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

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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.