Hydrodynamic Flow Model Research Articles

Deep Reinforcement Learning Technique for Traffic Metering in Connected Urban Street Networks

Proper metering can improve traffic operations in congested urban street networks. The available approaches either (a) use macroscopic fundamental diagrams to model traffic dynamics or (b) use numerical time–space discretization of the hydrodynamic traffic flow model with high computational requirements. Therefore, they either (a) do not represent traffic dynamics accurately or (b) are not suitable for online applications. This study introduces a deep reinforcement learning (DRL) methodology to capture traffic dynamics on a micro-level scale with the capability of capturing detailed traffic dynamics with low computational time. The DRL methodology employs two neural networks that map the location of connected vehicles in a network to traffic metering signal indications and estimate the objective function of the traffic metering problem. The outputs of the neural networks are used to construct a loss function, whose optimization provides the optimal parameters for the neural networks. Because of the complexity of the loss function, the gradient descent optimization technique with Monte Carlo simulation is used to optimize the loss function. The proposed methodology was tested on a simulated case study network in Vissim software with 20 intersections. The numerical results showed that the methodology increased throughput by 41.2% and 21.3% and reduced the total travel time of vehicles by 3.4% and 15.5% compared to a no-metering strategy. Comparing the computational time of the proposed methodology with one of the existing traffic metering approaches also showed the potential of the methodology for online applications.

Piston problem for the pressureless hydrodynamic traffic flow model

The hydrodynamic model can be used to describe traffic problems in transport. When the speed of the first car is less than the speed behind it, it leads to traffic jams. When the first car's speed is faster than the cars behind it, it leads to traffic evacuation. If we consider the first car to be a piston, then the speed of the piston will cause traffic jams and traffic evacuation. In this paper, we study the piston problem for the hydrodynamic model. The formation and propagation of shock wave, rarefaction wave, delta-shock wave, and vacuum can describe the phenomena of traffic jams, traffic evacuation, severe traffic jams, and traffic evacuation with traffic volume of zero, respectively. Therefore, for different traffic phenomena, we prove the existence of shock solution, rarefaction solution, delta shock solution, and vacuum solution. In addition, we perform some representative numerical simulations.

A hydrodynamic model of capillary flow in an axially symmetric tube with a non-slowly-varying cross section and a boundary slip

The capillary flow of a Newtonian and incompressible fluid in an axially symmetric horizontal tube with a non-slowly-varying cross section and a boundary slip is considered theoretically under the assumption that the Reynolds number is small enough for the Stokes approximation to be valid. Combining the Stokes equation with the hydrodynamic model assuming the Hagen–Poiseulle flow, a general formula for the capillary flow in a non-slowly-varying tube is derived. Using the newly derived formula, the capillary imbibition and the time evolution of meniscus in tubes with non-uniform cross sections such as a conical tube, a power-law-shaped diverging tube, and a power-law-shaped converging tube are reconsidered. The perturbation parameters and the corrections due to the non-slowly-varying effects are elucidated, and the new scaling formulas for the time evolution of the meniscus of these specific examples are derived. Our study could be useful for understanding various natural fluidic systems and for designing functional fluidic devices such as a diode and a switch.

The significance of radiative heat and mass transfer through a vertical sheet with chemical reaction: Designing by artificial approach Levenberg-Marquardt

The present study examines the impact of incorporating soft computing algorithms during neural network training on the evaluation and prediction performance of artificial neural networks. The research centers on a magneto hydrodynamic flow model (RHMT-VSCR) that depicts the movement of rotating fluid along a vertical sheet in a permeable medium. Furthermore, the model considers the impact of heat source-sink interactions, thermal radiation, and reactive species in conjunction with the effects of Hall current on the enhancement of energy and solute profiles. Because the induced magnetic field has a negligible magnetic Reynolds number, it is disregarded. A set of governing nonlinear PDEs is converted to a system of ODEs by applying an appropriate postulate of similarity variables in order to analyze the system. The multilayer perceptron network utilizes a supervised Levenberg–Marquardt Backpropagation algorithm (LMLA-BPNN) to ascertain the ideal quantity of neurons to be included in the hidden layer of artificial neural networks intended for modeling purposes. The bvp4c numerical approach is utilized to establish a continuous neural network mapping, which produces datasets that are utilized for the purposes of authentication, training, and testing. The range i.e., 10−2−10−8 of absolute error of the reference and target data demonstrates the optimal accuracy performance of LMLA-BPNN networks. After acquiring knowledge of neural network mapping, it is applied to approximate solutions for a wide range of scenarios through the manipulation of physical constraints, including suction/injection quantities, magnetic parameter, and porosity parameter, which influence the characteristics of flow, energy, and concentration.To assess the precision of the proposed method, statistical graphs based on regression, mean squared error analysis graphs, and error histogram graphs are employed. The results of the research demonstrate that the Levenberg-Marquardt Backpropagation neural network mappings' derivation, convergence, authentication, and stability were effectively validated.

The vanishing pressure limits of Riemann solutions for the Aw-Rascle hydrodynamic traffic flow model with the logarithmic equation of state

Two kinds of Riemann solutions for the Aw-Rascle hydrodynamic traffic flow model with the logarithmic equation of state are explicitly obtained by using the combination between 1-rarefaction or 1-shock wave along with 2-contact discontinuity. The formation of vacuum state and delta shock wave is identified and analyzed when the perturbation parameter in the pressure term drops to zero, where the intrinsic cavitation and concentration phenomena are surveyed and explored concretely. Additionally, several numerical results displaying the formation process of vacuum state and delta shock wave are also presented by taking three different perturbation parameters for comparison.

Hydrodynamic traffic flow models including random accidents: A kinetic derivation

Hydrodynamic traffic flow models including random accidents: A kinetic derivation

Sailing Performance of Wind-Powered Cargo Vessel in Unsteady Conditions

The need to reduce green-house gas emissions from shipping has reborn the interest in wind propulsion for commercial cargo vessels. This places new requirements on the tools used in ship design, as well as the methods usually applied in sailing yacht design. A range of design tools are used by designers at various stages in the design of wind-assisted ships and for different purposes. One important tool is the steady-state velocity prediction program (VPP) which is typically used to predict the speed of the vessel when sailing in a range of wind directions and wind speeds. Steady-state VPPs can be very efficient and fast and may be used to rapidly assess a large number of design alternatives. However, steady-state VPPs are not able to consider dynamic effects such as unsteady wave forces on the hull which may require the rudder to be active to control the heading and course of the vessel. This, in turn, leads to different mean forces than those predicted by a static VPP and therefore the sailing performance may be reduced compared to the predictions of a static VPP. Another effect of the ship’s motions in a seaway is that the angles of attack of the sails fluctuate, which can lead to different optimum sheeting angles and possibly a loss of performance. This study uses an unsteady 3D fully nonlinear potential flow hydrodynamic model coupled with an efficient lifting-line aerodynamic model to investigate the differences in sailing performance of a vessel sailing in steady conditions to the performance when sailing in a seaway and gusty wind based on a spatio-temporal wind model. The analysis shows clearly that the unsteady wind model affects the predicted performance. This is especially the case when sailing close-hauled and on a beam reach, where large changes in the local sail angles of attack can be observed.

Analysis of Convergence Behavior for the Overset Mesh Based Numerical Wave Tank in openfoam

Abstract This paper presents a solution verification and validation study for an overset mesh based numerical wave tank in openfoam, which considers the coupling between a free-surface hydrodynamic flow model, a rigid body motion model, and an overset mesh. The coupling between the rigid body motion solver and the free-surface flow solver was achieved in a segregated manner. Free decay of roll motion of a barge was modeled using the numerical wave tank, and the damping coefficient was selected as the target quantity for solution verification. The least-square based solution verification procedure was adopted, where one of the four types of error estimators was fit to the data in the least-square sense. Both structured and unstructured mesh were tested, and their effects on the convergence order, numerical uncertainty, and error were carefully investigated. From the numerical tests, it is found that the numerical wave tank exhibits a very good convergence property for the floating body problems with structured mesh, i.e., nearly second order in space and first order in time. However, when switching the body-fitted mesh to unstructured mesh, the grid convergence is reduced to first order. Unstructured mesh does not significantly affect the convergence order in time domain, but results in a larger uncertainty due to data scattering.

Developing a phenomenological model to simulate single and mixed scale formation during flow in porous media: Coupling a salt precipitation model with an ion transport equation under dynamic conditions

Water flooding and pressure maintenance are recommended to improve oil recovery practices after low recovery of petroleum reservoirs occurs during primary production. Salt crystal formation is a frequent occurrence when using these techniques. Several experimental, numerical, and theoretical studies have been done on the mechanisms underlying scaling and permeability reduction in porous media; however, there has not been a satisfactory model developed. This study developed a phenomenological model to predict formation damage caused by salt deposition. Compared with existing models, which provide a scaling tendency, the proposed model predicts the profile of scale deposition. The salt precipitation model simulates reactive fluid flow through porous media. A thermodynamic, kinetic, and flow hydrodynamic model was developed and coupled with the ion transport equation to describe the movement of ions. Further, a set of carefully designed dynamic experiments were conducted and the data were compared with the model predictions. Model forecasts and experimental data were observed to have an average absolute error (AAE) ranging from 0.68% to 5.94%, which indicates the model's suitability.

The effect of thermal-elastic deformation on the sealing performance of supercritical CO2 dry gas seal

PurposeThe purpose of this study is to investigate the sealing performance of S-CO2 dry gas seals (DGSs) by considering the effects of pressure-induced deformation, thermal deformation and coupling deformation.Design/methodology/approachA hydrodynamic lubrication flow model of S-CO2 DGS was established, and the model was solved using the finite difference and finite element methods. The pressure-induced deformation and thermal deformation of the sealing ring, as well as the sealing performance under the effects of pressure-induced deformation, thermal deformation and coupling deformation, were obtained.FindingsThe deformation of the sealing ring is mainly thermal deformation. The influence of pressure-induced deformation on leakage and gas film stiffness is greater than that of thermal deformation and coupling deformation. However, thermal deformation has a greater impact on friction torque and minimum film thickness than pressure-induced deformation and coupling deformation. The influence of deformations on sealing performance is important.Originality/valueThe sealing performance of S-CO2 DGSs was analyzed considering the effect of pressure-induced deformation, thermal deformation and coupling deformation, which can provide a theoretical basis for S-CO2 DGS optimization design.Peer reviewThe peer review history for this article is available at: https://publons.com/publon/10.1108/ILT-04-2023-0120/

Gas-hydrodynamics of gas-liquid flow in the reservoir-well system

A hydrodynamic model of gas-liquid flow in the reservoir-well system is developed and the boundary problem is solved.As the volume of liquid in the mixture is very small (5-10%), its effect on the gas-liquid mixture flow was considered only in terms of density change effect. It is assumed that liquid and gas particles move in the pipe at the same speed and there is no mass transfer.The gas is assumed to be ideal and an isothermal process is considered. The solution of some equations in initial and boundary conditions is found and the continuity condition at the well bottom is written, Pc(t) - bottom hole pressure is found. The borehole productivity is then determined by setting Pq.a (t) (taken from experiment).

Co-occurrence of pluvial and fluvial floods exacerbates inundation and economic losses: evidence from a scenario-based analysis in Longyan, China

Hydrodynamics and socio-economic impacts of compound floods as the result of co-occurring pluvial and fluvial floods, have not been well studied, which challenges conventional urban flood risk management that treats different types of floods separately. This study generates a high-resolution land surface digital surface model based on images from unmanned aerial vehicles, and incorporates it into a combined flooding model that integrates the 1D river hydrodynamic model, 1D urban drainage hydrodynamic model and 2D overland flow model, in order to simulate the inundation characteristics of extreme floods when local pluvial floods coincide with upstream fluvial floods, and in a further step to quantify the direct economic losses on urban physical systems. As demonstrated by an empirical study of Longyan, China, the combination of pluvial and fluvial floods leads to substantial exacerbation in inundation extent and depth. Consequently, the joining of pluvial and fluvial floods greatly amplifies economic losses to urban physical systems, indicating that ignoring co-occurrence of different types of floods will potentially lead to underestimation of flood risk and insufficient adaptation. Our study emphasizes the need to account for the co-occurrence of multiple types of floods in flood risk assessment and management, to avoid the shortcomings of fragmented responses.

“Digital Core” Technology and Supercomputer Computing

This article is focused on the modeling of multiphase hydrodynamic flows within “digital core” technology for the needs of the oil and gas industry. The essence of this technology is direct numerical simulation of the flows on the scale of the pore space of oil and gas reservoir rocks with direct resolution of the structure of this space and the dynamics of interphase boundaries. The importance of the development of high-performance computing facilities (supercomputers) for the successful implementation of this technology is emphasized. Work carried out by the Keldysh Institute of Applied Mathematics, RAS, in the field of mathematical models, computational algorithms, and their software implementation is described.

Numerical prediction of portal hypertension by a hydrodynamic blood flow model combing with the fractal theory

Portal hypertension (PH) can cause a series of complications, therefore, early prediction of PH is important. Traditional diagnostic methods are harmful to the human body, while other non-invasive methods are inaccurate and lack physical meaning. Combining various fractal theories and flow laws, we establish a complete portal system blood flow model from the Computed Tomography (CT) and angiography images. The portal vein pressure (PP) is obtained by the flow rate data from the Doppler ultrasound and the pressure–velocity relationship is established by the model. Three normal participants and 12 patients with portal hypertension were divided into three groups. For the three normal participants (Group A), their mean PP calculated by the model is 1752 Pa, falling into the normal range of PP. The mean PP of three patients with portal vein thrombosis (Group B) is 2357 Pa; and for the 9 patients with cirrhosis (Group C), their mean PP is 2915 Pa. These results validate the classification performance of the model. Moreover, the blood flow model can give early warning parameters of the corresponding portal vein trunk and portal vein microtubules for thrombosis and liver cirrhosis. This model presents the complete process of blood flow from sinusoids to the portal vein, adapts to the diagnosis of portal hypertension of thrombosis and liver cirrhosis, and provides a new method for noninvasive portal vein pressure detection from the perspective of biomechanics.

A Hydrodynamic Model of a Magnetized Jet Flow in the Magnetosphere

Abstract—A new hydrodynamic model of a quasi-stationary jet is presented. Approximation of the idealhydrodynamics of an incompressible fluid is found using an analytical solution, corresponding to a jet limitedin space, under conditions of compensation for the nonlinear effects of velocity and magnetic field in theequation of motion. An axially symmetric low-parameter model of a stationary jet was created for the boundaryconditions typical for jets in astrophysics and in laboratory modeling experiments, which makes it possibleto describe the structure of the velocity field and magnetic field.

Flood risk assessment of loss of life for a coastal city under the compound effect of storm surge and rainfall

Many coastal cities face the risk of compound floods induced by multiple factors such as storm surge and rainfall. Flood risk assessment helps mitigate and manage flood disasters in coastal cities. In current literature, study about compound flood risk assessment of loss of life is limited. This study assesses the risk of loss of life in compound floods under storm surge ad rainfall in the Macao Peninsula which is a coastal city. The joint probability for storm surge and rainfall occurrence was analyzed by copula function. The coupled hydrodynamic model of drainage and overland flow was established to simulate the compound flooding process. The risk of loss of life is quantitatively assessed based on the mortality function for flood scenarios with different probabilities. The western coastal area of Macao Peninsula is the most vulnerable area to flooding with higher mortality. Although the most area of Macao Peninsula has low mortality, there is still a serious loss of life in extreme floods due to high population density. This study provides guidance for quantitative compound flood risk assessment and risk management of loss of life in coastal cities.

A Hydrodynamic Model of a Magnetized Jet Flow in the Magnetosphere

A Hydrodynamic Model of a Magnetized Jet Flow in the Magnetosphere

Impact of thermal energy on MHD Casson fluid through a Forchheimer porous medium with inclined non-linear surface: A soft computing approach

In this work, the influence of thermal energy in term of heat source, thermal radiation and chemical reaction on magneto hydrodynamic Casson fluid flow model (MHD-CFM) over a nonlinear slanted extending surface with slip velocity in a Forchheimer permeable medium is numerically studied using the Levenberg Marquardt methodology with backpropagated learning mechanism. It is valuable to evaluate the flow of Cason fluids based on materials (such as drilling muds, clay coatings, various suspensions and certain lubricating oils, polymeric melts, and a wide range of colloids) in the occurrence of heat transfer. Using efficient data, PDEs of (MHD-CFM) were converted to ordinary differential equations. These obtained non-linear ODEs are then rectified using the computational power of the Lobatto IIIA approach to obtain a dataset of Levenberg Marquardt algorithm based trained neural networks (LMA-TNN) for six scenarios of this presented model, which were graphically represented using nftool to obtain regression, efficiency, fit curve, error bars, and trained state analysis. The velocity, temperature, and concentration profiles were computed, and the findings were presented. Additionally, the skin friction coefficient, Nusselt number, & local Sherwood number are explored. The graphs show that when values of radiation parameter and the Forchheimer porous media parameter increase, the temperature of the plate drops. In the existence of a chemical process and a high Schmidt number, the concentration drops. The accuracy achieved in terms of relative error demonstrates the validity and significance of the solution process.

Numerical modelling of hydraulic efficiency and pollution transport in waste stabilization ponds

This paper investigates the hydraulic performance and solute transport processes in waste stabilization ponds (WSPs). A numerical model comprised of Reynolds-averaged Navier–Stokes (RANS) flow hydrodynamic model with the standard k − ε turbulence closure coupled with the advection-diffusion solute transport model, is developed in a three-dimensional Cartesian coordinate system. The proposed numerical model is successfully validated against laboratory-scale physical modelling measurements of flow hydrodynamics and solute characteristics across trapezoidal pond geometry. The developed numerical model is adopted to run series of scenario-based simulations to investigate the effects of WSP's geometrical features and implementation of an island retrofitting on the hydraulic performance and treatment efficiency of the WSPs. Fifteen pond configurations with varying side-walls slope and island configurations are simulated. Vertical and horizontal structures of flow hydrodynamics across the pond are investigated. Solute transport processes are studied through determining residence time distribution (RTDs) curves based on numerical tracer simulations. Two deflector island configurations (parallel and rotated) are simulated to investigate their influence on enhancing the hydraulic performance of the WSP. The analysis of the numerical results indicates an overall positive impact of deflector island retrofitting on the hydraulic performance of the WSP. The side-walls slope are shown to play a key role in determining the overall performance of the WSP. For the cases with side-walls slope of 1:1, 0.5:1 and 0:1, the hydraulic efficiency of the WSP was enhanced by adding both parallel and rotated islands. However, for the cases with side-walls slope of 2:1 and 1.5:1, addition of island deflector is shown to have negative impacts on the hydraulic performance of the waste stabilization pond.

A Hybrid Approach to Improve Flood Forecasting by Combining a Hydrodynamic Flow Model and Artificial Neural Networks

Climate change is driving worsening flood events worldwide. In this study, a hybrid approach based on a combination of the optimization of a hydrodynamic model and an error correction modeling that exploit different aspects of the physical system is proposed to improve the forecasting accuracy of flood water levels. In the parameter optimization procedure for the hydrodynamic model, Manning’s roughness coefficients were estimated by considering their spatial distribution and temporal variation in unsteady flow conditions. In the following error correction procedure, the systematic errors of the optimized hydrodynamic model were captured by combining the input variable selection method using partial mutual information (PMI) and artificial neural networks (ANNs), and therefore, complementary information provided by the data was achieved. The developed ANNs were used to analyze the potential non-linear relationships between the considered state variables and simulation errors to predict systematic errors. To assess the hybrid forecasting approach (hydrodynamic model with an ANN-based error correction model), performances of the hydrodynamic model, two ANN-based water-level forecasting models (ANN1 and ANN2), and the hybrid model were compared. Regarding input candidates, ANN1 considers the historical observations only, and ANN2 considers not only the historical observations that used in ANN1 but also the prescribed boundary conditions required for the hydrodynamic forecast model. As a result, the hybrid model significantly improved the forecasting accuracy of flood water levels compared to individual models, which indicates that the hybrid model is able to take advantage of complementary strengths of both the hydrodynamic model and the ANN model. The optimization of the hydrodynamic model allowing spatially and temporally variable parameters estimated water levels with acceptable accuracy. Furthermore, the use of PMI-based input variable selection and optimized ANNs as error correction models for different sites were proven to successfully predict simulation errors in the hydrodynamic model. Hence, the parameter optimization of the hydrodynamic model coupled with error correction modeling for water level forecasting can be used to provide accurate information for flood management.