An objective classification of condensation regimes in direct contact condensation

Intensified heat treatment, using direct contact condensation (DCC), is applied in the production of dairy products to ensure a high level of food safety. The key challenge with DCC is the fouling due to the protein reactions that limits operational efficiency and sustainability. Using a condensation regime map can improve operational decision-making. Pilot plant scale experiments were conducted for a wide range of steam mass fluxes and inlet temperatures at high and low channel pressures. High-speed images were recorded and analyzed to obtain penetration lengths and plume area. The experimental data and image analysis supplemented with temperature and pressure measurement, were processed using machine learning (ML) to develop a data driven model to predict the regime maps. The linear discriminant analysis (LDA) was found to be the most suitable model. From the ML models it was also found that the best parameters to make a condensation regime map are the steam pressure, channel pressure, subcooling temperature, water Prandtl number, and the relative velocity ratio between gas and liquid. The condensation outcomes were presented with various two-dimensional regime maps. New regime maps are proposed using the Prandtl number and velocity ratio as dimensionless parameters.</p

Comparative analysis of drop-size measurement in highly dense sprays using shadowgraphy, PDA, and SLIPI

Atomization is a physical phenomenon that is widely encountered in many engineering and industrial applications, such as in combustion engines, spray coating, spray dryers and many more. Spray characterization involves the determination of the droplet size and velocity distributions (both probability density function and spatial). To determine these parameters experimentally, traditionally, microscopic shadowgraphy and Phase Doppler Anemometry (PDA) are used, because of their relative ease of use and high accuracy. However, the application of these techniques is limited to relatively less dense sprays. In highly dense sprays, the strong multiple scattering effects cause significant errors in the determination of relevant parameters. Therefore, the Structured Laser Illumination Planar Imaging (SLIPI) technique is adopted. In this work, comparative measurements are reported to assess the capabilities of these techniques for drop-size measurements in a highly dense spray originating from a pressure swirl nozzle

Experiments on floating bed rotating drums using magnetic particle tracking

Magnetic particle tracking (MPT) was employed to study a rotating drum filled with cork particles, using both air and water as interstitial medium. This noninvasive monitoring technique allows for the tracking of both particle translation and rotation in dry granular and liquid–solid systems. Measurements on the dry and floating bed rotating drum were compared and detailed analysis of the bed shape and velocity profiles was performed. It was found that the change of particle–wall and particle–particle interaction caused by the presence of water significantly affects the bed behavior. The decreased friction leads to slipping of the particles with respect to the wall, rendering the circulation rate largely insensitive to increased drum speed. It was also found that the liquid–particle interaction is determining for the behavior of the flowing layer. The well-defined experiments and in-depth characterization performed in this study provide an excellent validation case for multiphase flow models.</p

Stochastic DSMC model for large scale dense bubbly flows

Bubble columns are widely used in the chemical industry because of their simple design and high efficiency. The scale-up of these kinds of columns is challenging and time-consuming. Since high throughput is targeted, they are operated in the heterogeneous bubbling regime where the flow is complex and turbulent. Large-scale bubble columns can in principle be simulated using continuum models (TFM/MFM) with closures from more detailed models such as Front Tracking (FT) or Volume of Fluid (VOF). Multi-fluid models are capable of predicting the flow field, but to accurately describe mass transfer rates, an accurate interfacial area of the bubbles is required as well as mass transfer coefficients for dense bubble swarms. This requires the MFM to be coupled with models that can predict bubble size distributions. The Discrete Bubble Model (DBM) can be scaled up but the bubble-bubble interactions make it computationally very intensive. Stochastic Direct Simulation Monte Carlo (DSMC) methods treat the bubbles in a discrete manner while more efficiently handling the collisions compared to the DBM. The DSMC model has earlier been used for very small particles in the size range of Angstroms to microns where the particles are purely inertial at high Stokes numbers. In the work of Pawar et al. (2014) this was used for micrometer sized particles/droplets where this method proved to be 60 to 70 times faster than more classical methods like the Discrete Particle Model (DPM). In this work the DSMC method has been extended to finite sized bubbles/particles in the order of millimeters. A 4-way coupling (liquid-bubble-bubble) is achieved using the volume-averaged Navier Stokes equations. The model is verified first for monodisperse impinging particle streams without gas. Then the model is verified with the DBM of a 3D periodic bubble driven system. The collision frequencies are all within 10 percent accuracy and the speed up achieved per DEM time step is nearly 10 times compared to the DBM, which facilitates simulation of large systems

Progress in Applied CFD. Selected papers from 10th International Conference on Computational Fluid Dynamics in the Oil & Gas, Metallurgical and Process Industries

Bubble columns are widely used in the chemical industry because of their simple design and high efficiency. The scale-up of these kinds of columns is challenging and time-consuming. Since high throughput is targeted, they are operated in the heterogeneous bubbling regime where the flow is complex and turbulent. Large-scale bubble columns can in principle be simulated using continuum models (TFM/MFM) with closures from more detailed models such as Front Tracking (FT) or Volume of Fluid (VOF). Multi-fluid models are capable of predicting the flow field, but to accurately describe mass transfer rates, an accurate interfacial area of the bubbles is required as well as mass transfer coefficients for dense bubble swarms. This requires the MFM to be coupled with models that can predict bubble size distributions. The Discrete Bubble Model (DBM) can be scaled up but the bubble-bubble interactions make it computationally very intensive. Stochastic Direct Simulation Monte Carlo (DSMC) methods treat the bubbles in a discrete manner while more efficiently handling the collisions compared to the DBM. The DSMC model has earlier been used for very small particles in the size range of Angstroms to microns where the particles are purely inertial at high Stokes numbers. In the work of Pawar et al. (2014) this was used for micrometer sized particles/droplets where this method proved to be 60 to 70 times faster than more classical methods like the Discrete Particle Model (DPM). In this work the DSMC method has been extended to finite sized bubbles/particles in the order of millimeters. A 4-way coupling (liquid-bubble-bubble) is achieved using the volume-averaged Navier Stokes equations. The model is verified first for monodisperse impinging particle streams without gas. Then the model is verified with the DBM of a 3D periodic bubble driven system. The collision frequencies are all within 10 percent accuracy and the speed up achieved per DEM time step is nearly 10 times compared to the DBM, which facilitates simulation of large systems.publishedVersio

Hydrodynamic and heat transfer study of a fluidized bed by discrete particle simulations

A numerical simulation study was carried out to study the combined thermal behavior and hydrodynamics of a pseudo-2D fluidized bed using a computational fluid dynamics-discrete element method (CFD-DEM). To mimic the effect of heterogeneous exothermic reactions, a constant heat source was implemented in the particle energy equation. The effects of superficial gas velocity, bed height and heat source distribution were analyzed with the aid of averaged volume fraction and temperature distributions and velocity profiles. It was found that both the gas superficial velocity and the bed aspect ratio have a profound influence on fluidization behavior and temperature distributions