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首页> 外文期刊>Disease Prevention Daily. >University of Michigan Researchers Have Provided New Study Findings on Applied Physical Science (On the utility of a well-mixed model for predicting disease transmission on an urban bus)
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University of Michigan Researchers Have Provided New Study Findings on Applied Physical Science (On the utility of a well-mixed model for predicting disease transmission on an urban bus)

机译:密歇根大学的研究人员提供新的研究发现在应用物理科学(混合模型的效用预测疾病传播城市公共汽车上)

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2021 SEP 21 (NewsRx) - By a News Reporter-Staff News Editor at Disease Prevention Daily - Researchers detail new data in applied physical science. According to news originating from the University of Michigan by NewsRx editors, the research stated, "The transport of virus-laden aerosols from a host to a susceptible person is governed by complex turbulent airflow and physics related to breathing, coughing and sneezing, mechanical and passive ventilation, thermal buoyancy effects, surface deposition, masks, and air filtration." The news correspondents obtained a quote from the research from University of Michigan: "In this paper, we study the infection risk via airborne transmission on an urban bus using unsteady Reynolds-averaged Navier-Stokes equations and a passive-scalar model of the virus-laden aerosol concentration. Results from these simulations are directly compared to the widely used well-mixed model and show significant differences in the concentration field and number of inhaled particles. Specifically, in the limit of low mechanical ventilation rates, the well-mixed model will overpredict the concentration far from the infected passenger and substantially underpredict the concentration near the infected passenger."
机译:2021年9月21日(NewsRx)——由一个新闻记者新闻编辑在日常——疾病预防在应用物理研究人员详细的新数据科学。NewsRx编辑,密歇根大学的研究指出:“virus-laden的运输气溶胶的主机是一个敏感的人由复杂的湍流气流和物理有关呼吸、咳嗽和打喷嚏机械和被动通风,热浮力的影响,表面沉积、面具和空气过滤。”引用研究大学的密歇根州:“在这篇文章中,我们研究了感染城市公共汽车上通过空中传播风险使用不稳定Reynolds-averaged n - s方程的passive-scalar模型virus-laden气溶胶浓度。这些模拟直接相比广泛使用的混合模型和显示意义不同的浓度场和数量吸入的颗粒。机械通气率低的将overpredict混合模型远离被感染的乘客和浓度附近的浓度大大低估了受感染的乘客。”

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