Fluid Dynamical Systems Research Articles

Propagation of waves in a hydroelastic shell‐viscous liquid system, in the presence of gas bubbles

In this article, a thin‐walled isotropic infinitely long‐circular cylindrical elastic tube and the pulsed flow of an aerohydroelastic system within a compressible bubble fluid have been presented in 3D. The equation has been solved for an axisymmetric waves propagation in the liquid–shell system. The form and frequency of the specific oscillations formed in the shell–fluid dynamic system have been determined. The results obtained for the case when the material of thin‐walled pipes is made from various modifications of high‐strength steel and non‐classical polymeric materials, and water containing spherical air bubbles is taken as a two‐phase liquid. The fundamental iteration technique has been used to compute eigenvalues and corresponding eigenfunctions to represent field quantities with the help of Matlab software. The numerical results have been presented graphically.

A Four Step Scheme Approach to the Forward-Backward Stochastic Navier-Stokes Equations

In this paper, the authors presented a novel fluid dynamics system, the forward-backward stochastic Navier-Stokes equations in two dimensions for incompressible fluid flows. The well-posedness of the system is obtained through a two-step process. First, certain projections of the system to the finite dimensions are employed, and the existence and uniqueness of solutions in finite dimensions are proved via the four step scheme. Then the Galerkin approximation is used to show the existence and uniqueness of a solution to the system in an infinite dimensional functional setup.

Time-delay-locked dynamic mode decomposition for extracting flow patterns of compressor in experiment

Dynamic mode decomposition (DMD) is widely used in extracting the main features of high-dimensional nonlinear systems in computational fluid dynamics. However, it cannot be directly applied to real compressors due to the limitations of data acquisition over the entire flow field. In order to overcome this limitation, our paper proposes a time-delay-locked DMD algorithm. It combines the idea of phase locking, Hankel time-delay method, and DMD method. Based on it, the flow patterns are successfully extracted and the unsteady flow phenomena including rotating stall and rotating instability are explained as modal waves. Multi-peak spectrum nearly 1/2 BPF observed under off-design RI conditions is explained. In addition, multi-scale of rotating stall is analyzed and shock wave is captured.

Attention-enhanced neural network models for turbulence simulation

Deep neural network models have shown great potential in accelerating the simulation of fluid dynamic systems. Once trained, these models can make inferences within seconds, thus can be extremely efficient. However, it becomes more difficult for neural networks to make accurate predictions when the flow becomes more chaotic and turbulent at higher Reynolds numbers. One of the most important reasons is that existing models lack the mechanism to handle the unique characteristic of high-Reynolds-number turbulent flow; multi-scale flow structures are nonuniformly distributed and strongly nonequilibrium. In this work, we address this issue with the concept of visual attention: intuitively, we expect the attention module to capture the nonequilibrium of turbulence by automatically adjusting weights on different regions. We compare the model performance against a state-of-the-art neural network model as the baseline, the Fourier neural operator, on a two-dimensional turbulence prediction task. Numerical experiments show that the attention-enhanced neural network model outperforms existing state-of-the-art baselines, and can accurately reconstruct a variety of statistics and instantaneous spatial structures of turbulence at high Reynolds numbers. Furthermore, the attention mechanism provides 40% error reduction with 1% increase in parameters, at the same level of computational cost.

Novel resonance stability criterion for the 2D magnetically levitated servo-proportional valve

The leakage of the pilot stage of the 2D valve mainly depends on the size of its initial opening. According to the Routh criterion, the pilot stage of the two-dimensional magnetically levitated servo-proportional valve (2D-MSP valve) needs to be designed to have certain positive values to increase the damping ratio to improve valve stability, which leads to the leakage flow representing a non-negligible power loss. In order to reduce leakage flow and achieve goal of energy saving, this paper presents a novel resonance stability criterion by considering nonlinear characteristics of the fluid dynamic system. First, the 2D-MSP valve is regarded as a three-way valve-controlled differential cylinder system. Based on the frequency response of the resonance state, the energy conservation method is used to solve the flow “backfilling” area, the motion equation of the cylinder piston (valve spool displacement) and the pressure waveform of the sensing chamber under different opening and pressure amplitude ratio. Then, the analytical expression of the resonance peak amplitude is obtained and the resonance stability criterion is deduced. The result is compared with the Routh stability criterion, which illustrates that the positive openings of the pilot stage can be reduced to one-third of the original value. The prototype valve is then designed and manufactured based on the resonance stability criterion. The dynamic and static characteristics under different system pressures are measured. Experimental results show that the prototype valve is an over-damped system without any overshoot, which has excellent working stability, and its static and dynamic performance can meet the demands of the industry servo-proportional control system. The research work validates the effectiveness of the proposed resonance stability criterion.

On UAV source seeking with complex dynamic characteristics and multiple constraints: A cooperative standoff monitoring mode

Aiming at the source-seeking problem with complex dynamic characteristics and multiple constraints in a three-dimensional (3-D) environment, a cooperative standoff monitoring mode that uses unmanned aerial vehicles (UAVs) is proposed in this paper. First, the dynamic pollutant field is modeled by a 3-D advection-diffusion partial differential equation (PDE), and quantifiable indexes are proposed for source seeking around multiple constraints, which consist of UAV kinematics, no-fly zones and cumulative exposure to the high concentration of pollutants. Second, to eliminate the essential gradient information in source seeking of the dependence on known advection and diffusion coefficients in the PDE, UAVs cooperatively estimate the spatial gradient of the pollutant field by the least squares method. Third, a cooperative standoff monitoring mode for source seeking is proposed based on dynamic level set tracking and an interfered fluid dynamical system (IFDS), and receding horizon control (RHC) is introduced to improve the source-seeking effect. Finally, source localization is achieved by the UAVs with the multiple constraints being satisfied, and the effectiveness of the proposed method is validated by the simulation results.

A stochastic topology optimization algorithm for improved fluid dynamics systems

Abstract The use of topology optimization in the design of fluid dynamics systems is still in its infancy. With the decreasing cost of additive manufacture, the application of topology optimization in the design of structural components has begun to increase. This paper provides a method for using topology optimization to reduce the power dissipation of fluid dynamics systems, with the novelty of it being the first application of stochastic mechanisms in the design of 3D fluid–solid geometrical interfaces. The optimization algorithm uses the continuous adjoint method for sensitivity analysis and is optimized against an objective function for fluid power dissipation. The paper details the methodology behind a vanilla gradient descent approach before introducing stochastic behavior through a minibatch-based system. Both algorithms are then applied to a novel case study for an internal combustion engine's piston cooling gallery before the performance of each algorithm's resulting geometry is analyzed and compared. The vanilla gradient descent algorithm achieves an 8.9% improvement in pressure loss through the case study, and this is surpassed by the stochastic descent algorithm which achieved a 9.9% improvement, however this improvement came with a large time cost. Both approaches produced similarly unintuitive geometry solutions to successfully improve the performance of the cooling gallery.

Reliability Analysis of Hydrodynamic System for Robot Configuration

The failure tree and J-M model method are lack of analysis of the importance of each component model, which leads to the low reliability of the analysis results. In view of this problem, a Monte Carlo method based on the shape of the English long-distance robot is proposed. In view of the configuration of the robot, the realization process of the robot shape fluid dynamics system is analyzed. The frequency of accident is determined by Monte Carlo simulation, which is used as the reliability index of the system. In MATLAB, the reliability of the shape fluid dynamic system of robot is analyzed by Monte Carlo method. The system importance name and parameters are determined. The parameter conforms to the statistical function of random variables of each corresponding probability distribution function. According to the parameters, the function of the structure is established. The system is divided into reliable state, failure state and limit state with 0 as the dividing point, and the actual failure probability of the system is calculated. The numerical solution of log domain is simulated by the method of statistical calculation of random variables, and the actual failure probability is expressed by normal distribution function. The experimental results show that the actual failure probability of the method is lower than 5% under any working load, and the reliability of the analysis results is high.

Convolutional neural networks to predict the onset of oscillatory instabilities in turbulent systems.

Many fluid dynamic systems exhibit undesirable oscillatory instabilities due to positive feedback between fluctuations in their different subsystems. Thermoacoustic instability, aeroacoustic instability, and aeroelastic instability are some examples. When the fluid flow in the system is turbulent, the approach to such oscillatory instabilities occurs through a universal route characterized by a dynamical regime known as intermittency. In this paper, we extract the peculiar pattern of phase space attractors during the regime of intermittency by constructing recurrence networks corresponding to the phase space topology. We further train a convolutional neural network to classify the periodic and aperiodic structures in the recurrence networks and define a measure that indicates the proximity of the dynamical state to the onset of oscillatory instability. We show that this measure can predict the onset of oscillatory instabilities in three different fluid dynamic systems governed by different physical phenomena.

Numerical Computation of Specific Impulse and Internal Flow Parameters in Solid Fuel Rocket Motors with Two-Phase Сombustion Products

Eulerian --- Lagrangian method was used in the Fluent computational fluid dynamics system to calculate motion of the two-phase combustion products in the solid fuel rocket motor combustion chamber and nozzle. Condensed phase is assumed to consist of spherical particles with the same diameter, which dimensions are not changing along the motion trajectory. Flows with particle diameters of 3, 5, 7, 9, and 11 μm were investigated. Four versions of the engine combustion chamber configuration were examined: with slotted and smooth cylindrical charge channels, each with external and submerged nozzles. Gas flow and particle trajectories were calculated starting from the solid fuel surface and to the nozzle exit. Volumetric fields of particle concentrations, condensed phase velocities and temperatures, as well as turbulence degree in the solid propellant rocket engine flow duct were obtained. Values of particles velocity and temperature lag from the gas phase along the nozzle length were received. Influence of the charge channel shape, degree of the nozzle submersion and of the condensate particles size on the solid propellant rocket engine specific impulse were determined, and losses were estimated in comparison with the case of ideal flow

Mathematical modelling of a membrane-less redox flow battery based on immiscible electrolytes

We present a mathematical model to study the steady-state performance of a membrane-less reversible redox flow battery formed by two immiscible electrolytes that spontaneously form a liquid-liquid system separated by a well defined interface. The model assumes a two-dimensional battery with two coflowing electrolytes and flat electrodes at the channel walls. In this configuration, the analysis of the far downstream solution indicates that the interface remains stable in all the parameter range covered by this study. To simplify the description of the problem, we use the dilute solution theory to decouple the calculation of the velocity and species concentration fields. Once the velocity field is known, we obtain the distribution of the mobile ionic species along with the current and the electric potential field of the flowing electrolyte solution. The numerical integration of the problem provides the variation of the battery current density Iapp with the State of Charge (SoC) for different applied cell voltages Vcell. A detailed analysis of the concentration density plots indicates that the normal operation of the battery is interrupted when reactant depletion is achieved near the negative electrode both during charge and discharge. The effect of the electrolyte flow on the performance of the system is studied by varying the Reynolds, Re, and Péclet, Pe, numbers. As expected, the flow velocity only affects the polarization curve in the concentration polarization region, when Vcell is well below the equilibrium potential, resulting in limiting current densities that grow with Re as jlim∼Re0.3. In addition, both the single-pass conversion efficiency ψ and the product ψjlim decrease with Re. Concerning the later, the decay rate with Re exhibits a power law with an exponent that almost doubles previous theoretical predictions obtained by imposing a prescribed velocity profile for the electrolyte in a membrane-less laminar flow battery with a liquid oxidant and gaseous fuel. The present work constitutes the first modelling attempt that simultaneously solves the fluid dynamical system formed by the two immiscible electrolytes and the electrochemical problem that determines the response of the membrane-less battery. The proposed model could be used as a valuable tool to optimize future flow battery designs based on immiscible electrolytes.

Integrated Microscopy and Metabolomics to Test an Innovative Fluid Dynamic System for Skin Explants In Vitro.

The in vitro models are receiving growing attention in studies on skin permeation, penetration, and irritancy, especially for the preclinical development of new transcutaneous drugs. However, synthetic membranes or cell cultures are unable to effectively mimic the permeability and absorption features of the cutaneous barrier. The use of explanted skin samples maintained in a fluid dynamic environment would make it possible for an in vitro experimentation closer to in vivo physiological conditions. To this aim, in the present study, we have modified a bioreactor designed for cell culture to host explanted skin samples. The preservation of the skin was evaluated by combining light, transmission, and scanning electron microscopy, for the histo/cytological characterization, with nuclear magnetic resonance spectroscopy, for the identification in the culture medium of metabolites indicative of the functional state of the explants. Our morphological and metabolomics results demonstrated that fluid dynamic conditions ameliorate significantly the structural and functional preservation of skin explants in comparison with conventional culture conditions. Our in vitro system is, therefore, reliable to test novel therapeutic agents intended for transdermal administration in skin samples from biopsies or surgical materials, providing predictive information suitable for focused in vivo research and reducing animal experimentation.

An in silico validation framework for quantitative DCE-MRI techniques based on a dynamic digital phantom

Quantitative evaluation of an image processing method to perform as designed is central to both its utility and its ability to guide the data acquisition process. Unfortunately, these tasks can be quite challenging due to the difficulty of experimentally obtaining the “ground truth” data to which the output of a given processing method must be compared. One way to address this issue is via “digital phantoms”, which are numerical models that provide known biophysical properties of a particular object of interest. In this contribution, we propose an in silico validation framework for dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) acquisition and analysis methods that employs a novel dynamic digital phantom. The phantom provides a spatiotemporally-resolved representation of blood-interstitial flow and contrast agent delivery, where the former is solved by a 1D-3D coupled computational fluid dynamic system, and the latter described by an advection-diffusion equation. Furthermore, we establish a virtual simulator which takes as input the digital phantom, and produces realistic DCE-MRI data with controllable acquisition parameters. We assess the performance of a simulated standard-of-care acquisition (Protocol A) by its ability to generate contrast-enhanced MR images that separate vasculature from surrounding tissue, as measured by the contrast-to-noise ratio (CNR). We find that the CNR significantly decreases as the spatial resolution (SRA, where the subscript indicates Protocol A) or signal-to-noise ratio (SNRA) decreases. Specifically, with an SNRA / SRA = 75 dB / 30 μm, the median CNR is 77.30, whereas an SNRA / SRA = 5 dB / 300 μm reduces the CNR to 6.40. Additionally, we assess the performance of simulated ultra-fast acquisition (Protocol B) by its ability to generate DCE-MR images that capture contrast agent pharmacokinetics, as measured by error in the signal-enhancement ratio (SER) compared to ground truth (PESER). We find that PESER significantly decreases the as temporal resolution (TRB) increases. Similar results are reported for the effects of spatial resolution and signal-to-noise ratio on PESER. For example, with an SNRB / SRB / TRB = 5 dB / 300 μm / 10 s, the median PESER is 21.00%, whereas an SNRB / SRB / TRB = 75 dB / 60 μm / 1 s, yields a median PESER of 0.90%. These results indicate that our in silico framework can generate virtual MR images that capture effects of acquisition parameters on the ability of generated images to capture morphological or pharmacokinetic features. This validation framework is not only useful for investigations of perfusion-based MRI techniques, but also for the systematic evaluation and optimization new MRI acquisition, reconstruction, and image processing techniques.

CFD Investigations of Bath Dynamics in a Pilot-Scale TSL Furnace

The hydrodynamics of a Top Submerged Lance (TSL) slag bath are investigated here by means of Computational Fluid Dynamics (CFD) simulation. The object of the study is the pilot-scale furnace located at TU Bergakademie Freiberg, where air is injected beneath the slag bath with a top lance. The fluid dynamics system is evaluated at operating conditions, with experimentally measured slag physical properties and real flow rates. The numerical approach is based on the Volume Of Fluid (VOF) model, a front-tracking method that allows the interface to be geometrically reconstructed. Using a fine computational grid, the multiphase interactions are calculated with a high level of detail, revealing the mechanisms of bubble formation and bath dynamics. Two lance configurations are compared, with and without a swirler, and the effect on the hydrodynamics is discussed with regards to key features of the process, such as bubble dynamics, slag splashing, the interface area, rotational sloshing, and bath mixing. The model predicts bubble frequencies in the range of 2.5 to 3 Hz and captures rotational sloshing waves with half the frequencies of the bubble detachment. These results agree with real furnace data from the literature, proving the reliability of the computing model and adding value to the empirical understanding of the process, thanks to the direct observation of the resolved multiphase flow features. The comparative study indicates that the air swirler has an overall positive effect in addition to the proposed enhancement of lance cooling, with an increase in the bath mixing and a reduction in the splashing.

Automatic Fault Diagnosis Method for Hydrodynamic Control System

In order to improve the accuracy and speed of fault data acquisition of fluid power control system, this paper designs an automatic fault diagnosis method of fluid power control system based on English translation speech recognition. Firstly, the SDG model of the fluid dynamic control system is established, and the fault link is obtained and determined. Then the correlation dimension of data in fluid mechanics calculation is analyzed, and the fault data location is realized. On this basis, the fault classification model of the hydraulic power control system is established, and the automatic fault diagnosis of the hydraulic power control system is completed. Experiments show that the new fault diagnosis method can effectively improve the accuracy and speed of fault data acquisition of fluid power control system, the highest accuracy can reach 89.92%, the fastest speed is 11s, and improve the reliability of fault diagnosis results.

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.

Endocardial left atrial appendage occlusion in atrial fibrillation: computational fluid dynamics simulations to assess stroke risk

Abstract Funding Acknowledgements Type of funding sources: None. Background Percutaneous endocardial left atrial appendage (LAA) occlusion (LAAO) in non-valvular atrial fibrillation (AF) seems comparable to anticoagulation therapy (OAC) as regards thromboembolic risk reduction with a possible additional decrease in major bleeding. LAAO is currently limited to patients with contraindications to OAC, due to its high costs and procedural risks, but better pre-procedural planning and operative techniques might improve the outcome widening practical indications. Computational fluid dynamics (CFD) represents a valuable non-invasive approach to estimate physiologically significant hemodynamic parameters in a complex fluid dynamics system. It might provide a helpful in silico simulation of blood flow patterns within the LA and LAA by using 3D patient-specific models, allowing LAAO planning and effects prediction. Purpose This study’s aim was to simulate the fluid dynamics effects of LAAO in AF patients to predict patient-specific hemodynamic changes caused by applying the two most popular devices. Methods LAAO was simulated on the 3D LA anatomical models obtained from CT data in 5 AF patients, considering the device specific shape. CFD simulations in AF condition were performed both on the whole LA model and on the models with the LAAO performed with the two devices. Significant fluid dynamics indices (blood velocity, vortex structures, LAA ostium velocity, LA blood stasis) were computed to evaluate the changes in the flow patterns after LAAO in relation to the thrombogenic risk. Results Overall we found a more effective washout within the LA after LAAO, in terms of a different spatial distribution of velocities (see figure for a qualitative evaluation of LA blood flow velocity in one patient: (A) model with LA and LAA; models after LAAO applying the Amulet (B) and the Watchman (C) device) and vortex structures (after LAAO, they were decreased in number and increased in size). Moreover, a higher velocity at the mitral valve and at the LAA ostium (peak velocity: 12-17 cm/s in the models with LAA, 40-60 cm/s in LAAO_A and 35-65 in LAAO_W) was detected together with a slightly improved washout effect in terms of blood stasis with the Watchman device (stasis: 3.1-5.7% in the models with LAA, 1.9-4.1% in LAAO_A, 1.7-3.7% in LAAO_W). Conclusions A workflow for simulating the fluid dynamics effects of endocardial LAAO in AF was developed and tested. CFD provides a valuable tool to quantify hemodynamic changes after LAAO and assess thrombogenic risk in patient-specific LA and LAA. Our preliminary results suggest that endocardial LAAO favourably affects blood fluid dynamics in the LA. Abstract figure

Dynamic Analysis and FPGA Implementation of a Kolmogorov-Like Hyperchaotic System

Chaotic systems have high potential for engineering applications due to their extremely complex dynamics. In the paper, a five-dimensional (5D) Kolmogorov-like hyperchaotic system is proposed. First, the hyperchaotic property is uncovered, and numerical analysis shows that the system displays the coexistence of different kinds of attractors. This system presents a generalized form of fluid and forced-dissipative dynamic systems. The vector field of the hyperchaotic system is decomposed to inertial, internal, dissipative and external torques, respectively, and the energies are analyzed in detail. Then, the bound of the 5D dissipative hyperchaos is estimated with a constructed spherical function. Finally, the system passes the NIST tests and an FPGA platform is used to realize the hyperchaotic system.

Electrostatic wave breaking limit in a cold electronegative plasma with non-Maxwellian electrons

A one-dimensional multifluid hydrodynamic model has been adopted as basis for an investigation of the role of suprathermal electrons on the wave breaking amplitude limit for electrostatic excitations propagating in an electronegative plasma. A three-component plasma is considered, consisting of two inertial cold ion populations of opposite signs, evolving against a uniform background of (non-Maxwellian) electrons. A kappa-type (non-Maxwellian) distribution function is adopted for the electrons. By employing a traveling wave approximation, the first integral for the fluid-dynamical system has been derived, in the form of a pseudo-energy balance equation, and analyzed. The effect of intrinsic plasma parameters (namely the ion density ratio, the ion mass ratio, and the superthermal index of the nonthermal electrons) on the wave breaking amplitude limit is explored, by analyzing the phase space topology of the associated pseudopotential function. Our results are relevant to particle acceleration in Space environments and to recent experiments based on plasma-based accelerator schemes, where the simultaneous presence of negative ions and nonthermal electrons may be observed.

Port-Hamiltonian modeling of ideal fluid flow: Part I. Foundations and kinetic energy

In this two-parts paper, we present a systematic procedure to extend the known Hamiltonian model of ideal inviscid fluid flow on Riemannian manifolds in terms of Lie–Poisson structures to a port-Hamiltonian model in terms of Stokes-Dirac structures. The first novelty of the presented model is the inclusion of non-zero energy exchange through, and within, the spatial boundaries of the domain containing the fluid. The second novelty is that the port-Hamiltonian model is constructed as the interconnection of a small set of building blocks of open energetic subsystems. Depending only on the choice of subsystems one composes and their energy-aware interconnection, the geometric description of a wide range of fluid dynamical systems can be achieved. The constructed port-Hamiltonian models include a number of inviscid fluid dynamical systems with variable boundary conditions. Namely, compressible isentropic flow, compressible adiabatic flow, and incompressible flow. Furthermore, all the derived fluid flow models are valid covariantly and globally on n-dimensional Riemannian manifolds using differential geometric tools of exterior calculus.