BRITE Pivot: Micro-Macro Modeling of Reactive Flow and Rock Weathering Enhanced by Artificial Intelligence

反应性流动是发生在跨越几个数量级的时空尺度上的地质力学不稳定性的关键。将化学反应和孔隙变形引起的微观结构变化与可测量的物理和机械性能联系起来尤其具有挑战性，因为控制宏观流体流动的微观结构特征不同于控制弹性、塑性和脆性行为的微观结构特征。这项促进工程变革和公平进步的研究理念（BRITE）支点奖将部署人工智能（AI）策略来预测热-水-化学-机械（THCM）不稳定性的时空尺度，并在局部化发生时自动调整微观结构的表示。这种自适应多尺度建模方法将有助于提高长期地下地质储存设施的安全性和可持续性，并有助于了解基岩中的化学风化过程，这些过程在养分供应、滑坡灾害和全球碳循环中起着核心作用。计算机科学、应用力学、岩土工程和地貌学的整合旨在促进新材料设计和对固体和软物质行为的基本理解的融合研究，从而提供新的建模工具来破译生命规则，并通过深度神经网络利用数据革命，突出拓扑特征和现象之间的隐藏相关性。PI将为学生创造多学期的本科研究机会和国际研究经验，开发多样性/公平/包容（DEI）系列研讨会，并共同设计工程中的创新包容指标。人工智能在计算地质力学方面的探索尚处于起步阶段。人工智能与均质化理论的研究整合将引领应用力学的重大进展，包括开放热力学系统的建模，一类新的自适应微观宏观模型的发展以及在广泛时空尺度上的应用。该研究计划将对项目负责人和参与项目的学生进行培训、研究、传播和DEI活动，并围绕以下五个科学目标进行组织：(1)用全场方法构建受限反应流虚拟实验数据库；(2)训练和测试深度卷积神经网络（CNN）来识别吸引高时空变化场变量的微观结构特征；(3)用包含特异性特征时间充实Eshelby的均质化理论；(4)训练和测试深度CNN，以适应均匀化方案作为特征时间过去后微观结构变化和局部化的函数；(5)采用自适应均匀化方法求解地质力学的耦合THCM边值问题。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。

Reactive flow is key to geomechanical instabilities that occur over spatiotemporal scales spanning several orders of magnitude. It is particularly challenging to formally link the microstructure changes induced by chemical reactions and pore deformation to measurable physical and mechanical properties, because the microstructural features that govern macroscopic fluid flow differ from those that dominate elastic, plastic and brittle behaviors. This Boosting Research Ideas for Transformative and Equitable Advances in Engineering (BRITE) Pivot award will deploy Artificial Intelligence (AI) strategies to predict the spatiotemporal scales of thermo-hydro-chemo-mechanical (THCM) instabilities and automatically adapt the representation of the microstructure as localizations occur. This adaptive multi-scale modeling approach will help improve the safety and sustainability of long-term underground geological storage facilities and understanding of chemical weathering processes in the bedrock, which play a central role in nutrient supply, landslide hazards, and the global carbon cycle. The integration of computer science, applied mechanics, geotechnical engineering and geomorphology aims to grow convergence research towards the design of new materials and the fundamental understanding of the behavior of solid and soft matter, hence providing new modeling tools to decipher the rules of life and harness the data revolution through deep neural networks that will highlight hidden correlations between topological features and phenomena. The PI will create multi-semester undergraduate research opportunities and international research experiences for students, develop a diversity/equity/inclusion (DEI) seminar series and co-design innovative inclusion metrics in engineering.The exploration of AI for computational geomechanics is at its infancy. The researched integration of AI with the homogenization theory will spearhead impactful advances in applied mechanics, including the modeling of open thermodynamic systems, the development of a new class of adaptive micro-macro models and applications over a wide range of spatiotemporal scales. The research plan will integrate training, research, dissemination and DEI activities for the PI and the students involved in the project, and will be organized around the five following scientific objectives: (1) Construct a database of virtual experiments of confined reactive flow with a full-field method; (2) Train and test a deep convolutional neural network (CNN) to recognize microstructural features that attract high spatiotemporal variations of field variables; (3) Enrich Eshelby’s homogenization theory with inclusion-specific characteristic times; (4) Train and test a deep CNN to adapt the homogenization scheme as a function of the microstructure changes and localizations that occur after characteristic times have elapsed; (5) Solve coupled THCM boundary-value problems of geomechanics with the adaptive homogenization method.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.