CLIMA: A Digital Twin Modeling Framework for Climate Adaptive Vertical Infrastructure

该奖项支持专注于开发与垂直基础设施运营相关的温室气体(GHG)排放量化的新型数字孪生框架的研究，并通过设计和部署环境响应型建筑围护结构来最大限度地减少此类环境足迹。数字双胞胎建模是指创建物理资产的高保真三维表示，并通过来自传感器的实时数据流进行增强，然后将其输入实时预测模型。通过采用这一模式，实物资产和数字资产不断地相互联系(即，它们一起老化，因此称为“孪生”)。该研究项目将利用这些能力制定新的战略，以设计和运营配备了能够改变其几何形状和行为的外墙的建筑，以最大限度地增加照明和通风，同时最大限度地减少能源需求和相关的温室气体排放。这项工作的核心是匹兹堡大学内的一座仪表化程度很高的建筑，它将作为创建和验证这一新工具集的试验台。这项研究还将通过为研究生和本科生提供教育和推广活动、暑期研究实习以及中学和代表性不足的少数族裔推广计划来补充。研究的具体目标是创建数字工具，通过分析构成材料层面的所有结构和非结构构件，并评估如何利用环境适应性外墙构件来优化此类性能，从而对建筑的短期和长期性能进行全面评估。鉴于气候变化对需求造成很大程度的不确定性，该系统的反应与其环境相结合的非线性、依赖时间的性质实际上至关重要。基于政府间气候变化专门委员会第六次评估报告的大气环流模型将缩小规模，以供区域审议，并将在数字孪生框架中加以利用，其中将嵌入机械预测模型和机器学习算法，最终目标是指导气候适应性建筑的设计和运营，以最大限度地减少生命周期温室气体排放。该项目带来了定义新一代数字孪生工具的可能性，以执行垂直基础设施性能的全面评估，为与复杂民用系统的建设和运营相关的温室气体排放的实时量化和可视化铺平道路，同时揭示如何利用气候适应性来最大限度地减少碳足迹。该项目由土木工程基础设施工程(ECI)计划和土木工程司工程设计和系统工程(EDSE)计划支持，这一奖项反映了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。

This award supports research focusing on developing a novel Digital Twin framework for the quantification of Greenhouse Gas (GHG) emissions associated with the operation of vertical infrastructure, and minimizing such environmental footprint by designing and deploying environmentally responsive building envelopes. Digital twin modeling refers to the creation of a high-fidelity three-dimensional representation of a physical asset, which is augmented by live data streaming from sensors, which is then fed into real-time predictive models. By adopting this paradigm, the physical and digital assets are continuously linked to each other (i.e., they age together, hence the term "twin"). This research project will leverage such capabilities to devise new strategies to design and operate buildings equipped with façades capable of modifying their geometry and behavior to maximize lighting and ventilation, while minimizing energy requirements and associated GHG emissions. Core to this effort is a heavily instrumented building within the University of Pittsburgh that will serve as the test bed to create and validate this new toolset. The research will also be complemented by delivering educational and outreach activities for graduate and undergraduate students, summer research internships, as well as middle schools and underrepresented minority outreach programs.The specific goal of the research is to create digital tools to allow for a holistic assessment of the short- and long-term performance of the building, by analyzing all the structural and non-structural components at the level of the constituent materials and assessing how environmentally adaptive façade components can be leveraged to optimize such performance over time. The nonlinear, time-dependent nature of the response of the system in combination with its environment is, in fact, of crucial importance in view of the large degree of uncertainty on the demand resulting from climate change. General circulation models based on the Intergovernmental Panel on Climate Change’s Sixth Assessment Report will be downscaled for regional consideration and will be leveraged in the Digital Twin framework, in which mechanistic predictive models and Machine Learning algorithms will be embedded, with the ultimate goal of guiding the design and operation of climate-adaptive buildings to minimize life cycle GHG emissions. This project brings the potential to define a new generation of Digital Twin tools to perform comprehensive assessment of the performance of vertical infrastructure, paving the way for real-time quantification and visualization of GHG emissions associated with the construction and operation of complex civil systems, while at the same time shedding light on how climate adaptivity can be leveraged to minimize their carbon footprint.This project is supported by the Engineering for Civil Infrastructure (ECI) Program and the Engineering Design and Systems Engineering (EDSE) Program of the Division of Civil, Mechanical and Manufacturing Innovation (CMMI) of the Directorate for Engineering (ENG).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.