Combined Computational Fluid Dynamics Research Articles

Predictive Model and Optimization of Micromixers Geometry using Gaussian Process with Uncertainty Quantification and Genetic Algorithm

Abstract Microfluidic devices are gaining attention for their small size and ability to handle tiny fluid volumes. Mixing fluids efficiently at this scale, known as micromixing, is crucial. This article builds upon previous research by introducing a novel optimization approach in microfluidics, combining Computational Fluid Dynamics (CFD) with Machine Learning (ML) techniques. Our research focuses on enhancing global optimization techniques while minimizing computational costs. It draws inspiration from a Y-type micromixer, initially featuring cylindrical grooves on the main channel's surface and internal obstructions. Simulations, conducted using OpenFOAM software, evaluate the impact of circular obstructions on mixing percentage and pressure drop, considering variations in obstruction diameter and offset. A Gaussian Process (GP) was utilized to model the data, providing model uncertainty. This study optimizes geometries by using genetic algorithm (GA) based on the reduced order model provided by GP. Results align with previous research, showing that medium-sized obstructions (137 mm diameter, 10 mm offset) near the channel wall are optimal. This approach not only provides efficient microfluidic optimization with uncertainty quantification but also highlights the effectiveness of combining CFD and ML techniques in achieving desired outcomes.

Using computational fluid dynamics and deep learning for leak detection and localization in a smart water management system

Water leakages in distribution systems offer significant challenges, resulting in water loss, infrastructure damage, and environmental hazards. As urban populations grow and water resources become scarcer, water management systems’ efficiency becomes increasingly important. Traditional leak detection methods are inadequate and labour-intensive, making them unsuitable for large-scale infrastructure. In addition, limitations in the data representing pipes of varying diameters and materials under various leak scenarios leads to complexity in automatic water leak detection and localization. In response to this challenge, this research effort combines Computational Fluid Dynamics (CFD) and Deep Learning (DL) to enhance leak detection and localization. The study simulates water flow in pipes of varying diameters and materials under various leak scenarios using CFD. Consequentially deep learning models like the Bi-Layered ReLU Encoder + Softmax and Tri-Layered ReLU Encoder + Softmax are trained and tested. The results indicate improvements in detection accuracy by 2.4% and localization by 5.6%, highlighting the potential of this hybrid approach to enhance the reliability and efficiency of water management systems, ultimately promoting water conservation and reducing infrastructure damage.

Data-driven parameter identification of an equivalent mechanical model for large amplitude liquid sloshing

The accurate extraction of model parameters is vital to ensure that the equivalent mechanical model can precisely describe the dynamic behavior of large amplitude liquid sloshing. In this paper, the parameters of the moving pulsating ball model (MPBM), which is an equivalent mechanical model used to represent the large amplitude liquid sloshing, are extracted using a combination of computational fluid dynamics (CFD), experimental data, and data-driven algorithms. The arbitrary Lagrangian-Eulerian finite element method (ALE-FEM) is adopted to simulate three-dimension large amplitude liquid sloshing in the tank with high precision. The calculated results from the presented algorithm are compared with the experimental data to verify the reliability and validity. The typical parameters required by the MPBM mainly include equivalent sloshing mass, equivalent ball radius, and damping coefficient. These parameters are extracted by using a data-driven parameter optimization algorithm, which is based on the numerical calculation results of ALE-FEM. The accuracy of the MPBM with the equivalent model parameters extracted by using data-driven parameter optimization algorithm is investigated under two types of excitations: harmonic excitation and step excitation. The results show that the MPBM with equivalent model parameters extracted by a data-driven parameter optimization algorithm can precisely imitate the large amplitude liquid sloshing behavior and the method presented can provide significant reference for the overall design of spacecraft dynamics system.

Quantifying and reducing the cost of tagging: combining computational fluid dynamics and diving experiments to reduce impact from animal-borne tags.

Animal-borne instruments are essential research tools for ecologists and physiologists. An increasing number of studies have shown impacts of carrying a tag on behaviour and energetics, which can have implications for animal welfare and data validity. Such impacts are a result of the additional mass and/or drag loads, with the latter requiring empirical measurements or computational fluid dynamics (CFD) to estimate. To quantify and effectively minimize tag impacts from drag, a novel combined empirical and CFD approach is required. Here, we demonstrate such an approach using captive phocid seals and the widely used Sea Mammal Research Unit (SMRU) Instrumentation Group GPS/GSM tag. We (i) show a significant change in the behaviour of grey seals when carrying a tag (gen 1; associated with 16.4% additional drag); (ii) redesigned the tag (gen 2) resulting in a lower additional drag of 8.6%; (iii) show significant differences in behaviour when carrying a gen 2 compared to gen 1 tag, demonstrating that the redesign successfully reduced impact; and (iv) observed changes in the swim speed of seals that were consistent with predictions from CFD estimates of drag. The gen 2 instrument is now commercially available. This non-trivial case study should pave the way for similar studies in other taxa and species.

Aerodynamic study of a bifurcated turboprop engine inlet with a propeller for flow at ground suction conditions

An advanced turboprop inlet with a bypass duct plays a crucial role in preventing foreign object damage and ensuring a high-quality airflow to the engine. However, it also introduces an increased flow complexity and design challenge due to the interaction between the propeller and the bifurcated ducts. To address these issues, a combined experimental and computational fluid dynamics (CFD) study was conducted on the aerodynamic performance and flowfield characteristics of a turboprop inlet equipped with a bypass duct considering the propeller interference. A ground suction test bench was utilized for generating the working conditions and the performance was measured by using total pressure rakes and pressure scanners. It is found that the rotational propeller on the one hand does work on and thus increases the total pressure recovery of the inlet, however, on the other hand causes a turning effect on the inlet flowfield structure along the direction of rotation and increases total pressure and swirling flow distortions in the engine duct. Besides, the engine duct and the bypass duct interact with each other. The combined influence of suction effect and the profile induction of the inlet leads to the majority of the shed vortices being drawn into the engine duct. Lastly, the presence of deflectors installed in the engine duct is found to effectively mitigate the secondary flow, thereby reducing the swirl distortion within the engine duct. This study may provide a significant reference to the design and optimization of advanced turboprop inlets with bypass ducts.

Hydrodynamic Performance Improvement of a Tirhandil Yacht by Stern Form Modifications

This paper presents a comprehensive investigation to improve the hydrodynamic performance of a Tirhandil hull form by modification efforts on the stern region. The form improvement approach combines computational fluid dynamics (CFD) methods with computer-aided design (CAD) systems. The design process for the reference and modified models was carried out by using CAD systems. The hydrodynamic characteristics of the reference hull form were evaluated by employing CFD methods and it was determined that form improvements should be concentrated on the stern region. The modification process was conducted by considering constraints on the design variables in the stern region and the main dimensions of the reference model. A grid independence study was performed to evaluate various grid structures to determine the optimal mesh configuration for the numerical analyses. The SST k-Omega turbulence model was used for the numerical analyses to simulate turbulence structure around the hull form. Achieving around a 13.4% reduction in the total resistance coefficient, the modified model also exhibited decreased wave amplitudes, smoother wave transitions, and a significant reduction or cancellation of shoulder and stern waves.

CFD-DEM study on the blocking conformation of pre-filled sand control screen by layering method

Preventing screen blockage due to sand poses a significant challenge. The article combines computational fluid dynamics (CFD) and discrete element method (DEM) to establish a fully coupled four-way interaction model. A sophisticated size particle injection model was established, taking into account the particle size distribution (PSD) characteristics of the formation. A layering method was used to study the sand control behavior of a high-performance pre-filled screen. The study also quantitatively investigated the impact of factors such as median sand size (D50), screen structure, and fluid velocity on both wellbore-screen annulus and internal screen plugging patterns. The findings reveal that the sand retention rate within the wellbore-screen annulus exceeds 93.8 %. The risk of pre-filled screen blockage is minimized when the ratio of gravel diameter to sand diameter falls within the range of 3.3–5. At a gravel filling rate of 97 %, fine sand predominantly moves radially, resulting in a low risk of blockage at the screen's entrance layer, thereby favoring oil and gas production. In cases with elevated fine sand content, it is advisable to maintain a production flow rate below 3 m/s to mitigate localized erosion damage caused by increased pressure resulting from screen blockage. This model holds significant importance in optimizing completion tools and production parameters.

Finite Element Analysis and Computational Fluid Dynamics for the Flow Control of a Non-Return Multi-Door Reflux Valve

This paper presents a comprehensive analysis of a multi-door check valve using computational fluid dynamics (CFD) and finite element analysis (FEA) to evaluate flow performance under pressure test conditions, with an emphasis on its ability to prevent backflow. Check valves are essential components in various industries, ensuring fluid flow in one direction only while preventing reverse flow. The non-return multi-door reflux valve is increasingly preferred due to its superior backflow prevention, fluid control, and effective flow regulation. Rigorous testing under varying pressure conditions is essential to ensure that these valves perform optimally. This study uses CFD and FEA simulations to evaluate the structural integrity and flow characteristics of the valve, including pressure drop, flow velocity, backflow prevention effectiveness, and flow coefficient. A high-fidelity 3D model was created to simulate the valve’s behavior under various conditions, analyzing the effects of parameters such as the number of doors, their orientation, geometry, and operating conditions. The CFD results demonstrated a significant reduction in backflow and pressure drop across the valve. However, localized turbulence and flow separation near the valve doors, particularly under partially open conditions, have raised concerns about potential wear. The velocity profiles indicated a uniform distribution at full opening with laminar velocity profiles and minimal resistance to flow. The results of the FEA showed that the stresses induced by the fluid forces were below critical levels, with the highest stress concentrations observed around the hinge points of the valve doors. Although the valve structure remained intact under normal operating conditions, some areas may have required reinforcement to ensure long-term durability. Combined CFD and FEA analyses demonstrated that the valve effectively preserves system integrity, prevents backflow, and maintains consistent performance under various pressure and flow conditions. These findings provide valuable insights into design improvements, performance optimization, and enhancing the efficiency and reliability of reflux valve systems in industrial applications.

Simulation of special-shaped graded particulate hydraulic transport in deep-sea mining scenarios

Hydrodynamic transport is crucial in deep-sea mining engineering for conveying materials. This study investigates the impact of various particle shapes on slurry transport efficiency and flow characteristics at higher concentrations. A numerical method combining computational fluid dynamics (CFD) and the discrete element method (DEM) within the Euler-Lagrange framework is employed to simulate the flow and hydrodynamic properties of graded heterogeneous coarse particles in a deep-sea hydraulic lift pipe. The reliability of the simulations is verified through comparisons, focusing on feed concentration and particle gradation effects on dynamic characteristics and flow patterns. The study quantitatively analyzes concentration distributions under various flow regimes for different particle shapes and conveying concentrations in a vertical pipeline system. Flow characteristics under clogging conditions are described, considering flow transition and concentration distribution. Additionally, conditions for stable transportation are discussed concerning particle forces and transport reliability.

Combining Computational Fluid Dynamics, Structural Analysis, and Machine Learning to Predict Cerebrovascular Events: A Mild ML Approach

Background/Objectives: Cerebrovascular events, such as strokes, are often preceded by the rupture of atherosclerotic plaques in the carotid arteries. This work introduces a novel approach to predict the occurrence of such events by integrating computational fluid dynamics (CFD), structural analysis, and machine learning (ML) techniques. The objective is to develop a predictive model that combines both imaging and non-imaging data to assess the risk of carotid atherosclerosis and subsequent cerebrovascular events, ultimately improving clinical decision-making. Methods: A multidisciplinary approach was employed, utilizing 3D reconstruction techniques and blood-flow simulations to extract key plaque characteristics. These were combined with patient-specific clinical data for risk evaluation. The study involved 134 asymptomatic individuals diagnosed with carotid artery disease. Data imbalance was addressed using two distinct approaches, with the optimal method chosen for training a Gradient Boosting Tree (GBT) classifier. The model’s performance was evaluated in terms of accuracy, sensitivity, specificity, and ROC AUC. Results: The best-performing GBT model achieved a balanced accuracy of 88%, with a ROC AUC of 0.92, a sensitivity of 0.88, and a specificity of 0.91. This demonstrates the model’s high predictive power in identifying patients at risk for cerebrovascular events. Conclusions: The proposed method effectively combines CFD, structural analysis, and ML to predict cerebrovascular event risk in patients with carotid artery disease. By providing clinicians with a tool for better risk assessment, this approach has the potential to significantly enhance clinical decision-making and patient outcomes.

Synergistic impact mechanism of particle size and morphology in superalloy powders for additive manufacturing

The particle size and morphology of superalloy powders are crucial parameters that significantly influence the performance of additive manufacturing (AM) processes. This study investigates the effects of atomization pressure on these characteristics through a combination of computational fluid dynamics (CFD) simulations and vacuum induction melting gas atomization (VIGA) experiments. The CFD simulations revealed that increasing the atomization pressure from 2.0 MPa to 3.5 MPa resulted in a rise in maximum gas velocity from 526 m/s to 537 m/s and a reduction in median particle size (D50) from 60.9 μm to 37.5 μm. Subsequent experiments demonstrated a decrease in D50 from 52.9 μm to 35.6 μm, and sphericity from 0.9432 to 0.9377, as pressure increased. The particle size results of the atomization experiments and numerical simulations show strong consistency, validating the accuracy of the numerical simulation results. The volume of hollow particles also increased slightly in specific size fractions. These results suggest that higher atomization pressures produce finer powders with lower sphericity, but also promote particle adhesion, reducing the overall refinement effect. This study provides insights into optimizing atomization conditions for the precise control of superalloy powders in AM.

The impact of particle size and concentration on the characteristics of solid–fluid two-phase flow in vertical pipe under pulsating flow

Vertical pipe hydraulic conveying is currently considered the most promising method for deep-sea mining. The conveying velocity at the inlet of the vertical pipe is often close to a pulsating form due to the influence of the flexible pipe and the marine environment. This study employs a combined Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) approach to investigate the impact of various particle sizes and concentrations on the flow behavior of solid–fluid two-phase flow in a vertical pipe under pulsating flow. The study reveals that fluid velocities exhibit periodic fluctuations within the vertical pipe under pulsating inlet flow. Moreover, the periodic phenomenon of local increases in particle velocity and concentration becomes more pronounced as particle size and concentration increase. Larger particle sizes increase particle inertia, reducing the ability to follow the fluid and leading to more localized particle aggregation. Therefore, the flow pattern within the vertical pipe transitions from homogeneous flow to plug flow as particle size increases. Variations in fluid–solid interaction forces and particle collision forces are explored to explain this phenomenon. When the particle size exceeds a critical threshold, the collision force surpasses the fluid–solid interaction force, leading to the transition.

Thin-film evaporation in microchannels: A combined analytical and computational fluid dynamics approach to assessing meniscus curvature impact

Computational fluid dynamics (CFD) is employed to investigate the effects of variation in bulk meniscus curvature on evaporative heat and mass transfer in microchannels. Evaporation in microchannels presents a classic multiscale problem, encompassing both microscopic and macroscopic length scales in the thin-film and bulk meniscus regions, respectively. Therefore, the liquid-vapor interface or meniscus shape is first determined through thin-film evaporation analysis using numerical approaches found in the literature. However, a new approach for establishing boundary conditions is also proposed, allowing for the representation of varying curvatures within the bulk meniscus region. Then, the obtained meniscus thickness profiles are exported to a two-dimensional CFD framework. The primary advantage of this developed CFD framework lies in its capability to simultaneously analyze evaporative heat and mass transfer from both microscopic and macroscopic regions, a feature that enhances research endeavors concerning micro and miniature heat pipes. The developed model is applied to various rectangular microchannel sizes, ranging from 10 to 100 μm in width, with wall superheats varying from 0.1 to 3.0 K. The aim is to quantify the effects of variations in bulk meniscus curvature on the evaporative characteristics of the extended meniscus (comprising both thin-film and bulk meniscus regions), using the concept of thermal resistance. Consequently, based on the CFD simulation results, multiple regression analysis is employed to formulate the total thermal resistance in terms of independent parameters, namely the microchannel width, wall superheat, and the radius of curvature (Rc). It is observed that the radius of curvature has a marginal impact on the total thermal resistance, allowing its influence to be expressed by a power-law function of (Rc/Rm)0.102, where Rm represents the minimum radius of curvature.

Application of computational fluid dynamics to investigate pathophysiological mechanisms in exercise-induced laryngeal obstruction.

The underlying pathophysiological mechanisms of exercise-induced laryngeal obstruction (EILO) remain to be fully established. It is hypothesized that high inspiratory flow rates can exert a force on laryngeal airway walls that contribute to its inward collapse causing obstruction. Computational fluid dynamics (CFD) presents an opportunity to explore the distribution of forces in a patient-specific upper airway geometry. The current study combined exercise physiological data and CFD simulation to explore differences in airflow and force distribution between a patient with EILO and a healthy matched control. Participants underwent incremental exercise testing with continuous recording of respiratory airflow and laryngoscopic video, followed by an MRI scan. The respiratory and MRI data were used to generate a subject-specific CFD model of upper respiratory airflow. In patient with EILO, the posterior supraglottis experiences an inwardly directed net force, whose magnitude increases nonlinearly with larger flow rates, with slight changes in the direction toward the center of the airway. The control demonstrated an outwardly directed force at all regions of the wall, with a magnitude that increases linearly with larger flow rates. A comparison is made between the CFD results and endoscopic visualization of supraglottic collapse, and a very good agreement is found. The current study presents the first hybrid physiological and computational approach to investigate the pathophysiological mechanisms of EILO, with preliminary findings showing great potential, but should be used in larger sample sizes to confirm findings.NEW & NOTEWORTHY The current study is the first to use a hybrid combined computational fluid dynamics (CFD) and exercise physiology approach to investigate pathophysiology in exercise-induced laryngeal obstruction (EILO). The hybrid methodology is a promising approach to explore the pathophysiological mechanisms underlying the condition. Notable differences occur in the distribution of airflow and wall forces between the EILO and control participants, which align with symptoms and visual observations.

Predicting coronary artery occlusion risk from noninvasive images by combining CFD-FSI, cGAN and CNN

Wall Shear Stress (WSS) is one of the most important parameters used in cardiovascular fluid mechanics, and it provides a lot of information like the risk level caused by any vascular occlusion. Since WSS cannot be measured directly and other available relevant methods have issues like low resolution, uncertainty and high cost, this study proposes a novel method by combining computational fluid dynamics (CFD), fluid-structure interaction (FSI), conditional generative adversarial network (cGAN) and convolutional neural network (CNN) to predict coronary artery occlusion risk using only noninvasive images accurately and rapidly. First, a cGAN model called WSSGAN was developed to predict the WSS contours on the vessel wall by training and testing the model based on the calculated WSS contours using coupling CFD-FSI simulations. Then, an 11-layer CNN was used to classify the WSS contours into three grades of occlusions, i.e. low risk, medium risk and high risk. To verify the proposed method for predicting the coronary artery occlusion risk in a real case, the patient’s Magnetic Resonance Imaging (MRI) images were converted into a 3D geometry for use in the WASSGAN model. Then, the predicted WSS contours by the WSSGAN were entered into the CNN model to classify the occlusion grade.

Fluidization behavior of stirred gas–solid fluidized beds: A combined X-ray and CFD–DEM–IBM study

Stirred gas–solid fluidized bed reactors are commercially employed in polyolefin manufacturing, but the complex gas–solid contacting dynamics pose challenges in design, scale-up, and operation. In this study, the influence of agitation on the fluidization performance of Geldart B particles was investigated experimentally by X-ray imaging and pressure drop measurements and numerically by Computational Fluid Dynamics (CFD) - Discrete Element Method (DEM) - Immersed Boundary Method (IBM). The experimentally obtained minimum fluidization curve and time-averaged pressure drop show good qualitative agreement with the simulation results. Visual observations underscore that an increase in the angular velocity of the agitator results in reduced bubble size and improved bed homogeneity, as further evidenced by reduced pressure fluctuations. Furthermore, the simulations reveal that while the impeller enhances solids agitation, a proper design study is imperative, considering that static immersed bodies, such as the stirrer shaft, can adversely impact solids motion.

Proton exchange membrane-like alkaline water electrolysis using flow-engineered three-dimensional electrodes

For high rate water electrolysers, minimising Ohmic losses through efficient gas bubble evacuation away from the active electrode is as important as minimising activation losses by improving the electrode’s electrocatalytic properties. In this work, by a combined experimental and computational fluid dynamics (CFD) approach, we identify the topological parameters of flow-engineered 3-D electrodes that direct their performance towards enhanced bubble evacuation. In particular, we show that integrating Ni-based foam electrodes into a laterally-graded bi-layer zero-gap cell configuration allows for alkaline water electrolysis to become Proton Exchange Membrane (PEM)-like, even when keeping a state-of-the-art Zirfon diaphragm. Detailed CFD simulations, explicitly taking into account the entire 3-D electrode and cell topology, show that under a forced uniform upstream electrolyte flow, such a graded structure induces a high lateral velocity component in the direction normal to and away from the diaphragm. This work is therefore an invitation to start considering PEM-like cell designs for alkaline water electrolysis as well, in particular the use of square or rectangular electrodes in flow-through type electrochemical cells.

A Y-Type Air-Cooled Battery Thermal Management System with a Short Airflow Path for Temperature Uniformity

A Y-type air-cooled structure has been proposed to improve the heat dissipation efficiency and temperature uniformity of battery thermal management systems (BTMSs) by reducing the flow path of air. By combining computational fluid dynamics (CFD) methods, the influence of the depths of the distribution and convergence plenums on the airflow velocity through battery cells was analyzed to improve heat dissipation efficiency. Adjusting the width of the first and ninth cooling channels can change the air velocity of these two channels, thereby improving the temperature uniformity of the BTMS. Further discussion was conducted regarding the influences of inlet and outlet depths. When the inlet width and outlet width were 20 mm, the maximum temperature and maximum temperature difference of the Y-type BTMS were 39.84 °C and 0.066 °C at a discharge rate of 2.5 °C, respectively; these temperatures were 1.537 °C (3.68%) and 0.059 °C (47.2%) lower than those of the T-type model. Meanwhile, the energy consumption of the sample also decreased by 13.1%. The results indicate that the heat dissipation performance of the proposed Y-type BTMS was improved, achieving excellent temperature uniformity, and the energy consumption was also reduced.

CFD-DEM investigation of centrifugal slurry pump with polydisperse particle feeds

Polydisperse particle feed into centrifugal slurry complicates hydraulic performance and wall erosion but is poorly understood. This paper presents a numerical study of a centrifugal slurry pump, focusing on the effect of particle size distribution (PSD) using the combined computational fluid dynamics and discrete element method (CFD-DEM). It incorporates rigid impeller rotation and wall erosion. On this basis, the hydraulic and erosion model validity is verified by clear water performance and jet erosion test, respectively. Additionally, this CFD-DEM model is compared with the dense discrete phase model (DDPM), demonstrating more precise erosion prediction as its fully resolved particle-particle interactions. Generally, total pump erosion severity intensifies when the PSD is broadened. Compartment-wise, it indicates the necessity of tailoring flow structure to enlarge drag force, therefore, mitigate particle aggregation in the impeller. This in-house CFD-DEM model is promising for addressing particle-fluid flow problems in centrifugal pumps or coupling with data driven method.

Impact of Changing Inlet Modes in Ski Face Masks on Adolescent Skiing: A Finite Element Analysis Based on Head Models

Due to the material properties of current ski face masks for adolescents, moisture in exhaled air can become trapped within the material fibers and freeze, leading to potential issues such as breathing difficulties and increased risk of facial frostbite after prolonged skiing. This paper proposes a research approach combining computational fluid dynamics (CFD) and ergonomics to address these issues and enhance the comfort of adolescent skiers. We developed head and face mask models based on the head dimensions of 15–17-year-old males. For enclosed cavities, ensuring the smooth expulsion of exhaled air to prevent re-inhalation is the primary challenge. Through fluid simulation of airflow characteristics within the cavity, we evaluated three different inlet configurations. The results indicate that the location of the air inlets significantly affects the airflow characteristics within the cavity. The side inlet design (type II) showed an average face temperature of 35.35 °C, a 38.5% reduction in average CO2 concentration within the cavity, and a smaller vortex area compared to the other two inlet configurations. Although the difference in airflow velocity within the cavity among the three configurations was minimal, the average exit velocity differed by up to 0.11 m/s. Thus, we conclude that the side inlet configuration offers minimal obstruction to airflow circulation and better thermal insulation when used in the design of fully enclosed helmets. This enhances the safety and comfort of adolescent wearers during physical activities in cold environments. Through this study, we aim to further promote the development of skiing education, enhance the overall quality of adolescents’ skiing, and thus provide them with more opportunities for the future.