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

BRITE Pivot：人工智能增强的反应流和岩石风化的微观-宏观建模

• 批准号: 2416344
• 负责人: Chloe Arson
• 金额: $ 52.51万
• 依托单位: Cornell University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2024
• 资助国家: 美国
• 起止时间: 2024-01-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2416344&HistoricalAwards=false
• 关键词: BRITE; Pivot; Micro; Macro; Modeling

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.

反应流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正式联系起来尤其具有挑战性，因为支配宏观流体流动的微观结构特征不同于主导弹性、塑性和脆性行为的微观结构特征。这项促进工程转型和公平进步的研究理念奖(BRITE)枢轴奖将采用人工智能(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。这种自适应的多尺度模拟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发挥核心作用。计算机科学、应用力学、岩土工程和地貌学的集成旨在促进对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，通过将突出拓扑特征和现象之间隐藏的关联的深层神经网络来破译生命规则和利用数据革命。PI将为学生创造多学期的本科生研究机会和国际研究经验，开发多样性/公平性/包容性(DEI)研讨会系列，并共同设计工程领域的创新包容指标。人工智能在计算地质力学方面的探索尚处于初级阶段。人工智能与均匀化理论的研究集成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-宏观模型的开发以及在广泛的时空尺度上的应用。该研究计划将为PI和参与该项目的学生提供培训、研究、传播和DeI活动，并将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的虚拟实验数据库；(2)训练和测试深度卷积神经网络(CNN)以识别吸引场变量的高时空变化的微结构特征；(3)用特定于包裹体的特征时间丰富EShelby的均匀化理论；(4)训练和测试深度CNN，以适应作为特征时间过去后发生的微结构变化和局部化的函数的均化方案；(5)用自适应均化方法解决地质力学的耦合THCM边值问题。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。