Study Of Fluid Mechanics Research Articles

Flow topology and mixing in alveolar edema: Unsteady flow in interconnected cavities with moving walls

The study of alveolar fluid mechanics is critical for comprehending respiratory function and lung diseases, particularly in cases of alveolar lesions that result in significant structural and fluid dynamic changes. This study investigates the flow topology and chaotic mixing within both normal and edematous alveoli, where the alveoli in the edematous model are interconnected by pores. To numerically simulate alveolar flow, a mathematical model is developed to ascertain the key parameters of Reynolds number (Re) and alveolar expansion ratio. Subsequently, the flow fields are analyzed to determine wall shear stress (WSS) and to identify WSS critical points and critical points of velocity vector, with a thorough presentation of the various flow topologies corresponding to these critical points. Moreover, a dynamic mode decomposition-based method is introduced to track particle trajectories, and the exploration of chaotic mixing is conducted through tracer advection, Poincare map, and the calculation of finite-time Lyapunov exponents. Results indicate that the edematous model exhibits a higher Re and higher WSS due to the fluid properties. Within the alveoli, high WSS is usually localized at the pores. The pores increase critical points and alter flow topologies, significantly changing chaotic mixing. Additionally, Re and alveolar locations also affect mixing patterns. These findings are crucial for understanding alveolar physiology and designing inhaled drugs for lung diseases, considering the role of chaos in particle transport in the lung acini.

Various exact solutions of the (4+1)-dimensional Boiti–Leon–Manna–Pempinelli-like equation by using bilinear neural network method

In this paper, Bilinear neural network method is used to investigate the (4+1)-dimensional Boiti–Leon–Manna–Pempinelli-like (BLMP-like) equation, which can be utilized to model wave phenomena in incompressible fluids and in the study of fluid mechanics. On the basis of the generalized bilinear operators, the generalized bilinear equation is formulated. In the bilinear neural network method, a variety of neural network structures, including the single hidden layer and multiple hidden layers neural network are employed to investigate the exact solutions. By considering the special values of the parameters involved, the dynamical behavior of the solution is analyzed graphically. The effective method adopted significantly contributes to the understanding of nonlinear evolution equations in various fields, including plasma physics, mathematical physics, electromagnetism, and fluid dynamics.

Predicting pressure fields from incomplete velocity fields based on deep convolutional neural network

Acquisition of an instantaneous pressure field in a flow field is essential for experimental fluid dynamics research because pressure is associated with various flow phenomena. Machine learning has performed well in various fluid mechanics studies in recent years due to its nonlinearity. In this paper, we use the machine learning method to predict the pressure fields from incomplete velocity fields while reconstructing the missing velocity information, as exemplified by the stationary circular cylinder flow and circular cylinder undergoing forced oscillating problems, in which the incompleteness of the velocity field is caused by the optical path occlusion in experiments. We propose a network model based on convolutional neural networks. The experimental results show that the error of machine learning methods is around 4%, and even if the resolution of the training set is reduced, it does not affect the prediction accuracy of the network. For statistical averaging of prediction results, the network has high accuracy in predicting first-order quantities, but its ability to predict second-order quantities is limited. We also explore the effects of the amount of noise on the performance of networks. When applying the model to the forced oscillating cylinder problem, we use transfer learning to train the network given the similarity of the two problems, and the training error yields faster convergence and more minor convergence results. In addition to this, we use flow field data with a single vibration frequency to construct a single dataset, and flow field data with multiple vibration frequencies to construct a mixed dataset, and study the effect of different datasets on the prediction accuracy, which shows that it is difficult for neural networks to achieve both accuracy and generalization.

Numerical Study of Possion Equation in Subsonic and Supersonic Airfoil Flow

In the context of subsonic and supersonic airfoil flows in fluid mechanics, this study aims to numerically simulate the Poisson equation. The focus is on analyzing the variation of fluid flow under stable conditions of incompressible fluid. The research classifies the flow into four distinct cases based on the Mach number and its constancy and employs Python for intricate numerical computation. The culmination of the study involves generating Mach number contour plots and streamlining diagrams for each case using Python, which visually depicts the nuanced evolution of the flow field. Moreover, the study conducts a rigorous comparative analysis of the simulation results for the different cases, exploring the intricate differences in flow behavior across a spectrum of Mach numbers and flow conditions. The discussion section further broadens the scope of inquiry by addressing potential limitations and charting out prospective research directions. By providing a comprehensive exploration of the widespread application of the Poisson equation in fluid mechanics, this paper stands as a beacon for further investigations in the domain and presents invaluable insights for the progressive advancement of fluid mechanics studies in the future.

Various Flow in a Slender Tube: Mach Number and Domain Shape Influenced by Boundary Obstacles Via Small Disturbance Equations

With the exponential advancements in computer hardware and software in recent years, computational fluid dynamics (CFD) has emerged as a highly efficient alternative to traditional fluid mechanics studies. Its prowess in accurately predicting and analyzing flow mechanics has led to widespread adoption across diverse engineering disciplines. This research delves into a numerical analysis of flow conditions utilizing the small disturbance equation (SDE). The study bridges the gap between mathematical formulations and computational language, exemplified through Python code, by harnessing the successive over-relaxation (SOR) method, Neumann boundary conditions (BC), and the ghost point technique. The research underscores its predictions' precision in subsonic and supersonic scenarios by meticulously evaluating the resultant errors. This accuracy is further corroborated by examining overarching flow patterns and Mach number distributions. However, the program's outputs indicate a discernible lack of precision in the transonic case, with errors surpassing acceptable thresholds. This discrepancy is hypothesized to stem from potential mischaracterizations of points at the inflow and outflow boundaries. This study, thus, offers invaluable insights while also highlighting areas necessitating further refinement.

Characterizing bulk-driven acoustic streaming in air

The time-independent fluid flows induced by the attenuation of acoustic energy known as acoustic streaming have been experimentally measured in air using particle image velocimetry for two high powered ultrasonic transducers. The literature declares that these bulk-driven “Eckart” streaming flows are observed as turbulent jets in the direction of the acoustic wave propagation. The aim of this work is to increase the understanding of the coupling between the acoustic and fluid domains and to validate these well-established theoretical relationships. Langevin horns and focused arrays of parking sensor transducers, which operate at ∼26 and 40 kHz, respectively, and are both capable of producing sound pressure levels of up to 170dB, were used to assess the relationship between the first order acoustic field and the second order streaming velocity field. Streaming velocities of the order of 0.2 and 0.3 m/s were measured for the Langevin horn and the focused array, respectively. Two-step COMSOL Multiphysics models were created for both transducers, first solving the acoustic fields in the frequency domain, then using the results to drive a streaming volume force in a stationary fluid mechanics study. Both laminar and turbulent k-ε fluid mechanics models were compared to the experimental results.

Machine Learning Based Fluid-Transportation Monitoring and Controlling

The discipline of fluid mechanics is developing quickly, propelled by previously unheard-of data volumes from experiments, field measurements, and expansive simulations at various spatiotemporal scales. The field of machine learning (ML) provides a plethora of methods for gleaning insights from data that can be used to inform our understanding of the fluid dynamics at play. As an added bonus, ML algorithms can be used to automate duties associated with flow control and optimization, while also enhancing domain expertise. This article provides a review of the background, current state, and potential future applications of ML in fluid mechanics. We provide an introduction to the most fundamental ML approaches and describe their applications to the study, modelling, optimization, and management of fluid flows. From the standpoint of scientific inquiry, which treats data as an integral aspect of modelling, experiments, and simulations, the benefits and drawbacks of these approaches are discussed. Since ML provides a robust information-processing framework, it can supplement and potentially revolutionize conventional approaches to fluid mechanics study and industrial applications.

Study of Aerodynamics and Fluid Mechanics

Study of Aerodynamics and Fluid Mechanics

Numerical simulation of hydrodynamics in wet cooling tower packings

Wet cooling towers are used in many industrial processes but hydrodynamic behaviour of air-water counter flows in towers packing remains unknown. Hydrodynamics is directly linked with main operating issues (e.g. packing fouling reducing cooling efficiency). The objective of this work is to use Computational Fluid Dynamics (CFD) simulations to characterize local hydrodynamic parameters such as water film thickness, velocity or wall shear stress and system scale parameters such as wetting rate or interfacial area. After a theoretical study of fluid mechanics, Volume of Fluid (VOF) simulations, with dedicated models for air/water interface capture and solid/liquid contact angle, successfully reproduced water falling films on packing surface. Simulations allowed the calculation of main water flow local and system parameters. The results have pointed out trends of increasing liquid film thicknesses and wetting rates with increasing water flowrate in agreement with experimental observations. A correlation of the interfacial area has been proposed as a function of the hydrodynamic adimensional numbers to characterize transfer phenomena in the considered packing.

A direct numerical simulation display of the rotational frame preference of turbulence

An elementary observation of a laminar cylindrical Poiseuille–Couette flow profile reveals no distinction in the parabolic streamwise profile, from one without a cross-stream flow, in whatever reference frame the observation is made. This is because the laminar flow is in solid-body rotation and there is no fluid intrinsic rotation. Hence, the main streamwise Poiseuille flow is unaffected. On the contrary, in turbulent (unsteady) cylindrical axial-Couette flow, the rotational reference frame must be considered, and any observation from an external inertial reference frame can give outright incorrect results. However, even in axial turbulent pipe flow with axial rotation, the resultant effect of azimuthal velocity on the flow profile is still usually too low. Hence, the importance of consideration in the rotational frame is often overlooked. A common misconception in the study of fluid mechanics is that the position of the observer does not matter. In this direct numerical simulation study, firstly turbulent flow in a pipe with axial rotation is established. Then turbulent flow in the concentric pipe, with inner wall rotation, is used to show how tilted wall streak direction is oriented by the rotational reference frame and not the inertial reference frame.

Errors in Bernoulli's Law and How to Eliminate and Solve Them

Bernoulli's law is widely used in practice and the study of fluid mechanics. This law has existed since 1738 when the Hydrodynamica book was published. Until now, it has not been repaired. The base of the law has some inaccuracies that lead to mistakes in its application. Therefore, their repair is essential. This article uses theories of physics and mathematical logic to point out the law's flaws. Current knowledge determines that the law of conservation of energy is correct. This correctness is the main basis for pointing out the problems in Bernoulli's law. The form of approach and problem-solving is to consider exercises and classic examples in textbooks on fluid mechanics. After that, they are going to be compared with the right knowledge. From it, the truths are reached. The study's main results pointed out Bernoulli's fundamental mistake, from applying basic knowledge to formulating the law. The errors of practical applications based on this knowledge are also clarified. From these results, there will be fundamental changes when building the theory for other sites. A series of applications in real life also change deeply. At the same time, they are leading to saving money, time, labor, and labor results and opening new doors of knowledge and science

The fusion of flow field data with multiple fidelities

We propose a spatial-temporal multi-fidelity Gaussian process regression framework for the fusion of flow field data with various availabilities and fidelities but not sufficiently large to train neural networks commonly encountered in fluid mechanics studies. For example, fluid experiments lead to data with high fidelity but sparse in time and space, while most of the numerical data are generally regarded as less accurate but are spatially temporally continuous. The proposed framework aims at generating a new set of fused data by combining the merits of those in the spatial-temporal space. Numerical simulations [e.g., direct numerical simulation (DNS), large eddy simulation, Reynolds-averaged Navier–Stokes] of flow around a National Advisory Committee for Aeronautics 0012 airfoil are performed to collect the original raw data with various fidelities, and a fraction of the DNS result is used to mimic the high-fidelity but sparse experimental data. It is found that the accuracy of the fused data increases with the density of high-fidelity points until reaching a threshold, above which the fusion accuracy becomes insensitive. This limit can be overcome by introducing extra dimensions, such as the gradients of the low-fidelity data field. By examining the error fields, it is found that the high-fidelity points can tune low-fidelity fields but only within a limited local region. The accuracy can be firmly improved by introducing more high-fidelity points or higher levels of spatial gradients if the data set captures the temporal development.

Solution of Temperature Fluid Particle in Incompressible Dusty Fluid with The Effect of Week Induced Magnetic Field

It is possible for dust particles to naturally exist in fluids. In the study of fluid mechanics, these problems related to flow characteristics of temperature. How the magnetic field of suspended particulate matter affects the temperature axially symmetrical jet mixing of incompressible dusty fluid. we assume that the velocity and temperature in the jet deviate from the surrounding stream. To linearize the equation that was solved using Laplace Transformation, a perturbation method was used. The solution of temperature of the particle phase which is depends on fluid phase temperature with in the weak induced Magnetic field.

The Effect on The Volume and Semi Axes of a Conducting Spheroid Due to The Scaling on Its First Order Polarization Tensor

In order to enhance identification of objects in electrical imaging or metal detection, the polarization tensor is used to characterize the perturbation in electric or electromagnetic field due to the presence of the conducting objects. This is similar as describing the uniform fluid flow that is disturbed after a solid is immersed in the fluid during the study of fluid mechanics. Moreover, in some applications, it is beneficial to determine a spheroid based on the first order polarization tensor in order to understand what is actually represented by the tensor. The spheroid could share similar physical properties with the actual object represented by the polarization tensor. The purpose of this paper is to present how scaling on the matrix for the first order polarization tensor will affect the original spheroid represented by that first order polarization tensor. In the beginning, we revise the mathematical property regarding how scaling the semi axes of a conducting spheroid has an effect to its first order polarization tensor. After that, we give theoretical results with proofs on how scaling the matrix for the first order polarization tensor affects the volume and semi axes of the spheroid. Following that, some numerical examples are provided to further justify the theory. Here, different scalar factors will be used on the given first order polarization tensor before the new volume and semi axes of the spheroid are computed. In addition, we also investigate how the size of the scale on the first order polarization tensor influence the accuracy of computing the related volume and semi axes. In this case, it is found that a large error could occur to the volume and the semi axes when finding them by solving the first order PT with that has being scaled by a very large scaling factor or a too small scaling factor. A suggestion is then given on how to reduce the errors.

Computational Solvers for Iterative Hydraulic loss Calculations in Pipe Systems

Abstract —The study of fluid mechanics spans several engineering disciplines including Mechanical, Civil, Aerospace, Chemical, Environmental, Petroleum, and Biomedical Engineering. In all these disciplines, hydraulic loss calculations in pipes are extremely important. However, the iterative nature of the solution to these engineering problems makes it intricate and cumbersome to solve. Further, it gets very difficult to visualize the solutions to such iterative problems for a wide variety of cases. The current paper aims to bridge this gap by the creation of two open-source Excel-VBA based computational solvers. The first tool corresponds to the determination of the Darcy-Weisbach friction factor through the Colebrook Equation and its visualization on a Moody's chart, which can be effectively employed by engineering instructors as an active learning tool. Second, a complete tool covering all four kinds of pipe flow situations (including the iterative problems) has been developed. The developed computational tools were employed in an undergraduate Fluid Mechanics classroom and the detailed student responses were collected on ten aspects related to teaching and learning divided broadly under four categories – 'overall rating', 'student perceptions on self-learning', 'Improvement in teaching delivery', and 'recommendation for other courses'. The data collected from student responses were subjected to statistical analysis. The results of hypothesis testing and the p-value calculations clearly justify the immense usefulness of this tool in the improvement of the overall teaching-learning process of Fluid Mechanics. Finally, the developed computational tools are being hosted free on the web for the benefit of engineering instructors, learners and professionals alike. Keywords:Pipe losses; computational tool; Fluid Mechanics; Hydraulic loss; Moody's chart; Excel VBA.

Parameter Study of Geometrically Induced Flow Maldistribution in Shell and Tube Heat Exchangers

Abstract Shell and tube heat exchangers (STHEs) are the most common type of heat exchanger in preheat trains (PHT) of oil refineries and in chemical process plants. Most commercial design software tools for STHE assume uniform distribution over all tubes of a tube bundle. This leads to various challenges in the operation of the affected devices. Flow maldistribution reduces heat duty of STHE in many applications and supports fouling buildup in fluids that tend to particle, bio, and crystallization fouling (Verein Deutscher Ingenieure, ed., 2010, Heat Atlas, 2nd ed., VDI-Buch., Springer-Verlag). In this article, a fluid mechanics study about tube side flow distribution of crude oil and related hydrocarbons in two-pass PHT heat exchangers is described. It is shown that the amount of flow maldistribution varies significantly between the different STHE designs. Therefore, a parameter study was conducted to investigate reasons for maldistribution. For instance, the nozzles diameter, type, and orientation were identified as crucial parameters. In consequence, simple design suggestions for reducing tube side flow maldistribution are proposed.

Should the HVAD Be Viewed as a Bridge-to-HeartMate 3 Left Ventricular Assist Device?

Cardiovascular fluid mechanical studies of the native and mechanically assisted circulation in vivo are challenging to conduct experimentally, and conceptually sound mathematical models are lacking at present. With respect to experimental studies, highly simplified approaches are typically employed, from which extrapolations to the in vivo setting are made. Closed circuits incorporating so-called mock loops, in which fluid flows through tubing into a pump inlet, thence into the pump mechanism, through the pump outlet, thence into tubing downstream of the pump outlet, and back into the tubing upstream of the pump inlet, are examples of such simplified systems.

Techniques for Turbulence Tripping of Boundary Layers in RANS Simulations

The exact placement of the laminar–turbulent transition has a significant effect on relevant characteristics of the boundary layer and aerodynamics, such as drag, heat transfer and flow separation on e.g. wings and turbine blades. Tripping, which fixes the transition position, has been a valuable aid to wind-tunnel testing during the past 70 years, because it makes the transition independent of the local condition of the free-stream. Tripping helps to obey flow similarity for scaled models and serves as a passive control mechanism. Fundamental fluid-mechanics studies and many engineering developments are based on tripped cases. Therefore, it is essential for computational fluid dynamics (CFD) simulations to replicate the same forced transition, in spite of the advanced improvements in transition modelling. In the last decade, both direct numerical simulation (DNS) and large-eddy simulations (LES) include tripping methods in an effort to avoid the need for modeling the complex mechanisms associated with the natural transition process, which we would like to bring over to Reynolds-averaged Navier–Stokes (RANS) turbulence models. This paper investigates the implementation and performance of such a technique in RANS and specifically in the k-omega SST model. This study assesses RANS tripping with three alternatives: First, a recent approach of turbulence generation, denoted as turbulence-injection method (kI), is evaluated and investigated through different test cases; second, a predefined transition point is used in a traditional transition model (denoted as IM method); and third a novel formulation combining the two previous methods is proposed, denoted gamma -kI. The model is compared with DNS, LES and experimental data in a variety of test cases ranging from a turbulent boundary layer on a flat plate to the three-dimensional (3D) flow over a wing section. The desired tripping is achieved at the target location and the simulation results compare very well with the reference results. With the application of the novel model, the challenging transition region can be excluded from a simulation, and consequently more reliable results can be provided.

A novel design of Gaussian Wavelet Neural Networks for nonlinear Falkner-Skan systems in fluid dynamics

In this study, a design of integrated computational intelligent paradigm has been presented for numerical treatment of the one-dimensional boundary value problems represented with Falkner-Skan equations (FSE) by exploitation of Gaussian wavelet neural networks (GWNNs), genetic algorithms (GAs) and sequential quadratic programming (SQP), i.e., GWNN-GA-SQP. The GWNNs is used for mathematical modeling of the problem by constructing mean squared error based objective function while optimization of the cost function is initially conducted with efficacy of GAs as a global search and while fine tuning is performed with efficiency local search with SQP. The numerical results are obtained by proposed GWNN-GA-SQP for different FSEs arising in nonlinear regimes of computation fluid mechanics studies. A comparison of the results of proposed GWNN-GA-SQP stochastic numerical solver with reference state of the art solutions of Adams method establishes the accuracy, convergence and stability, which further endorsed through statistics on multiples runs. The T-Paired test is also applied to validate the effectiveness of the proposed GWNN-GA-SQP algorithm for solving nonlinear FSEs.

Bilinear Bäcklund transformation, breather- and travelling-wave solutions for a (2+1)-dimensional extended Kadomtsev–Petviashvili II equation in fluid mechanics

Fluid-mechanics studies are applied in mechanical engineering, biomedical engineering, oceanography, meteorology and astrophysics. In this paper, we investigate a (2+1)-dimensional extended Kadomtsev–Petviashvili II equation in fluid mechanics. Based on the Hirota bilinear method, we give a bilinear Bäcklund transformation. Via the extended homoclinic test technique, we construct the breather-wave solutions under certain constraints. We obtain the velocities of the breather waves, which depend on the coefficients in that equation. Besides, we derive the lump solutions with the periods of the breather-wave solutions tending to the infinity. Based on the polynomial-expansion method, travelling-wave solutions are constructed. We observe that the shapes of a breather wave and a lump remain unchanged during the propagation. We graphically discuss the effects of those coefficients on the breather wave and lump.