Planning: Digital Twin for Building Performance Simulation and Optimization in Adaptive Reuse Planning

现有建筑的适应性再利用是解决与基础设施老化、城市发展和环境可持续性相关的社会挑战的重要策略。然而，由于缺乏准确的数据和强大的工具，目前的方法在预测和优化建筑性能方面往往不足。在可持续和节能建筑设计领域，数字孪生技术等先进技术的未开发潜力与丰富的建筑数据变革解决方案相结合。将数字孪生技术纳入适应性再利用规划，不仅有助于有效利用资源和减少废物，还能显著减少对环境的影响，从而解决关键的可持续性问题。在该项目中，首席研究员（PI）将利用该规划拨款开发概念验证数字孪生系统，专门为适应性重用规划设计。PI将举办研讨会，促进各hbcu教师之间的知识交流，并与潜在的合作者和行业伙伴接触，为未来的研究奠定基础。该项目涉及开发一个复杂的数字孪生模型，该模型融合了各种数据源，包括建筑和结构参数、历史数据、环境因素和城市法规。利用机器学习算法和高性能计算的力量，该模型将对建筑性能进行实时模拟和预测。该项目旨在通过预测和优化建筑性能参数来提高能源效率，从而为适应性再利用规划提供信息。PI将为数据收集、模型开发、性能模拟和能源效率评估开发一个强大的方法。这一努力将产生一个用户友好的框架，使非专家能够进行复杂的模拟并做出明智的、节能的决策。这种方法为适应性再利用规划引入了一种革命性的、技术驱动的方法，通过提高过程的效率和效果，有可能改变这个领域，最终培育出更可持续的城市。在这个关键的规划阶段，重点是战略协调、吸引潜在合作者、进行初步调查和概念验证研究，从而为这一突破性研究奠定坚实的基础。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。

The adaptive reuse of existing buildings presents an essential strategy for addressing societal challenges related to aging infrastructure, urban development, and environmental sustainability. However, current methodologies often fall short in predicting and optimizing building performance due to a lack of accurate data and robust tools. In the realm of sustainable and energy-efficient building design, the untapped potential of advanced technologies such as digital twin technology, integrated with rich building data transformative solutions. Incorporating digital twin technology into adaptive reuse planning not only contributes to efficient resource utilization and waste reduction but also significantly reduces environmental impact, thereby addressing critical sustainability issues. In this project, the Principal Investigator (PI) will utilize this planning grant to develop a proof-of-concept digital twin system, specifically designed for adaptive reuse planning. The PI will conduct workshops to promote knowledge exchange among faculty members from various HBCUs and engage with potential collaborators and industry partners, laying the groundwork for the research ahead.The project involves developing a sophisticated digital twin model that incorporates diverse data sources, including architectural and structural parameters, historical data, environmental factors, and urban regulations. Leveraging the power of machine learning algorithms and high-performance computing, this model will perform real-time simulations and predictions of building performance. The project aims to enhance energy efficiency by predicting and optimizing building performance parameters, thus informing adaptive reuse planning. The PI will be developing a robust methodology for data collection, model development, performance simulation, and energy efficiency evaluation. This endeavor will result in a user-friendly framework that empowers non-experts to conduct complex simulations and make informed, energy-efficient decisions. This approach introduces a revolutionary, technology-driven method to adaptive reuse planning, potentially transforming the field by making the process more efficient and effective, ultimately fostering more sustainable cities. In this crucial planning stage, the focus is on strategic coordination, engaging potential collaborators, conducting preliminary investigations, and proof-of-concept studies, thereby laying a robust foundation for this ground-breaking research.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.