RAPID: Collaborative Research: Modeling, Analysis and Control of COVID-19 Spread in an Aircraft Cabin using Physics Informed Deep Learning

RAPID：协作研究：使用物理信息深度学习对机舱内的 COVID-19 传播进行建模、分析和控制

基本信息

批准号：
2031029
负责人：
Padmanabhan Seshaiyer
金额：
$ 6万
依托单位：
George Mason University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-06-01 至 2023-05-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031029&HistoricalAwards=false
关键词：
RAPID Collaborative Research Modeling Analysis

项目摘要

This project will model, analyze, predict, and present control mechanisms for a COVID-19 outbreak through an airborne infection in an aircraft cabin. As air travel resumes, it is expected that many passengers would be exposed to and possibly infected by the COVID-19 virus. As a result, there is an urgent need to rapidly develop solutions to determine the speed of the contagion by understanding the dynamics of the airflow inside aircraft. This research will combine four separate multi-physics models representing fluid dynamics, scalar transport, epidemiology, and airborne infection to analyze the spread of COVID-19 within a closed system such as an airplane. The multidisciplinary nature of this research will yield new algorithms at the interface of computational mathematics, deep learning, data science, epidemiology, and fluid dynamics and will provide novel techniques that can be directly applied to large-scale data to allow efficient and powerful data analysis. The project will also serve as valuable training for students. Open-source codes will be made available to the user community and will be open to contributions from end-users, academic researchers, industry members, practitioners, and government research labs. The research may also be extended to other physical spaces, such as marine vessels, trains, buses, or any other medium of public transportation systems.This research will accomplish the following specific objectives (a) develop a fully 3-dimensional computational model capturing realistic geometry and coupling four different physical and biological systems; (b) implement a hidden multi-physics neural network framework to enable data assimilation and; (c) evaluate the predictive capability using simulated, experimental and observational data in addition to developing and studying novel control and reinforcement learning mechanisms. The framework considers the characteristics of the exhalation of the droplets from COVID-19 infected members on an airplane that may not be wearing face masks, tracking the dispersion of these droplets, and tracking the inhalation of the droplets by susceptible passengers through these coupled multi-physics models. The research will help to develop a novel physics-informed deep-learning framework that will be capable of encoding the multi-physics system of equations modeled into the neural networks while being agnostic to the geometry or the initial and boundary conditions. Progress on the goals will provide advances in data-driven discovery, which will allow a better understanding of the impact of COVID-19. This grant is being awarded using funds made available by the Coronavirus Aid, Relief, and Economic Security (CARES) Act supplement allocated to MPS.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.

该项目将通过飞机舱中的空中感染进行建模，分析，预测和目前的控制机制，以造成19次爆发的爆发。随着航空旅行的恢复，预计许多乘客将暴露于19号病毒并可能感染。结果，迫切需要快速开发解决方案来通过了解飞机内部气流的动力学来确定传染的速度。这项研究将结合四个独立的多物理模型，这些模型代表流体动力学，标量传输，流行病学和空中感染，以分析Covid-19在封闭系统（例如飞机）中的传播。这项研究的多学科性质将在计算数学，深度学习，数据科学，流行病学和流体动力学的界面上产生新的算法，并将提供可直接应用于大规模数据的新技术，以允许有效且强大的数据分析。该项目还将成为学生的宝贵培训。开源代码将提供给用户社区，并将对最终用户，学术研究人员，行业成员，从业人员和政府研究实验室的贡献开放。这项研究还可以扩展到其他物理空间，例如海船，火车，公共汽车或任何其他公共交通系统的媒介。这项研究将实现以下特定目标（a）开发出完全3维的计算模型，以捕获现实的几何学并耦合四个不同的物理和生物系统；（b）实施隐藏的多物理神经网络框架以启用数据同化和；（c）除了开发和研究新颖的控制和增强学习机制外，还使用模拟，实验和观察数据来评估预测能力。该框架考虑了在飞机上呼出的液滴的特征，该飞机可能没有戴着口罩，跟踪这些液滴的分散，并通过这些耦合的多物质模型跟踪易感的载客对液滴吸入液滴的吸入。这项研究将有助于开发一个新颖的物理知识深度学习框架，该框架将能够编码建模到神经网络的多物理系统，同时对几何或初始和边界条件不可知。目标的进展将为数据驱动的发现提供进步，这将使人们更好地了解Covid-19的影响。该赠款是使用冠状病毒援助，救济和经济安全（CARES）法案提供的资金来授予的。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛影响审查标准通过评估来进行评估的。