-4644.
    [21]
    Gao W, Wu D, Song C, et al. Hybrid probabilistic interval analysis of bar structures with uncertainty using a mixed perturbation Monte-Carlo method[J]. Finite Elements in Analysis and Design, 2011, 47(7): 643-652.
    [22]
    Miao D, Huang W, Li Y, et al. Planning water resources systems under uncertainty using an interval-fuzzy De novo programming method[J]. Journal of Environmental Informatics, 2014, 24(1): 11-23.
    [23]
    Lü Z, Qiu Z Q. An iteration method for predicting static response of nonlinear structural systems with non-deterministic parameters[J]. Applied Mathematical Modelling, 2019, 68: 48-65.
    [24]
    Qiu Z Q, Xia H J. A novel interval linear programming based on probabilistic dominance[J]. Fuzzy and Systems, 2021, 434(S1): 20-47.
    [25]
    Dong G M, Tian J, Zhan H, et al. Groundwater flow determination using an interval parameter perturbation method[J]. Water, 2017, 9(12): 978.
    [26]
    Dong G M, Wang Y, Tian J, et al. Nonlinear expression of groundwater head interval based on the perturbation method[J]. Journal of Hydrologic Engineering, 2021, 26(8): 04021025.
    [27]
    Dong G M, Wang Y, Tian J, et al. Groundwater head uncertainty analysis in unsteady-state water flow models using the interval and perturbation methods[J]. Hydrogeology Journal, 2021, 29 (5): 1871-1883.
    [28]
    邱志平, 王晓军. 不确定性结构力学问题的集合理论凸方法[M]. 北京: 科学出版社, 2008.

    Qiu Z P, Wang X J. Convex method of set theory for uncertain structural mechanics problems[M]. Beijing: Science Press, 2008(in Chinese).
    [29]
    王颍. 二维承压水流地下水均衡项的区间分析方法及应用研究[D]. 江苏徐州: 中国矿业大学, 2020.

    Wang Y. Interval analysis method and application research of two-dimensional confined flow groundwater equilibrium term[D]. Xuzhou Jiangsu: China University of Mining and Technology, 2020(in Chinese with English abstract).
  • 加载中

Catalog

    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

    1. 本站搜索
    2. 百度学术搜索
    3. 万方数据库搜索
    4. CNKI搜索

    Article Metrics

    Article Views(309) PDF Downloads(37) Cited by()
    Proportional views
    Related

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return