MCA: Enhancing Discrete Fracture Network Modeling Using Evolutionary and Quantum Computing to Expand Opportunities of Convergence Research

该奖项全部或部分由2021年美国救援计划法案（公法117-2）资助。在广泛的领域中，裂隙岩石中流动和输运过程的有效和准确的数值模拟是必要的。离散裂隙网络（DFN）模拟方法是模拟裂隙岩体中地下水流和污染物运移的一种常用数值方法。该项目通过将DFN模型与进化和量子计算领域的技术相结合，增强了DFN模型的计算性能和功能。这将带来更强大的工具，以确保安全和可持续地使用断裂岩石系统，并将使地下水文学，量子计算和人工智能启发方法的科学进步。该项目还为断裂网络计算方法课程作出贡献，并为一名研究生提供培训。研究方法侧重于：1）裂缝网络的图形表示和降阶模型的确定，以包括与周围不可渗透岩石基质的扩散交换，2）进化计算优化（ECO）编码方案和适应度评估计算的定制，以进一步降低网络的复杂性，以及3）将量子计算算法用于大型线性系统以获得裂缝网络中跨多个尺度的流动解。现有的DFN模型将通过增加热传输能力来增强，以创建DFN热模型。DFN中骨干的识别，传统上是通过广泛的基于粒子的模拟或机器学习方法来完成的，在这个项目中将作为一个多目标优化问题来处理，其中ECO算法将同时优化骨干的“种群”而不是单个骨干，并且沿着有效的操作符（选择，交叉，变异）将确定最佳解决方案。通过提供改进的模拟平台和评估框架来评估裂缝网络中流动和运输过程的控制，该项目将有助于将DFN模型的应用扩展到更高复杂程度和更大规模的问题。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估来支持。

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2).Efficient and accurate numerical modeling of flow and transport processes in fractured rocks is necessary in a wide array of fields. One common numerical method to simulate groundwater flow and contaminant transport in fractured rocks is the discrete fracture network (DFN) modeling approach. The project enhances the computational performance and functionality of DFN models by integrating them with techniques from the field of evolutionary and quantum computing. This will lead to more robust tools for ensuring safe and sustainable use of fractured rock systems, and will enable scientific advances at the convergence of subsurface hydrology, quantum computing, and artificial-intelligence-inspired methods. The project also contributes to a course on computational methods in fracture networks and provides training to a graduate student. The research approach focuses on: 1) graph representations of fracture networks and determination of reduced-order models to include diffusional exchanges with the surrounding impermeable rock matrix, 2) customization of evolutionary computing optimization (ECO) encoding schemes and fitness evaluation computations to further reduce complexity of networks, and 3) use of quantum computing algorithms for large linear systems to obtain flow solutions across a multitude of scales in fracture networks. Existing DFN models will be enhanced by adding heat transport capabilities to create DFN-Thermal models. Identification of backbone in DFNs, which is traditionally done through extensive particle-based simulations or machine learning methods, will be approached in this project as a multi-objective optimization problem where ECO algorithms will simultaneously optimize a “population” of backbones rather than a single backbone, and along with effective operators (selection, crossover, mutation) would determine the optimal solution. By providing improved simulation platform and evaluation framework to assess controls on flow and transport processes in fracture networks, the project will serve to expand the application of DFN models to problems of higher degrees of complexity and at larger scales.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.