表面活性剂强化的DNAPLs污染含水层修复过程的数值模拟

根据含水层中水、表面活性剂和DNAPLs的运移规律和相互作用机理, 建立三维多相流数值模拟模型, 用以模拟表面活性剂强化的DNAPLs污染含水层的修复过程.将所建立的模型应用于一个被PCE污染的非均质含水层中, 并分别对污染物的污染过程以及修复过程进行模拟.研究结果表明: 数值模拟模型给出了表面活性剂强化含水层修复过程中非水相流体迁移转化的数学描述, 能够在短时间内、参数有限的条件下真实地刻画DNAPLs在含水层中的运移规律, 并能有效地模拟表面活性剂的修复过程.此外, 模拟结果显示, 由于表面活性剂对PCE的增溶增流作用, 有效地提高了PCE在水中的溶解性和迁移性, 其修复40 d的去除率达到63.5%, 与抽出处理法(去除率为31.8%)相比修复效果明显增强.      

渗透率空间变异性对重非水相流体运移的影响

中针对多孔介质中水、气和非水相流体的多相流运移特点,建立了由包气带至饱和带的 NAPLs- 水 - 气三相流模 型。用 Karhunen-Loeve 展开方法构建 4 个空间变异性不同的渗透率随机场。在此基础上,分别模拟 4 种情景重非水相流体 (DNAPLs)的泄漏运移过程。通过分析 DNAPLs 饱和度的空间分布以及其空间矩随时间的变化来探讨渗透率空间变异性对 DNAPLs 运移的影响。结果显示,随着渗透率空间变异性的增强,DNAPLs 饱和度的空间变异性增大。渗透率场中的优势通 道对 DNAPLs 运移有着显著影响,直接决定 DNAPLs 的运移路径和饱和度分布。渗透率场的各向异性降低了 DNAPLs 在垂向 上的运移速度,影响污染羽的形态。     

水动力和介质非均质性影响下DNAPLs运移数值模拟

选定氯苯为典型DNAPL建立理想的地表污染泄漏多相流运移模型,利用TOUGH系列软件TMVOC模块进行数值模拟,探讨地下水动力、介质非均质性及两种因素协同作用时对DNAPLs运移和分布的影响。研究结果表明:(1)水力梯度增大了DNAPL水平和垂向运移速率;当水平向水力梯度过大时,反而导致垂向速率减小。(2)透镜体主要发挥阻滞作用,绕过透镜体后,下游侧污染指入渗深度明显大于上游侧;水力梯度的存在进一步增强介质非均质性的影响,产生更多集聚和绕流,形成不规则的污染指和污染池交替组合形态。     

介质润湿性和非均质性对重非水相运移影响孔隙尺度机制研究

重非水相(DNAPLs)是地下水常见的有机污染物,理解其运移机制对于污染物修复具有重要意义.为解释实验中多孔介质润湿性对DNAPL的流动速度及残余饱和度的影响,使用多相流相场法,在构造的二维孔隙中模拟DNAPL液滴的下落过程.结果表明:模拟能准确刻画多相流界面的不稳定性,重现与实验室尺度相似的现象.文章提出的双界面模型...     

表面活性剂强化含水层修复过程的全局灵敏度

针对非水相流体污染含水层的表面活性剂强化修复过程,在多相流数值模拟模型以及径向基函数人工神经网络替代模型的基础上,应用Sobol法对影响修复效果的变量进行全局灵敏度分析。当替代模型的训练集包含12组和22组数据时,替代模型与模拟模型拟合的确定性系数分别为0.977 8和0.981 6,表明随着训练集数据的增多,替代模型与模拟模型的近似精度逐渐增加。灵敏度分析结果表明：对修复效果贡献最大的决策变量为总抽水量(总灵敏度为0.491 2),其次为修复时间(总灵敏度为0.468 5),表面活性剂浓度对修复效果的贡献最小(总灵敏度为0.124 2);各个变量之间存在着相互作用,但相互作用对输出响应的影响不大。     

基于GMS的三维TOUGH2模型及模拟

GMS和TOUGH2均是应用广泛的地下流动系统数值模拟软件,其中,GMS界面友好,功能强大;而TOUGH2虽具有强大的数值计算能力,但缺乏友好的可视化前后处理界面。本文借助GMS强大的前处理能力,基于概念模型建立了三维复杂模型,把GMS/MODFLOW三维数值模型(包括网格数据、岩性数据、初始和边界数据等)转化为TOUGH2数值模型进行数值计算。通过两个计算实例(含水层水平没有起伏和有起伏)对比分析了GMS/MODFLOW和TOUGH2计算结果的差异。结果显示,该方法可以快速地建立刻画复杂地质条件的TOUGH2模型,计算结果与GMS/MODFLOW差异很小,说明两个软件均有很高的可信度;同时,该方法发挥了两个软件各自的优势,为进行更为复杂的多相流动数值模拟提供了可行性。     

裂隙宽度空间变异性和泄漏条件对网络裂隙中DNAPLs运移影响研究

DNAPLs本身的化学性质、裂隙的几何性质以及泄漏条件等影响重非水相流体（DNAPLs）在裂隙介质中的运移和分布。针对DNAPLs的运移规律研究多集中在孔隙介质和单裂隙中,在随机网络裂隙中的研究较少。本研究生成随机网络裂隙是基于蒙特卡罗方法,运用像素扫描识别并输出裂隙的坐标和宽度,然后采用PetraSim模拟四氯乙烯(PCE)在随机网络裂隙中的运移,探讨裂隙宽度空间变异性和泄漏条件（包括泄漏速率和泄漏位置）对DNAPLs运移的影响。数值模拟结果表明:DNAPLs的空间展布和运移路径受裂隙宽度空间变异性影响,随着网络裂隙中裂隙宽度空间变异性增大,出现优势通道,DNAPLs运移的速率加快,DNAPLs的质心位置和饱和度空间分布发生明显变化;DNAPLs的运移速率和空间展布受泄漏速率影响,泄漏速率越大,DNAPLs运移速率越快,模型底部蓄积的DNAPLs的饱和度越大,DNAPLs的空间展布也越大;同一网络裂隙中,泄漏位置不同,导致DNAPLs的运移路径及分布范围不同,不同的泄漏位置重力方向裂隙空间变异性不同,导致DNAPLs运移路径和空间展布各不相同。研究结果可以丰富裂隙介质中DNAPLs运移机理研究,为裂隙介质中DNAPLs污染修复提供模型参考。     

结合蒸汽和空气注入修复多孔介质中DNAPL污染物的多目标多相流模拟优化

基于数值方法模拟对比分析仅注入蒸汽与同时注入蒸汽和空气两种情形下原位曝气方法修复多孔介质中DNAPL污染物(以TCE为例)的修复过程。结果显示蒸汽的注入速率和焓值对蒸汽锋面在含水层中的运移速率影响显著;同时注入蒸汽和空气能够有效抑制只注入蒸汽时出现的DNAPL再冷凝现象,从而提高修复效率。基于模拟-优化方法对同时注入蒸汽和空气修复的理想范例建立了多目标多相流优化模型,采用一种自适应通用多目标优化算法AMALGAM求解Pareto锋面。优化结果表明基于混合优化框架的AMALGAM优化算法能有效综合利用多种独立进化算法的优点,自适应地根据独立进化算法的优劣生成子代,保证子代种群最优,收敛速度较NSGA算法更快。     

SPARROW模型研究及应用进展

SPARROW(SPAtially Referenced Regressions On Watershed attributes)模型是由美国地质调查局开发的一个基于空间的计算流域营养物质污染负荷的非线性回归模型。它使用机理函数和空间分布模块来计算流域的污染负荷,从而弥补了许多经验回归模型的缺陷。基于模型的特性,其在流域污染负荷核算、水质响应模拟、采样点空间优化、流域最大日最大污染负荷计算与水环境管理等方面有较好的应用前景。对SPARROW模型的机理、结构、输入输出变量、应用现状及在我国的应用发展前景和可能的问题进行了全面阐述和讨论,并对SPARROW模型的改进模型—贝叶斯-SPARROW模型进行简要介绍。以期为该模型在中国水环境管理中的应用提供参考。     

控制海水入侵的地下水多目标模拟优化管理模型

为实现滨海含水层地下水开采-回灌方案优化、控制海水入侵面积和降低海水入侵损失等多重管理目标,建立了海水入侵条件下地下水多目标模拟优化管理模型SWT-NPTSGA。模拟模型采用基于变密度流的数值模拟程序SEAWAT来模拟海水入侵过程。优化模型采用小生境Pareto禁忌遗传混合算法NPTSGA来求解,该算法在保证多目标权衡解的收敛性和计算效率的前提下,能维护整个进化群体的全局多样性。将SWT-NPTSGA程序应用于一个理想滨海含水层地下水开采方案和人工回灌控制海水入侵的优化设计中,结果表明该管理模型能够同时处理最大化总抽水流量、最小化人工回灌总量和最小化海水入侵范围等3个目标函数之间的权衡关系。通过采用人工回灌海水入侵区的减灾策略,既能增加滨海地区的供水量,又可减少海水入侵的范围,由此进一步验证了模型的有效性和可靠性。     

基于集合卡尔曼滤波的多相流模型参数估计——以室内二维砂箱中重质非水相污染物入渗为例

重质非水相有机污染物(DNAPL)泄漏到地下后,其运移与分布特征受渗透率非均质性影响显著。为刻画DNAPL污染源区结构特征,需进行参数估计以描述水文地质参数的非均质性。本研究构建了基于集合卡尔曼滤波方法(EnKF)与多相流运移模型的同化方案,通过融合DNAPL饱和度观测数据推估非均质介质渗透率空间分布。通过二维砂箱实际与理想算例,验证了同化方法的推估效果,并探讨了不同因素对同化的影响。研究结果表明:基于EnKF方法同化饱和度观测资料可有效地推估非均质渗透率场;参数推估精度随观测时空密度的增大而提高;观测点位置分布对同化效果有所影响,布置在污染集中区域的观测数据对于参数估计具有较高的数据价值。     

利用趋势化随机参数场的地下水流数值模拟优化方法

水文地质参数场的刻画是建立地下水流数值模拟模型的关键问题和难点问题。通常来讲,参数场合理性程度越高,模型拟合精度越高。本次研究将随机方法和参数空间分布表达进行结合,提出了趋势化随机参数场的构建方法。以渗透系数为研究对象,首先利用MCMC采样和样本数据特征确定水文地质参数的基本数据结构,进而根据样本空间分布特征对其进行趋势化处理,最终形成趋势化的渗透系数场。通过算例分析,利用趋势化处理后的渗透系数场能够大幅提高模拟精度,相比传统赋均值方法其误差可降至原来的1/3。在北京大兴跌隆起地区进行的实例应用说明,趋势化渗透系数场对提升岩性粒径较大(中砂以上)地区模拟精度效果显著,案例中粗砂区域渗透系数经趋势化处理后平均拟合误差由2.76 m下降至0.64 m;而对岩性以细砂及以下粒径为主的区域模拟精度提升并不明显。总体来说,该方法可为地下水流数值模型的优化提供借鉴,提升模型拟合精度,从而更加合理地刻画地下水流系统。     

粗糙裂隙水、气两相流相对渗透系数模型与数值分析

粗糙裂隙水、气两相流相对渗透系数是岩体工程多相渗流以及水力耦合分析的重要参数。从粗糙裂隙的细观结构出发，基于毛细吸持理论和立方定理，提出了粗糙裂隙水、气两相流相对渗透系数模型。通过与具有不同空间分布的粗糙裂隙水、气两相流试验数据对比分析，验证了模型的准确性。为进一步验证理论模型对不同粗糙程度裂隙的适用性，基于SRAM与Invasion Percolation模型，提出了粗糙裂隙的开度分布生成以及水、气两相流数值分析方法，计算结果表明理论模型与数值数据基本吻合一致，且优于X模型、V-C模型以及Corey模型。     

An adaptive sampling approach to reduce uncertainty in slope stability analysis

An adaptive sampling approach is proposed, which can sample spatially varying shear strength parameters efficiently to reduce uncertainty in the slope stability analysis. This approach employs a limit equilibrium model and stochastic conditional methodology to determine the likely sampling locations. Karhunen-Loève expansion is used to conduct the conditional Monte Carlo simulation. A first-order analysis is also proposed to ease the computational burden associated with Monte Carlo simulation. These approaches are then tested using borehole data from a field site. Results indicate that the proposed adaptive sampling approach is an effective and efficient sampling scheme for reducing uncertainty in slope stability analysis.     

Fast linearized forecasts for subsurface flow data assimilation with ensemble Kalman filter

Ensemble methods present a practical framework for parameter estimation, performance prediction, and uncertainty quantification in subsurface flow and transport modeling. In particular, the ensemble Kalman filter (EnKF) has received significant attention for its promising performance in calibrating heterogeneous subsurface flow models. Since an ensemble of model realizations is used to compute the statistical moments needed to perform the EnKF updates, large ensemble sizes are needed to provide accurate updates and uncertainty assessment. However, for realistic problems that involve large-scale models with computationally demanding flow simulation runs, the EnKF implementation is limited to small-sized ensembles. As a result, spurious numerical correlations can develop and lead to inaccurate EnKF updates, which tend to underestimate or even eliminate the ensemble spread. Ad hoc practical remedies, such as localization, local analysis, and covariance inflation schemes, have been developed and applied to reduce the effect of sampling errors due to small ensemble sizes. In this paper, a fast linear approximate forecast method is proposed as an alternative approach to enable the use of large ensemble sizes in operational settings to obtain more improved sample statistics and EnKF updates. The proposed method first clusters a large number of initial geologic model realizations into a small number of groups. A representative member from each group is used to run a full forward flow simulation. The flow predictions for the remaining realizations in each group are approximated by a linearization around the full simulation results of the representative model (centroid) of the respective cluster. The linearization can be performed using either adjoint-based or ensemble-based gradients. Results from several numerical experiments with two-phase and three-phase flow systems in this paper suggest that the proposed method can be applied to improve the EnKF performance in large-scale problems where the number of full simulation is constrained.     

An Approach for Modeling Rock Discontinuous Mechanical Behavior Under Multiphase Fluid Flow Conditions

In this paper, the two computer codes TOUGH2 and RDCA (for “rock discontinuous cellular automaton”) are integrated for coupled hydromechanical analysis of multiphase fluid flow and discontinuous mechanical behavior in heterogeneous rock. TOUGH2 is a well-established code for geohydrological analysis involving multiphase, multicomponent fluid flow and heat transport; RDCA is a numerical model developed for simulating the nonlinear and discontinuous geomechanical behavior of rock. The RDCA incorporates the discontinuity of a fracture independently of the mesh, such that the fracture can be arbitrarily located within an element, while the fluid pressure calculated by TOUGH2 can be conveniently applied to fracture surfaces. We verify and demonstrate the coupled TOUGH–RDCA simulator by modeling a number of simulation examples related to coupled multiphase flow and geomechanical processes associated with the deep geological storage of carbon dioxide—including modeling of ground surface uplift, stress-dependent permeability, and the coupled multiphase flow and geomechanical behavior of fractures intersecting the caprock.     

非饱和土土-水特征曲线预估方法研究

在Wei等[1]基于多相孔隙介质非平衡渗流理论提出的多孔介质热动力学混合物理论模型的基础上,建立了一个能描述不考虑土骨架变形的非饱和土土-水特征曲线动态模型。依据非饱和土土-水特征曲线动态模型,推导出不考虑土骨架变形的气-水两相非饱和土的饱和度演化方程。利用饱和度演化方程并结合多步流动瞬态试验的成果,通过数值反演,提出了一种能利用多步流动瞬态试验数据快速确定非饱和土土-水特征曲线的方法。通过对低液限粉土及低液限黏土的多步流动瞬态试验研究发现,饱和度演化方程能较好地模拟非饱和土在小基质吸力步长变化下饱和度的变化规律。此外,通过对多步流动瞬态试验试样饱和度的模拟确定的非饱和土土-水特征曲线与联合测试系统测得的土-水特征曲线的结果较为一致。     

A surrogate-based simulation–optimization approach application to parameters’ identification for the HydroGeoSphere model

A systematic approach is needed to use water more productively, because water shortages limit socio-economic development in many parts of the world. The aim of this paper is to establish a surrogate-based simulation–optimization approach to identify parameter values for a fully integrated surface water and groundwater flow coupling simulation. A surface water and groundwater flow coupling simulation model was implemented using HydroGeoSphere (HGS) model and the parameter sensitivities in the model were analyzed using local sensitivity analysis method. The parameters that exerted a large influence on the output results of the HGS model were then selected as stochastic variables, and the stochastic variable data sets were generated using the latin hypercube sampling (LHS) method, which, thereby, were used as inputs in HGS model to obtain the corresponding outputs. On the basis of input and output data sets, a kriging surrogate model of the HGS model was then established and verified, and parameter values of HGS model were identified using a surrogate-based simulation–optimization approach. The results of this study show that parameters that exert a large influence on the simulation output results include hydraulic conductivity, porosity, the van genuchten parameter (\(\alpha\)), and channel manning coefficient. The established kriging surrogate model is an ideal alternative to the HGS model for simulating and predicting, while optimal parameter values can be identified effectively and accurately using the established approach. The results of this research reveal that huge computational loads can be mitigated while using the kriging surrogate as an alternative for a simulation model in the solution process of optimization model.     

Application of the two-stage Markov chain Monte Carlo method for characterization of fractured reservoirs using a surrogate flow model

In this paper, we develop a procedure for subsurface characterization of a fractured porous medium. The characterization involves sampling from a representation of a fracture’s permeability that has been suitably adjusted to the dynamic tracer cut measurement data. We propose to use a type of dual-porosity, dual-permeability model for tracer flow. This model is built into the Markov chain Monte Carlo (MCMC) method in which the permeability is sampled. The Bayesian statistical framework is used to set the acceptance criteria of these samples and is enforced through sampling from the posterior distribution of the permeability fields conditioned to dynamic tracer cut data. In order to get a sample from the distribution, we must solve a series of problems which requires a fine-scale solution of the dual model. As direct MCMC is a costly method with the possibility of a low acceptance rate, we introduce a two-stage MCMC alternative which requires a suitable coarse-scale solution method of the dual model. With this filtering process, we are able to decrease our computational time as well as increase the proposal acceptance rate. A number of numerical examples are presented to illustrate the performance of the method.