Recurrence CFD – A novel approach to simulate multiphase flows with strongly separated time scales

On Modelling Parasitic Solidification Due to Heat Loss at Submerged Entry Nozzle Region of Continuous Casting Mold

Continuous casting (CC) is one of the most important processes of steel production; it features a high production rate and close to the net shape. The quality improvement of final CC products is an important goal of scientific research. One of the defining issues of this goal is the stability of the casting process. The clogging of submerged entry nozzles (SENs) typically results in asymmetric mold flow, uneven solidification, meniscus fluctuations, and possible slag entrapment. Analyses of retained SENs have evidenced the solidification of entrapped melt inside clog material. The experimental study of these phenomena has significant difficulties that make numerical simulation a perfect investigation tool. In the present study, verified 2D simulations were performed with an advanced multi-material model based on a newly presented single mesh approach for the liquid and solid regions. Implemented as an in-house code using the OpenFOAM finite volume method libraries, it aggregated the liquid melt flow, solidification of the steel, and heat transfer through the refractory SENs, copper mold plates, and the slag layer, including its convection. The introduced novel technique dynamically couples the momentum at the steel/slag interface without complex multi-phase interface tracking. The following scenarios were studied: (i) SEN with proper fiber insulation, (ii) partial damage of SEN insulation, and (iii) complete damage of SEN insulation. A uniform 12 mm clog layer with 45% entrapped liquid steel was additionally considered. The simulations showed that parasitic solidification occurred inside an SEN bore with partially or completely absent insulation. SEN clogging was found to promote the solidification of the entrapped melt; without SEN insulation, it could overgrow the clogged region. The jet flow was shown to be accelerated due to the combined effect of the clogging and parasitic solidification; simultaneously, the superheat transport was impaired inside the mold cavity.

Modeling the coating layer thickness in a pharmaceutical coating process

Although mechanistic numerical simulations can offer great insights into a process, they are limited with respect to resolved process time. While statistical models provide long-term predictability, determining the underlying probability distributions is often challenging. In this work, detailed CFD-DEM simulations of a pharmaceutical Wurster coating process for microspheres are used to evaluate the input parameters for a novel Monte-Carlo simulation approach. The combined strengths of both modeling approaches make it possible to predict the coating mass and thickness distributions over the entire process time. It was observed that smaller beads receive a thicker coating layer since they pass the spray zone closer to the nozzle. Moreover, it was established that, in contrast to the airflow rate, the spray rate has a great impact on the inter-particle coating variability. A stochastic model was developed to investigate the relative contribution of coating layer variability and fill weight variability to the product non-uniformity in a capsule filling process of Multiple Unit Pellet Systems (MUPS).

Possibilities and Limits of Computational Fluid Dynamics-Discrete Element Method Simulations in Process Engineering: A Review of Recent Advancements and Future Trends

Fluid-solid systems play a major role in a wide variety of industries, from pharmaceutical and consumer goods to chemical plants and energy generation. Along with this variety of fields comes a diversity in apparatuses and applications, most prominently fluidized and spouted beds, granulators and mixers, pneumatic conveying, drying, agglomeration, coating, and combustion. The most promising approach for modeling the flow in these systems is the CFD-DEM method, coupling computational fluid dynamics (CFD) for the fluid phase and the discrete element method (DEM) for the particles. This article reviews the progress in modeling particle-fluid flows with the CFD-DEM method. A brief overview of the basic method as well as methodical extensions of it are given. Recent applications of this simulation approach to separation and classification units, fluidized beds for both particle formation and energy conversion, comminution units, filtration, and bioreactors are reviewed. Future trends are identified and discussed regarding their viability.

Coupled metabolic-hydrodynamic modeling enabling rational scale-up of industrial bioprocesses

Metabolomics aims to address what and how regulatory mechanisms are coordinated to achieve flux optimality, different metabolic objectives as well as appropriate adaptations to dynamic nutrient availability. Recent decades have witnessed that the integration of metabolomics and fluxomics within the goal of synthetic biology has arrived at generating the desired bioproducts with improved bioconversion efficiency. Absolute metabolite quantification by isotope dilution mass spectrometry represents a functional readout of cellular biochemistry and contributes to the establishment of metabolic (structured) models required in systems metabolic engineering. In industrial practices, population heterogeneity arising from fluctuating nutrient availability frequently leads to performance losses, that is reduced commercial metrics (titer, rate, and yield). Hence, the development of more stable producers and more predictable bioprocesses can benefit from a quantitative understanding of spatial and temporal cell-to-cell heterogeneity within industrial bioprocesses. Quantitative metabolomics analysis and metabolic modeling applied in computational fluid dynamics (CFD)-assisted scale-down simulators that mimic industrial heterogeneity such as fluctuations in nutrients, dissolved gases, and other stresses can procure informative clues for coping with issues during bioprocessing scale-up. In previous studies, only limited insights into the hydrodynamic conditions inside the industrial-scale bioreactor have been obtained, which makes case-by-case scale-up far from straightforward. Tracking the flow paths of cells circulating in large-scale bioreactors is a highly valuable tool for evaluating cellular performance in production tanks. The "lifelines" or "trajectories" of cells in industrial-scale bioreactors can be captured using Euler-Lagrange CFD simulation. This novel methodology can be further coupled with metabolic (structured) models to provide not only a statistical analysis of cell lifelines triggered by the environmental fluctuations but also a global assessment of the metabolic response to heterogeneity inside an industrial bioreactor. For the future, the industrial design should be dependent on the computational framework, and this integration work will allow bioprocess scale-up to the industrial scale with an end in mind.