Hydrodynamic Research Articles

Multiscale analysis of interlocking effects for polymer additive manufacturing on aluminum foam

AbstractHybrid structures are increasingly relevant for lightweight design and functional components. Various manufacturing techniques for creating hybrid components exist, often focusing on combining metal and polymer components. The present study examines a novel approach for manufacturing hybrid functional structures by directly printing thermoplastic onto aluminum profiles using closed‐cell aluminum foam, with the mechanical interlocking resulting in a structural bond. The filling of pores in the aluminum foam with the polymer affects the bond strength of this hybrid compound. Within a simulation‐based process design the additive manufacturing process parameters are investigated using a computational fluid dynamics (CFD) model with Fluid‐Structure Interaction for the pore‐filling process and a finite element (FE) model to evaluate the resulting bond strength. The material throughput, the nozzle distance, and the polymer temperature are the key process parameters taken into account. A parameterized, virtual foam model is used within this framework, where a linear movement of the extruder nozzle and the resulting polymer strand is investigated. This method includes the mesh conversion from the CFD results to the FE mesh for structural analysis by exporting the iso‐surface for the polymer geometry embedded in the pores of the aluminum foam structure. This approach made it possible to investigate the phenomena that occur in the process in more detail, particularly to quantify the influence of air inclusions. Thus, a decisive contribution to the hybridization of aluminum foams has been made, which opens up further potential for research and application in the long term.

A Synergistic Overview between Microfluidics and Numerical Research for Vascular Flow and Pathological Investigations

Vascular diseases are widespread, and sometimes such life-threatening medical disorders cause abnormal blood flow, blood particle damage, changes to flow dynamics, restricted blood flow, and other adverse effects. The study of vascular flow is crucial in clinical practice because it can shed light on the causes of stenosis, aneurysm, blood cancer, and many other such diseases, and guide the development of novel treatments and interventions. Microfluidics and computational fluid dynamics (CFDs) are two of the most promising new tools for investigating these phenomena. When compared to conventional experimental methods, microfluidics offers many benefits, including lower costs, smaller sample quantities, and increased control over fluid flow and parameters. In this paper, we address the strengths and weaknesses of computational and experimental approaches utilizing microfluidic devices to investigate the rheological properties of blood, the forces of action causing diseases related to cardiology, provide an overview of the models and methodologies of experiments, and the fabrication of devices utilized in these types of research, and portray the results achieved and their applications. We also discuss how these results can inform clinical practice and where future research should go. Overall, it provides insights into why a combination of both CFDs, and experimental methods can give even more detailed information on disease mechanisms recreated on a microfluidic platform, replicating the original biological system and aiding in developing the device or chip itself.

Determination of Turbulent Prandtl Number for Thermal Fluid Dynamics Simulation of HVAC Unit by Data Assimilation

Abstract Regarding high-precision temperature field prediction of a heating ventilation and air conditioning (HVAC) unit using thermal fluid dynamics simulation, the determination of the turbulent Prandtl number (Prt) was a key issue. In this study, we present an attempt to improve the accuracy of thermal fluid dynamics simulations in HVAC units by adjusting the Prt using data assimilation techniques. First, we simultaneously measured the velocity and temperature in the hot and cold air mixing zone of a simple HVAC model using particle image velocimetry and thermocouples. Second, we coupled data assimilation to the thermal fluid simulation model to determine the Prt in the mixing field. Finally, we proposed two functions of the Prt for high velocity and low velocity regions using multidimensional analysis. In the future, we believe that the Prt functions can be applied to thermal fluid simulations of actual HVAC unit to accurately predict performance without conducting prototype experiments, thereby contributing to reducing development cost and time of HVAC unit.

A solar air receiver with porous ceramic structures for process heat at above 1000 °C – Heat transfer analysis

Abstract Concentrated solar energy can be used as the source of heat at above 1000 °C for driving key energy-intensive industrial processes, such as cement manufacturing and metallurgical extraction, contributing to their decarbonization. The cornerstone technology is the solar receiver mounted on top of the solar tower, which absorbs the incident high-flux radiation and heats a heat transfer fluid. The proposed high-temperature solar receiver concept consists of a cavity containing a reticulated porous ceramic (RPC) structure for volumetric absorption of concentrated solar radiation entering through an open (windowless) aperture, which also serves for the access of ambient air used as the heat transfer fluid flowing across the RPC structure. A heat transfer analysis of the solar receiver is performed by means of two coupled models: a Monte Carlo (MC) ray-tracing model to solve the 3D radiative exchange and a computational fluid dynamics (CFD) model to solve the 2D convective and conductive heat transfer. Temperature distributions computed by the iteratively coupled models were compared with experimental data obtained by testing a lab-scale 5 kW receiver prototype with a silicon carbide RPC structure exposed to 3230 suns flux irradiation. The receiver model is applied to optimize its dimensions for maximum efficiency and to scale up for a 5 MW solar tower.

Convergence Acceleration of Favre-Averaged Non-Linear Harmonic Method

Abstract This paper develops a numerical procedure to accelerate the convergence of the Favre-averaged non-linear harmonic (FNLH) method. The scheme provides a unified mathematical framework for solving the sparse linear systems formed by the mean flow and the time-linearized harmonic flows of FNLH in an explicit or implicit fashion. The approach explores the similarity of the sparse linear systems of FNLH and leads to a memory-efficient procedure, so that its memory consumption does not depend on the number of harmonics to compute. The proposed method has been implemented in the industrial computational fluid dynamics solver Hydra. Three test cases are used to conduct a comparative study of explicit and implicit schemes in terms of convergence, computational efficiency, and memory consumption. Comparisons show that the implicit scheme yields better convergence than the explicit scheme and is also roughly 7–10 times more computationally efficient than the explicit scheme with four levels of multigrid. Furthermore, the implicit scheme consumes only approximately 50% of the memory required by the explicit scheme with four levels of multigrid. Compared with the full-annulus unsteady Reynolds-averaged Navier–Stokes simulations, the implicit scheme produces comparable results to URANS with computational time and memory consumption that are two orders of magnitude smaller.

A Mathematical Model of Interstitial Fluid Flow and Retinal Tissue Deformation in Macular Edema.

This study aims to develop a mathematical model to elucidate fluid circulation in the retina, focusing on the movement of interstitial fluid (comprising water and albumin) to understand the mechanisms underlying exudative macular edema (EME). The model integrates physiological factors such as retinal pigment epithelium (RPE) pumping, osmotic pressure gradients, and tissue deformation. It accounts for spatial variability in hydraulic conductivity (HC) across the retina and incorporates the structural role of Müller cells (MCs) in maintaining retinal stability. The model predicts that tissue deformation is maximal at the center of the fovea despite fluid exudation from blood capillaries occurring elsewhere, aligning with clinical observations. Additionally, the model suggests that spatial variability in HC across the thickness of the retina plays a protective role against fluid accumulation in the fovea. Despite inherent simplifications and uncertainties in parameter values, this study represents a step toward understanding the pathophysiology of EME. The findings provide insights into the mechanisms underlying fluid dynamics in the retina and fluid accumulation in the foveal region, showing that the specific conformation of Müller cells is likely to play a key role.

A Statistical Analysis of Fluid Interface Fluctuations: Exploring the Role of Viscosity Ratio

Understanding multiphase flow through porous media is integral to geologic carbon storage or hydrogen storage. The current modelling framework assumes each fluid present in the subsurface flows in its own continuously connected pathway. The restriction in flow caused by the presence of another fluid is modelled using relative permeability functions. However, dynamic fluid interfaces have been observed in experimental data, and these are not accounted for in relative permeability functions. In this work, we explore the occurrence of fluid fluctuations in the context of sizes, locations, and frequencies by altering the viscosity ratio for two-phase flow. We see that the fluctuations alter the connectivity of the fluid phases, which, in turn, influences the relative permeability of the fluid phases present.

Unveiling complex fault geometry and driving mechanisms: insights from a refined data processing and multiplet analysis of the 2010 Beni-Ilmane seismic sequence (NE Algeria)

Summary This paper offers a comprehensive re-analysis of the Beni-Ilmane 2010 seismic sequence, using a dataset that is 100 per cent larger than previous studies. This unprecedented sequence in Algeria features three mainshocks with magnitudes Mw 5.4, 5.1, and 5.1. Our approach involves high-precision relocation, which includes the development of a new 1D minimum velocity model, followed by a double-difference (DD) procedure and hierarchical clustering. We determined the focal mechanisms (FMs) for 128 key events and identified 21 multiplet groups using an average cross-correlation threshold of 0.8. Our analysis offers new insights into fault geometry and addresses ongoing debates, by proposing a seismotectonic model that reveals the activation of fourteen (14) fault segments during the sequence, in contrast to previous oversimplified models that suggested two or three faults. The computed stress field from the inversion of 128 FMs aligns with a tectonic loading force due to the convergence of the African and Eurasian plates. These findings highlight the complexity of the fault network in the study area and shed light on the role of strike-slip faults in shaping the thrust belt. We found a strong link between multiplet groups and fluid movement along the fault network. Analysis of the temporal history of these multiplet groups provides new insights into fluid dynamics timescales, with an estimated hydraulic diffusivity (D) of 0.36 m2/s suggesting a fluid pressure diffusion process. The observed expansion of the aftershock area with the logarithm of time and the existence of repeating earthquakes indicates, for the first time, an aseismic slip mechanism that adds an additional layer to the driven processes. In conclusion, our results suggest that the underlying mechanisms governing the BI-2010 seismic sequence involve a complex interplay of tectonic loading, coseismic stress transfer, fluid dynamics, and aseismic slip transients. We attempt to correlate our findings with various studies linking the structure, mechanics, and fluid flow properties of fault zones and fault systems. The activation of smaller fault segments potentially averted a larger quake, resulting in three moderate mainshocks and numerous aftershocks. This work not only enrich our understanding of seismic phenomena but also provides useful insights for seismic hazard assessment and risk mitigation strategies.

Study of the Effect of Initial Plate Temperature in Jet Impingement Cooling Process

The microstructure characteristics and the properties of rolled steels are significantly affected by the heat transfer and boiling phenomena occurring during the jet impingement cooling on run‐out tables (ROT). In this study, experiments are conducted using a full industrial‐scale ROT facility with rectangular plates made of low‐carbon stainless steel (type 316L). The plate is heated up to a temperature ranging from to , then rapidly impinged using a single circular water jet, and the temperature drop is captured using an infrared thermal camera (FLIR A615 25°–50 Hz type). The dissipated heat flux, estimated experimentally using a 2D inverse heat conduction analysis, ranges from 6.1 to 3.4 MW m−2 across different zones along the plate surface. The impact of different initial plate temperature on the boiling behavior is studied by developing a 2D‐computational fluid dynamics (CFD) model, and the results are closely aligned with the experimental findings. The results reveal that when estimating the heat flux from CFD simulations, the best accuracy is obtained when considering fluid temperature at a point close to the plate surface (about 1 μm above the surface). Furthermore, the maximum extracted heat flux (MHF) is significantly influenced by the initial temperature of the plate. Increasing the initial plate temperature from 500 to 900 °C led to an increase of 82% in the MHF in stagnation zone, and 137% increase in the parallel‐flow region. The CFD model presented in this study and the full calculation of the boiling curves numerically will pave the road for investigating various practical parameters in jet impingement cooling.

Numerical Investigation of Wake Characteristics for Scaled 20 kW Wind Turbine Models with Various Size Factors

Wind energy is essential for sustainable energy development, providing a clean and reliable energy source through the wind turbine. However, the vortices and turbulence generated as wind passes through turbines reduce wind speed and increase turbulence, leading to significant power losses for downstream turbines in wind farms. This study investigates wake characteristics in wind turbines by examining the effects of different scale ratios on wake dynamics, using both experimental and numerical approaches, utilizing scaled-down models of the Aeolos H-20 kW turbine at scales of 1:33, 1:50, and 1:67. The experimental component involved wind tunnel tests in an open-circuit tunnel with adjustable wind speeds and controlled turbulence intensity. Additionally, Computational Fluid Dynamics (CFD) simulations were conducted using STAR-CCM+ (Version 15.06.02) to numerically analyze the wake characteristics. Prior to the simulation, a convergence test was performed by varying grid density and y+ values to establish optimized simulation settings essential for accurately capturing wake dynamics. The results were validated against experimental data, reinforcing the reliability of the simulations. Despite minor inconsistencies in areas affected by tower and nacelle interference, the overall results strongly support the methodology’s effectiveness. The discrepancies between the experimental results and CFD simulations underscore the limitations of the rigid body assumption, which does not fully account for the deformation observed in the experiment.

Computational fluid dynamics simulation of ammonia leakage scenarios during ship-to-ship bunkering

Ammonia is emerging as a potential marine fuel, yet its toxicity and risk of accidental leaks pose significant safety challenges compared to other alternatives. This study focuses on the critical ship-to-ship bunkering process using Computational Fluid Dynamics (CFD) simulations to analyse diverse ammonia leakage scenarios considering various leak orientations relative to the wind and the ships. The numerical setup was initially validated against controlled experiments, demonstrating satisfactory agreement. Results indicate that atmospheric ammonia dispersion is significantly influenced by obstacles like the superstructure and hull in the near field, with diminishing effect in the far field. Conventional Gaussian plume models are inadequate within the first few hundred metres from the release source due to the complex near-field flow patterns and increased turbulence generated by the wakes of the hull and superstructure. These findings underscore the necessity for improved ship-scale dispersion models to accurately evaluate the impact of obstacles on ammonia dispersion. The consequence analysis reveals that upward leaks create larger risk zones with potential for irreversible health effects, while horizontal leaks may expose personnel to life-threatening concentrations. Vertical leaks in crosswind configurations generate lethal zones covering tens of metres on deck. These results highlight the importance of considering the exact location of the transfer hose relative to the ship and all potential wind orientations in risk assessments. Given the extensive range of potential dispersion patterns and the inadequacy of conventional Gaussian dispersion models in this context, we strongly recommend vessel-specific CFD simulations before certifying ammonia bunkering operations as safe. This approach is crucial for accurate risk assessment at the ship scale, where complex geometries and multi-directional flows invalidate the assumptions of traditional point-source, uni-directional dispersion models.

A Mixed Convection Model for Estimating the Critical Velocity to Prevent Smoke Backlayering in Tunnels

AbstractA novel mathematical model for the critical ventilation velocity to prevent smoke backlayering in tunnels is presented, addressing limitations of prior approaches. The basis of the model is a rigorous characterisation of the physical processes by the characteristic quantities. Empirical parameters within the new model are determined, to align with results from both full-size and small-scale tunnel experiments. Data from numerical simulations (CFD, Computational Fluid Dynamics), validated by known test data, are then used to estimate the effects of tunnel slope and other parameters on the critical velocity. The model is seen to approximate the critical velocity well, following all trends identified by test data and CFD parameter studies. The empirically calibrated equation permits prediction of the critical velocity beyond the narrow range of tunnel geometries where known results already give an answer. The resulting equation has practical application for tunnel design.

The aqueous humour dynamics in primary angle closure disease: a computational study

PurposeTo create a computational fluid dynamics (CFD) model of ocular anterior segment for primary angle closure diseases (PACD) and assess the aqueous humour (AH) dynamics in different angle closure ranges...

Research on Carbon Dioxide Computational Fluid Dynamics Simulation of Urban Green Spaces under Different Vegetation Spatial Layout Morphologies

Urban green spaces (UGSs) are considered an important natural approach for improving urban climatic conditions, promoting sustainable urban development, and advancing the global “Carbon Peak and Carbon Neutrality” targets. Previous studies have found that different vegetation spatial morphologies significantly impact the capacity to obstruct and absorb CO2, but it is not yet well understood which morphology can retain and absorb more CO2. This study takes Nantong Central Park as an example and conducts a CFD (Computational Fluid Dynamics) carbon flow simulation for CO2 under different vegetation spatial morphologies to identify their CO2 retention and absorption effects. First, the carbon sink benefits of elements such as “vegetation, soil, and wetlands” within the park were calculated, and the elements with the highest carbon sink benefits were identified. Then, the park was divided into carbon welcoming zones, carbon flow zones, and carbon shadow zones for carbon flow simulation with the highest carbon sink benefits. The results show that in the carbon welcome area, the one-block long fan-shaped plant community with a spatial density of 40 m thickness can best meet the requirements of absorption and induction of a small amount of carbon dioxide, with the smallest air vortex and uniform distribution of carbon dioxide in the surrounding area. In the carbon flow area, combined with the visual effect, the planting pattern of 6 m spacing herringbone combined with natural structure was adopted, which has a good carbon dioxide blocking and absorption capacity. In the carbon-shaded area, a herringbone planting pattern with a total width of 40 m and a base angle of 60° was chosen, which had the strongest hindrance and absorption capacity. Urban park environment optimization can use Fluent simulation to analyze the flow of carbon dioxide between different elements affected by wind dynamics at the same time. Based on the results, the form, layout, and spatial distance are adjusted and optimized. This study can better guide the spatial layout of vegetation and contribute to the realization of the goal of “carbon peak and carbon neutrality”.

Fluid Dynamics of Interacting Rotor Wake with a Water Surface

Rotor-type cross-media vehicles always induce considerably complex mixed air–water flows when approaching the water surface, resulting in relative thrust loss and structural damage on rotor. The interactions between a water surface and rotor wake bring potential risks to the cross-media process, which is known as the near-water effect of the rotor. In this paper, experimental investigations are used to explore the fluid dynamics of the near-water effect of the rotor. Qualitative droplet observation was carried out on the 0.25 m and 0.56 m diameter commercial rotor blades and the 0.07 m diameter ducted fan near the water surface first to gain a qualitative understanding of droplet characteristics. The results show that the rotor wake caused water surface deformation, droplet tearing off, splashing, and entrainment into the rotor disk. The depression formed by the rotor downwash flow impacting the water surface is named as three modes: dimpling, splashing, and penetrating, and the correlation between the depression modes and the aerodynamic characteristics of the rotor is primary analyzed. The flow mechanisms of dimpling mode were studied using the particle image velocimetry (PIV) technique. The results showed that the cavity and liquid crown obviously alter the flow direction of water surface jets, but not all rotors near water enter the vortex ring state. Two splashing mechanisms were revealed, including the direct ejection of droplets at the rim of depression and the tearing of liquid crown by the water surface jets. The blade tip vortex in the surface jet is a potential cause of entrainment into the rotor disk and secondary breakup of the droplet.

Reducing spatial discretization error on coarse CFD simulations using an openFOAM-embedded deep learning framework

AbstractWe propose a method for reducing the spatial discretization error of coarse computational fluid dynamics (CFD) problems by enhancing the quality of low-resolution simulations using deep learning. We feed the model with fine-grid data after projecting it to the coarse-grid discretization. We substitute the default differencing scheme for the convection term by a feed-forward neural network that interpolates velocities from cell centers to face values to produce velocities that approximate the down-sampled fine-grid data well. The deep learning framework incorporates the open-source CFD code OpenFOAM, resulting in an end-to-end differentiable model. We automatically differentiate the CFD physics using a discrete adjoint code version. We present a fast communication method between TensorFlow (Python) and OpenFOAM (c++) that accelerates the training process. We applied the model to the flow past a square cylinder problem, reducing the error from 120% to 25% in the velocity for simulations inside the training distribution compared to the traditional solver using an x8 coarser mesh. For simulations outside the training distribution, the error reduction in the velocities was about 50%. The training is affordable in terms of time and data samples since the architecture exploits the local features of the physics.

Numerical Simulation of the Unsteady Airwake of the Liaoning Carrier Based on the DDES Model Coupled with Overset Grid

The wake behind an aircraft carrier under heavy wind condition is a key concern in ship design. The Chinese Liaoning ship’s upturned bow and the island on the deck could cause serious flow separation in the landing and take-off area. The flow separation induces strong velocity gradients and intense pulsations in the flow field. In addition, the sway of the aircraft carrier caused by waves could also intensify the flow separation. The complex flow field poses a significant risk to the shipboard aircraft take-off and landing operation. Therefore, accurately predicting the wake of an aircraft carrier during wave action motion is of great interest for design optimization and recovery aircraft control. In this research, the aerodynamic around an aircraft carrier (i.e., Liaoning) was analyzed using the computational fluid dynamics technique. The validity of two turbulence models was verified through comparison with the existing data from the literature. The upturned bow take-off deck and the right-hand island were the main areas where flow separation occurred. Delayed detached eddy simulation (DDES), which combines the advantages of LES and RANS, was adopted to capture the full-scale spatial and temporal flow information. The DDES was also coupled with the overset grid to calculate the flow field characteristics under the effect of hull sway. The downwash area at 15° starboard wind became shorter when the hull was stationary, while the upwash area and turbulence intensity increased. The respective characteristics of the wake flow field in the stationary and swaying state of the ship were investigated, and the flow separation showed a clear periodic when the ship was swaying. Comprehensive analysis of the time-dependent flow characteristic of the approach line for fixed-wing naval aircraft is also presented.

CFD Investigation on Combined Ventilation System for Multilayer-Caged-Laying Hen Houses.

Mechanical ventilation is an important means of environmental control in multitier laying hen cages. The mainstream ventilation mode currently in use, negative-pressure ventilation (NPV), has the drawbacks of a large temperature difference before and after adjustment and uneven air velocity distribution. To solve these problems, this study designed and analyzed a combined positive and negative-pressure ventilation system for laying hen cages. According to the principle of the conservation of mass to increase the inlet flow in the negative-pressure ventilation system on the basis of the addition of the pressure-wind body-built positive-and-negative-pressure-combined ventilation (PNCV) system, further, computational fluid dynamics (CFD) simulation was performed to analyze the distribution of environmental parameters in the chicken cage zone (CZ) with inlet angles of positive-pressure fans set at 45°, 90°, and 30°. Simulation results showed that the PNCV system increased the average air velocity in the CZ from 0.94 m/s to 1.04 m/s, 1.28 m/s, and 0.99 m/s by actively blowing air into the cage. The maximum temperature difference in the CZ with the PNCV system was 2.91 °C, 1.80 °C, and 3.78 °C, which were all lower than 4.46 °C, the maximum temperature difference in the CZ with the NPV system. Moreover, the relative humidity remained below 80% for the PNCV system and between 80% and 85% for the NPV system. Compared with the NPV system, the PNCV system increased the vertical airflow movement, causing significant cooling and dehumidifying effects. Hence, the proposed system provides an effective new ventilation mode for achieving efficient and accurate environmental control in laying hen cages.

Inverse Problem of Permeability Field under Multi-Well Conditions Using TgCNN-Based Surrogate Model

Under the condition of multiple wells, the inverse problem of two-phase flow typically requires hundreds of forward runs of the simulator to achieve meaningful coverage, leading to a substantial computational workload in reservoir numerical simulations. To tackle this challenge, we propose an innovative approach leveraging a surrogate model named TgCNN (Theory-guided Convolutional Neural Network). This method integrates deep learning with computational fluid dynamics simulations to predict the behavior of two-phase flow. The model is not solely data-driven but also incorporates scientific theory. It comprises a coupled permeability module, a pressure module, and a water saturation module. The accuracy of the surrogate model was comprehensively tested from multiple perspectives in this study. Subsequently, efforts were made to address the permeability-field inverse problem under multi-well conditions by combining the surrogate model with the Ensemble Random Maximum Likelihood (EnRML) algorithm. The research findings indicate that modifying the network structure allows for improved integration of the outputs, resulting in prediction accuracy and computational efficiency. The TgCNN surrogate model demonstrated outstanding predictive performance and computational efficiency in two-phase flow. By combining the surrogate model with the EnRML algorithm, the inversion results closely aligned with those from the commercial simulation software, significantly improving the computational efficiency.

Energy and fluid dynamics of PEM fuel cells in automotive applications: integration strategies for enhanced performance

Fuel cells using proton exchange membranes, or PEMs, are leading the way in the adoption of sustainable vehicle technology. This study investigates the synergistic integration of PEM fuel cells with advanced battery systems in fuel cell vehicles (FCVs) to enhance energy management and efficiency. By implementing a hybrid energy management strategy that combines the consistent power output of PEM fuel cells with the dynamic response capabilities of lithium-ion batteries, the system achieves a 15% increase in fuel efficiency and a 20% reduction in hydrogen consumption compared to conventional configurations. Simulations conducted over various driving cycles, including urban, suburban, and highway scenarios, reveal that this hybrid setup not only improves operational flexibility but also reduces the dependency on the PEM fuel cell during low-demand periods by up to 30%. These results imply that the suggested integration approach greatly improves the sustainability and overall performance of FCVs, establishing them as a workable option for lowering emissions associated with transportation.