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

EAGER：室内环境中 COVID-19 传播的建模和控制

• 批准号: 2114439
• 负责人: Munther Dahleh
• 金额: $ 15万
• 依托单位: Massachusetts Institute of Technology
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-06-01 至 2022-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2114439&HistoricalAwards=false
• 关键词: EAGER; Modeling; Control; COVID; 19

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.

尽管关于新冠肺炎仍有很多未知之处，但观察性研究已经证明，微小的冠状病毒颗粒可以在空气中飞行几个小时，并且旅行距离远远超过6英尺的社交距离。因此，这增加了在受限的室内空间，如餐馆、办公室和宿舍接触病毒的风险。尽管它们通常不是为大流行情况而设计的，但如果设计和控制得当，供暖、通风和空调(HVAC)系统可以显著减少疾病在室内空间的传播。该合作研究项目是麻省理工学院和三菱电机研究实验室(MERL)在控制理论、流体动力学和机器学习方面的协同研究活动，以实现建筑环境中暖通空调系统的优化设计和控制，从而将乘员的暴露风险降至最低。该项目将利用疾病传播的现实计算模型以及室内热流体动力学，为暖通空调系统的优化控制建立一个新的理论框架。这将为企业主和大厦管理人员提供暖通空调运行的一般指导方针。这项研究将把拟议的框架与数据驱动的降阶模型相结合，并采用强化学习技术来开发易于计算的算法，用于暖通空调系统的自适应在线控制，以遏制冠状病毒的传播。首先，该项目将通过对不同水平的模拟复杂性进行系统研究，并分析它们对携带病原体的微滴和气溶胶传输的影响，来解决制定可靠、新冠肺炎安全指南所需的最低模拟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，这项研究将开发一个数据驱动的自适应控制框架，可以用于设计即插即用的暖通空调系统控制器。这项拟议的研究将具有重大的社会效益和经济效益，因为它为设计可通过负担得起的暖通空调设备(如通风机和风扇)实现的针对特定病例的感染控制指南的改进方法奠定了基础。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。