Eulerian- lagrangian dense discrete phase model (DDPM) of stenotic LAD coronary arteries in comparison with single phase modeling.

In computational fluid dynamic studies related to blood flow, investigating the behavior of blood particles is crucial, especially red blood cells as they constitute a significant proportion of blood particles. Additionally, studying red blood cell movements is necessary, especially in stenotic artery geometries. A new multiphase scheme was utilized to demonstrate the effect of red blood cells on hemodynamics in complex coronary arteries and investigate the consequence of their motion. To investigate the effect of red blood cell movement on flow, the dense discrete phase model (DDPM) was used. This simulation was performed in 3D coronary arteries with different degrees of stenosis, utilizing blood pressure as inlet and outlet boundary conditions while assuming the arterial wall to be rigid. The model prediction shows good agreement with experimental data. Velocity values were comparable in both single-phase and two-phase flow simulations, but the shear stress in two-phase modeling had higher values. In the two-phase DDPM modeling, the recirculation areas indicated a higher probability of atherosclerosis plaque re-formation in the pre-stenosis area compared to the stenosis and post-stenosis areas. The DDPM model was found to be more effective in obtaining shear stress values in the artery. Additionally, this model provides good results compared to the single-phase model in investigating the movement of particles along the artery as well as recirculation areas that lead to the deposition of particles.

A numerical approach for preventing the dispersion of infectious disease in a meeting room.

Airborne transmission of respiratory aerosols carrying infectious viruses has generated many concerns about cross-contamination risks, particularly in indoor environments. ANSYS Fluent software has been used to investigate the dispersion of the viral particles generated during a coughing event and their transport dynamics inside a safe social-distance meeting room. Computational fluid dynamics based on coupled Eulerian-Lagrangian techniques are used to explore the characteristics of the airflow field in the domain. The main objective of this study is to investigate the effects of the window opening frequency, exhaust layouts, and the location of the air conditioner systems on the dispersion of the particles. The results show that reducing the output capacity by raising the concentration of suspended particles and increasing their traveled distance caused a growth in the individuals' exposure to contaminants. Moreover, decreasing the distance between the ventilation systems installed location and the ceiling can drop the fraction of the suspended particles by over 35%, and the number of individuals who are subjected to becoming infected by viral particles drops from 6 to 2. As well, the results demonstrated when the direction of input airflow and generated particles were the same, the fraction of suspended particles of 4.125%, whereas if the inputs were shifted to the opposite direction of particle injection, the fraction of particles in fluid increased by 5.000%.

Passenger exposure to respiratory aerosols in a train cabin: Effects of window, injection source, output flow location.

Nowadays the use of public transportation (PT) has been identified as high risk as due to the transfer of particles carrying the coronavirus from an infected passenger to others. This study puts forward a new computational framework for predicting the spread of droplets produced while the infected passenger talking inside the cabin of a train during various scenarios, including the changes in the outflows' location and the infected passenger's position. CFD was used to conduct the study, using the Euler-Lagrange approach to capture the transmission of particles, and Reynolds-averaged Navier-Stokes equations (RANS) to compute the airflow field. The results revealed that opening the window reduces the duration of particles inside the domain. So that when the window is open, the particle's shelf time can decrease to 25 percent comparing with closed mode. It was found that the passenger sitting next to the infected passenger encountered the highest infection risk. The conclusions made in this work show that the most desirable situation is obtained when the infected passenger is sitting next to the exits, whether the window is closed or open. The results of this paper offer comprehensive insights into how to keep indoor environments safe against infection aerosols.

Investigating the effect of air conditioning on the distribution and transmission of COVID-19 virus particles.

The effect of indoor airflow has been confirmed on the diffusion and transmission of droplets generated when talking or sneezing by a person with a viral respiratory infection such as COVID-19. The present study to investigate the effect of airflow in an indoor environment (a classroom) on the distribution and transmission of droplets emitted from speaking and cough by an infected person. A numerical analysis to investigate the persistence and deposition of particles on the surfaces of desks and the faces of residents (teacher and students) under various scenarios, including the opening of windows. This study puts forward two types of conditions while the teacher is speaking and the cough of some students for the distribution of pathogenic particles. Computational Fluid Dynamics used to conduct the study, using the Euler-Lagrange approach to capture the transport of the particles, and the RANS equations to compute the airflow field in the classroom. The results indicate the significant effect of air conditioning and open window close to the infected person in reducing environmental pathogens. Moreover, the concentrations of virus particles increase greatly near the output; hence, the presence of people in these areas increases the risk of contracting the disease. Furthermore, when all the windows are closed, due to the low output capacity, the particles spread in all areas of the domain and increase the risk of infection. Therefore, it is recommended that the window be open in indoors environment especially the window next to the speaker.

Effects of different non-Newtonian models on unsteady blood flow hemodynamics in patient-specific arterial models with in-vivo validation.

The aim of this study is to demonstrate the implications of using different blood rheological models in the simulation of blood flow dynamics in atherosclerotic coronary arteries. Computational fluid dynamics simulation was performed using three-dimensional (3D) patient-specific models of diseased left anterior descending (LAD) coronary arteries with varying degrees of stenosis severity. The three-dimensional arterial models were reconstructed from 3D quantitative coronary angiography, and input flow conditions were prescribed with blood flow conditions measured in-vivo. Different blood viscosity models were used for the simulations, and they include Newtonian and also non-Newtonian models such as Bingham, Carreau, Carreau-Yasuda, Casson, modified Casson, Cross, modified Cross, simplified Cross, Herschel Bulkley, Kuang-Luo (K-L), PowellErying, modified PowellErying, Power-law, Quemada and Walburn-Schneck models. Results from this study show that the time-averaged velocity at the centre of the arteries produced in the CFD simulations that uses the Carreau, modified Casson or Quemada blood viscosity models corresponded exceptionally well with the clinical measurements regardless of stenosis severities and hence, highlights the usefulness of these models to determine the potential determinants of blood vessel wall integrity such as dynamic blood viscosity, blood velocity and wall shear stress.