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

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.