Fluids

The aerodynamic characterization of propeller-driven UAVs is often constrained by the unfeasibility of testing the complete airframe–propeller assembly in a wind tunnel, since geometric scaling prevents simultaneous similarity of both the airframe and the propeller. To address this limitation, this work presents an integrated experimental–numerical methodology that reconstructs the full-scale free-air aerodynamic behaviour of a tractor-propeller UAV combining wind-tunnel measurements of the scaled airframe (without the propeller) and the full-scale propeller. Computational fluid dynamics (CFD) is not used to predict the full-scale UAV directly; it is used to predict differences between matched configurations, while the absolute aerodynamic level remains anchored to experiments. Dedicated CFD simulations are carried out to isolate three distinct physical contributions: scale effects, wind-tunnel blockage, and propeller installation effects. In the developed methodology, numerical simulations complement the experimental data to obtain corrected full-scale aerodynamic coefficients and propulsive maps together with a longitudinal force-equilibrium model used to determine the longitudinal force-equilibrium operating point. The reconstruction shows that scale and wind-tunnel blockage effects primarily alter the airframe aerodynamic characteristics, with a minor influence on equilibrium incidence, while propeller installation produces a substantial thrust augmentation due to airframe-induced inflow modification. Accounting for these effects leads to an overprediction of the propeller rotational speed by approximately 23% when installation effects are neglected, demonstrating that the installed performance cannot be obtained by a linear superposition of isolated airframe and isolated propeller data.

Lack of available data, ease of clinical use and lack of evidence for prognostic benefit are arguably the key limitations to clinical uptake for any model. This paper reports the development of an image-based analysis protocol, coupling 0D left heart and systemic circulation components with a 3D aortic valve model to measure and to predict the pressure gradient across the aortic valve at rest. The model is personalized using routine clinical data available for aortic valve patients, augmented by additional image data (transesophageal echo and/or CT) to support valve characterization. Computed aortic pressure gradient both pre- and post-intervention was compared with clinical measurements based on Doppler ultrasound for a cohort of 21 patients with aortic valve disease. Correlation for the diseased state measures were adequate (R2= 0.81) for those cases for which associated image data was deemed to be of acceptable quality for segmentation to support a 3D computational fluid dynamics analysis but poor otherwise. Post treatment correlation was reasonable (R2= 0.47) for all cases. Importantly, the personalized model presented here describes the interaction between the patient’s cardiovascular system, including the heart and circulation, and the valve, rather than evaluating the valve in isolation.

A Bargmann spacetime is a constrained five-dimensional setting that, while introducing no new physical degrees of freedom beyond those of ordinary four-dimensional spacetime, permits Galilei physics to be expressed with a tensor formalism that respects the distinction between mass and energy while affording the conceptual and technical advantages of a spacetime metric. This framework offers a route to a strong-field ‘Galilei general relativity’ approximating the usual Poincaré general relativity introduced by Einstein. In preparation for modeling core-collapse supernovae, where such an approximation would be useful, this work generalizes the kinetic-energy–momentum–mass 5-flux T and its associated spacetime tensor law from a simple fluid of constant particle mass to a baryon fluid whose multiple nuclear species can interconvert rest mass and internal energy. The spacetime tensor law on Bargmann–Galilei spacetime BG and its decompositions relative to comoving (‘Lagrangian’) and fiducial (‘Eulerian’) observers are derived in detail. The formalism is rendered more suitable for core-collapse supernova modeling by an extension from strict BG to a regime that might be denoted as BG+: microscopically Poincaré yet macroscopically Galilei. This extension accommodates energy generation by nuclear composition changes and allows comoving energy density and pressure to contribute relative to mass density, while preserving the simplifications of Galilei bulk fluid flow and the streamlined geometry governed by the Bargmann–Galilei spacetime metric.

Bubble columns are widely used in gas–liquid processes, yet predicting bubble hydrodynamics remains challenging because of the coupled effects of operating conditions and wall confinement. This study experimentally investigates the influence of confinement, gas flow rate, axial position, and liquid flow configuration on bubble size, rise velocity, and shape in rectangular bubble columns using high-speed visualization and shadowgraphy measurements. Experiments are performed at three confinement ratios, λ=3.7, λ=11, and λ=18.5. The results show that confinement strongly modifies bubble formation, growth, velocity, and shape. Under strong confinement, larger bubbles, higher aspect ratios, and significant axial increases in bubble size are observed, indicating continued bubble enlargement along the column height. Bubble rise velocity and dimensionless velocity are also strongly affected by confinement, whereas liquid flow configuration mainly influences bubble velocity under weak confinement. Furthermore, visual observations reveal the onset of transient heterogeneous flow structures under strong confinement despite conventional flow-regime maps predicting homogeneous flow. Existing aspect-ratio correlations reproduce the general trend but do not fully account for confinement effects. These findings demonstrate that confinement is a governing parameter in rectangular bubble columns and should be explicitly considered in future hydrodynamic and mass transfer models for confined gas–liquid systems.

A computational fluid dynamics and conjugate heat transfer (CFD+CHT) methodology is developed for the thermal simulation of a commercial prismatic LiFePO4 cell under charging and discharging operating conditions. The approach couples a three-dimensional representation of the battery, including a simplified description of its internal layered structure, with an electrochemical–thermal heat-generation model implemented as a temperature- and time-dependent volumetric source term. The heat source is applied within the active layers of the cell and updated during the transient simulation according to the local thermal state and to the evolution of the state of charge. The methodology is applied to 1C and 2C cycles under natural convection and forced-air cooling at free-stream velocities of 10ms−1 and 20ms−1. A dedicated wind-tunnel campaign is carried out on the same cell, instrumented with type-K thermocouples distributed over its external surfaces, to provide experimental data for model validation. The results show that the proposed framework accurately reproduces the main wall-temperature trends observed experimentally. Under natural convection, the temperature distribution remains nearly uniform, whereas forced convection produces more pronounced vertical and in-plane gradients. For the charge cycles, the comparison between CFD predictions and end-of-cycle measurements yields a mean absolute error (MAE) of 0.66∘C and a root-mean-square error (RMSE) of 0.82∘C over 168 measurement locations. The discharge cycles yield a comparable level of agreement (MAE 0.65∘C, RMSE 0.81∘C over 168 probe points), confirming the predictive capability of the methodology for both operating modes.

This study presents a systematic mapping of methanol spray autoignition, lift-off, and flame development across an engine-relevant range of ambient temperatures (1000–1200 K), injection pressures (70–130 MPa), and O2 concentrations (21–15 vol.%), using a single fixed injector and optical configuration. In addition, the study reports a dual-fuel strategy to address the low-temperature instability challenges highlighted by the mapping. Within this dataset, ignition delay increases with a lower ambient temperature, reduced injection pressure, or a lower O2 concentration, while the lift-off length increases with a lower temperature and higher injection pressure. Schlieren imaging consistently captures ignition in the mid-axial region of the jet, softening of spray-head gradients before high-temperature ignition, and occasional upstream ignition sites during the diffusion-controlled phase that affect the flame base position. At the lowest tested temperature of 1000 K, methanol autoignites over a wide ignition-delay range (±1.33 ms), with combustion occurring outside the chamber’s field of view. The corresponding heat-release profile cannot be interpreted conclusively under the current test configuration. Introducing a pilot jet at this condition enables methanol to ignite shortly after the start of injection over a much narrower range (∼±0.10 ms). The resulting combustion event remains within the field of view and occurs much closer to the nozzle compared with its autoignition counterpart.

Undular bores are classical shallow-water phenomena in which a sharp transition between two flow states is replaced, in a dispersive theory, by an oscillatory wave train. In non-dispersive shallow-water theory, the bore is associated with an apparent loss of mechanical energy. In dispersive models, this energy can be interpreted as being redistributed into the oscillatory tail. The aim of this short article is to formulate a possible extension of this interpretation when surface tension is included. The capillary contribution modifies the long-wave dispersion coefficient through a Bond-number-dependent term and adds an additional surface energy to the total energy functional. We derive the basic capillary-gravity KdV scaling, identify the modified energy density, and discuss how surface tension may affect the amplitude, wavelength, and energy flux of the trailing oscillations. The proposed direction is relevant for small-scale laboratory bores, tidal-bore fronts, and shallow tidal currents in which a rapid transition generates short dispersive oscillations. Special attention is paid to the critical value Bo=1/3, where the classical KdV dispersion vanishes, and a fifth-order correction is required.

Laboratory experiments and numerical modeling are essential tools for understanding the performance of engineered tsunami mitigation measures, enabling controlled investigation of complex hydrodynamic processes that are difficult to capture in real events. This review critically evaluates current research on key structural countermeasures, seawalls, breakwaters, and water-filled canals, focusing on findings from physical modeling and computational simulations. Evidence from numerical and laboratory studies demonstrates that properly designed mitigation structures can reduce tsunami wave energy, delay inland inundation, and decrease forces on downstream infrastructure. The effectiveness of these measures is strongly influenced by structural geometry, placement, and maintenance, as well as by accurate representation of flow dynamics in experiments and simulations. Despite significant advances, important gaps remain, including the validation of numerical models against high-fidelity experiments, the assessment of extreme events, and the evaluation of hybrid or integrated strategies combining multiple mitigation measures. This review identifies these gaps and highlights research priorities aimed at improving predictive capabilities, optimizing structural designs, and supporting the development of reliable, scalable, and context-specific tsunami mitigation solutions.

The article presents the results of systematic numerical studies on the efficacy of low-concentration nanoemulsions for enhanced oil recovery. A series of computational investigations was conducted to examine the displacement regimes of oil from digital core models with varying permeability using the developed low-concentration diesel fuel-based nanoemulsions. The volume fraction of diesel fuel in the emulsions was 1 vol.%. The volume fraction of the emulsifier ranged from 0.05% to 0.4%. The nanoemulsions demonstrated high efficiency across the entire range of permeabilities considered. It was shown that the behavior of the displacement front for water and for emulsions differs fundamentally. The waterflood front for emulsions is significantly more uniform and exhibits more complete cross-sectional saturation of pore channels compared to water flooding. With an increase in the capillary number, the oil displacement coefficient achieved by nanoemulsions increases. However, the maximum incremental effect from the use of nanoemulsions is observed at the minimum values of the capillary number. This finding indicates that the primary mechanisms underlying the positive impact of emulsions on oil displacement are the reduction in interfacial tension and the improvement of wettability.

Agitator reactors are widely used in chemical production processes, and their structural design has a significant impact on power consumption. Therefore, this study performs numerical simulations of multi-stage cup-shaped paddle agitators with different geometric parameters, and discusses in detail the effects of the paddle spacing (S/H), the ratio of the upper paddle length to the reactor radius (Ls/R), the ratio of the lower paddle length to the reactor radius (Lx/R), and the ratio of the upper to lower paddle lengths (Ls/Lx) on the reactor’s power characteristics and internal flow field. Equations were derived to relate the power number (Np) to parameters such as Re, Ls/R, and Lx/R. The study found that, at the same Reynolds number, torque exhibits a slight upward trend as the paddle spacing increases; the best mixing effect is achieved when S/H is 0.333. Based on this pitch, when Ls/R, and Lx/R exceed 0.67, the mixing process fails to form a stable and complete radial circulation; when Ls/R and Lx/R are less than 0.53, the high-velocity zone in the flow field decreases, leading to the formation of dead zones. Therefore, selecting a multi-stage cup-shaped impeller with an Ls/R value of 0.53, an Lx/R value of 0.53, and Ls/Lx of 1 can achieve better mixing results with lower power consumption. These findings provide a reference for the energy-efficient optimization design of multi-stage cup-shaped impeller mixers in industrial applications.

In the context of aircraft engine technologies, sprays are used to inject water into the engine cycle to enhance efficiency and reduce emissions. Accurate specification of droplet injection boundary conditions is therefore essential for reliable numerical predictions. This study presents a numerical validation of a water spray configuration previously characterized using phase Doppler anemometry. An Euler/Lagrange approach is applied to simulate the spray using two distinct injection strategies: an array of injector points (Case 1) and a solid-cone injector (Case 2). Numerical results are compared with experimental data to assess droplet size and velocity distributions. Both approaches capture the main spray characteristics, while Case 1 provides improved agreement due to a more accurate representation of the injection conditions. In addition, the influence of droplet-droplet collisions is investigated using different collision-regime maps. While the collision models lead to significantly different collision outcomes, only minor differences are observed in spray characteristics, with noticeable deviations occurring in the downstream region. Overall, the results demonstrate the importance of accurate injection modeling for reliable spray predictions, while simpler injection approaches remain viable with reduced accuracy. The influence of collision modeling is limited under the present conditions and for the investigated spray metrics, providing insight into its role and limitations in polydisperse sprays.

This study utilizes a large-scale numerical simulation model to investigate the hydrodynamic behavior and particle transport characteristics of gas-liquid-solid three-phase flow in vertical wellbores featuring multi-source confluence and curved geometries. Simulation results indicate that increasing flow velocity shifts the dominant control mechanism from surface tension to inertial forces, transitioning the flow pattern from slug flow to churn flow. In curved pipe sections, centrifugal phase separation and geometric shielding effects cause significant flow asymmetry and maintain large bubble stability at the inner wall. Additionally, the multi-inlet structure induces shear rate gradients that result in the spatial coexistence of two distinct bubble scales. Furthermore, localized gas concentrations exceeding 70% at the upper inlet can trigger severe gas-locking phenomena and intense pressure pulsations.

A reinforcement-learning-based wall-modeled large-eddy simulation (RL-WMLES) framework is proposed to improve the physical consistency of near-wall turbulence predictions. In this approach, a reinforcement learning agent is coupled with the WMLES solver to dynamically adjust a compensating stress term, with the objective of enforcing agreement between the LES solution and the law of the wall. The agent is trained using the proximal policy optimization (PPO) algorithm, where the state is defined as the discrepancy between the near-wall LES velocity and the wall-model prediction, and the action corresponds to modifying a parameterized support viscosity distribution. The proposed method is implemented within a high-performance CFD solver and trained on turbulent channel flow. Numerical results demonstrate that the trained agent effectively reduces the log-layer mismatch and significantly improves the accuracy of near-wall velocity predictions. Furthermore, the RL-WMLES framework exhibits a degree of generalization capability: the trained agent performs robustly with varying levels of numerical dissipation and Reynolds numbers. By introducing a simple interpolation strategy, the same agent can be successfully applied to configurations with different matching locations. Overall, the RL-WMLES framework provides a flexible and data-driven approach for enforcing physical constraints in turbulence modeling. The method shows strong potential for extension to more complex flows.

We present a material distribution topology optimization (TO) framework that directly generates capacity-specific radial trims for severe-service control valves. The method uses an out-of-plane resistance modified two-dimensional turbulence model and objective functions that maximize directional change to create tortuous pressure-staging geometries at predefined channel depths. Four trims targeting non-dimensional capacities (CV) of 0.672, 0.96 (two objectives), and 1.248 were optimized, MSLA-printed, and tested in a globe valve using IEC 60534 procedures. The measured capacities ranged from -13.7% to +4.8% of the targets for a fully 2D optimization process, dropping to a maximum of 7.8% when coupled with a hybrid 3D tuning step. Acoustic detection indicated incipient cavitation at a pressure drop ratios greater than 0.87 for the most highly staged design and 0.73 for the highest capacity design, which is consistent with our simulations of the flow field before fabrication. These results demonstrate that TO can deliver fit-to-service, capacity-tuned trims with excellent cavitation suppression, reducing reliance on large parametric design libraries.

This article reviews the linear solvers available in OpenFOAM and assesses their impact on the convergence behaviour of the SIMPLE algorithm. The discretisation of transport equations in CFD results in large and sparse linear systems, for which the choice of linear solver strongly influences the computational time. Although the solver does not change the final discrete solution, the difference in speed and robustness between the solvers can be more than one order of magnitude. A brief overview is given concerning how the velocity and pressure fields are decoupled in OpenFOAM, followed by a detailed review of the main linear solver families, including direct methods, basic iterative methods, multigrid methods and Krylov subspace methods, with attention to their practical strengths and weaknesses. The performance of the most advanced solvers is evaluated on a full-scale non-reacting kiln case consisting of 2.3 million cells. The pressure-corrector equation is identified as the main bottleneck in the SIMPLE algorithm. The conjugate gradient (CG) solver with a multigrid (MG) preconditioner is found to be the fastest and most stable method, achieving speed-ups of up to a factor of 7 compared to the slower advanced methods. Using MG as a preconditioner also improves the robustness of the Bi-CGStab method.

An optical lens focuses light and a similar device can be developed to focus surface water waves. A detailed description of such hydrodynamic lenses is given, for which the focusing is induced by shaping the bathymetry of the bottom. Classically, the Luneburg lens uses a specific radial variation of the refractive index. The modified Luneburg lens (MLL) introduces an extra degree of freedom, permitting the focal point to be tuned. It is shown how to design the MLL for water waves, and then its performance is evaluated. Compared with a simple parabolic-shaped mount, the MLL is shown to be free of spherical aberration, resulting in a focus with larger intensity and smaller size of the focal point. Moreover, the focusing properties can be tuned and enhanced thanks to the possibility of changing the position of the focal point. The focusing quality of the MLL is described in all water-depth regimes (covering dispersive and non-dispersive waves) and the focusing of linear and nonlinear waves is revealed experimentally. The option of moving the focal point outside the lens, where the water depth is constant, may be useful when locating devices for harvesting wave energy.

Spray impingement cooling is a well-established heat removal technique employed across a wide range of industrial processes. A particularly significant cooling regime arises when the temperature of the cooled surface surpasses the Leidenfrost temperature of the spray. Developing an accurate numerical framework for this regime holds considerable potential for optimising industrial applications such as cryogenic machining and spray quenching. This paper presents a Eulerian-Lagrangian Conjugate Heat Transfer (CHT) model tailored for spray impingement under Leidenfrost conditions. Two heat transfer sub-models are incorporated to characterise droplet-solid thermal interaction: the first, developed by Breitenbach, is grounded in a theoretical analysis of the droplet impingement process, while the second, proposed by Deb, relies on a semi-empirical correlation. Both models were validated against an experimental correlation obtained from a literature study on orthogonal water spray impingement, yielding mean relative errors of 3.54% for the Deb model and 5.2% for the Breitenbach model across a broad range of operating conditions and surface temperatures.

Additive manufacturing (AM) introduces surface roughness that is much larger than that in chemically etched printed circuit heat exchanger (PCHE) channels, limiting the applicability of established design correlation. In this study, four selective laser melting (SLM) 3D-printed stainless steel test sections were tested, namely two semicircular and two rounded-edge semicircular channels, at hydraulic diameters of 2 mm and 4 mm. Water was used as the test fluid in the experiment, with a Reynolds number ranging from 500 to 7000 and wall heat flux ranging from 20 to 90 kW/m2. Scanning electron microscopy image characterization shows significant material accumulation concentrated at the rounded edges of the as-built channels. The experimental results show that for the entire flow regime, the printed rounded edge increases the friction factor by approximately 9% for 2 mm and 4 mm channels. The filleting design would increase the effective hydraulic roughness in small-diameter AM channels. The SLM 3D-printed rougher channel has a lower transition Reynolds number and higher turbulent friction factors compared to the etching channel. The data were compared with existing smooth PCHE channel data and rough AM mini-channel correlation, and two empirical correlations were developed for SLM 3D-printed mini-channels for transition and turbulent regimes.

High-speed train aerodynamics have mainly been improved by passive design methods, such as streamlined noses, local fairings, and surface smoothing. These methods have achieved clear benefits, but several important aerodynamic problems remain difficult to solve by geometry optimization alone. Open-air drag is still affected by tail flow separation, base-pressure recovery, and disturbances around bogies and the underbody; crosswind safety is influenced by unsteady leeward-side separation and wake asymmetry; slipstream behavior depends on wake vortices, boundary-layer development, and complex near-ground underbody flow; and tunnel-related pressure transients arise from compression-wave generation, propagation, and reflection. These coupled effects mean that one fixed train shape cannot perform optimally in all operating conditions. For this reason, this review proposes that active flow control (AFC) should not be regarded only as a drag-reduction or stability-improvement technique for high-speed trains. Instead, it should be understood as a mission-adaptive aerodynamic control framework, in which different control actions are used for different operating scenarios. This paper first clarifies that passive optimization is increasingly subject to diminishing returns under multi-objective and engineering constraints. It then reviews AFC studies on drag reduction, base-pressure recovery, wake and slipstream control, underbody flow conditioning, crosswind mitigation, and tunnel pressure-wave suppression. Related AFC studies on bluff bodies, road vehicles, and other separated flows are included only when their physical relevance to trains is clear. The review further distinguishes gross aerodynamic improvement from net energy gain and identifies actuator power, durability, maintainability, acoustic impact, validation level, and full-scale transferability as decisive feasibility factors. Current research is still dominated by open-loop numerical studies with simplified actuation. Future work should therefore move toward multi-objective, closed-loop, energy-aware, sensor–actuator-integrated, and explainable machine-learning-assisted AFC. The main message is that the next step in train aerodynamics is not simply a better fixed shape, but a control-enabled train that can selectively redistribute aerodynamic authority across its mission profile.

Gas–liquid reactors (especially bubble columns (BCs)) are the most widely used in both the chemical and biochemical industries [...]