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.

该奖项是根据2021年的《 Anericue Plan Act》（公共法律117-2）进行的在裂缝岩石中模拟流量和污染物的方法是离散的骨折网络，通过将其与来自LLD量子量的技术的技术集成在一起，从而增强了DFN模型的计算机性能。 ACE水文学，量子计算和人工智能启发的方法。岩石矩阵，2）自定义跨越骨折网络中的量表的进化构造s n ocods of School Searths System，以创建DFN-thermal模型。或机器rning方法，将在本项目中批准为多目标问题，在这种问题中，生态算法将同时优化骨架而不是单个骨架，并且与有效的操作员（选择，交叉，突变）一起，将降低最佳状态丁的改进的模拟平台框架可以评估裂缝网络中流动和运输过程的控制，以扩大更高程度和较大尺度的DFN模型的应用。该奖项反映了NSF'SF'SF'Story Mission，已被认为是值得的。使用Toundation的智力优点和更广泛的影响审查标准通过评估来支持。