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

MCA：利用进化和量子计算增强离散断裂网络建模，扩大融合研究的机会

• 批准号: 2123481
• 负责人: Rishi Parashar
• 金额: $ 39.99万
• 依托单位: Nevada System of Higher Education, Desert Research Institute
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-01 至 2024-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2123481&HistoricalAwards=false
• 关键词: MCA; Enhancing; Discrete; Fracture; Network

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.

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。在许多领域，对裂隙岩体的流动和输运过程进行高效、准确的数值模拟是必要的。离散裂隙网络（DFN）建模方法是模拟裂隙岩石中地下水流动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，以确保裂缝岩石系统的安全和可持续利用，并将在地下水文学、量子计算和人工智能启发方法的融合方面实现科学进步。该项目还为裂缝网络计算方法课程做出了贡献，并为研究生提供了培训。研究方法主要集中在：1)裂缝网络的图表示和降阶模型的确定，以包括与周围不透水岩石矩阵的扩散交换；2)定制进化计算优化（ECO）编码方案和适应度评估计算，以进一步降低网络的复杂性；3)在大型线性系统中使用量子计算算法，以获得裂缝网络中多个尺度的流动解。现有DFN模型将通过增加热传输功能来增强DFN- thermal模型。传统上通过广泛的基于粒子的模拟或机器学习方法来识别DFNs中的主干，在这个项目中将作为一个多目标优化问题来处理，其中ECO算法将同时优化主干的“种群”而不是单个主干，并与有效的算子（选择、交叉、突变）一起确定最佳解决方案。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输送过程的控制，该项目将有助于扩大DFN模型在更高复杂程度和更大范围内的应用。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。