Computational Fluid Dynamics Approach Research Articles

Modelling Heat and Mass Transfer in High-Capacity Natural Convection Solar Dryers

Predicting solar dryer performance under different environmental conditions or assessing their performance to dry different grains is challenging since repeatable full-scale tests are expensive and time consuming. In the present study, computational fluid dynamics approach was used to model the drying of maize in high-capacity dryers such as greenhouse and solar bubble dryer. The absorption of short-wave radiation and the greenhouse effect in the dryer with incident solar radiation was modelled using a dual-band spectrum. The distribution of airflow, temperature, and absolute humidity was analysed in this study to optimise the drying process of maize. Additionally, these results were also used to quantify the drying rate of both greenhouse and solar bubble dryer. The greenhouse dryer model overpredicted the dryer temperatures by an average of 0.12%, and overpredicted absolute humidity by 0.38 per cent. The average Root-Mean-Square Error (RMSE) of temperature prediction was 1.8 °C, and the average RMSE for absolute humidity was 0.0042 for the greenhouse model. On the other hand, the solar bubble dryer model underpredicted temperatures by 1.7%, and underpredicted humidity values by 0.3 per cent. The mean absolute percentage error for the temperature and absolute humidity prediction of the solar bubble dryer model was 1.69% and 0.28%, respectively. The predicted and observed spatial variation in the temperature was similar for both dryers.

Modeling and analysis of particle behavior in fluidized bed bioreactors during non-Newtonian sewage treatment

The stability of biofilms attached to support particles plays a crucial role in fluidized bed bioreactors (FBBRs) when treating non-Newtonian sewage. Biofilm instability is governed by particle-particle (P-P) and particle-wall (P-W) collisions. This paper presents a numerical study of particle behavior in a FBBR using a combined approach of computational fluid dynamics (CFD) and discrete element method (DEM) tailored for non-Newtonian flow systems. Following validation, the CFD-DEM model is utilized to quantify the FBBR performance regarding bed expansion, void fraction distribution, and P-P/W interactions. The impacts of three variables are examined, including sewage superficial velocity and two rheological properties, i.e., sewage temperature and solid concentration. The results reveal that increasing sewage superficial velocity and sewage solid concentration or decreasing sewage temperature, leads to a transition in particle flow from symmetrical to asymmetrical in the radial direction, along with a circulating flow at the lower part of the bed. The relationships between the variables and the bed expansion, uniformity, and P-P/W interactions are also established, identifying optimum conditions to enhance bed uniformity and reduce P-P/W interactions. Overall, the findings demonstrate the value of the CFD-DEM model for understanding, designing, and optimizing FBBRs.

Numerical analysis of the journal bearings of Keban Hydroelectric Power Plant using different type nanofluids

Energy production in line with demand rapidly increases. Fossil fuel systems in use today pose a great threat to the future of the world and in this sense, the interest to the renewable energy sources such as hydroelectric energy systems is increasing. In this study, the heating problem of the journal bearings one of the parts of the hydroelectric energy systems was evaluated, various analysis were performed with the Computational Fluid Dynamics (CFD) approach to eliminate this problem and the results obtained were shared. Initial analyses were performed and evaluated for Mobil DTE 68 oil, which was commonly used as a refrigerant in journal bearings. Then, Al2O3 nanoparticles at concentrations of 3%, 7% and 10% were then added to the refrigerant oil and necessary analyses were performed for these three conditions. Finally, similar analyses were performed in the 3%, 7% and 10% concentration for SiO2. When the obtained temperature changes were examined accordingly, it was obtained that the increase in the concentration of nanoparticles exhibited a characteristic that was inversely proportional to the surface temperature. With the addition of nanoparticles, surface temperatures have been observed to decrease from 80°C to 68°C, but the effect on sharp corners is quite low. In this sense, it has been obtained that nanoparticles can significantly increase the thermal characteristics of Mobil DTE 68 oil, and it has been concluded that nanofluids may be an alternative solution for the overheating problem that occurs in journal bearings.

Novel method for temperature prediction in rotary kiln process through machine learning and CFD

Blast furnace high-proportion pellet smelting stands as a pivotal pathway towards achieving low-carbon and environmentally friendly ironmaking. However, challenges in pellet ore production, particularly through the rotary kiln method, have surfaced due to the scarcity of iron ore resources, leading to issues like “ringing” that hamper production efficiency and energy conservation. Effectively predicting and optimally controlling the temperature field within rotary kilns emerges as a crucial strategy to address this pressing challenge. In a pioneering effort, this paper introduces a data-driven machine learning methodology designed to expedite the prediction of temperature field distributions within rotary kilns. Through a detailed analysis of field data from the factory and the insights of experts, the paper has discussed on the key factors influencing the temperature field dynamics in rotary kilns. Leveraging Computational Fluid Dynamics (CFD), a numerical simulation model was established to replicate rotary kiln oxy-coal combustion scenarios. A comprehensive dataset was generated using high-throughput computing technology, encompassing a variety of conditions such as coal injection rates, return air temperature, return airflow volume, fixed carbon content of pulverized coal, and volatile fraction of pulverized coal. This dataset included a total of 625 distinct cases. Subsequently, employing machine learning techniques including random forest (RF) and genetic algorithm-optimized Random forest (GA-RF), predictive models were developed for rotary kiln temperature field dynamics. A rigorous comparative analysis was conducted to explore the impact of different machine learning dataset sizes on the predictive accuracy of the models. The results of this analysis demonstrate that, enhanced by the genetic algorithm of RF, it surpasses the performance of the standard RF approach. This enhancement resulted in a significant improvement in prediction accuracy, as evidenced by the reduction of the Root Mean Square Error (RMSE) from 25.0879 to 17.5330 and the Mean Absolute Error (MAE) from an initial value of 6.5600 to 4.7661. Furthermore, the novel soft-measurement method introduced in this paper significantly reduces simulation time by several orders of magnitude. It has diminished the response time from the traditional industrial methods, which took two to three weeks, to approximately 10 s, as compared to conventional CFD approaches. Remarkably, this approach successfully preserves a high level of accuracy. The implications of these research findings are far-reaching,providing novel insights into the acceleration of CFD simulation calculations and the design of soft measurement sensors customized for black-box reactors in industrial processes.

Effect of personalized air curtain combined with mixing ventilation on dispersion of aerosols released at different velocities from respiratory activities during close contact

Release velocities of aerosols vary among people and respiratory activities. To better promote the application of personalized air curtain (PAC) in practice, this work investigated the performance of PAC combined with mixing ventilation in terms of dispersion of aerosols released at different velocities using a simulation approach of computational fluid dynamics (CFD). The healthy person (HP) and infected person (IP) were in face-to-face and face-to-back scenarios during close contact. Results showed that the aerosol dispersion was highly affected by aerosol release velocities. When aerosols were released from 2.0 m/s to 8.0 m/s, the deposition rate was increased by more than 3 times and 10 times in the face-to-face and face-to-back scenarios, respectively. The intake fraction was increased by over 3 times in the face-to-face scenario. PAC could significantly reduce the intake fraction and deposition rate. When aerosols were released at 8.0 m/s, PAC could reduce the intake fraction and deposition rate by 76% and 66% in the face-to-face scenario, respectively. A higher PAC velocity did not necessarily lead to a better performance. The appropriate PAC velocity of 3.0–5.0 m/s was suggested in this work. The significantly reduced intake fraction and deposition rate suggest that PAC as an advanced ventilation system can be a promising control measure to prevent the spread of respiratory diseases in indoor environments. Additionally, different performances of PAC indicate the importance of investigating the appropriate PAC velocities in relation to aerosol release velocities. Otherwise, more energy is consumed by PAC, but the best performance is not achieved.

A comprehensive heat transfer prediction model for tubular moving bed heat exchangers using CFD-DEM: Validation and sensitivity analysis

This study established a complete heat transfer prediction model that integrates four heat transfer sub-models, including contact heat conduction, gas film heat conduction, heat radiation, and heat convection, along with an artificial softening correction based on combined approach of computational fluid dynamics and discrete element method (CFD-DEM). The model’s precision was confirmed by experimental results with relative errors below 5 %. The discussion on the heat flux contributions of the above four heat transfer mechanisms shows that with an initial particle temperature of 200℃, tube wall temperature of 30℃, and particle descent velocity of 1.3 mm/s, gas film conduction heat flux is the primary contributor to the total tube wall heat flux at 60.5 %, followed by radiation heat flux (19.5 %), contact conduction heat flux (12.1 %), and convection heat flux (7.9 %). Besides, the presentation of physical fields related to particles and the fluid showcases the model’s capability in elucidating the heat and mass transfer characteristics of the particle–fluid-wall system within tubular MBHEs. Furthermore, a local nominal range sensitivity analysis, along with a study of factors’ influence rules, was performed to quantify the effects of the 8 key model parameters on heat transfer results. The analysis indicates that fluid thermal conductivity is the predominant factor with its nondimensionalized first-order sensitivity coefficient of 54.9 %, succeeded by particle-to-wall angle factor (15.4 %), wall emissivity (14.4 %), gas film thickness (9.6 %), wall thermal conductivity (2.3 %), wall roughness (-1.6 %), particle Young’s modulus (-1.4 %), and particle specific heat capacity (0.4 %). Finally, their impacts on the heat prediction results were presented in detail.

Analysis of the Effect of Coordination Number on Permeability of the Three-Dimensional (3D) Rock Model Using the Lattice Boltzmann Method (LBM)

Understanding the correlation between coordination number and permeability is crucial for predicting fluid flow behavior in hydrocarbon reservoirs. In this study, we investigated the influence of coordination number on permeability in three-dimensional (3D) rock models. The research methodology involved the creation of synthetic 3D rock models, incorporating pores networks with varying coordination numbers. Utilizing the Lattice Boltzmann Methods (LBM), a computational fluid dynamics approach, we simulated fluid flow through these synthetic rock models and quantified their permeability. Our findings demonstrated a strong dependency of permeability on the coordination number of synthetic rock models. The application of the Lattice Boltzmann Method (LBM) proved to be an effective tool for understanding fluid flow behavior in porous rock formations and can serve as a basis for further optimization of reservoir management strategies to maximize hydrocarbon exploitation.

Two-Phase Crude Oil-Water Flow Through Different Pipes: An Experimental Investigation Coupled with Computational Fluid Dynamics Approach.

The present study deals with two-phase non-Newtonian pseudoplastic crude oil and water flow inside horizontal pipes simulated by ANSYS. The study helps predict velocity and velocity profiles, as well as pressure drop during two-phase crude-oil-water flow, without complex calculations. Computational fluid dynamics (CFD) analysis will be very important in reducing the experimental cost and the effort of data acquisition. Three independent horizontal stainless steel pipes (SS-304) with inner diameters of 1 in., 1.5 in., and 2 in. were used to circulate crude oil with 5, 10, and 15% v/v water for simulation purposes. The entire length of the pipes, along with their surfaces, were insulated to reduce heat loss. A grid size of 221,365 was selected as the optimal grid. Two-phase flow phenomena, pressure drop calculations, shear stress on the walls, along with the rate of shear strain, and phase analysis were studied. Moreover, velocity changes from the wall to the center, causing a velocity gradient and shear strain rate, but at the center, no velocity variation (velocity gradient) was observed between the layers of the fluid. The precision of the simulation was investigated using three error parameters, such as mean square error, Nash-Sutcliffe efficiency, and RMSE-standard deviation of observation ratio. From the simulation, it was found that CFD analysis holds good agreement with experimental results. The uncertainty analysis demonstrated that our CFD model is helpful in predicting the rheological parameters very accurately. The study aids in identifying and predicting fluid flow phenomena inside horizontal straight pipes in a very effective way.

Hole cleaning evaluation and installation spacing optimization of cuttings bed remover in extended-reach drilling

In extended-reach or long-horizontal drilling, cuttings usually deposit at the bottom of the annulus. Once cuttings accumulate to a certain thickness, complex problems such as excessive torque and drag, tubing buckling, and pipe stuck probably occur, which results in a lot of non-productive time and remedial operations. Cuttings bed remover can efficiently destroy deposited cuttings in time through hydraulic and mechanical stirring effects. This paper aims to build a method for hole cleaning evaluation and installation spacing optimization of cuttings bed remover to improve the wellbore cleaning effect. Firstly, a Computational Fluid Dynamics approach with Eulerian–Eulerian multiphase model was utilized to investigate the mechanism of cuttings transportation, and a new type of cuttings bed remover was designed. Next, an evaluation method of hole cleaning effect of remover was established. After that, the effects of several drilling parameters on hole cleaning including flow rate of drilling fluid, rotational speed of drillpipe, rate of penetration, wellbore size, rheological property of drilling fluid, and remover eccentricity on the performance of cuttings bed remover were investigated. The results demonstrate that the new type of remover with streamline blade performs better than conventional removers. The efficiency of hole cleaning is greatly improved by increasing the rotational speed of drillpipe, flow rate of drilling fluid, remover eccentricity, and 6 rpm Fann dial reading for drilling fluid. While higher rate of penetration and large wellbore size result in worse hole cleaning. These findings can serve as an important guide for the structure optimization design of cuttings bed remover and installation spacing of removers.

Numerical Simulation on Erosion and Stress on a Small-Diameter Hydrajet Fracturing Tool.

Conventional hydrajet fracturing techniques are often frustrated when they are applied to some specific well types, such as casing-damaged and small-diameter wells. It is of great significance to investigate the erosion and stress on a small-diameter hydrajet fracturing tool during its service and clarify the relevant influencing factors. Based on the solid-liquid two-phase flow theory and erosion model, a numerical simulation was conducted on the erosion and stress on a small-diameter hydrajet fracturing tool by using the computational fluid dynamics approach in order to understand how the inlet flow rate, particle size, and particle mass concentration affect the erosion and stress on the tool. The results show that the erosion on the small-diameter hydrajet fracturing tool is generally a cutting erosion of proppant particles on the tool body. Such erosion occurs on the lower wall of the nozzle, and the erosion at the upper-0° nozzle is higher in rate and smaller in area than that at the lower-180° nozzle. The maximum stress of the small-diameter hydrajet fracturing tool is concentrated on the upper and lower walls of the upper and lower nozzles, especially the lower part inside the upper nozzle. The maximum erosion rate, average erosion rate, and maximum stress on the wall near the nozzle during fracturing increase as the inlet flow rate and particle mass concentration increase and decrease as the proppant particle size increases.

Numerical investigation on boiling crisis in a full-length 5 × 5 fuel assembly under typical pressurized water reactor conditions

Critical heat flux (CHF) presents a research priority in the Pressurized Water Reactor (PWR) safety, as it can lead to the severe accidents and release radioactive materials. In this study, a wall heat flux partition model, proposed by Demarly [1], was employed within the two-fluid model by Computational Fluid Dynamics (CFD) methodology to investigate the characteristics of CHF under typical PWR conditions. A multi-scale interface model was coupled to consider the interfacial transfers in bulk flow during flow regime transition. The models were validated by using experimental data on subcooled flow boiling, boiling crisis in vertical tubes, and rod bundles. The PWR sub-channel and bundle test (PSBT) benchmark, comprising a full-length 5 × 5 fuel assembly with three different types of spacers, was simulated in this study. The CHF value was evaluated, demonstrating an acceptable accuracy compared to the experimental data, with a deviation of less than 10 % in the selected conditions. The CHF location in the cross section was successfully predicted by CFD approach, while the axial location of CHF was underestimated than experimental measurement. Besides, the impacts of mixing vanes with different deflection angles on boiling heat transfer characteristics were analyzed, revealing the crucial role of mixing vanes in the distribution of vapor and dry-patch area fraction in both the circumferential and flow directions.

Investigation of Compressor Cascade Flow Using Physics-Informed Neural Networks with Adaptive Learning Strategy

In this study, we utilize the emerging physics-informed neural networks (PINNs) approach for the first time to predict the flowfield of a compressor cascade. Different from conventional training methods, a new adaptive learning strategy that mitigates gradient imbalance through incorporating adaptive weights in conjunction with a dynamically adjusting learning rate is used during the training process to improve the convergence of PINNs. The performance of PINNs is assessed here by solving both the forward and inverse problems. In the forward problem, by encapsulating the physical relations among relevant variables, PINNs demonstrate their effectiveness in accurately forecasting the compressor’s flowfield. PINNs also show obvious advantages over the traditional computational fluid dynamics (CFD) approaches, particularly in scenarios lacking complete boundary conditions, as is often the case in inverse engineering problems. PINNs successfully reconstruct the flowfield of the compressor cascade solely based on partial velocity vectors and near-wall pressure information. Furthermore, PINNs show robust performance in the environment of various levels of aleatory uncertainties stemming from labeled data. This research provides evidence that PINNs can offer turbomachinery designers an additional and promising option alongside the current dominant CFD methods.

Optimization of Injection Source Settings for SNCR Numerical Simulation of Low-Water Content Biomass Boilers with Blending.

In order to control NOx emissions and meet China's ultralow emission standards, a numerical simulation based on the computational fluid dynamics (CFD) approach is performed for the optimization of the reductant injection volume, number of injection sources, distribution, and injection direction for the flue gas denitrification process of a circulating fluidized bed boiler (CFB) blended with low-water content biomass in a 168 MW unit of a thermal power plant. Using the target power plant boiler entity as a template, a simplified geometric model is established, 1:1, and the mass fractions of each flue gas component set by the inlet boundary conditions are O22, H2O11.6, CO216.2%, and NO0.05%(about 134 ppm), and the reduction reactions under different optimized conditions are numerically simulated using the SNCR model in ANSYS Fluent 2021 R1. The simulation results under each condition were analyzed. The results show that the optimal ammonia-to-nitrogen ratio should be taken as NSR = 1.25, the denitrification efficiencies of 81.00, 81.63, and 82.74% at the three outlets are high, and the ammonia escapes of 1.76, 2.08, and 9.42 mg/s are within a reasonable range; increasing the number of injection sources can significantly reduce the disturbance of the flue gas flow field by reductant injection; the direction of injection is parallel to the direction of the flue gas flow, and the line of the injection source is orthogonal to the direction of the flue gas flow, which is conducive to the mixing of the reductant and flue gas; the optimized boiler denitrification efficiency reaches 74.2%, meeting the ultralow emission requirements of nitrogen oxides and ammonia escape.

Development and Validation of Computational Fluid Dynamics Model of Ladle Furnace with Electromagnetic Stirring System.

Numerical methods are crucial to supporting the development of new technology in different industries, especially steelmaking, where many phenomena cannot be directly measured or observed under industrial conditions. As a result, further designing and optimizing steelmaking equipment and technology are not easy tasks. At the same time, numerical approaches enable modeling of various phenomena controlling material behavior and, thus, understanding the physics behind the processes occurring in different metallurgical devices. With this, it is possible to design and develop new technological solutions that improve the quality of steel products and minimize the negative impact on the environment. However, the usage of numerical approaches without proper validation can lead to misleading results and conclusions. Therefore, in this paper, the authors focus on the development of the CFD-based (computational fluid dynamics) approach to investigate the liquid steel flow inside one metallurgical device, namely a ladle furnace combined with an EMS (electromagnetic stirring) system. First, a numerical simulation of electromagnetic stirring in a scaled mercury model of a ladle furnace was carried out. The numerical results, such as stirring speed and turbulent kinetic energy, were compared with measurements in the mercury model. It was found that the results of the transient multiphase CFD model achieve good agreement with the measurements, but a free surface should be included in the CFD model to simulate the instability of the flow pattern in the mercury model. Based on the developed model, a full-scale industrial ladle furnace with electromagnetic stirring was also simulated and presented. This research confirms that such a coupled model can be used to design new types of EMS devices that improve molten steel flow in metallurgical equipment.

Optimization of a thermal energy storage system enhanced with fins using generative adversarial networks method

Optimizing fin configurations is an effective method to improve the performance of a thermal energy storage system. The present research aims to determine the optimal fin spacing and inclination angle to minimize the melting process of a phase change material (PCM) in a rectangular enclosure, which is exposed to a constant temperate finned wall while other walls are adiabatic. Oleic acid (OA) is used as a PCM with a melting temperature of 287–288 K. Computational fluid dynamics (CFD) simulations are employed to comprehensively investigate the thermal behavior of the system by solving continuity, momentum, and energy equations simultaneously. Initially, fifteen different datasets are simulated using CFD methods to explore various fin configurations. After that, fifteen additional datasets are generated to verify the reliability of the generative adversarial networks (GANs) method, which supplements the CFD approach. An ensemble learning approach is implemented to create a hybrid dataset consisting of thirty datasets, and then the optimum dataset is determined. To validate this approach, a single numerical simulation is conducted at the optimum dataset, and the obtained optimum value, minimum melting time of PCM, is compared with the result obtained from CFD simulation. It is identified as the optimal fin configuration, resulting in a fin spacing of 15.75 mm and an inclination angle of −11.6°, highlights the efficacy of the combined CFD and GANs approach. The melting time captured from the numerical simulation and the one produced with GANs differed by about 4.7 %. The current study concludes that the GANs method might be effectively worthwhile in many energy storage applications.

Numerical parametric study of a sweeping-vortex low-frequency fluidic oscillator

Fluidic oscillators are garnering increasing attention in various fields due to their self-induced oscillation characteristics. This study proposes a novel fluidic oscillator, the sweeping-vortex fluidic oscillator (SVFO), characterized by unique feedback properties. These properties exploit fluid feedback from the side opposite the attached wall and the spin delay of the captive vortex to yield low-frequency oscillating jets. The oscillation characteristics and the influence of main geometric parameters were investigated using computational fluid dynamics approach. Results show that the evolution of the flow field within the SVFO demonstrates an intricately dynamic interacting. The deflection switching of the main jet is primarily controlled by a pair of counter-rotating moving vortexes. Geometric parameters influence the stability of the dynamic evolution of both captive and moving vortexes, thereby altering the signal components of the output performance transmitted to the inlet. The overall width of the oscillator has a pronounced impact on the oscillation frequency, and with an increase in the overall width, a maximum frequency increase of 87 % can be observed. Furthermore, the height of the sweeping chamber and the diameter of the vortex chamber outlet have a noticeable effect on the pressure pulse amplitude. A pressure fluctuation coefficient greater than 1.1 was recorded. The results also suggest that the geometric parameters of the fluidic oscillator need to be designed within a reasonable range; otherwise, the jet oscillator will not function. A more refined optimization of geometric parameters could be obtained with the aim of achieving a stable main jet, feedback flow of appropriate intensity, and optimal attachment conditions. This work reveals the oscillation mechanism and the influence of geometric parameters on SVFOs, providing a theoretical basis for designing related oscillators.

A Comprehensive Parametric Study on CO2 Removal from Natural Gas by Hollow Fiber Membrane Contactor: A Computational Fluid Dynamics Approach

AbstractThis study examined essential factors in the use of hollow fiber membranes that affect CO2 removal efficiency. In the simulation, a finite element model for a membrane with ten fibers was used. Each fiber is 175 mm long with inner radius of 0.75 mm and outer radius of 1.5 mm. Liquid and gas flow rates were set at 100–800 mL min−1, and adsorbent concentration was adjusted in the range of 200–1500 mol m−3 for monoethanolamine, piperazine (PZ), and ethylenediamine absorbents. Increasing the liquid flow rate, gas flow rate, and absorbent concentration leads to an increase, decrease, and increase in efficiency, respectively. Thus, using PZ as absorbent with a concentration of 1080 mol m−3 liquid and gas flow rates of 400 and 180 mL min−1, respectively, showed a CO2 removal efficiency of > 95 % for a membrane effective length of 0.3 m.

Multi-Objective Optimization of the Microchannel Heat Sink Used for Combustor of the Gas Turbine

This research presents a surrogate model and computational fluid dynamic analysis-based multi-objective optimization approach for microchannel heat sinks. The Non-dominated Sorting Genetic Algorithm is suggested to obtain the optimal solution set, and the Kriging model is employed to lower the simulation’s computational cost. The physical model consists of a coolant chamber, a mainstream chamber, and a solid board equipped with staggered Zigzag cooling channels. Five design variables are examined in relation to the geometric characteristics of the microchannel heat sinks: the length of inlet of the cooling channels, the width of the cooling channels, the length of the “zigzag”, the height of the cooling channels, and the periodic spanwise width. The optimal geometry is established by choosing the averaged cooling effectiveness and coolant mass flow rate which enters the mainstream chamber through the microchannel heat sinks as separate objectives. From the Pareto front, the optimal microchannel heat sinks structures are obtained. Three optimized results are studied, including the maximum cooling effectiveness, minimum coolant mass flow rate, and a compromise between the both objectives; a reference case using the median is compared as well. Numerical assessments corresponding to the four cases are performed, and the flow and cooling performance are compared. Furthermore, an analysis is conducted on the mechanisms that impact the ideal geometric parameters for cooling performance.

Droplet deposition of agrochemical spraying: Comparison of results from a random‐walk model and CFD simulations

AbstractThe effectiveness of agricultural spraying processes depends considerably on the ability of the atomized droplets to reach the target site in the desired amount. In this work, two mathematical models to study the trajectories and deposition of atomized droplets are implemented and compared. On the one hand, a computational fluid dynamics (CFD) coupled with discrete phase model (DPM) is implemented to calculate the trajectories of atomized droplets and determine distances at which the droplets are deposited. The continuous phase (atmospheric air) is modelled by continuity, momentum, and energy equations. On the other hand, a Lagrangian random‐walk (LRW) model based on force and energy balances to predict the pulverization process of a nozzle is formulated and implemented in Python. Both models take into account the effects of drag, gravity, buoyancy, and evaporation on individual droplets, as well as the impact of atmospheric stability and dispersion. By tracking a large number of trajectories, meaningful estimates of dispersal statistics can be obtained. The LRW model accurately replicated the trajectories, deposition distances, and final diameters of atomized droplets for three atmospheric stability cases, compared with CFD simulation results. The results of both models agreed that 100 μm droplets were most susceptible to wind‐induced spray drift, depositing at the furthest distances from the nozzle. In addition, 50 μm droplets exhibited a significant tendency to evaporate entirely before reaching the ground. The LRW model is found to be a cost‐effective alternative for estimating spray drift compared to the computationally intensive CFD approach.

Added mass analysis of submersible using computational fluid dynamics

An estimation of the resistance acting on a manned submersible can be performed using computational fluid dynamics (CFD). The resistance force acting on a vehicle can be steady state or transient state drag. Steady state drag comes when a vehicle moves at a constant velocity but drag value increases or decreases when the vehicle accelerates or decelerates. Transient state drag acts on a vehicle when velocity changes. The addition or reduction in drag value is due to the added mass. This paper discusses two CFD approaches to calculate longitudinal added mass. The second CFD method is preferred due to the inaccuracy and assumptions involved in the first method. The preferred CFD method can be used to obtain the added mass for both standard shapes, as well as for complicated geometries like manned submersibles. The drag and added mass of the ellipsoid are calculated using CFD and compared with analytical and experimental results for validation. Additionally, an acceleration sensitivity study indicates that added mass is independent of acceleration. CFD methods proposed here are simple and time-efficient. By using this method, a submersible’s added mass can be calculated without employing expensive experimental methods or other CFD methods. Highlights A vehicle’s steady-state drag develops when moving at constant velocity, but the amount of drag increases or decreases when accelerating or decelerating The CFD method can be used to obtain the added mass of more complicated geometries like manned submersibles and standard shapes like spheres and cylinders An acceleration sensitivity study indicates that added mass is independent of acceleration