Predict Particle Trajectories Research Articles

Machine learning-enhanced predictive modeling for arbitrary deterministic lateral displacement design and test.

The separation of biological particles like cells and macromolecules from liquid samples is vital in clinical medicine, supporting liquid biopsies and diagnostics. Deterministic Lateral Displacement (DLD) is prominent for sorting particles in microfluidics by size. However, the design, fabrication, and testing of DLDs are complex and time-consuming. Researchers typically rely on finite element analysis to predict particle trajectories, which are crucial in evaluating the performance of DLD. Traditional particle trajectory predictions through finite element analysis often inaccurately reflect experimental results due to manufacturing and experimental variabilities. To address this issue, we introduced a machine learning-enhanced approach, combining past experimental data and advanced modeling techniques. Our method, using a dataset of 132 experiments from 40 DLD chips and integrating finite element simulation with a microfluidic-optimized particle simulation algorithm (MOPSA) and a Random Forest model, improves trajectory prediction and critical size determination without physical tests. This enhanced accuracy in simulation across various DLD chips speeds up development. Our model, validated against three DLD chip designs, showed a high correlation between predicted and experimental particle trajectories, streamlining chip development for clinical applications.

FLUID-GPT (Fast Learning to Understand and Investigate Dynamics with a Generative Pre-Trained Transformer): Efficient Predictions of Particle Trajectories and Erosion.

The deleterious impact of erosion due to high-velocity particle impingement adversely affects a variety of engineering and industrial systems, resulting in irreversible mechanical wear of materials/components. Brute force computational fluid dynamics (CFD) calculations are commonly used to predict surface erosion by directly solving the Navier-Stokes equations for fluid and particle dynamics; however, these numerical approaches often require significant computational resources. In contrast, recent data-driven approaches using machine learning (ML) have shown immense promise for more efficient and accurate predictions to sidestep computationally demanding CFD calculations. To this end, we have developed FLUID-GPT (Fast Learning to Understand and Investigate Dynamics with a Generative Pre-Trained Transformer), a new hybrid ML architecture for accurately predicting particle trajectories and erosion on an industrial-scale steam header geometry. Our FLUID-GPT approach utilizes a Generative Pre-Trained Transformer 2 (GPT-2) with a convolutional neural network (CNN) for the first time to predict surface erosion using only information from five initial conditions: particle size, main-inlet speed, main-inlet pressure, subinlet speed, and subinlet pressure. Compared to the bidirectional long- and short-term memory (BiLSTM) ML techniques used in previous work, our FLUID-GPT model is much more accurate (a 54% decrease in the mean squared error) and efficient (70% less training time). Our work demonstrates that FLUID-GPT is an accurate and efficient ML approach for predicting time-series trajectories and their subsequent spatial erosion patterns in these complex dynamic systems.

Improvement of Dust Particle Suction Efficiency by Controlling the Airflow of a Regenerative Air Sweeper

In a regenerative air sweeper, airflow and dust particles entering the system are filtered and recirculated within the system. The uncirculated portion of the exhaust air in the system spreads to the ambient air, and PM2.5 dust in the air can poison the environment and adversely affect human health. The development of an airflow control system to reduce road dust emissions and improve air quality was the main contribution of this study. A regenerative air sweeper airflow control system is designed to direct the air from the centrifugal fan back into the pickup head to fully absorb the dust particles and balance the positive and negative air pressures inside the pickup head. The modeling and analysis of the dust control system were performed using an experimental test rig system. A mathematical model of the fundamental parameters of the regenerative air sweeper and dust control system was established. Computational fluid dynamics (CFD) ANSYS was used for the analysis to determine the direction of airflow via the suction and inlet ducts. The discrete particle model (DPM) accurately predicted particle trajectories and measured the suction efficiency of particles of different shapes and types. By controlling the circulating harmful air flow in the system, the amount of PM2.5 released into the atmosphere was reduced by 90%. The suction efficiency of the 200 μm sized sand particles was higher than 95%. The results provide theoretical and methodological assistance for the development of improved road sweeper dust control systems.

Potassium carbonate decomposition modeling within rotating detonation engines for direct power extraction applications

Recently, there has been a significant interest in detonation-based combustion systems, such as rotating detonation engines (RDE), due to potential performance advantages in propulsion and energy applications. This includes improvements in thermodynamic efficiency, inherently high gas velocities, and the possibility of achieving an increase in total pressure (i.e. pressure gain) through the combustion process. Due to approximating a constant volume combustion process, these devices also tend to exhibit extremely high local gas temperatures relative to a comparable constant pressure combustor. These advantages overlap well with desirable performance characteristics of direct power extraction (DPE) technologies, such as a magnetohydrodynamic (MHD) generator. Typically, in DPE systems hot combustion products are seeded with an easily ionizable material such as potassium carbonate (K2CO3) in order to boost the electrical conductivity. However, due to the short gas residence times within an RDE, it was unclear whether forming an electrically conductive combustion plasma would be feasible for integration with a downstream MHD generator. A model is presented which describes the heating, decomposition, and ionization of solid K2CO3 particles and aqueous solutions of K2CO3 in water, for a given initial particle diameter. This model was combined with available computational fluid dynamics (CFD) data for an oxygen-methane RDE in a one-way coupled Eulerian-Lagrangian framework to predict particle trajectories and the corresponding heating, decomposition, and ionization histories. Electrical conductivities were computed using a previously developed model, and a method was proposed to determine an equivalent average electrical conductivity. Results show that particle sizes below ∼30 µm are able to fully decompose before reaching the exit of the RDE. While the one-way coupled nature of the simulations precluded rigorous evaluation of the effects of seed material on detonability, a substantial temperature reduction is expected at the detonation wave front due to heating and decomposition. A preliminary comparison is presented between the RDE and an equivalent constant pressure adiabatic combustor, showing a potential performance advantage for the RDE.

Sensitivity of Skill Score Metric to Validate Lagrangian Simulations in Coastal Areas: Recommendations for Search and Rescue Applications

Search and rescue (SAR) modeling applications, mostly based on Lagrangian tracking particle algorithms, rely on the accuracy of met-ocean forecast models. Skill assessment methods are therefore required to evaluate the performance of ocean models in predicting particle trajectories. The Skill Score (SS), based on the Normalized Cumulative Lagrangian Separation (NCLS) distance between simulated and satellite-tracked drifter trajectories, is a commonly used metric. However, its applicability in coastal areas, where most of the SAR incidents occur, is difficult and sometimes unfeasible, because of the high variability that characterizes the coastal dynamics and the lack of drifter observations. In this study, we assess the performance of four models available in the Ibiza Channel (Western Mediterranean Sea) and evaluate the applicability of the SS in such coastal risk-prone regions seeking for a functional implementation in the context of SAR operations. We analyze the SS sensitivity to different forecast horizons and examine the best way to quantify the average model performance, to avoid biased conclusions. Our results show that the SS increases with forecast time in most cases. At short forecast times (i.e., 6 h), the SS exhibits a much higher variability due to the short trajectory lengths observed compared to the separation distance obtained at timescales not properly resolved by the models. However, longer forecast times lead to the overestimation of the SS due to the high variability of the surface currents. Findings also show that the averaged SS, as originally defined, can be misleading because of the imposition of a lower limit value of zero. To properly evaluate the averaged skill of the models, a revision of its definition, the so-called SS∗, is recommended. Furthermore, whereas drifters only provide assessment along their drifting paths, we show that trajectories derived from high-frequency radar (HFR) effectively provide information about the spatial distribution of the model performance inside the HFR coverage. HFR-derived trajectories could therefore be used for complementing drifter observations. The SS is, on average, more favorable to coarser-resolution models because of the double-penalty error, whereas higher-resolution models show both very low and very high performance during the experiments.

High velocity measurements of particle rebound characteristics under erosive conditions of high pressure compressors

Ingested sand particles may lead to severe erosion in aero engine compressors resulting in performance deterioration. Predicting particle trajectories through a turbo machine requires accurate estimation of the particle rebound. The experimental investigation of particle impact is a challenging task with high demands on measurement equipment due to the combination of small particle sizes with high impact velocities. Therefore high velocity impacts with a realistic combination of both, impact velocity and particle size, have not been investigated experimentally yet. In this work high velocity impacts have been measured for 60 μm sized quartz particles impacting stainless steel (1.4301) and Ti6Al4V targets. The experiments were conducted in a sand-blast type erosion test rig designed to meet compressor specific boundary conditions representative for a third stage axial compressor of civil jet engines. Particle Shadow Velocimetry is used to measure the particle velocities in the test rig. A dimensional analysis of normal impact is presented to discuss the effect of particle size and impact velocity on the rebound characteristics. It was found that the widely used approach to implement the energy loss via a simple curve-fit that solely depends on the impact angle is not valid throughout the broad range of possible impact events in aero engine compressors. In addition, for a realistic combination of particle size and impact velocity, particle fracture was observed. The fracture alters the Stokes Number of the particles and with that the particle trajectory after first impact. Hence the accurate prediction of the particle-wall interaction includes not only the change of particle direction and velocity upon impact but the treatment of the particle fracture. This is especially relevant for high impact velocities and high normal impact velocities.

Predicting particle trajectories in oceanic flows using artificial neural networks

Predicting ocean transport has many practical applications ranging from search and rescue operations to predicting the spread of oil, debris, and biogeochemical tracers, yet trajectory prediction remains a challenge for existing ocean modeling techniques. General circulation models require high resolution observational data in order to be properly initialized, but these data do not exist for the ocean. Statistical models are difficult to tune with existing data and are often too simple to accurately encapsulate turbulent flows. Here we investigate a data-driven approach to ocean transport prediction wherein the goal is to first learn from available data instead of prescribed laws of physics and then apply this information to new data. More specifically, we explore whether simple artificial neural networks (ANNs) are capable of learning to predict 2D particle trajectories using only previous velocity observations. ANNs are trained in two ways: first, a so-called “one-to-one ANN” uses a particle’s most recently observed velocity to predict its velocity six hours later, and second, a “time series ANN” uses the past 24 hours’ worth of velocity observations to predict the next 24 h. We present a proof-of-concept considering particles in a hierarchy of simulated flow regimes ranging from uniform, steady flow to more complex scenarios with interacting scales of motion and then substantiate our approach on trajectories in modeled flows generated by a high-resolution Hybrid Coordinate Ocean Model for a mesoscale eddy in the northern Gulf of Mexico. We also assess ANN sensitivity to the prediction window over which forecasts are made, the number of training particles used, and the size of the network. ANNs successfully predict 24 h trajectories within the temporal bounds of the training data with forecast errors around half those of both rudimentary persistence and classical ARIMA models. Predicting beyond the domain of the training data leads to forecast errors comparable to ARIMA models. Our results suggest that ANNs offer promising potential as a data-driven approach to forecasting material transport in the ocean.

Numerical and experimental investigation on the performance of a ventilated chamber for low-cost PM sensor calibration

Particulate matter (PM) sensors are popular for distributed monitoring due to their portability, low-cost and networking capabilities. To provide reliable data, these sensors have to be calibrated periodically in the laboratory. This is often done in chambers with several of them co-located, alongside several research grade instruments. It is important to confirm that the aerosol is well distributed to ensure reliability and accuracy of the calibration tests. This paper describes a modeling and experimental study of these chambers used for calibration of PM sensors. The experimental setup is described and calibration results for the low-cost PM sensors (MAXIMA sensor, Applied Particle Technology, Inc.) using research-grade instruments (GRIMM 11-C, GRIMM Technologies, Inc.) are presented. A computational fluid dynamics (CFD) model including effects of turbulence and Brownian motion is used to predict particle trajectories and distribution in the chamber. The simulated particle number concentration agrees well with the measured values in the chamber. Having the CFD model, the suitability of three proposed chamber designs (fan off; fan on-parallel; fan on-diagonal) are explored for a variety of conditions. Operating conditions that result in uniform concentrations are established using the model. The recommended design for the PM sensor calibration in a rectangular ventilated chamber is: parallel fan layout, fan pressure of 12 Pa, and inlet air flow rate between 10 and 15 L/min. This study shows that the numerical modeling is available to understand the performance of a calibration chamber as well as optimizing the chamber design. A broader perspective is that the results validate the use of low-cost PM sensors for checking the distribution of aerosols in chambers.

Inertia-magnetic particle sorting in microfluidic devices: a numerical parametric investigation

Microfluidic-based sorting systems are an integral part of many biological applications, where sorting of cells, microorganisms, and particles is of interest. In this paper, a computational fluid dynamics model is established to expand investigations on a hybrid microparticle sorting method, which combines inertia-magnetic focusing and hydrodynamic separation, known as multiplex inertia-magnetic fractionation (MIMF). This microfluidic device consists of two regions, i.e. a narrow microchannel with a magnet on its side for inertial and magnetophoretic focusing of particles and a downstream wide hydrodynamic expansion zone for particles’ separation and imaging. A Lagrangian–Eulerian framework was adopted to simulate particle trajectories using the ANSYS-Fluent discrete phase modeling (DPM) approach. Acting forces that were considered to predict particle trajectories included the drag, inertial lift, Saffman lift, gravitational, and magnetophoretic forces. User-defined functions were used for inertial lift and magnetophoretic forces that are not built-in relations in the ANSYS-Fluent DPM. Numerical results were verified and validated against the experimental data for MIMF of 5 and 11 µm magnetic particles at flow rates of 0.5–5 mL/h. Particles fractionation throughput and purity in the expansion region could be predicted with errors of 6% and 2%, respectfully. The validated model was then used to perform a numerical parametric study on the unknown effects of magnetization, particle size, higher flow rates, and fluid viscosity on MIMF. The presented numerical approach can be used as a tool for future experimental design of inertia-magnetophoretic microfluidic particle sorting devices.

Wave-driven flow induced by suspended and submerged canopies

Coastal canopies deliver a wide range of ecosystem services through the flow and transport processes within and around the canopies. This study presents a numerical investigation on the effect of suspended/floating canopies on wave-driven current around canopy using a non-hydrostatic model SWASH. The model results by SWASH for a submerged canopy are first validated with experiment measurements and compared with predictions from two Volume-of-Fluid (VOF) based free surface flow models. Then the effects of suspended/floating canopies on wave attenuation and vertical mean flow structures are examined using SWASH. The model results show that a strong shoreward wave-driven current is generated at the top of a suspended canopy in the same direction as the wave, same as that at the top of a benthic canopy. In contrast, a seaward mean current is generated at the bottom of a suspended canopy in the opposite direction to the wave. The predicted particle trajectories indicate that due to flow attenuation within canopy, at the bottom of the canopy, the fluid particles travel faster at the bottom half of their orbits outside the canopy in the opposite direction of wave propagation than at the top half of their orbits within the canopy in the direction of wave propagation. This leads to open particle orbits and a time‐averaged mean current in the opposite direction of wave propagation. The present study is the first attempt that reveals and examines this physical phenomena that would play an important role in particulate transport and exchange across the suspended canopies. The maximum magnitudes of wave induced currents at the top and bottom of canopy are consistent with a recent empirical formula extracted from the observations for submerged canopy. The predicted wave decay agrees well with a three-layer analytical solution for suspended and submerged canopy and indicates that the canopy vertical location plays an important role in wave attenuation and canopy induced currents. The spatial distribution patterns of the wave-driven current clearly support the notion that the current strength generated at the canopy interface is a function of horizontal oscillatory velocity, vertical orbital excursion and canopy density. The spanwise extent of the mean current is examined by a 3-D simulation where the canopy occupies only half the flume width. Numerical results show that the wave-driven current is mainly confined to the part of flume occupied by the canopy and that the current strength is soon attenuated to zero towards the other part of flume through a mixing-layer-like transition region at the canopy side interface.

Numerical modeling of sand particle erosion in return bends in gas-particle two-phase flow

In gas and oil industry, erosion damages to pipe lines bends and elbows due to the presence of sand particles have been a challenging issue. In this study a computational model approach was for evaluating the erosion rates in different vertical return bends including sharp bend, standard elbow, 180° pipe bend and long elbow. The airflow in the pipe was simulated using the SIMPLE method and the k-ω SST turbulence model. An Eulerian-Lagrangian approach was used for predicting particle trajectories and the corresponding erosion rates. Different particle sizes and mass flow rates were considered and Oka model for evaluating the erosion rate was used in these simulations. Under the same conditions, the simulation results indicated that the sharp return bends experience the highest erosion rates and the 180° bends experience the lowest erosion among the studied configurations. It was also found that the erosion rate is linearly proportional to the mass flow rate of particles for all cases studied.

Effect of the Number of Injectors on the Mixing Process in a Rapidly Mixed Type Tubular Flame Burner

Three-dimensional simulations are performed to study the non-reactive mixing process in a rapidly mixed type tubular flame burner (RTFB). The current work examines the effect of the number of injectors (N= 2, 4 and 6) on the mixing process by focusing on three criterions (Flow structure, local swirl intensity and mixing layer thickness). The Discrete Phase Model (DPM) is used to track the particle trajectories. Validation of the numerical results is carried out by comparing the predicted particle trajectories, central recirculation zone (CRZ) and tangential velocity results to the experimental data. It is concluded that the model offers a satisfactory prediction of the flow field in a RTFB. Numerical results show that, for the same geometrical swirl number (Sw) and the same Reynolds number (ReT), the increasing of the number of injectors enhances the mixing process by generating a larger reverse flow and reducing the mixing layer thickness. It is also concluded that the local swirl intensity along of the RTFB can be correlated in terms of geometric swirl number and number of injectors.

Inertially focused diamagnetic particle separation in ferrofluids

Continuous flow separation of target particles from a mixture is essential to many chemical and biomedical applications. There has recently been an increasing interest in the integration of active and passive particle separation techniques for enhanced sensitivity and flexibility. We demonstrate herein the proof-of-concept of a ferrofluid-based hybrid microfluidic technique that combines passive inertial focusing with active magnetic deflection to separate diamagnetic particles by size. The two operations take place in series in a continuous flow through a straight rectangular microchannel with a nearby permanent magnet. We also develop a three-dimensional numerical model to simulate the transport of diamagnetic particles during their inertial focusing and magnetic separation processes in the entire microchannel. The predicted particle trajectories are found to be approximately consistent with the experimental observations at different ferrofluid flow rates and ferrofluid concentrations.

Exploiting the Wall-Induced Non-inertial Lift in Electrokinetic Flow for a Continuous Particle Separation by Size

Separating particles from a heterogeneous mixture is important and necessary in many engineering and biomedical applications. Electrokinetic flow-based continuous particle separation has thus far been realized primarily by the use of particle dielectrophoresis induced in constricted and/or curved microchannels. We develop in this work a new electrokinetic method that exploits the wall-induced non-inertial lift in a straight uniform microchannel to continuously separate particles by intrinsic properties (e.g., size and surface charge). Such an electrically originated lift force arises from the asymmetric electric field distribution around a particle nearby a planar dielectric wall. We demonstrate this method through separating both a binary and ternary mixture of dispersed polystyrene microspheres by size in a T-shaped microchannel. A semi-analytical model is also developed to simulate and understand the particle separation process. The predicted particle trajectories in the entire microchannel agree reasonably well with the experimental measurements.

Experimental and numerical analysis of performance of two fluted mixer designs

In this paper, the effect of the Maddock mixer design on its performance in the single screw extrusion of High-density Polyethylene (HDPE) melt has been investigated experimentally on a special extrusion line equipped with a barrel having several glass windows as well as theoretically using a three-dimensional finite element method simulation. The red–green–blue spectral analysis has been used for the experimental quantification of the mixing efficiency, whereas a generalised Newtonian model (with Carreau–Yasuda viscosity function) and a viscoelastic modified White–Metzner model have been utilised in flow simulations. Based on the performed experimental work, it has been found that the mixer with no undercut wiping flight (‘Closed’ mixer) has a faster mixing/purging but a similar final mixing performance as the mixer with undercut wiping flight (‘Open’ mixer). It has also been revealed that the ‘Open’ mixer creates an unwanted dead zone (in the form of the stagnation layer of material rotating between the mixer and the barrel) at which the polymer melt degradation may take place. It has been found that based on the stress, residence time and pathline calculation, it is possible to predict the experimentally observed tendency of the ‘Closed’ mixer for a faster mixing/purging in comparison with the ‘Open’ mixer as well as the presence of a stagnation layer in the ‘Open’ mixer. The performed theoretical parametric study indicates that the gradual opening of the wiping gap causes the pressure driven flow to become more dominant than the drag flow in shearing as well as in the wiping gap independently of the utilised rheological model. On the other hand, the predicted particle trajectories inside the mixer were found to be shorter for the viscoelastic model, tending to occur mainly in the middle of the channel and thus leaving the mixer a little bit faster in comparison with the purely viscous calculations. Finally, it has been revealed that for the highest tested wiping flight opening, the viscoelastic modified White–Metzner model predicts back flow over the wiping flight, whereas the purely viscous Carreau–Yasuda does not, which can be explained by the elongational viscosity, considered in the viscoelastic modified White–Metzner model. This suggests that the modified White–Metzner model should be preferred more than the purely viscous Carreau–Yasuda model in the mixing element die design optimisation.

Microfluidic electrical sorting of particles based on shape in a spiral microchannel.

Shape is an intrinsic marker of cell cycle, an important factor for identifying a bioparticle, and also a useful indicator of cell state for disease diagnostics. Therefore, shape can be a specific marker in label-free particle and cell separation for various chemical and biological applications. We demonstrate in this work a continuous-flow electrical sorting of spherical and peanut-shaped particles of similar volumes in an asymmetric double-spiral microchannel. It exploits curvature-induced dielectrophoresis to focus particles to a tight stream in the first spiral without any sheath flow and subsequently displace them to shape-dependent flow paths in the second spiral without any external force. We also develop a numerical model to simulate and understand this shape-based particle sorting in spiral microchannels. The predicted particle trajectories agree qualitatively with the experimental observation.

Experimental and Numerical Study of Dilute Gas-Solid Flow inside a 90° Horizontal Square Pipe Bend

A pneumatic test rig is built to test a curved 90° square bend in an open-circuit horizontal-to-horizontal suction wind tunnel system. Sand particles are used to represent the solid phase with a wide range of particle diameters. Velocity profiles are constructed by measuring the gas velocity using a 3-hole probe. Flow patterns inside the bend duct are introduced using sparks caused by burning sticks of incense with the air flow inside the piping system for flow visualization purpose. Numerical calculations are performed by Lagrangian-particle tracking model for predicting particle trajectories of dispersed phase, and standard k-ε model for predicting the turbulent gas-solid flows in bends. Comparisons made between the theoretical results and experimental data for the velocity vectors and particle trajectories show good agreement.

Thermal Treatment of Al2O3, MgO, and CeO2 Granulated Powders by Induction Thermal Plasma: A Numerical Approach

A plasma–particle interactive flow model has been developed for the investigation of plasma temperature, velocity, particle temperature, diameter, and flight path during in-fight thermal treatment of granulated powders – Al2O3, MgO, and CeO2 in induction thermal plasma, taking into account of plasma particle interaction and particle loading effects. In this model, the conservative equations are solved to investigate the influence of feed-rate and particle diameter on the melting behavior of granulated powders and to predict particle trajectories, temperature histories etc. In this paper attention is given to the effects of carrier gas flow-rate, powder feed-rate and secondary gas (oxygen) flow-rate on the particles' final diameter and trajectory for Al2O3, MgO, and CeO2 powders. Results show that the particles diameter after thermal treatment is strongly dependent on the powder feed-rate, oxygen gas flow-rate and carrier gas flow-rate. Among the three types of powders, particle diameter decrement is significant in case of MgO and less significant in case of Al2O3; and the effect of oxygen flow-rate on the particle diameter decrement is significant in case of MgO. Particles trajectories are wider for smaller particles than that of larger particles. Particle temperature is lowest for MgO and highest for Al2O3 particles.

Predicting particle trajectories on an electrodynamic screen – Theory and experiment

Abstract The electrodynamic screen, or EDS, was first introduced to the electrostatics community in the early 1970's. Since that time, it has been studied by several research groups who have investigated its use as a means to remove unwanted particles from insulator surfaces. In the typical EDS, interdigitated electrodes are embedded or deposited on the surface, then energized by three-phase square or sinusoidal voltages of magnitude 500–1200 V at frequencies between 5 and 200 Hz. The resulting electric field entrains previously deposited particles and moves them laterally across the surface. Electrodynamic screens have been proposed for use on solar cells in Mars and Moon space missions, as well as the removal of dust from renewable-energy solar collectors such as photovoltaic panels, solar reflectors, and mirrors. Though often considered merely a nuisance, dust has the potential to partially or totally obscure the solar flux incident on any solar collector. Development of EDS technology has evolved mostly from a “trial and error” approach to choosing such parameters as electrode spacing, depth, and width, as well as voltage magnitude and frequency. Though some theoretical studies may be found in the literature, a more detailed understanding would be valuable in optimizing EDS performance for a particular application and composition of ambient dust, which may vary with geographical location with respect to size, permittivity, and conductivity. A theoretical understanding of how particles are entrained and made to move on an EDS will facilitate proper choice of relevant parameters in future EDS systems. Our work thus attempts to analyze the motion of dust particles subject to all the forces encountered at the surface of an EDS, including the coulomb and dielectrophoretic forces, as well as aerodynamic drag, gravity, and friction. Previous work has shown that particles entrained in the traveling-wave electrostatic field of an EDS exhibit one of two behaviors: ordered, lateral motion; and semi-chaotic motion. These two behaviors have been found both experimentally and in theoretical simulations. This paper attempts to correlate trajectory computations with video observations of these behaviors as recorded in a laboratory setting.

Three-Dimensional Spray-Drying Model Based on Comprehensive Formulation of Drying Kinetics

In our previous 2D axisymmetric study of the spray-drying process, it was shown that the introduction of comprehensive formulation of drying kinetics had a substantial influence on both predicted gas and particle flow patterns in a spray dryer. On the other hand, it was demonstrated that the concept of 2D axisymmetric modeling is suitable for fast and low-resource consumptive numerical calculations of the spray-drying process and that the predicted velocity, temperature, and vapor mass fraction are of reasonable accuracy. However, due to their restrictions, 2D axisymmetric simulations fail to predict asymmetry of flow patterns and the presence of the transversal air flow and cannot provide actual 3D representation of particle trajectories inside the spray chamber. Therefore, in the present study a novel 3D theoretical model of multiscale multiphase transport phenomena in a steady-state spray-drying process is proposed for predicting more realistic particles paths, residence times, temperatures, and moisture contents. A drying process of silica slurry in a short-form pilot-scale spray dryer fitted with a pressure nozzle atomizer is investigated. The simulated 3D drying behavior of the dispersed phase and flow patterns of air velocity, temperature, and humidity are compared with the previously published results of 2D axisymmetric modeling. A significant influence of both drying kinetics and number of utilized dimensions on the predicted particle trajectories and transport phenomena in the drying chamber is observed. Hence, a proper formulation of the droplet drying kinetics and realistic 3D flow modeling is crucial for accurate numerical representation of the actual spray dryer performance.