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

该奖项支持的研究重点是开发一种新型的数字孪生框架，用于量化与垂直基础设施运营相关的温室气体（GHG）排放，并通过设计和部署环保型建筑围护结构来最大限度地减少此类环境足迹。数字孪生模型是指创建物理资产的高保真三维表示，通过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。通过采用这种模式，物理和数字资产不断地相互联系（即，他们一起长大，因此称为“双胞胎”）。该研究项目将利用这些能力制定新的策略，设计和运营配备有立面的建筑物，这些立面能够修改其几何形状和行为，以最大限度地提高照明和通风，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个装有大量仪器的建筑，它将作为创建和验证这个新工具集的测试平台。该研究还将通过为研究生和本科生提供教育和外展活动、暑期研究实习以及中学和代表性不足的少数民族外展计划来补充。该研究的具体目标是创建数字化工具，以便对建筑的短期和长期性能进行全面评估，通过分析所有结构和非结构部件的组成材料水平，并评估如何利用环境适应性立面部件来优化此类性能。实际上，鉴于气候变化造成的需求存在很大程度的不确定性，系统响应与其环境结合的非线性和时间依赖性至关重要。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被缩小规模以供区域考虑，并将在数字孪生框架中得到利用，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潜力，以全面评估垂直基础设施的性能，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了道路，同时阐明如何利用气候适应性来最大限度地减少碳足迹。该项目得到工程部的支持该奖项反映了NSF的法定使命，并通过使用基金会的智力价值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.