A CFD Digital Twin to Understand Miscible Fluid Blending

Thomas, John A.; Sinha, Kushal; Shivkumar, Gayathri; Cao, Lei; Funck, Marina; Shang, Sherwin; Nere, Nandkishor K.

doi:10.1208/s12249-021-01972-5

Cited by 22 publications

(19 citation statements)

References 19 publications

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“…This resolution is also sufficient to fully resolve flow near the walls, which are characterized by a minimum y + value of 0.3 mm 18 . We note that this lattice size exceeds the resolution required to predict grid‐independent impeller power number and bulk flow characteristics 30,31 …”

Section: Direct Two‐fluid Modelmentioning

confidence: 96%

“…18 We note that this lattice size exceeds the resolution required to predict grid-independent impeller power number and bulk flow characteristics. 30,31 Both fluids were assigned a density and viscosity of 1000 kg/m 3 and 10 À6 m 2 /s. The surface tension between the fluids was set to 0.04 N/m.…”

Section: Model Set-upmentioning

confidence: 99%

“…We apply a lattice spacing of 0.667 mm, which is sufficient to properly resolve flow and energy dissipation across the vessel. 30,31 Since we are only solving for the time-evolution of the continuous phase, this 4Â coarsening of the lattice spacing generates a computational domain that is 64Â smaller than the domain used to model the direct two-fluid simulations. Moreover, the timestep in these E-L simulations is 4Â larger than those applied in the direct two-fluid simulations.…”

Section: Mathematical Descriptionmentioning

confidence: 99%

“…The impeller and tank topology are the same as the 1 L system presented in Figure 1. We apply a lattice spacing of 0.667 mm, which is sufficient to properly resolve flow and energy dissipation across the vessel 30,31 . Since we are only solving for the time‐evolution of the continuous phase, this 4× coarsening of the lattice spacing generates a computational domain that is 64× smaller than the domain used to model the direct two‐fluid simulations.…”

Section: E–l Simulationsmentioning

confidence: 99%

See 3 more Smart Citations

Predicting the diameters of droplets produced in turbulent liquid–liquid dispersion

Thomas¹,

DeVincentis²,

Wutz

et al. 2022

AIChE Journal

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The droplet size distribution in liquid-liquid dispersions is a complex convolution of impeller speed, impeller type, fluid properties, and flow conditions. In this work, we present three a priori modeling approaches for predicting the droplet diameter distributions as a function of system operating conditions. In the first approach, called the two-fluid approach, we use high-resolution solutions to the Navier-Stokes equations to directly model the flow of each phase and the corresponding droplet breakup/coalescence events.In the second approach, based on an Eulerian-Lagrangian model, we describe the dispersed fluid as individual spheres undergoing ongoing breakup and coalescence events per user-defined interaction kernels. In the third approach, called the Eulerian-Parcel model, we model a sub-set of the droplets in the Eulerian-Lagrangian model to estimate the overall behavior of the entire droplet population. We discuss output from each model within the context of predictions from first principles turbulence theory and measured data.

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Section: Direct Two‐fluid Modelmentioning

confidence: 96%

Section: Model Set-upmentioning

confidence: 99%

Section: Mathematical Descriptionmentioning

confidence: 99%

Section: E–l Simulationsmentioning

confidence: 99%

See 2 more Smart Citations

Predicting the diameters of droplets produced in turbulent liquid–liquid dispersion

Thomas¹,

DeVincentis²,

Wutz

et al. 2022

AIChE Journal

View full text Add to dashboard Cite

show abstract

“…Prediction quality of obtained steady flow fields and power numbers are shown by Particle Image Velocimetry (PIV) or laser-doppler anemometry (LDA) and torque measurements [11,12,19,28]. LB methods have just recently become popular in the biopharmaceutical industry due to the availability of new commercial solvers [27,29,30]. Only a few articles have been published on LB LES for stirred tanks, lacking information on the quality of flow field prediction.…”

Section: Introductionmentioning

confidence: 99%

Validation of Novel Lattice Boltzmann Large Eddy Simulations (LB LES) for Equipment Characterization in Biopharma

et al. 2021

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Detailed process and equipment knowledge is crucial for the successful production of biopharmaceuticals. An essential part is the characterization of equipment for which Computational Fluid Dynamics (CFD) is an important tool. While the steady, Reynolds-averaged Navier–Stokes (RANS) k − ε approach has been extensively reviewed in the literature and may be used for fast equipment characterization in terms of power number determination, transient schemes have to be further investigated and validated to gain more detailed insights into flow patterns because they are the method of choice for mixing time simulations. Due to the availability of commercial solvers, such as M-Star CFD, Lattice Boltzmann simulations have recently become popular in the industry, as they are easy to set up and require relatively low computing power. However, extensive validation studies for transient Lattice Boltzmann Large Eddy Simulations (LB LES) are still missing. In this study, transient LB LES were applied to simulate a 3 L bioreactor system. The results were compared to novel 4D particle tracking (4D PTV) experiments, which resolve the motion of thousands of passive tracer particles on their journey through the bioreactor. Steady simulations for the determination of the power number followed a structured workflow, including grid studies and rotating reference frame volume studies, resulting in high prediction accuracy with less than 11% deviation, compared to experimental data. Likewise, deviations for the transient simulations were less than 10% after computational demand was reduced as a result of prior grid studies. The time averaged flow fields from LB LES were in good accordance with the novel 4D PTV data. Moreover, 4D PTV data enabled the validation of transient flow structures by analyzing Lagrangian particle trajectories. This enables a more detailed determination of mixing times and mass transfer as well as local exposure times of local velocity and shear stress peaks. For the purpose of standardization of common industry CFD models, steady RANS simulations for the 3 L vessel were included in this study as well.

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Computational prediction of blend time in a large‐scale viral inactivation process for monoclonal antibodies biomanufacturing

Sirasitthichoke

Hoang

Phalak

et al. 2022

Biotech & Bioengineering

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Viral inactivation (VI) is a process widely used across the pharmaceutical industry to eliminate the cytotoxicity resulting from trace levels of viruses introduced by adventitious agents. This process requires adding Triton X‐100, a non‐ionic detergent solution, to the protein solution and allowing sufficient time for this agent to inactivate the viruses. Differences in process parameters associated with vessel designs, aeration rate, and many other physical attributes can introduce variability in the process, thus making predicting the required blending time to achieve the desired homogeneity of Triton X‐100 more critical and complex. In this study we utilized a CFD model based on the lattice Boltzmann method (LBM) to predict the blend time to homogenize a Triton X‐100 solution added during a typical full‐scale commercial VI process in a vessel equipped with an HE‐3‐impeller for different modalities of the Triton X‐100 addition (batch vs. continuous). Although direct experimental progress of the blending process was not possible because of GMP restrictions, the degree of homogeneity measured at the end of the process confirmed that Triton X‐100 was appropriately dispersed, as required, and as computationally predicted here. The results obtained in this study were used to support actual production at the biomanufacturing site.

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A CFD Digital Twin to Understand Miscible Fluid Blending

Cited by 22 publications

References 19 publications

Predicting the diameters of droplets produced in turbulent liquid–liquid dispersion

Predicting the diameters of droplets produced in turbulent liquid–liquid dispersion

Validation of Novel Lattice Boltzmann Large Eddy Simulations (LB LES) for Equipment Characterization in Biopharma

Computational prediction of blend time in a large‐scale viral inactivation process for monoclonal antibodies biomanufacturing

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