Stationary Flow Research Articles

Stationary regimes and parametrization of ekman friction in the Karman model of flow induced by external vortical body force

The detailed study of stationary regimes of Karman axisimmetric flow induced by external vortical body force is done. It is extracted two stationary regimes – with small (Batchelor regime) and with substantial (Stewartson regime) secondary circulation. The diagram of regimes existence is plotted in the space of flow parameters – Rossby and small Ekman numbers. For the flow decaying to the stationary flow in the Batchelor regime a theoretical model is proposed with which it was possible to derive a parametrization of linear friction coefficient, Ekman pumping velocity, stationary pressure from mean flow characterictics (vorticity and divergence). In the Stewartson regime a parameterization of the stationary flow is proposed and also numerically studied a decay rate. It is shown a good agreement between theoretical and numerical model results.

Novel dopamine-derived carbon modified fe-based metal–organic framework enhanced aptasensor rendering ultrasensitive electrochemical detection of oxytetracycline

In the present work, an aptasensor with dual binding sites based on Au NPs modified with self-polymerized polydopamine (PDA) and Fe-MOF is successfully constructed. In terms of standard electron transfer, the cyclic voltammetry (CV) analysis showcases a rate constant (ks) of 96.0 s−1, signifying an exceptional electron transfer rate of oxytetracycline at the sensor interface of Apt/Au@PDA@NH2-MIL-101(Fe)/GCE. Further deepening the investigation into electrochemical kinetics using chronoamperometry (CA), we investigated the electrocatalytic parameters (Kcat) of Au@PDA@NH2-MIL-101(Fe) in relation to oxytetracycline. The results divulged an impressive average Kcat value of 3.06 × 105 M−1 S-1. This evidence robustly suggests the superior electrocatalytic activity of Au@PDA@NH2-MIL-101(Fe) when it comes to oxytetracycline oxidation. A custom-designed portable rapid detection platform was developed, achieving detection limits of 6.88 nM under static water flow conditions and 5.56 nM under dynamic conditions. Remarkably, the self-polymerization of PDA on Au NPs not only refines their size but also boosts their interaction with biomolecules, thereby promising excellent biocompatibility. Moreover, the abundance of –COOH groups and unsaturated Fe3+ sites on the NH2-MIL-101(Fe) surface furnishes a considerable number of grafting sites for the aptamer. More importantly, the redox interplay between Au@PDA and NH2-MIL-101(Fe) accelerates electron movement, thus amplifying the electrochemical signal and the detection sensitivity for oxytetracycline.

Numerical simulation study of aluminum particle evaporation combustion process based on SPH method

A large variety of metal fuels is usually added to the modern solid rocket propellants to improve the propellant energy and motor specific impulse and to suppress high frequency unstable combustion. Among them, aluminum is the most common additive. The combustion process of aluminum can significantly affect the combustion characteristics of a solid rocket motor. In this paper, the SPH (Smoothed Particle Hydrodynamics) method is used to simulate the combustion process of aluminum particles. First, the SPH discrete equations with an evaporation combustion model are derived. On this basis, the evaporation combustion process of aluminum particles is numerically simulated. The results show that when aluminum particles are heated and evaporated in a static flow field, an alumina shell will be formed on the surface, and further thermal expansion will cause the alumina shell to break. The molten aluminum will spray out, and an aluminum cap will be formed on the surface. The “microburst process” will be similar to the gel droplet. In the convective environment, the flame structure of aluminum particles will be obviously peach shaped, which will wrap aluminum particles at the bottom. The simulation results are consistent with the experimental results.

The Role of Critical Thinking in Mitigating Social Network Addiction: A Study of TikTok and Instagram Users

This study addresses the growing concern of social network (SN) addiction, with a focus on TikTok and Instagram. Guided by the Uses and Gratifications Theory (UGT), we explored the motivations (escapism and social interaction), attitudes (critical thinking), and states (flow and sense of belonging) that influence SN use. Our objective was to investigate whether critical thinking acts as a protective factor against SN addiction. A sample of 332 university students completed questionnaires assessing motivations, attitudes, states, and SN addiction. Critical thinking was measured using the Critical Thinking Attitude Scale (CTAS), and critical thinking skills were assessed through the Critical Reasoning Assessment (CRA). Statistical analyses revealed significant associations between motivations, critical thinking, states, and SN addiction. Specifically, critical thinking (CTAS scores) demonstrated a negative correlation with SN addiction (r = −0.34, p < 0.01), indicating that higher critical thinking is associated with lower SN addiction. Regression analysis further indicated that escapism (β = 0.45, p < 0.01) and social interaction (β = 0.31, p < 0.05) positively predicted SN addiction, while critical thinking negatively predicted SN addiction (β = −0.28, p < 0.01). Additionally, states of flow and sense of belonging showed significant positive correlations with SN addiction (r = 0.42, p < 0.01 and r = 0.37, p < 0.01, respectively). These findings highlight the potential of critical thinking as a safeguard against SN addiction. This study offers valuable insights into the intricate dynamics of SN use, with implications for promoting healthier digital engagement. Understanding the factors influencing SN addiction and the roles of motivations, dispositions, and states can inform interventions aimed at fostering responsible and mindful online behaviors.

Biofilm formation of Lactiplantibacillus plantarum food isolates under flow and resistance to disinfectant agents

Bacterial biofilms formed in food processing environments can be resilient against cleaning and disinfection causing recontamination and spoilage of foods. We investigated the biofilm formation of six Lactiplantibacillus plantarum food spoilage isolates (FBR1-FBR6) using WCFS1 as a reference strain, and examined the impact of benzalkonium chloride (BKC) and peracetic acid (PAA) on planktonic and biofilm cells formed under static and dynamic flow conditions. We used a custom-designed setup composed of a 48-well plate with 0.8 ml culture volumes. We quantified biofilm formation under static and dynamic flow conditions with a flow rate of 3.2 ml/h using plate counting, Crystal Violet (CV) staining, and fluorescence staining techniques. Our findings revealed significant differences in biofilm formation and disinfectant resistance among studied strains and cell types. We observed that flow promoted biofilm formation in some strains and increased the number of culturable cells within biofilms in all strains. Furthermore, biofilm cells demonstrated higher resistance to disinfectants in comparison to planktonic cells for certain strains. Interestingly, cells from dispersed under flow biofilms show higher resistance to disinfectants than cells from static biofilms. The results indicate the importance of flow conditions in influencing L. plantarum food isolates biofilm formation and disinfection resistance, which may have implications for product contamination and spoilage risks.

Flow boiling in a rectangular micro/mini channel with self-rewetting and non-rewetting fluids

In this experimental investigation the influence of self-rewetting during flow boiling was studied in a horizontal microchannel during one-sided uniform heating. The channel had a width and height of 6 mm and 0.3 mm respectively. Local wall temperature and direct in-time flow visualisation was performed. The working fluids under consideration included water as base reference, pure 1-butanol, pure ethanol, dilute butanol-water self-rewetting mixtures, and a dilute ethanol-water binary mixture. Tests were conducted at mass fluxes of 10, 15 and 25 kg/m2s, over a range of heat fluxes to produce both single phase and two-phase quasi steady state flow data. Within the wall temperature ranges of interest, the butanol-water mixtures (5 % and 7 % by volume butanol) had surface tension characteristics that would draw liquid toward local wall hotspots. Local and average wall temperatures and heat transfer coefficients were obtained and considered against the governing flow pattern visualisations. In contrast to the other fluids, the self-rewetting butanol-water mixtures exhibited significantly more stable flow and more predictable saturation flow boiling heat transfer coefficient trends across all heat fluxes under consideration. At the lowest mass flux, it was found that an increase in the heat flux resulted in an improvement in the self-rewetting fluids heat transfer performance, while at higher mass fluxes this dependence was diminished. Across all mass fluxes and heat fluxes the 5 % butanol-water outperformed its 7 % butanol-water counterpart. The tendency of self-rewetting to reduce local dry-out, even at low mass fluxes are demonstrated.

Instabilities and particle-induced patterns in co-rotating suspension Taylor–Couette flow

The first experimental results on pattern transitions in the co-rotation regime (i.e. the rotation ratio $\varOmega = \omega _o/\omega _i > 0$ , where $\omega _i$ and $\omega _o$ are the angular speeds of the inner and outer cylinders, respectively) of the Taylor–Couette flow (TCF) are reported for a neutrally buoyant suspension of non-colloidal particles, up to a particle volume fraction of $\phi = 0.3$ . While the stationary Taylor vortex flow (TVF) is the primary bifurcating state in dilute suspensions ( $\phi \leq ~0.05$ ), the non-axisymmetric oscillatory states, such as the spiral vortex flow (SVF) and the ribbon (RIB), appear as primary bifurcations with increasing particle loading, with an overall de-stabilization of the primary bifurcating states (TVF/SVF/RIB) being found with increasing $\phi$ for all $\varOmega \geq ~0$ . At small co-rotations ( $\varOmega \sim 0$ ), the particles play the dual role of stabilization ( $\phi < 0.1$ ) and destabilization ( $\phi \geq ~0.1$ ) on the secondary/tertiary oscillatory states. The distinctive features of the ‘particle-induced’ spiral vortices are identified and contrasted with those of the ‘fluid-induced’ spirals that operate in the counter-rotation regime.

Stationary Flows of the ES-BGK Model with the Correct Prandtl Number

Stationary Flows of the ES-BGK Model with the Correct Prandtl Number

Experimental study of a novel guided sequential immersion cooling system for battery thermal management

Immersion cooling exhibits superior cooling performance compared to traditional battery thermal management systems (BTMS). However, a significant challenge of immersion cooling is the spatial variation of temperature within both the coolant and lithium-ion batteries (LIBs). This research proposes a guided sequential immersion cooling (GSIC) BTMS to address this issue. Experimental studies were conducted to evaluate the heat dissipation performance of the GSIC structure under various conditions, including extreme loads. The results indicate that under the static flow immersion cooling (SFIC) scheme, cooling the tabs significantly influences the overall performance. Immersing the tabs can reduce the maximum battery temperature by 10.403 °C, although the spatial variation of temperature persists. Under forced flow immersion cooling (FFIC) conditions, increasing the coolant flow rate dissipates the heat generated by LIBs more effectively. Even at an extreme discharge rate of 5C, the maximum temperature remains below 45 °C. The average temperature reduction at the tabs is greater than the battery body, and with increased flow rates, the temperature difference between the two can be maintained within 1 °C under all conditions. As the flow rate keeps increasing, the average temperature at the tabs gets even lower than the battery body at low loads. This demonstrates that the GSIC BTMS can suppress the temperature rise at the tabs, which is a critical heat risk. Moreover, the temperature uniformity of the LIBs module is improved. As the flow rate increases, the temperature difference within the LIBs module decreases when the discharge rates is between 1C and 5C. Theoretical analysis confirms that increasing flow rates for low loads can suppress temperature rise, albeit with increased power consumption and reduced cooling efficiency. High flow rates are more suitable for high load conditions of the LIBs. These results validate the feasibility of the GSIC BTMS and provide new insights for the development of immersion cooling BTMS.

Specular Surface Detection with Deep Static Specular Flow and Highlight

Specular Surface Detection with Deep Static Specular Flow and Highlight

Feasibility Testing of the Bionet Sonar Ultrasound Transcutaneous Energy Transmission (UTET) System for Wireless Power and Communication of a LVAD.

To address the clinical need for totally implantable mechanical circulatory support devices, Bionet Sonar is developing a novel Ultrasonic Transcutaneous Energy Transmission (UTET) system that is designed to eliminate external power and/or data communication drivelines. UTET systems were designed, fabricated, and pre-clinically tested using a non-clinical HeartWare HVAD in static and dynamic mock flow loop and acute animal models over a range of pump speeds (1800, 2400, 3000 RPM) and tissue analogue thicknesses (5, 10, 15mm). The prototypes demonstrated feasibility as evidenced by meeting/exceeding function, operation, and performance metrics with no system failures, including achieving receiver (harvested) power exceeding HVAD power requirements and data communication rates of 10kB/s and pump speed control (> 95% sensitivity and specificity) for all experimental test conditions, and within healthy tissue temperature range with no acute tissue damage. During early-stage development and testing, engineering challenges for UTET size reduction and stable and safe operation were identified, with solutions and plans to address the limitations in future design iterations also presented.

High spatiotemporal resolution free surface detection using cost-effective video equipment and computer vision techniques in nearly stationary flow along a transparent wall in the laboratory

The identification of the air–water interface in free surface flows traditionally involves intrusive techniques or costly equipment. Non-intrusive alternatives, such as computer vision, are emerging as highly effective substitutes or supplements for more invasive techniques in laboratory measurements, thanks to their straightforward implementation and cost efficiency. This research specifically delves in the conjunction of various naive techniques, exploring their collective precision in detecting the air–water interface along transparent walls in laboratory. A detection technique based on the double gradient of the image is applied and thoroughly examined. The study progresses through multiple refinement stages, culminating in a method that is both cost effective and easy to implement. This methodology allows for large-scale, high resolution measurements (200 mm × 1800 frames per video at a 0.25 mm, 50 Hz resolution), offering both spatial and temporal measurements by adeptly detecting the free surface along transparent walls.

Computational investigation of both geometric and fluidic compressible turbulent thrust vectoring, using a Coanda based nozzle

This study addresses the challenge of enhancing aircraft maneuverability, particularly for vertical landing and takeoff, focusing on the fluidic aerial Coanda high efficiency orienting jet nozzle that employs the Coanda effect to achieve thrust vectoring. This research advances understanding of the interplay between geometric and fluidic factors in thrust vectoring. Stationary, turbulent, and compressible flow conditions are assumed, employing Favre-averaged Reynolds-averaged Navier–Stokes approach with the standard k-ε model. Computational solutions were obtained using a pressure-based finite volume method and a structured computational grid. The key findings include thrust vectoring enhancement due to an increase in the total mass flow rate, septum position (at no shock wave-related issues), and Reynolds number. In addition, shock wave formation (at specific mass flow rates and septum positions) considerably affects thrust vectoring. These insights are crucial for optimizing Coanda-based nozzle design in advanced propulsion systems, including in unmanned aircraft vehicles.

Numerical Implementation of Boundary Conditions on Bubbles in a Microchannel with Stationary Laminar Flow

Numerical Implementation of Boundary Conditions on Bubbles in a Microchannel with Stationary Laminar Flow

Numerical solution of nanofluids mixing processes in T-mixer equipped titled gate

AbstractIn this study, a numerical simulation technique is employed to predicate the temperature distribution and velocity profile data of cold and hot nanofluids within a T-mixer was studied. The mixing of nanofluid flow with Al2O3 nanoparticles of 50 nm flows at Φ = 0.4 vol.% in a T-shaped mixer. The present numerical problem has been solved using the COMSOL Multiphysics version 5.4. Six angle of inclination was studied (θ = 15, 30, 45, 60, 75, and 90°) of the gate and evaluated its effects on the temperatures and velocity contour in the T-junction. The study's findings indicated that the presence of a gate in a stationary, non-rotating flow regime has a noteworthy impact on the stationary vortex flow. Also, the mixing occurs more quickly at angles of 45 or 60°. Mixing at a 30° or 90° angle took longer.

A comparative study of shale oil transport behavior in graphene and kerogen nanopores with various roughness via molecular dynamics simulations

Understanding the transport behavior of molecules within nanopores is crucial in various fields, including nanofluidic, bioengineering and geophysics. To elucidate oil flow within shale organic nanopores, the simplified carbon nanopores (e.g., graphene and CNTs) and realistic kerogen nanopores have been extensively applied. The variation in pore structure models leads to divergent findings in oil flow behavior. This study employs molecular dynamics simulations to compare alkane flows within graphene and kerogen nano-slits with similar surface roughness. We observe that using graphene nanopores with ultra-smooth surfaces may introduce errors of several orders of magnitudes in flux calculations. Conversely, when accounting for similar roughness, the static characteristics and flow behaviors in graphene and kerogen slit pores are largely comparable. The relative error of flux and the flow velocity could be lower than 6% when using graphene to replace the realistic kerogen. Alkane molecules exhibit a parabolic velocity profile, with a no-slip condition adjacent to the rough surfaces. In addition, when applying the Hagen-Poiseullie equation for alkane flow in rough nanopores, Gibbs dividing surface is recommended for defining the pore radius, reducing the error of flux predicted to below 10%. Our work fulfills the gap in accessing the suitability of using graphene as a model for oil transport in shale organic nanopores, and provides valuable insights and guidance for future studies on nanofluidic in shale, including molecular simulations, nanofluidic experiment designs, and multi-scale modeling.

An invitation to resolvent analysis

AbstractResolvent analysis is a powerful tool that can reveal the linear amplification mechanisms between the forcing inputs and the response outputs about a base flow. These mechanisms can be revealed in terms of a pair of forcing and response modes and the associated energy gains (amplification magnitude) at a given frequency. The linear relationship that ties the forcing and the response is represented through the resolvent operator (transfer function), which is constructed through spatially discretizing the linearized Navier–Stokes operator. One of the unique strengths of resolvent analysis is its ability to analyze statistically stationary turbulent flows. In light of the increasing interest in using resolvent analysis to study a variety of flows, we offer this guide in hopes of removing the hurdle for students and researchers to initiate the development of a resolvent analysis code and its applications to their problems of interest. To achieve this goal, we discuss various aspects of resolvent analysis and its role in identifying dominant flow structures about the base flow. The discussion in this paper revolves around the compressible Navier–Stokes equations in the most general manner. We cover essential considerations ranging from selecting the base flow and appropriate energy norms to the intricacies of constructing the linear operator and performing eigenvalue and singular value decompositions. Throughout the paper, we offer details and know-how that may not be available to readers in a collective manner elsewhere. Towards the end of this paper, examples are offered to demonstrate the practical applicability of resolvent analysis, aiming to guide readers through its implementation and inspire further extensions. We invite readers to consider resolvent analysis as a companion for their research endeavors.

Effects of incorporating bentonite, metakaolin, microsilica, and calcium carbonate on the rheological properties of portland cement-based 3D printing inks

Cementitious mixtures for 3D printing require specific rheological properties in the fresh state, which is why it is necessary to incorporate admixtures and/or mineral additions (modifiers) into such mixtures. The objectives of this article were to compare the effects of bentonite (Ben) (1 %–5 %) and mineral additions, such as metakaolin (MK) (5 % and 10 %), microsilica (MS) (5 % and 10 %) and calcium carbonate (Ca) (5 % and 10 %), on the rheological properties in the fresh state and to determine the printability of cementitious mixtures based on ordinary Portland cement (OPC). Among the rheological properties, the static yield stress (τ0), flow behavior, viscosity (η) and thixotropic of the mixtures were evaluated. Complementarily, the properties of materials in the fresh state, such as the settlement (minislump), flow rate (flow table) and buildability, were determined. The printability was evaluated through laboratory-scale additive manufacturing tests by printing from a hollow cylinder (unfilled) 50.8 mm in diameter and 208 mm in height (≈52 layers). The results showed that the incorporation of Ben between 1 % and 5 % had a significant effect on the rheological properties of the mixtures in the fresh state, increasing τ0 to 203.8 % (5 % Ben) and structural regeneration (thixotropy) to 98.5 %. Ben decreased the fluidity of the mixtures while significantly increasing the buildability. Among the mineral additions used, MS and MK increased the rheological properties of the mixtures as a function of the amount incorporated (5 % and 10 %, respectively), while Ca had a fluidifying effect on the mixtures, thereby affecting the buildability during the 3D printing process. This phenomenon was corrected by simultaneous adding 3 % Ben. The results obtained were considered a starting point for the design of OPC-based 3D printing mixtures with these modifiers (Ben, MK, MS and Ca).

Enhanced hydrate formation at the liquid CO2-brine interface with shear flow for solid CO2 sequestration

The hydrate-based CO2 sequestration provides an alternative solution for reducing CO2 emissions. However, the hydrate film that forms preferentially at the CO2-water interface can severely restrict hydrate conversion rate. How to enhance CO2 hydrate formation remains a crucial question. Here, we constructed static and shear flow contact models of liquid CO2 and brine in a cubic cavity and investigated their hydrate growth behavior at different subcoolings from perspectives of morphology and kinetics. For static contact, nucleation usually occurs at the CO2-brine interface, and high subcooling increases the degree of hydrate film wrinkling and the CO2 hydrate formation amount. The shear flow can delay the explosive growth of hydrates and tends to nucleate in bottom water; meanwhile, the continuous flow causes the overall extrusion deformation of hydrate film until the injection hole is blocked. Compared to static contact, this forced CO2-H2O contact caused by film deformation enhanced the CO2 hydration amount by 2.68–3.62 times at the subcoolings of 6.94–3.25 K. Increasing the flow rate can further delay hydrate appearance and enhance hydrate growth. We also speculate on the hydration patterns of CO2 in sediment pores under static and flowing conditions, and think that maintaining long-term flow can be an effective means to improve CO2 hydrate storage and prevent blockage occurring in the pore throat. Our work provides new insights into the growth behavior of CO2 hydrate for future CO2 storage in submarine sediments.

Physics guided machine learning modelling of compressor stall flutter

Modern aircraft engines need to meet ever more stringent requirements that greatly increase the complexity of design, which strives for enhanced performance, reduced operating costs, emissions and noise simultaneously. The drive for performance leads to the development of thin, lightweight, highly loaded fan and compressor blades which are increasingly more prone to incur high, sustained vibratory stresses and aeroelastic problems such as flutter. The current practice employs preliminary design tools for flutter that are often based on empiricism or simplified analytical models, requiring extensive use of computational fluid dynamics to verify aeroelastic stability. As the industry moves to new designs, fast and accurate prediction tools are needed. In this work, data-driven techniques are employed to model the aeroelastic response of compressor blades. Machine learning has been applied to a plethora of engineering problems, with particular success in the field of turbulence modelling. However, conventional, black-box data-driven methods based on simple input parameters require large databases and are unable to generalise. In this work a combination of machine learning techniques and reduced order models is proposed to address both limitations at the same time. Previous knowledge of flutter is introduced in the physics guided framework by formulating relevant, steady state input features, and by injecting results from low-fidelity analytical models. The models are tested on several unseen cascades and it is found that training on even a single geometry yields accurate results. The models developed here allow flutter prediction of fan and compressor flutter stability based on the steady state flow only without a need for any CPU intensive unsteady simulations. Hence, one can predict flutter stability of a given blade for different mechanical properties (mode shape, frequency) at near zero additional cost once the mean flow is known.