EAGER: Modeling and Control of COVID-19 Transmission in Indoor Environments

尽管对COVID-19还有很多未知之处，但观察研究表明，微小的冠状病毒颗粒可以通过空气传播，因此可以在空中停留几个小时，飞行距离远远超过6英尺的社交距离指导。因此，这增加了在餐馆、办公室和宿舍等密闭室内空间接触病毒的风险。虽然它们通常不是为流行病而设计的，但如果设计和控制得当，供暖、通风和空调（HVAC）系统可以显著减轻疾病在室内空间的传播。麻省理工学院和三菱电机研究实验室（MERL）之间的合作研究项目协同控制理论，流体动力学和机器学习方面的研究活动，以实现建筑环境HVAC系统的优化设计和控制，从而最大限度地减少居住者的暴露风险。该项目将利用疾病传播的现实计算模型和室内热流体动力学，为HVAC系统的优化控制建立一个新的理论框架。这将为企业主和建筑管理者提供暖通空调操作的一般指导方针。该研究将把提出的框架与数据驱动的降阶模型结合起来，并采用强化学习技术开发计算上易于处理的算法，用于自适应在线控制HVAC系统，以遏制冠状病毒的传播。首先，该项目将通过系统研究不同级别的建模复杂性并分析其对携带病原体的液滴和气溶胶运输的影响，解决制定可靠的COVID-19安全指南所需的最低建模复杂性水平的不确定性。其次，该项目将开发一个最优控制框架，研究病毒传播的流动物理和室内空间的一般气流。第三，本研究将开发一个数据驱动的自适应控制框架，可用于设计HVAC系统的即插即用控制器。拟议的研究将通过为改进设计特定病例感染控制指南的方法奠定基础，从而具有显着的社会和经济效益，这些指南可以通过负担得起的暖通空调设备（如呼吸机和风扇）来实现。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。

Although much remains unknown about COVID-19, observational studies have demonstrated that tiny coronavirus particles can become airborne and hence, remain aloft in air for a few hours and travel distances much longer than the 6-feet guidance of the social distancing. This consequently increases the risk of exposure to the virus in confined indoor spaces, such as, restaurants, offices, and dormitories. Although they are generally not designed for pandemic situations, heating, ventilation, and air-conditioning (HVAC) systems, if properly designed and controlled, can significantly mitigate the spread of diseases in indoor spaces. This collaborative research project between MIT and Mitsubishi Electric Research Laboratories (MERL) synergies research activities in control theory, fluid dynamics, and machine learning to enable optimal design and control of HVAC systems for built environments so as to minimize the exposure risk of occupants. The project will establish a new theoretical framework for optimal control of HVAC systems employing realistic computational models of the disease transmission as well as the indoors thermofluid dynamics. This will provide business owners and building managers with general guidelines for the HVAC operations. The research will integrate the proposed framework with data-driven, reduced-order models and employ reinforcement learning techniques to develop computationally tractable algorithms for adaptive, online control of the HVAC systems in order to contain the spread of the coronavirus. First, the project will resolve the uncertainty that exists about the minimum level of modeling complexity required for developing reliable, COVID-19 safety guidelines by systematic study of different levels of modeling complexities and analyzing their effects on the transport of pathogen-laden droplets and aerosols. Second, the project will develop an optimal control framework that studies the flow physics of the virus transmission and the general airflow in the indoor space. Third, this research will develop a data driven, adaptive control framework that can be used to design plug and play controllers for HVAC systems. The proposed research will have significant social and economic benefits by setting the foundation for an improved methodology in designing case-specific infection control guidelines that can be realized by affordable HVAC devices, such as, ventilators and fans.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.