CAREER: Reliable Superinsulated Building Envelopes via Predictive Multiphysics Modeling

This award is funded in whole or in part under the American Rescue Plan Act of 2021 (Public Law 117-2). This Faculty Early Career Development (CAREER) award supports research to investigate the design of additively manufactured insulation components of building envelope (roofs and walls) based on novel high-performance materials, using predictive computational modeling, and applying rigorous uncertainty quantification methodologies. The extraordinary properties of fiber-reinforced silica aerogel composites make them the most promising insulation materials that can significantly reduce energy consumption and carbon dioxide emission of next-generation buildings, leading to extensive environmental benefits, improving human health and welfare, and enhancing national economic competitiveness. This project will establish validated computational capabilities to fundamentally understand the functional behavior of aerogel composites and exploit their superinsulation properties in modern building envelopes. The knowledge created in this project will positively impact other fields, such as energy storage technologies, thermal management in aerospace engineering, and other composite materials employed in almost all engineering systems. An educational and outreach program will also be established centered around computational engineering that embraces self-directed, portable, and lifelong learnings. The activities consist of (1) stimulating collaborative skills in engineering education, (2) training workshops for high-school and undergraduate students with focus on involving women and minorities, (3) improving gender diversity in the computing workforce, and (4) enhancing partnerships between academia and industry through collaborations and joint workshops. The specific goal of this research is to create predictive computational models that cope with all aspects of uncertainties and can guide the discovery of multi-functional and high-performance building insulation components. The research objectives are to: (1) establish new multiphysics models of fiber-reinforced silica aerogel composites; (2) validate the predictive reliability of the model against experimental measurements using a Bayesian framework; (3) leverage the predictive model to design low-cost, multi-material thermal breaks with desired mechanical resiliency, thermal insulation, and soundproofing performances; and (4) fabricate and test the designed components to validate their performance and iteratively inform modeling refinement. These advancements will be made possible by novel microstructural-based theories of materials, accelerated Bayesian calibration solutions, unique optimization algorithms for design under uncertainty, and rigorous validation and verification of computational models. The overarching theme of this research is harnessing multi-functional capacities of additive manufactured aerogel composites to open up the possibility for high-volume fabrication of more resilient and sustainable building insulation components. This project will support the PI’s long-term vision to uncover engineering systems with new performance regimes using physics-based predictive computational modeling. 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.