Real Flow Research Articles

A Thread-Safe Lattice Boltzmann Model for Multicomponent Turbulent Jet Simulations

In this work an optimized multicomponent lattice Boltzmann (LB) model is deployed to simulate axisymmetric turbulent jets of a fluid evolving in a quiescent, immiscible environment over a wide range of dynamic regimes. The implementation of the multicomponent LB code achieves peak performance on graphic processing units (GPUs) with a significant reduction of the memory footprint, retains the algorithmic simplicity inherent to standard LB computing, and, being based on a high-order extension of the thread-safe LB algorithm, allows us to perform stable simulations at vanishingly low viscosities. The proposed approach opens attractive prospects for high-performance computing simulations of realistic turbulent flows with interfaces on GPU-based architectures.

Ignition study of hypergolic solid fuels through counterflow experiment using hydrogen peroxide spray

Hypergolic solid fuels provide rapid and reliable ignition for hybrid rockets, offering significant advantages over conventional methods. However, a practical experimental approach is necessary to study ignition and combustion behavior under realistic oxidizer flow conditions. This study introduced a novel counterflow spray experiment using 90 % rocket-grade hydrogen peroxide sprays applied to low-density-polyethylene-based fuel pellets embedded with sodium borohydride and manganese acetate tetrahydrate. The results showed that sodium borohydride caused vigorous reactions, damaging the pellets, while the addition of manganese acetate tetrahydrate moderated this effect. Hypergolic ignition and sustained combustion were achieved at oxidizer mass flow rates of 0.38–0.57 g/s, with ignition delay times of 15 to 30 ms, consistent with droplet test results. In addition, reignition was successful under the same flow conditions, although the char layer formed on pellet surface after the first ignition limited the surface additive reactivity, leading to longer ignition delay times. 3D surface analysis provided quantitative data on surface roughness across various fuel compositions and flow rates. This novel counterflow spray experiment is expected to be both effective and simple, with the ability to offer valuable insights into hypergolic ignition and combustion process.

Reconstruction of blood flow velocity with deep learning information fusion from spectral ct projections and vessel geometry

In this work, we investigate a new deep learning reconstruction method of blood flow velocity within deformed vessels from contrast enhanced X-ray projections and vessel geometry. The principle of the method is to perform linear or nonlinear dimension reductions on the Radon projections and on the mesh of the vessel. These low dimensional projections are then fused to obtain the velocity field in the vessel. The accuracy of the reconstruction method is proved using various neural network architectures with realistic unsteady blood flows. The approach leverages the vessel geometry information and outperforms the simple PCA-net.

Pressure-equilibrium semi-implicit solver for real fluids

In this study, a semi-implicit pressure-based scheme which can suppress the spurious pressure oscillations is developed for real fluid flows. Conservation properties relevant to the proposed scheme are investigated through one-dimensional numerical simulations of the nitrogen interface advection. The efficiency and validity of this scheme are carefully examined with two- and three-dimensional numerical simulations of cryogenic nitrogen jets. From the one-dimensional simulation results, the conservation properties are well conserved as the past studies. The two-dimensional simulation results show that the developed scheme can reduce the computational cost by more than 92% (13–14 times faster) compared with the conventional density-based explicit solver employing the double flux model. It is also found that, through the three-dimensional simulation results, the developed scheme can predict the structure of the nitrogen jet under both transcritical and supercritical conditions, while taking into account the effects of real fluid properties on the mixing and spatiotemporal evolution of this jet, with excellent accuracy.

Short-term inbound passenger flow prediction at high-speed railway stations considering the departure passenger arrival pattern

Accurate prediction of short-term inbound passenger flow at high-speed railway stations is of great significance for the refined operation of stations, the formulation of emergency plans, and the provision of intelligent services. The arrival of passengers traveling on the same train at the same station shows a similar pattern, which is called the departure passenger arrival pattern (DPAP). The short-term inbound passenger flow at the station is composed of the short-term inbound passenger flow of all waiting trains within the same period. Inspired by this, this paper develops an ensemble prediction model based on the time series decomposition modeling strategy to introduce the DPAP to the short-term inbound passenger flow prediction at stations. Firstly, we propose a new framework for studying the DPAP to calculate the fitted station short-term inbound passenger flow, which is only affected by the DPAP. During this process, we find that 7minutes is the optimal time granularity. Secondly, based on the singular spectrum analysis, we prove that the DPAP is the determining factor affecting the station short-term inbound passenger flow. Finally, we propose an ensemble prediction model that considers the DPAP to achieve short-term inbound passenger flow prediction at stations. The model consists of two parts: the deterministic and stochastic components prediction, where the former is predicted by the fitted station short-term inbound passenger flow, and the latter is achieved by the combination of historical stochastic components and weather type with the help of the Seq2Seq model based on time attention mechanism. Using real inbound passenger flow data, we compare the proposed model with 13 benchmark models and the results show that under different training and prediction steps, our model achieves optimal prediction performance, whether in all-day period and the busiest period of the station. Through further ablation experiments, it has been proven that the introduction of the DPAP effectively improves the prediction accuracy. Our model can provide scientific support for the intelligent operation of stations and the refined management of passenger flow.

Extension of the local domain-free discretization method to large eddy simulation of compressible flows

Most of the flow problems encountered in practical engineering are wall-bounded turbulent flows at high Reynolds numbers. Wall-modeled large eddy simulation (WMLES) is one of the most viable approaches for predicting these realistic flows. Immersed boundary (IB) approach is an efficient computational technique to solve flow problems involving complex and/or moving geometries. This work extends a sharp-interface IB method, named the local domain-free discretion (DFD), to WMLES of compressible flows at high Reynolds numbers. An equilibrium wall model based on solving the simplified compressible turbulent boundary layer equations is utilized to alleviate the requirement of high near-wall mesh resolution. In conjunction with the approximate boundary conditions prescribed by the modeled wall shear stress and wall heat flux, the tangential velocity and temperature at an exterior dependent node are evaluated. Then, the closure of the discrete form of governing equations at an interior node in the immediate vicinity of the immersed wall is accomplished. A simple non-equilibrium correction of the wall shear stress provided by the equilibrium wall model is introduced explicitly. The WMLES/DFD method is applied to a supersonic zero-pressure-gradient turbulent boundary layer flow, a shock wave/flat-plate boundary layer interaction, a supersonic compression ramp flow and high-speed turbulent Couette flows with various thermal boundary conditions. The influence of grid resolution is investigated in the simulation of zero-pressure-gradient turbulent boundary layer flow. By comparing the computed results with the referenced experimental data and/or numerical results, the accuracy and ability of the WMLES/DFD method to simulate compressible turbulent flows are verified.

Large eddy simulation of near-surface boundary layer dynamics over patchy snow

The near-surface boundary layer over patchy snow is highly heterogeneous and dynamic. Layers of opposing stability coexist within only a few horizontal meters. Conventional experimental methods to investigate this layer suffer from limitations related to the fixed positions of eddy covariance sensors. To overcome these difficulties, we set up a centimeter-resolution large eddy simulation of flow across an idealised transition from bare ground to snow. We force the simulation with high-frequency eddy covariance data recorded during a field campaign. We show that the model can represent the real flow by comparing it to independent eddy covariance data. However, the simulation underestimates vertical wind speed fluctuations, especially at high frequencies. Sensitivity analyses show that this is influenced by grid resolution and surface roughness representation but not much by subgrid-scale parameterization. Nevertheless, the model can reproduce the experimentally observed plumes of warm air intermittently detaching from bare ground and being advected over snow. This process is highly dynamic, with time scales of only a few seconds. We can show that the growth of a stable internal boundary layer adjacent to the snow surface can be approximated by a power law. With low wind speeds, deeper stable layers develop, while strong wind speeds limit the growth. Even close to the surface, the buoyancy fluxes are heterogeneous and driven by terrain variations, which also induce the frequent decoupling of a thin layer adjacent to the snow surface. Our simulations point the path towards generalizing point-based and aerial measurements to three dimensions.

Nonlinear deterministic dynamic analysis of a GPL micropipe conveying pulsating laminar flow

In several industries, the handling of fluid-filled micropipes is common. New-brand structures are increasingly being manufactured using advanced materials. This article tackles one of the crucial dynamic analyses that an advanced fluid-filled micropipe encounters. The dynamics of a graphene nanoplatelets-reinforced micropipe (GPL micropipe) under pulsating laminar flow for the first time is examined. The Euler-Bernoulli beam model follows the modified couple stress theory (MCST) and von-Karman’s nonlinear strain to formulate the problem. The impression of the flow profile exerted by a real flow as a consequence of fluid viscosity, is taken into account. The plug flow model results are compared to those regarding real flow consideration. Using the method of multiple scales (MMS), we determine the instability area and the steady state response of the GPL micropipe subjected to the principal parametric resonance of one of its modes due to pulsating laminar flow by applying this method to the discretized couple nonlinear gyroscopic governing equations. The Floquet theory treats the linear equations and fourth-order Runge–Kutta (RK4) method attacks nonlinear ones to validate MMS findings. It is confirmed that the 0.3 % addition of the GPL to matrix phase significantly enhances its advanced attributes, making it a highly effective material. The GPL reinforcement phase in the FGO pattern increases the critical flow velocity that exhibits the micropipe to static instability by 37.98 % , and critical flow stimulation amplitude fraction by 152.19 % , while declines instability area bandwidth by 36.95 % , and steady state response by 67.17 % relative to a non-reinforced micropipe. A denser flow degrades the corresponding values by 18.40 % , 42.59 % , 41.67 % , and 227.28 % in comparison to a lighter fluid. The declared findings provide crucial insights into the key design elements affecting significantly the functioning of fluid-filled GPL micropipes system, and regulate future research and expand openings for industry applications.

Dynamic event-triggered integrated task and motion planning for process-aware source seeking

The process-aware source seeking (PASS) problem in flow fields aims to find an informative trajectory to reach an unknown source location while taking the energy consumption in the flow fields into consideration. Taking advantage of the dynamic flow field partition technique, this paper formulates this problem as a task and motion planning (TAMP) problem and proposes a bi-level hierarchical planning framework to decouple the planning of inter-region transition and inner-region trajectory by introducing inter-region junctions. An integrated strategy is developed to enable efficient upper-level planning by investigating the optimal solution of the lower-level planner. In order to leverage the information acquisition and computational burden, a dynamic event-triggered mechanism is introduced to enable asynchronized estimation, region partitioning and re-plans. The proposed algorithm provides guaranteed convergence of the trajectory, and achieves automatic trade-offs of both exploration-exploitation and accuracy-efficiency. Simulations in a highly complicated and realistic ocean surface flow field validate the merits of the proposed algorithm, which demonstrates a significant reduction in computational burden without compromising planning optimality.

Developing algorithm and dispatching rules for scheduling a realistic flexible flow shop with setups to minimize total weighted tardiness

Developing algorithm and dispatching rules for scheduling a realistic flexible flow shop with setups to minimize total weighted tardiness

Zeolite intervention in soil nitrate dynamics: insights from column experiments and modelling

This study aims to address the harmful effects of excessive nitrogen fertilizer by investigating the mobility-retardance effect of zeolite on nitrate movement in loam soil columns. Disturbed and undisturbed soil columns, treated with zeolite as well as control soil columns, were analysed over a span of three years. Breakthrough curves generated from the experiments were evaluated using the HYDRUS-1D code with mobile–immobile water (MIM) and dual-permeability (DP) models. Our findings highlight that zeolite application significantly reduced nitrate mobility, with recovery rates decreasing by 8% in disturbed and 11% in undisturbed columns. The DP model, which accurately reflects real soil water flow conditions, outperformed the MIM model in predictive accuracy. This research offers a novel long-term perspective on the benefits of zeolite in enhancing soil properties and mitigating nitrate leaching, recommending the DP model for superior prediction of contaminant transport in structured soils within the vadose zone.

Indoor environmental quality assessment in passively ventilated classrooms in Germany and estimation of ventilation energy losses

In this field study we present an approach for the comprehensive and room-specific assessment of parameters with the overall aim to realize energy-efficient provision of hygienically harmless and thermally comfortable indoor environmental quality in naturally ventilated non-residential buildings. The approach is based on (i) conformity assessment of room design parameters, (ii) empirical determination of theoretically expected occupant-specific supply air flow rates and corresponding air exchange rates, (iii) experimental determination of real occupant-specific supply air flow rates and corresponding air exchange rates, (iv) measurement of indoor environmental exposure conditions of T, RH, cCO2, cPM2.5 and cTVOC, and (v) determination of real energy demands for the prevailing ventilation scheme. Underlying assessment criteria comprise the indoor environmental parameters of category II of EN 16798-1: Temperature T = 20 °C–24 °C, and relative humidity RH = 25 %–60 % as well as the guide values of the German Federal Environment Agency for cCO2cPM2.5 and cTVOC of 1000 ppm, 15 μg m−3, and 1 mg m−3, respectively.Investigation objects are six naturally ventilated classrooms of a German secondary school. Major factors influencing indoor environmental quality in these classrooms are the specific room volume per occupant and the window opening area. It is concluded that the rigorous implementation of ventilation recommendations laid down by the German Federal Environment Agency is ineffective with respect to anticipated indoor environmental parameters and inefficient with respect to ventilation energy losses on the order of about 10 kWh m−2 a−1 to 30 kWh m−2 a−1.

A multi-modal high pressure and high temperature reaction cell for combined x-ray spectroscopy, scattering, and imaging.

A free-standing and compact reaction cell for combined in situ/operando x-ray spectroscopy, scattering, and imaging measurements at high pressures and high temperatures is described. The cell permits measurements under realistic operating conditions (up to 50bar and 1000°C), under static and flow conditions (up to 100 ml/min), over a wide range of hard x-ray energies, variable detection modes (transmission, fluorescence, and scattering), and at all angles of rotation. An operando XAS, x-ray fluorescence, x-ray computed tomography, and x-ray diffraction computed tomography case study on the reduction of a heterogeneous catalyst is presented to illustrate the performance of the reaction cell.

Stochastic dual dynamic programming for optimal power flow problems under uncertainty

Planning in the power sector has to take into account the physical laws of alternating current (AC) power flows as well as uncertainty in the data of the problems, both of which greatly complicate optimal decision making. We propose a computationally tractable framework to solve multi-stage stochastic optimal power flow (OPF) problems in AC power systems. Our approach uses recent results on dual convex semi-definite programming (SDP) relaxations of OPF problems in order to adapt the stochastic dual dynamic programming (SDDP) algorithm for problems with a Markovian structure. We show that the usual SDDP lower bound remains valid and that the algorithm converges to a globally optimal policy of the stochastic AC-OPF problem as long as the SDP relaxations are tight. To test the practical viability of our approach, we set up a case study of a storage siting, sizing, and operations problem. We show that the convex SDP relaxation of the stochastic problem is usually tight and discuss ways to obtain near-optimal physically feasible solutions when this is not the case. The algorithm finds a physically feasible policy with an optimality gap of 3% and yields a significant added value of 27% over a rolling deterministic policy, which leads to overly optimistic policies and underinvestment in flexibility. This suggests that the common industry practice of assuming direct current and deterministic problems should be reevaluated by considering models that incorporate realistic AC flows and stochastic elements in the data as potentially more realistic alternatives.

A comprehensive review of advances in physics-informed neural networks and their applications in complex fluid dynamics

Physics-informed neural networks (PINNs) represent an emerging computational paradigm that incorporates observed data patterns and the fundamental physical laws of a given problem domain. This approach provides significant advantages in addressing diverse difficulties in the field of complex fluid dynamics. We thoroughly investigated the design of the model architecture, the optimization of the convergence rate, and the development of computational modules for PINNs. However, efficiently and accurately utilizing PINNs to resolve complex fluid dynamics problems remain an enormous barrier. For instance, rapidly deriving surrogate models for turbulence from known data and accurately characterizing flow details in multiphase flow fields present substantial difficulties. Additionally, the prediction of parameters in multi-physics coupled models, achieving balance across all scales in multiscale modeling, and developing standardized test sets encompassing complex fluid dynamic problems are urgent technical breakthroughs needed. This paper discusses the latest advancements in PINNs and their potential applications in complex fluid dynamics, including turbulence, multiphase flows, multi-field coupled flows, and multiscale flows. Furthermore, we analyze the challenges that PINNs face in addressing these fluid dynamics problems and outline future trends in their growth. Our objective is to enhance the integration of deep learning and complex fluid dynamics, facilitating the resolution of more realistic and complex flow problems.

The influence of non-Newtonian behaviors of blood on the hemodynamics past a bileaflet mechanical heart valve

This study employs extensive three-dimensional direct numerical simulations to investigate the hemodynamics around a bileaflet mechanical heart valve. In particular, this study focuses on assessing whether non-Newtonian rheological behaviors of blood, such as shear-thinning and yield stress behaviors, exert an influence on hemodynamics compared to the simplistic Newtonian behavior under both steady inflow and physiologically realistic pulsatile flow conditions. Under steady inflow conditions, the study reveals that blood rheology impacts velocity and pressure field variations, as well as the values of clinically important surface and time-averaged parameters like wall shear stress (WSS) and pressure recovery. Notably, this influence is most pronounced at low Reynolds numbers, gradually diminishing as the Reynolds number increases. For instance, surface-averaged WSS values obtained with the non-Newtonian shear-thinning power-law model exceed those obtained with the Newtonian model. At Re=750, this difference reaches around 67%, reducing to less than 1% at Re=5000. Correspondingly, pressure recovery downstream of the valve leaflets is lower for the shear-thinning blood than the constant viscosity one, with the difference decreasing as the Reynolds number increases. On the other hand, in pulsatile flow conditions, jets formed between the leaflets and the valve housing wall are shorter than steady inflow conditions. Additionally, surface-averaged wall shear stress and blood damage (BD) parameter values are higher (with differences more than 13% and 47%, respectively) during the peak stage of the cardiac cycle, especially for blood exhibiting non-Newtonian yield stress characteristics compared to the shear-thinning or constant viscosity characteristics. Therefore, blood non-Newtonian behaviors, including shear-thinning and yield stress behaviors, exert a considerable influence on the hemodynamics around a mechanical heart valve. All in all, the findings of this study demonstrate the importance of considering non-Newtonian blood behaviors when designing blood-contacting medical devices, such as mechanical heart valves, to enhance functionality and performance.

A multi-layer constrained spectral k-embedded clustering methodology approach for intelligent partitioning of power grid to enhance resiliency in transmission networks

The intentional controlled islanding by intelligent partitioning of the power grid is considered as essential to protect the grid from cascading events, faults, and High Impact Low Probability (HILP) events. To enhance the resilience, stability, and security of the power grid, the proposed model in this paper intentionally divides the affected power network into islands. This paper presents an intelligent partitioning approach to create an islanding solution through multilayer graphs using spectral clustering. The controlled islanding algorithm uses a multi-criteria objective function that considers the correlation coefficients among the frequency of the buses and minimal disturbances in the real and reactive power. The proposed control technique is implemented in two phases. The first phase employs correlation coefficients between frequency of the buses and modularity clustering to identify clusters of coherent buses. During the second stage, all nodes are categorised into groups using Multi-level constrained Spectral Clustering (ML-CSC) to determine the solution for Intentional controlled Islanding that satisfies bus coherency with the minimum level of disruptions to real and reactive power flows across the boundaries. The proposed algorithm for resolving the generator coherency issue and an intelligent islanding solution is demonstrated by simulation experiments conducted on an IEEE 39-bus transmission test system developed in DIGSILENT Powerfactory version 2023. The MATLAB version R2023a is used to construct the ML-CSC control method. The results demonstrated that the proposed ML-CSC algorithm substantially impacts the functioning of the power system, enabling the formation of intelligent islanding during abnormal conditions. Also, the results clearly show that instead of using single-layer spectral clustering, the multi-layer spectral clustering yields a better intentional islanding solution with minimum power flow mismatch which enhances the transient stability of the islands.

Digital traffic state analysis for urban regions considering complex multi-directional flow changes

In this research, a digital analytical model based on three-flow full-cycle state macroscopic fundamental diagram (MFD) with observable different perimeter control management and flow loading modes has been proposed on the basis of real network and peak hour traffic flow loading scenarios in Rostov-on-Don. It is known that the aim of digital management of urban intelligent traffic is to ultimately optimize the overall state of multi-area traffic operation in the city. The goal of this research was to provide a strong basis to establish digital management strategies for intelligent transport, determine the change trends of steady-state equilibrium of regional traffic flow and their effects on full-cycle states of traffic flow under various peripheral control management strategies and traffic loading patterns. Simulation results confirmed that with the evolution of traffic state, stricter peripheral control management always results in a wider attraction area ultimately giving rise to steady-state equilibrium of traffic flow.

Flow stability and permeability in a nonrandom porous medium analog

The estimation of the permeability of porous media to fluids is of fundamental importance in fields as diverse as oil and gas industry, agriculture, hydrology, and medicine. Despite more than 150 years since the publication of Darcy's linear law for flow in porous media, several questions remain regarding the range of validity of this law, the constancy of the permeability coefficient, and how to define the transition from Darcy flow to other flow regimes. This study is a numerical investigation of the permeability and flow stability in a nonrandom quasi-tridimensional porous medium analog. The effect of increasing pressure gradient on the velocity field and on the estimation of Darcy and Darcy–Forchheimer coefficients is investigated for three different obstacles radius. The transition from Darcy flow to nonlinear behavior is associated with the formation of jets in the outlet of the porous medium and development of flow instabilities. Different representations of the Reynolds number proved adequate to detect deviation from the linear law. The instantaneous permeability calculated at each pressure gradient was sensitive to flow velocity, in agreement with previous studies stating that permeability cannot be conceptualized as a constant for real flows.

Numerical study on flow field characteristics and performance of a Mach 10 internal injection kerosene-fueled oblique detonation engine

Oblique detonation engines (ODE) have significant potential for hypersonic propulsion, yet there is a paucity of research investigating internal injection ODEs. In this study, a numerical simulation of the internal flow field of a Mach 10 internal injection kerosene-fueled ODE is conducted. The fuel mixing, pre-combustion, and combustor wave structure in the flow field are analyzed in a situation closer to the real flow field conditions. Further studies have demonstrated that alterations to the upper wall initiation position of nozzle can influence the separation zone, flow field stability and the engine performance. An upper wall initiation position that is too far forward will increase the separation zone area and reduce the engine thrust. Conversely, an upper wall initiation position that is too far back will lead to flow field destabilization and eventually thermal choking. Finally, the effects of increasing the equivalent ratio on the flow field structure and engine performance for a certain configuration are analyzed. The results demonstrate that when the equivalent ratio is elevated, an increase in either the bottom or top incoming flow equivalent ratio results in a transformation of the wave structure within the combustor due to the presence of the incoming boundary layer and the subsonic zone. A large-scale separation zone will form at the bottom of the combustor, resulting in a reduction in nozzle thrust. However, the wedge drag is reduced more, thereby increasing the engine's specific impulse.