The integration of CFD modeling and simulation into plume measurement programs

During a postulated main steam line break (MSLB) event of a Pressurized Water Reactor (PWR) initiated at the Hot Zero Power (HZP) condition, increased heat removal from the broken steam generator (SG) on the secondary side that significantly reduces the coolant temperature on the primary side, and cold primary coolant enters the reactor vessel through the affected loop resulting in asymmetric temperature and mass flux distributions into the reactor core. A plant safety analysis under the MSLB condition needs to account for the thermal and mass flux asymmetry effects on the reactor core response due to the colder water flowing from the affected SG and the reactor coolant system (RCS) to reactor vessel. High resolution computational fluid dynamics (CFD) methodology with ANSYS CFX (Version 16.1) software was applied to analyze the flow behaviors and thermal-hydraulic phenomena and to study the thermal mixing and asymmetry effects in the downcomer and lower-plenum of a typical Westinghouse design four-loop PWR under the MSLB conditions. Two scenarios were considered for the CFD simulation distinct by reactor coolant pump status: (1) Low-flow case: without offsite power where the reactor core is cooled through natural circulation (2) High-flow case: with offsite power available and the reactor coolant pumps in operation The CFX CFD modeling and simulation were based on the reactor vessel boundary conditions from a system code transient simulation at the limiting time steps with respect to thermal margin of the fuel design. The geometric model included the vessel downcomer and the lower internals up to the reactor core inlet below the fuel assemblies. The results of CFD simulation show the different flow patterns and temperature distributions at the reactor core inlet for the low-flow case and for the high-flow case. Thermal asymmetric effect exists in both cases, but in the low-flow case, cold flow enters into core inlets at the opposite side of faulted loop located, and in the high-flow case cold flow enters into core inlets at the same side of faulted loop located. A mass flux asymmetric effect exists in both cases, but for the low-flow case, the core inlet mass flow distribution is more uniform than that for the high-flow case. The reactor core inlet distributions under the MSLB condition were further evaluated through comparisons with the results from the STAR-CCM+ (Version 10.04.01) CFD modeling and simulation. The evaluation showed that the simulation results are in good agreement with the STAR-CCM+ predictions and consistent with the phenomenon observed in an experiment published in open literature and site engineer judgment based on the available detected data.

This paper demonstrates the application of computational fluid dynamic (CFD) simulation and response surface methodology (RSM) in analyzing the thermal performance of a high input/outputs, seven chips, indirect liquid cooled multichip module which will be applied in a kind of supercomputer. A series of similar experiments and corresponding CFD simulations are conducted firstly to evaluate the validity of CFD simulation method and determine the interfacial thermal resistance of thermal grease iteratively, and then a three-dimensional CFD model is established to investigate the heat transfer and fluid flow of the multichip module. Based on the CFD model, the individual effects of factors such as thermal conductivity of the thermal interface material and thermal grease, thickness of the chips, space between chips, solder bump patterns, solder ball patterns, flow velocity and liquid inlet temperature on the thermal performance of the module are studied with one-factor-at-a-time experimentation, and after that, four significant factors are selected to establish a response surface model of the maximum temperature of the module with central composite design based RSM and analysis of variance.

Background Cardiovascular diseases are the leading cause of death throughout the developed world, attributed to approximately 17.8 million deaths worldwide in 2017 with increasing prevalence due to the aging population. Cardiovascular diseases generally result in heart failure. While the best treatment option for heart failure patients is heart transplantation, there is a severe deficiency in the availability of donor hearts. Rotary Blood Pumps (RBPs) utilised as Ventricular Assist Devices (VADs) provide an alternative treatment option. These devices are small implantable pumps that support the failing heart by providing power to augment circulation. The development of RBPs generally begins with initial designs obtained using traditional pump design methods (such as that developed by Stepanoff). However, studies have shown that this approach produces RBP prototypes far from optimal in design. Traditional theory relies on design constants derived empirically for large industrial pumps and these do not scale down well when applied to the much smaller RBPs. The initial designs are therefore generally quite poor and require an iterative build-and-test approach to obtain suitable pump prototypes – a process that is expensive and time consuming. Therefore, by improving the methodology for obtaining initial designs to better reflect the final product, development time can be greatly reduced. A popular avenue for analysing the effect of design variations and to further develop early prototypes of RBPs is to employ Computational Fluid Dynamics (CFD) simulations. These numerical simulations provide detailed data regarding the flow fields within these devices. However, a range of simulation options is available, leading to a wide range of potential predictions. In an attempt to provide a benchmark case, the FDA presented a challenge in which a pump design and test conditions were defined, allowing for direct comparison amongst different simulation approaches from a number of labs/RBP developers. The purpose of this thesis was to produce a gross design tool to provide a good starting point in RBP prototyping and a CFD simulation approach for verification that can also be used as a design refinement tool. Methods Formulating a design method for pumps requires the generation of empirical data. A number of pump design variables was identified as having an impact on pump performance, and a large number of experimental tests would have been needed to test the influence of each. Instead, a Design of Experiments (DOE) was utilised to streamline the process. The DOE outputs a relatively small number of tests required to fit a statistical model. Each design specified by the DOE was examined experimentally using a custom-built automated pump test platform to generate a number of performance measures. The obtained results were used to formulate a Response Surface Method (RSM) statistical model that showed acceptable fit to the input data. Coupled with desirability functions, the RSM model allowed for design optimisation. This tool essentially replaces Stepanoff’s traditional design methodology. The RSM model provides a robust tool that allows the user flexibility in design optimisation goals. The FDA pump was investigated in this thesis and a wide variety of simulation approaches was examined to determine which was most accurate. A range of factors were considered which included: mesh density, interface position between the rotating and stationary zones, steady vs. transient simulations, discretisation schemes, time step size and choice of turbulence model. The most appropriate option from each investigative study was selected to determine a recommended simulation approach. Final simulations were performed using these recommendations and were compared to the FDA experimental results to confirm the suitability of the suggested settings. Determination of Centrifugal Blood Pump Characteristics using CFD and Experimental Analysis iii The statistical model developed was used to design two different impellers as validation test cases. The first impeller was designed to optimise the maximum efficiency, P – Q curve slope and efficiency consistency. The second impeller was designed to mimic the approach used in traditional design methods for RBPs in setting a target design point as the primary objective and the aforementioned factors (from the first impeller) as secondary objectives. These two case studies underwent statistical performance predictions, CFD simulations, PIV analysis and experimental hydraulic testing to validate the statistical and CFD models. Results From the initial CFD study, a hybrid SBES turbulence model with full transient simulation on a fine grid with small time steps proved to be the most suitable both in terms of pressure rise generated by the FDA pump and resulting velocity fields when compared to published experimental results. From these findings the CFD modelling strategy was established. CFD results for the two validation pumps showed pressure rises matching the experimental data (8% and 1% difference for each impeller) within an acceptable range (<10% from the mean). The simulated velocity fields also closely replicated the PIV data for the majority of the flow domain. The statistical performance predictions well reflected those measured experimentally with the majority of data points falling within its confidence intervals. The hydraulic results also supported the main goal of this thesis, whereby an impeller generated using the statistical model, operated far closer to the target design point than that of a blood pump designed following Stepanoff’s methodology. Overall, both the statistical model and CFD approach provided accurate predictions and the purpose of the thesis was achieved. Final Remarks The statistical and CFD models developed in this thesis yield an effective design tool and verification methodology and show improvement over the current traditional design methods and accuracy in simulated results. Ultimately, the utilisation of these tools will lead to a reduction in the development time for new RBPs and provide a good understanding of the flow dynamics within these pumps, leading to improved pump designs reaching patients sooner. These tools are readily generalizable and could be adopted as design tools now.

Introduction: Computational fluid dynamics (CFD) has been increasingly used to analyse cardiovascular hemodynamics through simulations to predict the behaviour of blood flow in the cardiovascular system. Hypothesis: It is hypothesized that CFD modelling assists in clinical decision-making by providing detailed analysis of flow patterns in patients with aortic dissection. Methods: A search of biomedical database for English literature was performed to identify studies investigating the CFD applications in type B aortic dissection. Only studies dealing with patient’s data were included, while reports based on phantom studies were excluded from the analysis. Results: Twelve studies were found to meet selection criteria and were included in the analysis. CFD simulations were based on a selected CT or MRI imaging data in 8 studies, while in the remaining four studies, CFD simulations were derived from 3 cases in two studies, and 4 and 8 cases in one study, respectively. CFD models and simulations were found to successfully capture the complex regions of hemodynamic flow by demonstrating low velocities in the false lumen and high velocities in the true lumen. In the true lumen, high time-averaged wall shear stress (TAWSS) was seen in the regions around the tear, while in the false lumen, TAWSS was less uniform due to recirculating flow in the dilated region (Figure). Presence of an additional re-entry tear was shown to provide an extra return path for blood back to the true lumen during systole, and an extra flow path into the false lumen during diastole. Following endovascular stent grafting, large WSS magnitudes were eliminated in the true lumen, while in the false lumen, the maximum WSS was reported to be reduced by more than a factor of 2. Conclusions: CFD offers insight into the hemodynamic factors such as velocity field and wall shear stress, and CFD models and simulations improve understanding of hemodynamics in type B aortic dissection.

<div class="section abstract"><div class="htmlview paragraph">Unsteady flow over automotive side-view mirrors may cause flow-induced vibrations of the mirror assembly which can result in blurred rear-view images, adversely affecting marketability through customer comfort and quality perception. Prior research has identified two mechanisms by which aerodynamically induced vibrations are introduced in the mirror. The first mechanism is unsteady pressure loading on the mirror face due to the unsteady wake, causing direct vibration of the mirror glass. The second mechanism, and the focus of this study, is a fluctuating loading on the mirror housing caused by an unsteady separation zone on the outer portion of the housing.</div><div class="htmlview paragraph">A time-dependent Computational Fluid Dynamics (CFD) methodology was developed to correctly model mirror wake behavior, and thereby predict flow-induced mirror vibration to improve performance estimations. The unsteady CFD methodology utilizes Detached Eddy Simulation (DES) to resolve the turbulent shear layer and wake regions around the mirror. To validate the methodology, qualitative flow characteristics and quantitative results observed in the CFD simulations were compared to unsteady PIV and surface pressure measurements obtained in wind tunnel tests.</div><div class="htmlview paragraph">An agreeable correlation was developed between CFD and PIV test results for the qualitative separation and wake behavior. The quantitative frequency component of the fluctuating flow was captured by CFD and showed a reasonable correlation in both frequency and magnitude to the measured surface pressure data. Additionally, a correlation was developed between mirror vibration frequencies obtained using accelerometers and frequencies observed in both surface pressure data and CFD simulation. Based on the correlated results, the CFD methodology provides a predictive foundation for evaluating mirror vibration design criteria and is an incremental step towards eliminating the need for prototype tooling and physical testing to guarantee performance target achievement as part of the development process.</div></div>

In the early design phase of automotive sector, the flow field around the vehicle is important in decision making on design changes. It would consume a lot of money and time for multiple prototypes development if adopt traditional testing method which is wind tunnel test. Thus, numerical method such as Computational Fluid Dynamics (CFD) simulation plays an important role here. It is very often simulation results been compared with wind tunnel data. However, with various mesh types, meshing methodology, discretization methods and different solver control options in CFD simulation, users may feel low confidence level with the generated simulation results. Thus, a robust modeling and simulation guideline which would help in accurate prediction should be developed due to the industry’s demand for accuracy when comparing CFD to wind tunnel results within short turnaround time. In this paper, a CFD modeling and simulation study was conducted on a simplified automotive model to validate with wind tunnel test results. The wind tunnel environment was reproduced in the simulation setup to include same boundary conditions. Meshing guidelines, turbulence model comparisons and also the best practice for solver setup with respect to accuracy will be presented. Overall, CFD modeling and simulation methods applied in this paper are able to validate the results from experiment accurately within small yaw ranges.

Oil-water emulsion effects on a seven-stage electrical submersible pump (ESP) performance are studied experimentally and numerically with computational fluid dynamics (CFD) simulation. At different oil-water fractions, temperatures, and ESP rotational speeds, the performance of the third stage was measured. The intention of this work is to validate and compare experimental data with CFD simulation results. In addition, flow structures can be visualized through the CFD simulation for oil-water emulsion flows. Density and mass flow rate are measured using the mass flowmeter while the emulsion effective viscosity is derived from the pipe viscometer installed downstream of the ESP. CFD simulations are carried out with estimated droplet sizes of the dispersed phase for oil-water flows, and the results are compared with the experiments. For the wall roughness, a higher value is used due to the extended use of the ESP. The three-dimensional, steady-state Reynolds-Averaged Navier–Stokes (RANS) equations with standard shear stress transport (SST) turbulence model are solved in ANSYS CFX solver by employing the frozen-rotor technique. With high-quality structured hexahedral mesh grids, the simulated pressure increment is compared with corresponding experimental results. For single-phase cases, results show considerable differences compared to experiments, which may be partially due to neglecting leakage losses in CFD simulations. When the two-phase simulation is conducted, it is confirmed that the solver takes oil and water as dispersion instead of emulsion, for which rheological behavior is not reflected. This is because the solver is based on two sets of Navier-Stokes equations for each phase. Consequently, the better approach for simulating the emulsion flow in ESPs by CFD methodology is to assume a single-phase, pseudo-homogeneous fluid with the rheology depending on the oil and water fractions and properties.

The automobile industry has relied on computational fluid dynamics (CFD) simulations to analyze and optimize the coating and curing processes, speed up product development, and lower the cost of product development. However, CFD modelings of these processes are computationally expensive due to the complexity of the models and the large number of simulations needed, especially when its used complex sprays such as the nithrothermal electrospray. As a result, more efficient methods must be developed to reduce computing time without compromising accuracy. In this article, we analyze how deep learning techniques can be used to predict coating and curing processes using electrospray CFD simulation. A dataset of 3D Eulerian-Lagrangian CFD simulations of coating and curing processes employing electro-spray for the automotive industry has been used to train different deep-learning models. We investigated how hyperparameters such as batch size and layer count affected deep learning model performance compared to conventional CFD simulations. For this, we evaluated the deep learning models’ efficiency and accuracy in terms of computing time. We also investigated how hyperparameters such as batch size and layer count affected deep learning model performance. Also, we’ve looked at the target’s final droplet deposition, and distribution that is required to accurately estimate the distribution. Furthermore, we studied the percentage of snapshots of the droplet distribution electrospray necessary to predict the target’s final deposition from the Lagrangian distribution. According to our findings, deep learning models can drastically reduce the amount of time needed to run CFD simulations. Depending on the model and hyperparameters applied, we can forecast the whole CFD simulation by utilizing somewhere between 10% and 15% of the initial spray development. Also, we discovered that the use of recurrent cells as an LSTM model outperformed the other models in terms of accuracy and computational efficiency, where the LSTM layers can extract better the features of the input snapshots. Overall, our research demonstrates the potential of deep learning techniques to significantly shorten the computing time of CFD simulations of coating and curing processes for the automotive sector. The results of this study have significant implications for coating and curing process design and optimization in the automobile industry as well as in other industries where CFD simulations are frequently employed.

While designing the power requirements of a ship, the most important factor to be considered is the ship resistance, or the sea drag forces acting on the ship. It is important to have an estimate of the ship resistance while designing the propulsion system since the power required to overcome the sea drag forces contribute to ‘losses’ in the propulsion system. There are three main methods to calculate ship resistance: Statistical methods like the Holtrop-Mennen (HM) method, numerical analysis or CFD (Computational Fluid Dynamics) simulations, and model testing, i.e. scaled model tests in towing tanks. At the start of the design stage, when only basic ship parameters are available, only statistical models like the HM method can be used. Numerical analysis/ CFD simulations and model tests can be performed only when the complete 3D design of the ship is completed. The present paper aims at predicting the calm water ship resistance using CFD simulations, using the Flow-3D software package. A case study of a roll-on/roll-off passenger (RoPax) ferry was investigated. Ship resistance was calculated at various ship speeds. Since the mesh affects the results in any CFD simulation, multiple meshes were used to check the mesh sensitivity. The results from the simulations were compared with the estimate from the HM method. The results from simulations agreed well with the HM method for low ship speeds. The difference in the results was considerably high compared to the HM method for higher ship speeds. The capability of Flow-3D to perform ship resistance analysis was demonstrated.

Numerically simulating the thermal–hydraulic characteristics within the fuel rod bundle using CFD methodology

Flow patterns in cerebral aneurysms are clinically important. Information on inflow patterns into aneurysms is especially helpful in preventing a recurrence after coil embolization. Computational fluid dynamics (CFD) simulations of patient-specific cerebral aneurysms are feasible and provide information on flow patterns. However, flow visualization by CFD simulations is challenging for recurrent aneurysms after coil embolization because coils make it difficult to obtain precise geometry of the recurrent aneurysms. In this study, we assessed the feasibility of flow visualization of recurrent aneurysms using 3D phase-contrast magnetic resonance imaging (PC-MRI). Time-of-flight magnetic resonance angiography and 3D PC-MRI were performed in eight cases of recurrent aneurysms after coil embolization. We attempted to visualize flow inside the aneurysms using data of 3D PC-MRI and evaluated the visualization. Additionally, CFD simulations were performed in a single case. Inflow into aneurysms was visualized in all eight cases (100%). Flow patterns inside aneurysms were visualized in six cases (75%), and these were associated with a large size of recurrent aneurysms (mean size, 10.3 mm for visualized cases vs. 4.8 mm for unvisualized cases; p = 0.046, Mann-Whitney test). Flow patterns were similar between PC-MRI and CFD simulations. PC-MRI was faster and easier for observing inflow patterns than CFD simulations. This is the first study to demonstrate that flow visualization of recurrent aneurysms by 3D PC-MRI is feasible. This technique may be more practical and easier than CFD simulations, and may provide clinically helpful information.

A severe accident involving core melt in a nuclear reactor is a major concern especially after Fukushima. Thus, to mitigate the effects of core melt accidents, an ex-vessel core catcher is being developed for Advanced Indian Nuclear Reactors. The core catcher design envisages using special refractory material. The cooling strategy of the core catcher is one of the key components in the design of the core catcher. Performing a full-scale prototypic experiment is extremely challenging and prohibitory due to the involvement of very high temperature and presence of radioactive materials. Therefore, a computational fluid dynamics (CFD) model capable of simulating the coolability of the melt pool is important to develop. In the present work, a two-dimensional (2D) CFD model was developed to understand the heat transfer phenomenon and solidification of the heat-generating simulant melt pool. The 2D symmetry geometry of the simulated core catcher vessel was used. The CFD model considers appropriate models for melting and solidification to understand crust formation in the melt pool and the k-ε turbulence model to resolve turbulence inside the melt pool. A decay heat of 1 MW/m3 was also considered inside the melt pool. The CFD simulation results were compared with the authors’ experimental results. The experiment involved a scaled-down ex-vessel core catcher model (CCM) employing electrical heaters to simulate decay heat. The experiment was carried out by melting about 25 L of sodium borosilicate glass using a cold crucible induction furnace at about 1200°C and cooling it in the scaled-down CCM. The scaled-down CCM was strategically cooled in three phases, namely, air cooled, indirect side cooling, and complete top flooding. To overcome the complexities of simulation of the initial melt pour condition, the CFD simulation was initialized with the temperatures just after the melt pour was completed in the experiment. Similar to the experimental conditions, the CFD simulations were carried out in three phases by changing the boundary condition. Comparison of the temperatures of the melt pool by the CFD simulations and experiments at different locations gave reasonable agreement. The evolution of crust formation, melt pool temperatures, core catcher inner wall temperatures, and heat flux distribution were investigated in detail using the CFD model.

The goal of this study is to compare mainstream Computational Fluid Dynamics (CFD) with the widely used 1D transient model LedaFlow in their ability to predict riser induced slug flow and to determine if it is relevant for the offshore oil and gas industry to consider making the switch from LedaFlow to CFD. Presently, the industry use relatively simple 1D-models, such as LedaFlow, to predict flow patterns in pipelines. The reduction in cost of computational power in recent years have made it relevant to compare the performance of these codes with high fidelity CFD simulations. A laboratory test facility was used to obtain data for pressure and mass flow rates for the two-phase flow of air and water. A benchmark case of slug flow served for evaluation of the numerical models. A 3D unsteady CFD simulation was performed based on Reynolds-Averaged Navier-Stokes (RANS) formulation and the Volume of Fluid (VOF) model using the open-source CFD code OpenFOAM. Unsteady simulations using the commercial 1D LedaFlow solver were performed using the same boundary conditions and fluid properties as the CFD simulation. Both the CFD and LedaFlow model underpredicted the experimentally determined slug frequency by 22% and 16% respectively. Both models predicted a classical blowout, in which the riser is completely evacuated of water, while only a partial evacuation of the riser was observed experimentally. The CFD model had a runtime of 57 h while the LedaFlow model had a runtime of 13 min. It can be concluded that the prediction capabilities of the CFD and LedaFlow models are similar for riser-induced slug flow while the CFD model is much more computational intensive.

1. Background of SSME Flowmeter Behavior Minor variations in the SSME’ fuel flowmeter constant (KJ during engine operation has been shown to be the result of wakes of the upstream hexagonal web flow straightener periodically partially stalling the rotor blades, thereby changing the lift on the blades and the rotation speed of the rotor. (This flowmeter constant relates the rotor speed to the engine flowrate.) Moreover, an unsteady, two-dimensional computational fluid dynamics (CFD) model of the flowmeter has shown this wake-induced partial stall disappearing as the straightener-rotor distance is doubled, in accord with the existing SSME flowmeter database for the previous “egg crate” flowmeter. These observations have led to a new flowmeter design which has been shown in three-dimensional CFD computations (consistent with both the previous twodimensional analyses and with existing correlations for airfoil stall) to be much less susceptible to stalling instabilities. The Space Shuttle Main Engine (SSME) Fuel Flowmeter is located in the duct between the low and high pressure fuel turbopumps. In the flowmeter the rotation rate of a 4-blade rotor positioned downstream of two flow straighteners is employed to measure the engine fuel flowrate and thereby control the engine mixture ratio via the engine controller. Since the current flight flowmeter configuration was incorporated into the SSME in the early eighties, some minor variations (i.e., less than 1%) in flowmeter behavior have been observed. Although these effects are small, if they could be quantified and understood, the SSME performance could be improved. The initial flowmeter incorporated an “egg crate” design for the two flow straighteners which turn the duct flow to make it more uniform and parallel after it has come out of the 90’ bend just upstream of the flowmeter. In the egg crate, the vanes of the flow straightener were in a * Senior Engineering Specialist, CFD Technology Center ’ Associate Technical Fellow, CFD Technology Center, Senior Member, AIAA ** Member of Technical Staff, CFD Technology Center % Listed Alphabetically $ Senior Engineering Specialist, RISC CFD Group Copyright 01999 by the American Institute of Aeronautics and Astronautics, Inc. All rights reserved. Figure 1: Egg Crate Flowmeter Configuration rectangular configuration (see Figure 1) and at no point did the wake of the vanes line up with the rotor blade. The vanes in the current flow straighteners are in a hexagonal web configuration (see Figure 2) allowing the wakes to line up with the rotor blade; moreover, for reasons of structural support, the rotorstraightener distance has been reduced in the present design by approximately half when compared with the egg crate design, thereby increasing the extent of fluid dynamic wake interactions. Two types of minor variations have been observed with the present design, flowmeter constant Kr “shifting” and flowmeter “aliasing”. The aliasing phenomena is illustrated in Figure 3 in which the perceived engine flowrate begins to oscillate with a large amplitude and perceived low frequency. This is always accompanied by the phenomena shown in Figure 4 where the previously calibrated flowmeter constant seems to “shift” . One explanation for this type of behavior is that the fluid dynamic forces on the blade have become unsteady at a relatively low frequency, causing the blade to bend with these unsteady forces or to rotate at an oscillatory speed as the lift and drag on the blade vary periodically and affect the blade rotation through the balance of torques on the blade. The possibility of the blade bending periodically and affecting the sensor measurements in a manner required to cause the observed test results has been largely ruled out through observing that all four blades of the rotor act in phase, suggesting that the rotor rotates as a rigid body, but at a sinusoidal speed.

This paper presents the predictions of deposition patterns using CFD simulations based on transient-flow behaviour of a 1.6 m high, 0.8 m diameter, pilot-scale spray dryer, following from previous studies assessing the use of Computational Fluid Dynamics (CFD) simulations to predict the deposition on a plate in a simple box configuration. The predicted deposition fluxes here have been compared with experimental data for the deposition fluxes of skim milk, maltodextrin and water. The CFD simulation results suggested that the effect of transient air flows on the vertical patterns of deposition fluxes with distance up the dryer wall for no inlet air swirl is small. The CFD simulations underpredicted the experimental values of the deposition fluxes by approximately 50%, but the simulations predicted the same experimental trends when changing the main air flow rate through the dryer. The experimentally-measured deposition fluxes were 38%, on average, higher at a main air flow rate of 113 kg/h compared with those at a flow rate of 88 kg/h. The CFD simulations predicted an average increase in deposition flux of 26% at 113 kg/h compared with 88 kg/h, so the trends with this change in operating conditions have been predicted well by the CFD simulations. One-way particle coupling has therefore shown correct trends in the deposition fluxes with respect to both positions in the dryer and different operating conditions, and such one-way coupling is several orders of magnitude faster than the more rigorous two-way coupling.