Numerical Simulation of Air Flow in Multiple Beds Intensive Care Unit of Hospital

Abstract

Indoor air quality ventilation diminishes airborne respiratory and other transmission in hospitals. Airflow in associate medical care Unit (ICU) may be provided through natural mean and additionally by the assistance of mechanical ventilation. Natural ventilation might not be enough to satisfy the requirement of ventilation for associate degree ICU. In the present study, numerical simulation of the airflow pattern and contaminant movement using Computational Fluid Dynamics (CFD) has been carried out for multiple bed hospital ICU with different inlet angles to examine path of contaminant transfer in the hospital. The measurement of air velocity is used as an input and standard k-ɛ turbulence model used in simulation work. Grid Independence Test (GIT) of hospital ICU has been carried out using high-quality tetrahedral unstructured mesh. In order to predict CFD simulations accurately, flow pattern has been validated using model of ICU with four bed and patient occupied with light source. Results shows that increasing rate of air flow change decreases the mean age of air. Importance of outlet position is high for transfer of contaminant particle from ICU.

Experimental and Numerical Investigation of Contaminant Control in Intensive Care Unit: A Case Study of Raipur, India

“…[31][32][33][34][35][36][37][38][39][40][41][42][43][44]. While CFD is an effective tool for numerical study, other numerical study involving ANN may be conducted for enhancing the results, as studies reported earlier have confirmed the same [45][46][47][48][49][50][51][52][53][54].…”

Airborne and aerosol pathogen transmission modeling of respiratory events in buildings: An overview of computational fluid dynamics

“…COVID-19 transport in enclosed spaces has been studied in different types of buildings such as hospitals ( Yu et al, 2017 , Qian and Zheng, 2018 , Verma et al, 2017 , Yam et al, 2011 , Anghel et al, 2020 , Sahu et al, 2019 , Leung et al, 2005 , Chow et al, 2006 , Bang et al, 2018 , Zhu et al, 2012 ), public transportation ( Zhang and Li, 2012 , Han et al, 2014 , Farag and Khalil, 2015 ), schools ( Coleman and Sigler, 2020 , Feng et al, 2012 ), offices ( He et al, 2011 , Azimi and Stephens, 2013 ), and residential buildings ( Gao et al, 2009 , Li et al, 2005 ). Reviewing the literature reveals that the background airflow pattern is a pivotal factor in airborne disease transmission, which is highly affected by different parameters related to (1) space (e.g., geometry and layout, usage type, equipment and furniture, and infiltration rate from cracks and voids), (2) HVAC (e.g., type of ventilation system, supply air parameters), (3) occupant (e.g., respiration performance, activity type such as walking, sitting or other types of activities ( Morawska, 2005 , Zhang et al, 2019 ).…”

“…In this regard, a brief overview of CFD technical details employed in previous studies with important insights is presented in Table 1 . These studies are summarized in different groups of HVAC-design ( Yu et al, 2017 , Anghel et al, 2020 , Jacob et al, 2019 , Mead-Hunter et al, 2013 , Arjmandi et al, 2021 , Sahu et al, 2019 , Leung et al, 2005 , Zhu et al, 2012 , He et al, 2011 , Zhang et al, 2019 , Gao and Niu, 2007 , Kang et al, 2015 , Feng et al, 2012 ), HVAC-disinfection ( Atci et al, 2020 , Noakes et al, 2006 , Feng et al, 2021 ), natural ventilation ( Gao et al, 2009 , Motamedi et al, 2021 ), partitioning ( Cheong and Lee, 2018 ) and PPE ( Kumar et al, 2020 , Pendar and Páscoa, 2020 ). In the HVACdesign as the most common theme, the impact of various HVAC strategies such as mixing ventilation (MV), displacement ventilation (DV), and under-floor air distribution (UFAD) on particle dispersion has been widely studied ( Feng et al, 2012 , Liu et al, 2018 , Jacob et al, 2019 , Correia et al, 2020 , Curseu et al, 2009 , Beggs et al, 2008 , Anghel et al, 2020 , Memarzadeh and Xu, 2012 , Gao and Niu, 2007 , Kang et al, 2015 , Feng et al, 2012 ).…”

“… An HVAC system can lead to the distribution of infectious droplets on both directly exposed surfaces and less exposed or hidden areas. ( Sahu et al, 2019 ) HVAC-design Airborne infections transmission study using particle tracking module Standard k- ε Droplet size of 1μm Wall: Adiabatic boundary type Inlet: Velocity inlet at two inlet grids Comparison of air flow pattern with experimental data ICU room 6.3 (L) m × 5.8 m (W) × 3.0 m (H) – 385,000 Tetrahedral unstructured cells Yes The importance of outlet position for transferring contaminant particles is highlighted in hospitals. ( Leung et al, 2005 ) HVAC-design Simulate the transport of droplets and bioaerosols for design optimization of local exhaust ventilation Standard k-ε Three particle sizes of 1, 10, and 50 µm in diameter Wall: Adiabatic boundary type Inlet-mouth: Sneezing velocity of 100m/s Comparison of airflow and aerosol concentration with experimental data A single patient room 6.7 (L) m × 6.0 m (W) × 2.7 m (H) – 1.6 million tetrahedral unstructured cells No Local exhaust ventilation is a promising strategy to remove the contagious pollutants for health care workers who need to be in close contact with patients.…”

“…Also, among the RANS turbulence models, the RNG k-ε model is tested and validated to be an appropriate choice ( Yu et al, 2017 , He et al, 2011 , Zhang et al, 2007 , Launder and Sharma, 1974 ) as it can provide better accuracy and stability in terms of low Reynolds number and near-wall flows ( Yu et al, 2017 , Feng et al, 2012 ). Furthermore, in some studies presented in Table 1 , the airflow condition is assumed isothermal ( Sahu et al, 2019 , Leung et al, 2005 , Gao and Niu, 2007 , Kang et al, 2015 , Atci et al, 2020 ). This assumption is mostly observed in studies that have a relatively small spatial domain to analyze while the investigation is mainly considered for sneezing and coughing, where particles are released at a high rate and the buoyancy effect is less effective ( Leung et al, 2005 ).…”