FlumeX: A modular flume design for laboratory-based marine fluid-substrate studies.

PURPOSE: Reconstructive obstacles in composite head and neck defects are compounded in re-operated, irradiated, and vessel-depleted surgical fields. In cases that require multiple free flaps, recipient vessel availability and inset logistics become challenging. Strategic flow-through flap configurations mitigate these issues by providing a built-in option for inflow to a second flap. This approach permits use of one native recipient vessel, increased reach of the inflow vessels, and greater flexibility to configure soft tissue and bony flap inset. METHODS: 38 head and neck free flap cases were reviewed from an academic hospital in New Orleans, Louisiana, taking place between March 2019 - April 2021. Nine cases utilized flow-through free flaps for reconstruction. RESULTS: Seven oncologic and two traumatic patients (N=9) received multiple flaps arranged in flow-through configuration (ALT: 78%; Fibula: 78%; DCIA: 22%; Peroneal Artery Perforator: 11%; MSAP: 11%) for reconstruction. Configurations involved ALT -> Fibula (56%), ALT -> DCIA (22%), Fibula -> Peroneal artery perforator (11%), Fibula -> MSAP (11%). Recipient vessels included facial (78%), transverse cervical (11%), and occipital (11%) arteries. No flap failures occurred, though complications included infection (22%), dehiscence (44%), hematoma (22%), thrombosis (11%), and others (33%). CONCLUSIONS: In head and neck reconstruction, the use of the flow-through principle enables uninterrupted vascular flow for two distinct free flaps in single stage reconstruction for patients with vessel-depleted, irradiated, and re-operated fields.

Computational Fluid Dynamics (CFD) surely has played a fundamental role in the design of the bodies and shapes of both commercial and racing vehicles in the last decades. This circumstance was mainly due to the connected substantial improvement in the design timings and to the possibility of producing numerous flow field and surface data that are difficult to obtain from a physical experimental method. Such a local analysis leads to a further understanding of the interactions of components with the overall aerodynamics. The development of wing performances, with respect to racing vehicles, has to deal with very short times but also with a very detailed description of the physics occurring. Starting from these constraints, a coupling procedure was developed by the joining of a CFD code with a Finite Element Method (FEM) structural code to better evaluate the aerodynamic performance of the wing deformed under the fluid dynamic loads. Combined with experimental data, these insights allow a better understanding of the involved flow mechanisms The codes coupling has been managed as it follows: the fluid dynamic pressure loads have been passed to the structural solver. The output, in terms of airfoil deformation, has been re-imported into the CFD solver in order to compute the updated fluid dynamic field. These steps have to be repeated till the reaching of the convergence of the fluid dynamic and deformation fields. The final result consists of the fluid dynamic field around the deformed configuration of the wing. The described procedure has been applied to a standard racing vehicle front wing, which was tested under different operating conditions in terms of vehicle speed and angle of attack. Results obtained are encouraging and suggest to further investigate the possibilities offered by this procedure.

11R3. Perturbation Methods for Differential Equations. - BK Shivamoggi (Dept of Math, Univ of Central Florida, Orlando FL 32816-1364). Birkhauser Boston, Cambridge MA. 2003. 354 pp. ISBN 0-8176-4189-0. $59.95.Reviewed by J Awrejcewicz (Dept of Autom and Biomech, Tech Univ of Lodz, 1/15 Stefanowskiego St, Lodz, 90-924, Poland).This book is focused on perturbation methods mainly applied to solve both ordinary and partial differential equations, as its title implies. As explained by the author, one of the unusual features of the treatment is motivated by his lecture notes devoted to a mix of students in applied mathematics, physics and engineering. Therefore, it is intended to serve as a textbook for both undergraduale students of the previously mentioned branches of science. However, I wonder if the students will be able to understand fully physical aspects of many various examples of completely separated fields such as solid mechanics, fluid dynamics and plasma physics. This aspect has been probably understood by the author, who added many appendices to the chapters. On the other hand, looking for the cited 26 references authored or co-authored by BK Shivamoggi, it is not surprising that his research covers the above-mentioned branches of science. This book can serve also as an example how an asymptotic analysis may easily move between various different disciplines. The book is 354 pages long and has 130 references. It is divided into seven chapters. Chapter 1 introduces a reader with asymptotic series and expansions of some arbitrarily chosen functions. It can be treated as a brief panoramic picture to the further problems dealt with the book. In Chapter 2 regular perturbation methods are addressed. First algebraic equations are considered (four examples), then differential equations are analyzed (four examples), and finally partial differential equations are studied (1 example). The author originally introduced some of the outlined examples (for example, Section 2.5 is devoted to application to fluid dynamics published already by the author in 1998) and some were taken from other cited sources. Eight exercises are given at the end of this chapter to be solved by a reader or student. In Chapter 3 the method of strained coordinates (parameters) is described. In Section 3.2, the Poincare´-Lindteadt-Lighthill method of perturbed eigenvalues is briefly stated with the supplement three examples. In addition, the eigenfunction expansion method (Section 3.3), Lighthill’s method of shifting singularities (Section 3.4), and the Pritulo’s method of renormalization (Section 3.5) are presented with supporting examples. It is worth noticing that the applications come from various fields including wave propagation in a homogeneous medium, nonlinear buckling of elastic columns, and a few examples within the field of fluid dynamics and plasma physics. The main limitation of the strained coordinates method, ie, an incapability of determining transient responses of dissipative systems, is illustrated and discussed. Nine exercises are added for the reader to solve. Chapter 4 discusses the method of averaging. After a brief introduction, the Krylov-Bogoliubov method of averaging is described and two classical examples adopted from the Nayfeh work are given. Section 4.3 includes one sentence describing the so called generalized Krylov-Bogoliubov-Mitropolski method, and then two classical examples of the Duffing and van der Pol oscillators are considered. Witham’s average Lagrangian method is addressed in Section 4.4 using a nonlinear dispersive wave propagation problem. In the next section the Hamiltonian perturbation method is introduced followed by three examples. Then the averaged Lagrangian method is applied to study a nonlinear evolution of a modulated gravity wave packet on the surface of a fluid. At the end of the chapter, seven exercises are included. The method of matched asymptotic expansions is described in Chapter 5. After a brief introduction and physical motivation the method of matched asymptotic expansion is explained through a simple example by computing inner, outer, and composite expansions. Applying Cole (1968) and Keviorkian and Cole (1996) results, the linear hyperbolic partial differential equation is analyzed in Section 5.4, the elliptic equations are described in section 5.5, and the parabolic equations are analyzed in Section 5.6. The interior layers are illustrated in Section 5.7 using an example introduced earlier by Lagerstrom (1988). In Section 5.8 Latta’s (1951) method of composite expansions are illustrated via three examples (two of them are borrowed form Nayfeh (1973) and Keller (1968)). Section 5.9 titled Turning-point problems, includes a description of the JWKB approximation [with two examples borrowed from Holmes (1995)], the solution near the turning point and the Langer’s method. An application of the matched asymptotic expansion is taken from the field of fluid dynamics. Namely, a boundary layer flow past a flat plate is studied. Next, ten exercises to be solved follow. A method of multiple scales is illustrated in Chapter 6. After a brief introduction to the method, the differential equations with constant coefficients are addressed in Section 6.2, where eight examples are included (six of them are borrowed form other references). Struble’s method is described in Section 6.3, where two examples are given. In Section 6.4 differential equations with slowly varying coefficients are considered. Two supplemented examples illustrate application of the multiple scale method. The generalized multiple scale method, following Nayfeh (1964), is presented via two boundary-value problems. The considered applications include dynamic buckling of a thin elastic plate (solid mechanics) and a few examples taken from fields of fluid dynamics and plasma physics. The chapter finishes with eleven examples to be solved. The last chapter, 7, is devoted to miscellaneous perturbation method. The main purpose of this chapter is to describe some special perturbation techniques that are very useful in some applications. The series of discussed methods include a quantum-field-theoretic perturbative procedure and a perturbation method for linear stochastic differential equations. Four exercises to be solved are given at the end of this chapter. Since Perturbation Methods for Differential Equations covers a great deal of material, it is recommended to students and researchers, already familiar with solid and fluid mechanics, as well as with plasma physics. In general the figures and tables are fine, and the index is adequate, hence I recommend the book to be purchased by both individuals and libraries.

Natural organic matter (NOM) in roof runoff and its impact on the Fe 0 treatment system of dissolved metals

Abstract:Hydrocarbon distribution rules in the deep and shallow parts of sedimentary basins are considerably different, particularly in the following four aspects. First, the critical porosity for hydrocarbon migration is much lower in the deep parts of basins: at a depth of 7000 m, hydrocarbons can accumulate only in rocks with porosity less than 5%. However, in the shallow parts of basins (i.e., depths of around 1000 m), hydrocarbon can accumulate in rocks only when porosity is over 20%. Second, hydrocarbon reservoirs tend to exhibit negative pressures after hydrocarbon accumulation at depth, with a pressure coefficient less than 0.7. However, hydrocarbon reservoirs at shallow depths tend to exhibit high pressure after hydrocarbon accumulation. Third, deep reservoirs tend to exhibit characteristics of oil (–gas)–water inversion, indicating that the oil (gas) accumulated under the water. However, the oil (gas) tends to accumulate over water in shallow reservoirs. Fourth, continuous unconventional tight hydrocarbon reservoirs are distributed widely in deep reservoirs, where the buoyancy force is not the primary dynamic force and the caprock is not involved during the process of hydrocarbon accumulation. Conversely, the majority of hydrocarbons in shallow regions accumulate in traps with complex structures. The results of this study indicate that two dynamic boundary conditions are primarily responsible for the above phenomena: a lower limit to the buoyancy force and the lower limit of hydrocarbon accumulation overall, corresponding to about 10%–12% porosity and irreducible water saturation of 100%, respectively. These two dynamic boundary conditions were used to divide sedimentary basins into three different dynamic fields of hydrocarbon accumulation: the free fluid dynamic field, limit fluid dynamic field, and restrain fluid dynamic field. The free fluid dynamic field is located between the surface and the lower limit of the buoyancy force, such that hydrocarbons in this field migrate and accumulate under the influence of, for example, the buoyancy force, pressure, hydrodynamic force, and capillary force. The hydrocarbon reservoirs formed are characterized as “four high,” indicating that they accumulate in high structures, are sealed in high locations, migrate into areas of high porosity, and are stored in reservoirs at high pressure. The basic features of distribution and accumulation in this case include hydrocarbon migration as a result of the buoyancy force and formation of a reservoir by a caprock. The limit fluid dynamic field is located between the lower limit of the buoyancy force and the lower limit of hydrocarbon accumulation overall; the hydrocarbon migrates and accumulates as a result of, for example, the molecular expansion force and the capillary force. The hydrocarbon reservoirs formed are characterized as “four low,” indicating that hydrocarbons accumulate in low structures, migrate into areas of low porosity, and accumulate in reservoirs with low pressure, and that oil(–gas)–water inversion occurs at low locations. Continuous hydrocarbon accumulation over a large area is a basic feature of this field. The restrain fluid dynamic field is located under the bottom of hydrocarbon accumulation, such that the entire pore space is filled with water. Hydrocarbons migrate as a result of the molecular diffusion force only. This field lacks many of the basic conditions required for formation of hydrocarbon reservoirs: there is no effective porosity, movable fluid, or hydrocarbon accumulation, and potential for hydrocarbon exploration is low. Many conventional hydrocarbon resources have been discovered and exploited in the free fluid dynamic field of shallow reservoirs, where exploration potential was previously considered to be low. Continuous unconventional tight hydrocarbon resources have been discovered in the limit fluid dynamic field of deep reservoirs; the exploration potential of this setting is thought to be tremendous, indicating that future exploration should be focused primarily in this direction.

Experiments on the aeroacoustics of an under-expanded supersonic jet impinging on a flat plate are presented and thoroughly discussed. A wide range of nozzle pressure ratios and of nozzle-to-plate distances has been analyzed with particular attention to the behavior of the discrete component of the noise. The investigation has been carried out by means of acoustic, particle image velocimetry and wall pressure measurements. The analysis of the relationship between the acoustic data and the fluid dynamic fields allows to examine the different source mechanisms of the discrete component of the noise and to evaluate the link between the jet flow structure and the acoustic tone features. Specifically, two ranges of nozzle pressure ratio have been observed showing different acoustic behaviors, characterized by distinct mechanisms of discrete noise generation. These regions are separated by a range of nozzle pressure ratios where impinging tones are not observed. The present experimental data extend previously published results, improving the analysis of the connection between fluid dynamic and acoustic fields and leading to a better comprehension of the impinging tone source mechanisms.

Fluid dynamic calculations play a crucial role in understanding marine biochemical dynamic processes, impacting the behavior, interactions, and distribution of biochemical components in aquatic environments. The numerical simulation of fluid dynamics is a challenging task, particularly in real-world scenarios where fluid motion is highly complex. Traditional numerical simulation methods enhance accuracy by increasing the resolution of the computational grid. However, this approach comes with a higher computational demand. Recent advancements have introduced an alternative by leveraging deep learning techniques for fluid dynamic simulations. These methods utilize discretized learned coefficients to achieve high-precision solutions on low-resolution grids, effectively reducing the computational burden while maintaining accuracy. Yet, existing fluid numerical simulation methods based on deep learning are limited by their single-scale analysis of spatially correlated physical fields, which fails to capture the diverse scale characteristics inherent in flow fields governed by complex laws in different physical space. Additionally, these models lack an effective approach to enhance correlation interactions among dynamic fields within the same system. To tackle these challenges, we propose the Spatial Multi-Scale Feature Extract Neural Network based on Physical Heterogeneous Interaction (PHI-SMFE). The PHI module is designed to extract heterogeneity and interaction information from diverse dynamic fields, while the SMFE module focuses on capturing multi-scale features in fluid dynamic fields. We utilize channel-biased convolution to implement a separation strategy, reducing the processing of redundant feature information. Furthermore, the traditional solution module based on the finite volume method is integrated into the network to facilitate the numerical solution of the discretized dynamic field in subsequent time steps. Comparative analysis with the current state-of-the-art model reveals that our proposed method offers a 41% increase in simulation accuracy and a 12.7% decrease in inference time during the iterative evolution of unsteady flow. These results underscore the superior performance of our model in terms of both simulation accuracy and computational speedup, establishing it as a state-of-the-art solution.

Understanding role of reactor configuration in electrochemical decomplexation of Ni-EDTA via experiments and simulations

Most existing convolutional neural-network-based super-resolution (SR) methods focus on designing effective neural blocks but rarely describe the image SR mechanism from the perspective of image evolution in the SR process. In this study, we explore a new research routine by abstracting the movement of pixels in the reconstruction process as the flow of fluid in the field of fluid dynamics (FD), where explicit motion laws of particles have been discovered. Specifically, a novel fluid micelle network is devised for image SR based on the theory of FD that follows the residual learning scheme but learns the residual structure by solving the finite difference equation in FD. The pixel motion equation in the SR process is derived from the Navier-Stokes (N-S) FD equation, establishing a guided branch that is aware of edge information. Thus, the second-order residual drives the network for feature extraction, and the guided branch corrects the direction of the pixel stream to supplement the details. Experiments on popular benchmarks and a real-world microscope chip image dataset demonstrate that the proposed method outperforms other modern methods in terms of both objective metrics and visual quality. The proposed method can also reconstruct clear geometric structures, offering the potential for real-world applications.

PurposeThe purpose of this paper is to study the effects of uniform injection and suction through a perforated pentagonal cylinder on the flow field and heat transfer.Design/methodology/approachThe finite-volume method has been used to solve the ensemble-averaged Navier-Stokes equations for incompressible flow at moderate Reynolds number (Re = 22,000) with the k-ɛ turbulence model equations.FindingsA computational fluid dynamics analysis of turbulent flow past a non-regular pentagonal cylinder with three different aspect ratios aspect ratios has been carried out to investigate the effects of uniform injection/suction through the front and all surfaces of the cylinder. It is found that flow field parameters such as drag coefficient, pressure coefficient and Nusselt number are affected considerably in some cases depend on injection/suction rate (Γ) and perforated wall position.Research limitations/implicationsTo optimize the efficiency of the injection and suction through a perforated surface, both wide-ranging and intensive further studies are required. Using various perforation ratios and injection/suction intensities are some possibilities.Practical implicationsControl of the vortex shedding and wake region behind bluff bodies is of vital interest in fluid dynamics. Therefore, applying uniform injection and suction from a perforated bluff body into the main flow can be used as a drag reduction mechanism, thermal protection and heat transfer enhancement.Originality/valueThis study provides unique insights into the active flow control method around pentagonal cylinders that can be useful for researchers in the field of fluid dynamics and aeronautics.

Variable coefficient-informed neural network for PDE inverse problem in fluid dynamics

Syringe irrigation remains a widely used irrigant delivery method during root canal treatment. An interdisciplinary approach involving well-established methods from the field of fluid dynamics can provide new insights into the mechanisms involved in cleaning and disinfection of the root canal system by this method. In addition to the equipment used clinically (syringes and needles), this chapter will also discuss the physical properties of commonly used irrigants, the flow developed inside the root canal system, irrigant refreshment, forces applied on the root canal wall, entrapment and removal of air bubbles, and the anatomical challenges faced by syringe irrigation. Essential background knowledge on fluid dynamics will also be provided.

Diffusion-type problems in (nearly) unbounded domains play important roles in various fields of fluid dynamics, biology, and materials science. The aim of this paper is to construct accurate absorbing boundary conditions (ABCs) suitable for classical (local) as well as nonlocal peridynamic (PD) diffusion models. The main focus of the present study is on the PD diffusion formulation. The majority of the PD diffusion models proposed so far are applied to bounded domains only. In this study, we propose an effective way to handle unbounded domains both with PD and classical diffusion models. For the former, we employ a meshfree discretization, whereas for the latter the finite element method (FEM) is employed. The proposed ABCs are time-dependent and Dirichlet-type, making the approach easy to implement in the available models. The performance of the approach, in terms of accuracy and stability, is illustrated by numerical examples in 1D, 2D, and 3D.

The influence of polymers on entropy generation processes is substantial, particularly in the fields of fluid dynamics and rheology. The FENE-P (Finitely Extensible Nonlinear Elastic-Peterlin) model describes the polymer’s dynamics as a result of the interaction between the stretching caused by the velocity gradient and the elastic force that restores the polymer to its equilibrium position. Models such as FENE-P aid in understanding and predicting polymer flow behaviour allowing for the reduction of entropy generation by optimizing system designs. A continuum approach is employed to express the heat flux vector and polymer confirmation tensor of the model. The study investigates the complex relationship between polymer conformation, flow dynamics, and heat transfer taking into account the thermophoresis (Soret effect) and mass diffusion-thermal diffusion coupling (Dufour effect) phenomena to optimize processes by reducing entropy. This study illuminates polymer’s significance in entropy minimization, improving engineering design methodologies and applications in materials science, chemical engineering, and fluid dynamics. As result, the presence of polymers leads to a substantial decrease in the total entropy of the system. This understanding provides opportunities for enhancing heat transfer systems, thereby facilitating the development of more efficient and sustainable technology.

Multiphase microfluidics offer a wide range of functionalities in the fields of fluid dynamics, biology, particle synthesis, and, more recently, also in logical computation. In this article, we describe the hysteresis of immiscible, multiphase flow obtained in hydrophilic, microfluidic systems at a T-junction. Stable and unstable state behaviors, in the form of segmented and parallel flow patterns of oil and water, were reliably produced, depending upon the history of the flow rates applied to the phases. The transition mechanisms between the two states were analyzed both experimentally and using numerical simulations, describing how the physical and fluid dynamic parameters influenced the hysteretic behavior of the flow. The characteristics of these multiphase systems render them suitable to be used as pressure comparators and also for the implementation of microfluidic logic operations.