DataSheet1_CFD simulations to study bed characteristics in gas–solid fluidized beds with binary mixtures of Geldart B particles: II quantitative analysis.pdf

Hydrodynamics of fluidized beds with binary mixtures of particles is important in many industrial applications. The binary particles are generally in the Geldart particle range. In our earlier work, (Part I) of this work simulations were carried out and qualitative analysis was presented. Quantitative predictions of gas velocity and particle velocity profiles have been presented in the present work, which is Part II of the two-part work on computational fluid dynamics (CFD) simulations of binary fluidized beds. It was observed that the dynamics of the bed vary for different binary mixtures and are a strong function of superficial velocity and bed height. Mixing and segregation in beds for two different initial bed heights and six different binary mixtures and superficial velocities have been identified. Segregation is prominent for binary mixtures with 20 wt.% and 80 wt.% of large particles, whereas mixing is observed in 40 wt.% and 60 wt.% large particle mixtures. Bypassing of gas near the walls is prominently seen for 60 wt.% large particles with gas velocities as high as 5 m/s. Time-averaged axial particle volume fractions have been observed to be lower in the dilute phase with large undulations in the middle whenever the bed is well mixed for central axial profiles. The axial volume fraction profiles also confirm the mixing and segregation for the 40 wt.% and 20 wt.% composition of large particles for the operating conditions considered for the study. Bed height expansion is linear until a certain superficial velocity with the increase or decrease depending on the superficial velocity or bed height of operation. Furthermore, correlations for minimum fluidization velocity and pressure drops from the literature have been compared with experimental results. The simulated data have been considered for the development of a correlation for minimum fluidization velocity. The predicted results match experimental data with a 10%–15% deviation.</p

Experimental and Computational Investigation of Microbubble Formation in a Single Capillary Embedded T‑junction Microfluidic Device

In recent years, there has been a notable increase in the interest toward microfluidic devices for microbubble synthesis. The upsurge can be primarily attributed to the exceptional control these devices offer in terms of both the size and the size distribution of microbubbles. Among various microfluidic devices available, capillary-embedded T-junction microfluidic (CETM) devices have been extensively used for the synthesis of microbubbles. One distinguishing feature of CETM devices from conventional T-junction devices is the existence of a wall at the right-most end, which causes a backflow of the continuous phase at the mixing zone during microbubble formation. The back flow at the mixing zone can have several implications during microbubble formation. It can possibly affect the local velocity and shearing force at the mixing zone, which in turn can affect the size and production rate of the microbubbles. Therefore, in this work, we experimentally and computationally understand the process of microbubble formation in CETM devices. The process is modeled using computational fluid dynamics (CFD) with the volume-of-fluid approach, which solves the Navier–Stokes equations for both the gas and liquid phases. Three scenarios with a constant liquid velocity of 0.053 m/s with varying gas velocity and three with a constant gas velocity of 0.049 m/s at different liquid velocities were explored. Increase in the liquid and gas velocity during microbubble formation was found to enhance production rates in both experiments and simulations. Additionally, the change in microbubble size with the change in liquid velocity was found to agree closely with the findings of the simulation with a coefficient of variation of 10%. When plotted against the time required for microbubble generation, the fluctuations in the pressure showed recurrent crests and troughs throughout the microbubble formation process. The understanding of microbubble formation in CETM devices in the presence of backflow will allow improvement in size reduction of microbubbles

Disparities in care by insurance status for individuals with rheumatoid arthritis: analysis of the medical expenditure panel survey, 2006–2009

<p><b>Objective:</b> Treatment guidelines for rheumatoid arthritis (RA) recommend early, aggressive treatment with nonbiologic and biologic disease-modifying antirheumatic drugs (DMARDs) to minimize long-term disability. We aimed to assess differences in medical resource utilization, drug therapy, and health outcomes among RA patients by insurance type in the United States.</p> <p><b>Methods:</b> Individuals with a self-reported diagnosis of RA were identified in the Medical Expenditure Panel Survey (MEPS) database, 2006–2009. Data regarding sociodemographic characteristics, insurance type and status, and outcomes (including health care resource utilization, prescription medication use, health status, and patient-reported barriers to health care) were extracted. Multivariable regression analyses were used to examine the impact of insurance type (private, Medicare, Medicaid, or uninsured) on outcome measures while controlling for age group, sex, and race/ethnicity.</p> <p><b>Results:</b> A total of 693 individuals with a self-reported diagnosis of RA during the study period were identified; 423 were aged 18–64 years and 270 were aged ≥65 years. Among patients aged 18–64, those with Medicaid or who were uninsured were less likely than those with private insurance to visit a rheumatologist (adjusted odds ratio [aOR] 0.13 and 0.17, respectively; <i>p</i> < .001) and to receive biologic DMARDS (aOR 0.09 [<i>p</i> < .001] and 0.16 [<i>p</i> < .01], respectively); those with Medicaid were also less likely to receive nonbiologic DMARDS (aOR 0.26 [<i>p</i> < .01]). Those with Medicaid were more likely than those with private insurance to be unable/delayed in getting prescription drugs (aOR 2.9 [<i>p</i> < .05]), to experience cognitive, social, and physical limitations (aOR 8.7 [<i>p</i> < .001], 4.7 [<i>p</i> < .001], and 2.5 [<i>p</i> < .05], respectively); they also reported significantly lower general health and health-related quality of life. Patients aged ≥65 experienced greater equity in care and outcomes.</p> <p><b>Conclusions:</b> Younger RA patients with Medicaid (including those who receive coverage under the Medicaid expansion component of the Affordable Care Act) may be at risk for inadequate treatment.</p

Experimental and Computational Investigation of Microbubble Formation in a Single Capillary Embedded T‑junction Microfluidic Device

In recent years, there has been a notable increase in the interest toward microfluidic devices for microbubble synthesis. The upsurge can be primarily attributed to the exceptional control these devices offer in terms of both the size and the size distribution of microbubbles. Among various microfluidic devices available, capillary-embedded T-junction microfluidic (CETM) devices have been extensively used for the synthesis of microbubbles. One distinguishing feature of CETM devices from conventional T-junction devices is the existence of a wall at the right-most end, which causes a backflow of the continuous phase at the mixing zone during microbubble formation. The back flow at the mixing zone can have several implications during microbubble formation. It can possibly affect the local velocity and shearing force at the mixing zone, which in turn can affect the size and production rate of the microbubbles. Therefore, in this work, we experimentally and computationally understand the process of microbubble formation in CETM devices. The process is modeled using computational fluid dynamics (CFD) with the volume-of-fluid approach, which solves the Navier–Stokes equations for both the gas and liquid phases. Three scenarios with a constant liquid velocity of 0.053 m/s with varying gas velocity and three with a constant gas velocity of 0.049 m/s at different liquid velocities were explored. Increase in the liquid and gas velocity during microbubble formation was found to enhance production rates in both experiments and simulations. Additionally, the change in microbubble size with the change in liquid velocity was found to agree closely with the findings of the simulation with a coefficient of variation of 10%. When plotted against the time required for microbubble generation, the fluctuations in the pressure showed recurrent crests and troughs throughout the microbubble formation process. The understanding of microbubble formation in CETM devices in the presence of backflow will allow improvement in size reduction of microbubbles

Experimental and Computational Investigation of Microbubble Formation in a Single Capillary Embedded T‑junction Microfluidic Device

In recent years, there has been a notable increase in the interest toward microfluidic devices for microbubble synthesis. The upsurge can be primarily attributed to the exceptional control these devices offer in terms of both the size and the size distribution of microbubbles. Among various microfluidic devices available, capillary-embedded T-junction microfluidic (CETM) devices have been extensively used for the synthesis of microbubbles. One distinguishing feature of CETM devices from conventional T-junction devices is the existence of a wall at the right-most end, which causes a backflow of the continuous phase at the mixing zone during microbubble formation. The back flow at the mixing zone can have several implications during microbubble formation. It can possibly affect the local velocity and shearing force at the mixing zone, which in turn can affect the size and production rate of the microbubbles. Therefore, in this work, we experimentally and computationally understand the process of microbubble formation in CETM devices. The process is modeled using computational fluid dynamics (CFD) with the volume-of-fluid approach, which solves the Navier–Stokes equations for both the gas and liquid phases. Three scenarios with a constant liquid velocity of 0.053 m/s with varying gas velocity and three with a constant gas velocity of 0.049 m/s at different liquid velocities were explored. Increase in the liquid and gas velocity during microbubble formation was found to enhance production rates in both experiments and simulations. Additionally, the change in microbubble size with the change in liquid velocity was found to agree closely with the findings of the simulation with a coefficient of variation of 10%. When plotted against the time required for microbubble generation, the fluctuations in the pressure showed recurrent crests and troughs throughout the microbubble formation process. The understanding of microbubble formation in CETM devices in the presence of backflow will allow improvement in size reduction of microbubbles

Experimental and Computational Investigation of Microbubble Formation in a Single Capillary Embedded T‑junction Microfluidic Device

In recent years, there has been a notable increase in the interest toward microfluidic devices for microbubble synthesis. The upsurge can be primarily attributed to the exceptional control these devices offer in terms of both the size and the size distribution of microbubbles. Among various microfluidic devices available, capillary-embedded T-junction microfluidic (CETM) devices have been extensively used for the synthesis of microbubbles. One distinguishing feature of CETM devices from conventional T-junction devices is the existence of a wall at the right-most end, which causes a backflow of the continuous phase at the mixing zone during microbubble formation. The back flow at the mixing zone can have several implications during microbubble formation. It can possibly affect the local velocity and shearing force at the mixing zone, which in turn can affect the size and production rate of the microbubbles. Therefore, in this work, we experimentally and computationally understand the process of microbubble formation in CETM devices. The process is modeled using computational fluid dynamics (CFD) with the volume-of-fluid approach, which solves the Navier–Stokes equations for both the gas and liquid phases. Three scenarios with a constant liquid velocity of 0.053 m/s with varying gas velocity and three with a constant gas velocity of 0.049 m/s at different liquid velocities were explored. Increase in the liquid and gas velocity during microbubble formation was found to enhance production rates in both experiments and simulations. Additionally, the change in microbubble size with the change in liquid velocity was found to agree closely with the findings of the simulation with a coefficient of variation of 10%. When plotted against the time required for microbubble generation, the fluctuations in the pressure showed recurrent crests and troughs throughout the microbubble formation process. The understanding of microbubble formation in CETM devices in the presence of backflow will allow improvement in size reduction of microbubbles

Experimental and Computational Investigation of Microbubble Formation in a Single Capillary Embedded T‑junction Microfluidic Device

In recent years, there has been a notable increase in the interest toward microfluidic devices for microbubble synthesis. The upsurge can be primarily attributed to the exceptional control these devices offer in terms of both the size and the size distribution of microbubbles. Among various microfluidic devices available, capillary-embedded T-junction microfluidic (CETM) devices have been extensively used for the synthesis of microbubbles. One distinguishing feature of CETM devices from conventional T-junction devices is the existence of a wall at the right-most end, which causes a backflow of the continuous phase at the mixing zone during microbubble formation. The back flow at the mixing zone can have several implications during microbubble formation. It can possibly affect the local velocity and shearing force at the mixing zone, which in turn can affect the size and production rate of the microbubbles. Therefore, in this work, we experimentally and computationally understand the process of microbubble formation in CETM devices. The process is modeled using computational fluid dynamics (CFD) with the volume-of-fluid approach, which solves the Navier–Stokes equations for both the gas and liquid phases. Three scenarios with a constant liquid velocity of 0.053 m/s with varying gas velocity and three with a constant gas velocity of 0.049 m/s at different liquid velocities were explored. Increase in the liquid and gas velocity during microbubble formation was found to enhance production rates in both experiments and simulations. Additionally, the change in microbubble size with the change in liquid velocity was found to agree closely with the findings of the simulation with a coefficient of variation of 10%. When plotted against the time required for microbubble generation, the fluctuations in the pressure showed recurrent crests and troughs throughout the microbubble formation process. The understanding of microbubble formation in CETM devices in the presence of backflow will allow improvement in size reduction of microbubbles

Experimental and Computational Investigation of Microbubble Formation in a Single Capillary Embedded T‑junction Microfluidic Device

In recent years, there has been a notable increase in the interest toward microfluidic devices for microbubble synthesis. The upsurge can be primarily attributed to the exceptional control these devices offer in terms of both the size and the size distribution of microbubbles. Among various microfluidic devices available, capillary-embedded T-junction microfluidic (CETM) devices have been extensively used for the synthesis of microbubbles. One distinguishing feature of CETM devices from conventional T-junction devices is the existence of a wall at the right-most end, which causes a backflow of the continuous phase at the mixing zone during microbubble formation. The back flow at the mixing zone can have several implications during microbubble formation. It can possibly affect the local velocity and shearing force at the mixing zone, which in turn can affect the size and production rate of the microbubbles. Therefore, in this work, we experimentally and computationally understand the process of microbubble formation in CETM devices. The process is modeled using computational fluid dynamics (CFD) with the volume-of-fluid approach, which solves the Navier–Stokes equations for both the gas and liquid phases. Three scenarios with a constant liquid velocity of 0.053 m/s with varying gas velocity and three with a constant gas velocity of 0.049 m/s at different liquid velocities were explored. Increase in the liquid and gas velocity during microbubble formation was found to enhance production rates in both experiments and simulations. Additionally, the change in microbubble size with the change in liquid velocity was found to agree closely with the findings of the simulation with a coefficient of variation of 10%. When plotted against the time required for microbubble generation, the fluctuations in the pressure showed recurrent crests and troughs throughout the microbubble formation process. The understanding of microbubble formation in CETM devices in the presence of backflow will allow improvement in size reduction of microbubbles

Experimental and Computational Investigation of Microbubble Formation in a Single Capillary Embedded T‑junction Microfluidic Device

In recent years, there has been a notable increase in the interest toward microfluidic devices for microbubble synthesis. The upsurge can be primarily attributed to the exceptional control these devices offer in terms of both the size and the size distribution of microbubbles. Among various microfluidic devices available, capillary-embedded T-junction microfluidic (CETM) devices have been extensively used for the synthesis of microbubbles. One distinguishing feature of CETM devices from conventional T-junction devices is the existence of a wall at the right-most end, which causes a backflow of the continuous phase at the mixing zone during microbubble formation. The back flow at the mixing zone can have several implications during microbubble formation. It can possibly affect the local velocity and shearing force at the mixing zone, which in turn can affect the size and production rate of the microbubbles. Therefore, in this work, we experimentally and computationally understand the process of microbubble formation in CETM devices. The process is modeled using computational fluid dynamics (CFD) with the volume-of-fluid approach, which solves the Navier–Stokes equations for both the gas and liquid phases. Three scenarios with a constant liquid velocity of 0.053 m/s with varying gas velocity and three with a constant gas velocity of 0.049 m/s at different liquid velocities were explored. Increase in the liquid and gas velocity during microbubble formation was found to enhance production rates in both experiments and simulations. Additionally, the change in microbubble size with the change in liquid velocity was found to agree closely with the findings of the simulation with a coefficient of variation of 10%. When plotted against the time required for microbubble generation, the fluctuations in the pressure showed recurrent crests and troughs throughout the microbubble formation process. The understanding of microbubble formation in CETM devices in the presence of backflow will allow improvement in size reduction of microbubbles

Cost-effectiveness of enzalutamide versus apalutamide versus androgen deprivation therapy alone for the treatment of metastatic castration-sensitive prostate cancer in Canada

There are no direct comparisons of the relative cost-effectiveness of second-generation anti-androgens (enzalutamide and apalutamide) used in managing metastatic castration-sensitive prostate cancer (mCSPC) in Canada. This study compared the cost-effectiveness of enzalutamide versus apalutamide versus androgen deprivation therapy (ADT) alone (standard of care) in patients with mCSPC from the Canadian public payer perspective using a Markov model with a 15-year time horizon. Efficacy data for enzalutamide and ADT alone were informed by the ARCHES and ENZAMET clinical trials, while a Bayesian network meta-analysis enabled comparison with apalutamide and ADT alone. Over the 15-year period, enzalutamide achieved the highest number of life-years (LY, 7.6) and quality-adjusted life-years (QALY, 5.62) compared with apalutamide (LY, 6.1; QALY, 4.59) and ADTs (LY, 4.9; QALY, 3.61). Enzalutamide incurred the most costs ($349,345) compared with apalutamide ($294,349) and ADT ($162,550). Sequential analysis showed that enzalutamide lies on the cost-effectiveness frontier with ADT alone (incremental cost-effectiveness ratio:$92,868/QALY), with apalutamide extendedly dominated through enzalutamide and ADT alone. Limitations include the heterogeneity of the studies included in the network meta-analysis and the validations for the treatment sequencing assumptions in the modeling. Enzalutamide was the most effective treatment option for mCSPC in the Canadian market, with the greatest LYs and QALYs, and incurred the most costs.</p