A Digital-Twin and Machine-Learning Framework for Ventilation System Optimization for Capturing Infectious Disease Respiratory Emissions

被引:15
|
作者
Zohdi, T. I. [1 ]
机构
[1] Univ Calif Berkeley, Dept Mech Engn, 6195 Etcheverry Hall, Berkeley, CA 94720 USA
关键词
ELEMENT-METHOD; PARTICLE; DROPLETS; DISINFECTION; COMPUTATION; SIMULATION; RADIATION; AGENTS;
D O I
10.1007/s11831-021-09609-3
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
The pandemic of 2019 has led to an enormous interest in all aspects of modeling and simulation of infectious diseases. One central issue is the redesign and deployment of ventilation systems to mitigate the transmission of infectious diseases, produced by respiratory emissions such as coughs. This work seeks to develop a combined Digital-Twin and Machine-Learning framework to optimize ventilation systems by building on rapidly computable respiratory emission models developed in Zohdi (Comput Mech 64:1025-1034, 2020). This framework ascertains the placement and flow rates of multiple ventilation units, in order to optimally sequester particles released from respiratory emissions such as coughs, sneezes, etc. Numerical examples are provided to illustrate the framework.
引用
收藏
页码:4317 / 4329
页数:13
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