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.

该项目将对通过机舱内空气传播感染爆发的 COVID-19 疫情进行建模、分析、预测和展示控制机制。随着航空旅行的恢复，预计许多乘客将接触并可能感染 COVID-19 病毒。因此，迫切需要快速开发解决方案，通过了解飞机内部气流的动态来确定传染速度。这项研究将结合代表流体动力学、标量传输、流行病学和空气传播感染的四个独立的多物理模型来分析 COVID-19 在飞机等封闭系统内的传播。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接口上产生新的算法，并将提供可直接应用于大规模数据的新技术，以实现高效、强大的数据分析。该项目还将为学生提供宝贵的培训。开源代码将提供给用户社区，并向最终用户、学术研究人员、行业成员、从业者和政府研究实验室开放贡献。该研究还可以扩展到其他物理空间，例如船舶、火车、公共汽车或任何其他公共交通系统媒介。这项研究将实现以下具体目标（a）开发一个完整的 3 维计算模型，捕获真实的数据几何和耦合四种不同的物理和生物系统； (b) 实施隐藏的多物理场神经网络框架以实现数据同化； (c) 除了开发和研究新的控制和强化学习机制之外，还使用模拟、实验和观察数据评估预测能力。该框架考虑了飞机上可能未戴口罩的 COVID-19 感染成员呼出飞沫的特征，跟踪这些飞沫的分散情况，并通过这些耦合的多方跟踪易感乘客吸入飞沫的情况。物理模型。该研究将有助于开发一种新颖的基于物理的深度学习框架，该框架能够将建模到神经网络中的多物理方程系统进行编码，同时与几何形状或初始条件和边界条件无关。这些目标的进展将推动数据驱动的发现取得进展，从而更好地了解 COVID-19 的影响。这笔赠款是使用分配给 MPS 的冠状病毒援助、救济和经济安全 (CARES) 法案补充提供的资金来授予的。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评估进行评估，被认为值得支持。影响审查标准。