CAREER: Reliable Superinsulated Building Envelopes via Predictive Multiphysics Modeling

该奖项全部或部分由《2021年美国救援计划法案》（公法117-2）资助。该学院早期职业发展（Career）奖支持研究基于新型高性能材料的建筑围护结构（屋顶和墙壁）增材制造隔热组件的设计，使用预测计算建模，并应用严格的不确定性量化方法。纤维增强二氧化硅气凝胶复合材料的非凡性能使其成为最有前途的保温材料，可以显著降低下一代建筑的能耗和二氧化碳排放，带来广泛的环境效益，改善人类健康和福利，提高国家经济竞争力。该项目将建立有效的计算能力，从根本上了解气凝胶复合材料的功能行为，并利用其在现代建筑围护结构中的超保温性能。该项目所创造的知识将对其他领域产生积极影响，例如能源存储技术、航空航天工程中的热管理以及几乎所有工程系统中使用的其他复合材料。还将建立一个以计算机工程为中心的教育和推广项目，该项目包括自我指导、可移植和终身学习。这些活动包括：(1)促进工程教育中的协作技能；(2)为高中和本科生提供培训讲习班，重点是让妇女和少数族裔参与其中；(3)改善计算机工作人员中的性别多样性；(4)通过合作和联合讲习班加强学术界和工业界之间的伙伴关系。本研究的具体目标是创建预测计算模型，以应对各方面的不确定性，并可以指导发现多功能和高性能的建筑保温构件。研究目标是：(1)建立新型纤维增强二氧化硅气凝胶复合材料的多物理场模型；(2)利用贝叶斯框架对比实验测量验证模型的预测可靠性；(3)利用预测模型设计具有理想机械弹性、隔热和隔音性能的低成本、多材料热断裂；(4)制造和测试所设计的组件以验证其性能，并迭代地为建模精化提供信息。这些进步将通过新的基于材料微结构的理论，加速贝叶斯校准解决方案，不确定设计的独特优化算法，以及严格的验证和验证计算模型而成为可能。本研究的首要主题是利用增材制造气凝胶复合材料的多功能能力，为大批量制造更具弹性和可持续性的建筑保温构件开辟可能性。该项目将支持PI的长期愿景，即使用基于物理的预测计算建模来发现具有新性能机制的工程系统。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识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 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.