Stochastic Flow and Transport in Heterogeneous Soils: A New Approach for Measurement Conditioning

9805357Li人们越来越普遍地认识到，溶质在非均质土壤中的运移是非常难以预测的。使预测复杂化的因素包括地质异质性和数据限制。该项目将研究一种新的方法来预测场尺度的流动和溶质迁移，它以一种系统的方式考虑了可变性和不确定性。这种新的方法基于分布式随机估计理论，该理论将现场测量与随机地下水模型的预测相结合。该方法可用于在修复开始之前确定危险废物场地地下水污染的程度和严重程度，并在修复过程中评估清理技术的进展。随机模型提供了整个污染场地的水头和溶质浓度的均值和方差的先验估计。只要有新的水力传导性、水头和/或浓度的测量结果，这些估计值就会更新。这些估计可用于指导取样井的布置和评估场地特征的准确性。研究项目的目的是在方向上具有方法性，并解决棘手的概念和计算问题。该项目将开发一种新的算法，用于耦合流动和运输调节，不受线性、平稳性或遍历假设的限制。具体地说，该项目将把PI的非平稳谱方法扩展到迭代测量条件，并为其有效实施提供了一种新的技术。该方法将极大地提高估计精度，并显著增加可用随机方法分析的场地表征问题的规模和复杂性。第一阶段将测试地下水流动问题的非平稳谱条件。第一阶段研究的目的是：1)论证新理论和具体目标阶段是应用程序的具体实例；2)通过与现有求解技术的比较，论证新方法的准确性、效率和稳健性；3)论证新方法在大型地下水系统中的可行性。第二阶段将测试三维流动和输运耦合问题的新方法，并将其应用于预测和表征实际现场的地下水流动和污染。按照国家科学基金会的建议，第二阶段的实施将推迟到第一阶段成功完成。我们稍后将向NSF提交一份修订后的提案，并根据第一阶段的结果为第二阶段制定完善的研究计划，并为第二阶段的调查提出令人信服的理由。

9805357LiIt is becoming widely recognized that solute transport in heterogeneous soils are extremely difficult to predict. Factors, which complicate the prediction, include geological heterogeneity and data limitations. This project will investigate a new approach to predict field-scale flow and solute transport which accounts for variability and uncertainty in a systematic way. The new approach is based on the distributed stochastic estimation theory which combines field measurements with predictions from a stochastic groundwater model. The approach can be applied to characterize the extent and severity of groundwater contamination at hazardous waste sites before remediation begins and to evaluate the progress of cleanup techniques during remediation. The stochastic model provides prior estimates of the mean and variance of head and solute concentration throughout a contaminated site. These estimates are updated whenever new measurements of hydraulic conductivity, head and/or concentration become available. These estimates may be used to guide the placement of sampling wells and to evaluate the accuracy of the site characterization.The research project is intended to be methodological in orientation and address tough conceptual and computationalproblems. The project will develop a new algorithm for coupled flow and transport conditioning that is not restricted by the assumptions of linearity, stationarity, or ergodicity. Specifically, the project will extend the PI's nonstationary spectral approach to iterative measurement conditioning and present a novel technique for its efficient implementation. The approach should greatly improve the estimation accuracy and dramatically increases the size and complexity of the site characterization problems that can be analyzed with stochastic methods.The proposed research will proceed in two phases. The first phase will test nonstationary spectral conditioning on a groundwater flow problem. The objective of phase 1 study is to 1) demonstrate the new theory and specific objective phase is to application procedure using a concrete example, 2) demonstrate the accuracy, efficiency, and robustness of the new approach by comparing it with existing solution techniques, and 3) demonstrate the feasibility of the new approach for large groundwater systems. The second phase will test the new approach for coupled three-dimensional flow and transport problems and apply it to predict and characterize groundwater flow and contamination at a real field site. As suggested by NSF implementation of phase 2 will be deferred until phase 1 is successfully completed. We will later submit a revised proposal to NSF with a refined research plan for the second phase based on phase 1 results and make a compelling case for phase 2 investigation.