Heterogeneous Flow Research Articles

Exploring the Impact of Conditionally Automated Driving Vehicles Transferring Control to Human Drivers on the Stability of Heterogeneous Traffic Flow

Exploring the Impact of Conditionally Automated Driving Vehicles Transferring Control to Human Drivers on the Stability of Heterogeneous Traffic Flow

Base saturation flow rate (BSFR) and its effect on performance of pretimed signalized intersection with non-lane based urban heterogeneous traffic.

Signalized intersections that are pretimed are commonly used in rapidly developing cities with diverse traffic patterns, including those found in Indonesia. These intersections are a common source of congestion and delays in road networks, particularly when there is non-lane based (NLB) traffic in urban areas. Accurate estimation of both the base saturation flow rate and capacity is essential for this type of facility, as an error in the prediction of the base saturation flow rate can result in significant bias in capacity evaluation and design at signalized intersections. The estimation of capacity at signalized intersections is critical for ensuring optimal signal timings, minimizing delay, and reducing congestion. Heterogeneous traffic, which refers to the presence of various types of vehicles with distinct static and dynamic characteristics, is a common phenomenon. To address this issue, this paper presents a modeling approach for the SFR that takes into account heterogeneous traffic and NLB movements. Indonesia, being an archipelago with 34 provinces, served as the focus of this study, which specifically concentrated on Banda Aceh, the capital province of Aceh province. Employing primary and observed data collected at a specific, predetermined signalized timing, this study aimed to investigate the impact of intersection geometry and heterogeneous traffic composition on the SFR. By adopting the modeling approach for NLB movements, the study formulated the BSFR model. To estimate the scale parameters of the BSFR, the multiple linear regression approach was utilized. The analysis results reveal that the existing BSFR based on the IHCM formula gives a substantially biased estimation because the PCEs from the Indonesian Highway Capacity Manual (IHCM) are underestimated. This source of error could be partially due to the heterogeneous (varied vehicle composition) traffic flow with NLB movements, unlike that observed under the prevailing conditions of IHCM 1997. The empirical results show that the existing IHCM should be improved to avoid overestimation, particularly for effective approach width (We) greater than 4.5 m. A comparison of the BSFR prediction model between IHCM's PCEs and new PCEs shows that the BSFR is predicted more accurately in the latter case. This finding demonstrates that the existing IHCM can be adjusted in two ways: adjusting PCEs or calibrating the BSFR formula. The proposed models can also enhance the accuracy of BSFR prediction, leading to better signalized intersection capacity estimation, providing support for designing traffic operation, alleviating traffic congestion, and reducing congestion delay within the city.

Linear Stability of Heterogeneous Traffic Flow Incorporating V2X Communication and Intra-Platoon Spatial Distribution

Linear Stability of Heterogeneous Traffic Flow Incorporating V2X Communication and Intra-Platoon Spatial Distribution

Boundary Control for Suppressing Chaotic Response to Dynamic Hydrogen Blending in a Gas Pipeline

It is known that periodic forcing of nonlinear flows can result in a chaotic response under certain conditions. Such non-periodic and chaotic solutions have been observed in simulations of heterogeneous gas flow in a pipeline with periodic, time-varying boundary conditions. In this paper, we examine a proportional feedback law for boundary control of a parabolic partial differential equation system that represents the flow of two gases through a pipe. We demonstrate that periodic variation of the mass fraction of the lighter gas at the pipe inlet can result in the chaotic propagation of gas pressure waves, and show that appropriate flow control can suppress this response. We examine phase space solutions for the single pipe system subject to boundary control, and use numerical experiments to characterize conditions for the controller gain to suppress chaos.

Blood shear stress during the cardiac cycle and endothelial cell orientation and polarity in the carotid artery of male and female mice.

Introduction: Blood flow produces fluid shear stress (SS), a frictional force parallel to the blood flow, on the endothelial cell (EC) layer of the lumen of the vessels. ECs themselves are sensitive to this frictional force in terms of directionality and intensity. The aim of this study was to determine the physiological shear stress value during the cardiac cycle and EC polarity and orientation from blood flow in healthy male and female mouse carotid artery. Methods: Experimentation is done on anesthetized male and female 8-week-old C5BL/6J mice. In vivo measurements of maximum blood velocity and vessel diameter in diastole and systole were performed on the right common carotid artery by Doppler ultrasound imaging. Blood viscosity (total and plasmatic) and hematocrit were determined on blood samples. For SS calculation, we developed a new method assuming heterogenous blood flow, i.e., a red cell central plug flow surrounded by a peripheral plasma sheath flow, and computing SS from vessel diameter and hemodynamical measurements (maximal blood velocity, hematocrit and plasmatic viscosity). Results: Results were compared with the classical method assuming a homogenous blood flow with constant apparent total blood viscosity. EC polarity and orientation were determined ex vivo on the carotid endothelium by confocal imaging after labeling of the EC nucleus and Golgi apparatus. Diastolic and systolic SS were 6 ± 2.5 Pa and 30 ± 6.5 Pa, respectively. Total blood and plasmatic viscosity was 4 ± 0.5 cP and 1.27 cP, respectively. ECs were polarized and significantly oriented against blood flow. No sex difference was identified.

МЕТОДЫ ПРОЕКТИРОВАНИЯ ПНЕВМАТИЧЕСКИХ СИСТЕМ

In this paper, the parameters of a two-phase heterogeneous gas-particle flow (for example, “airseed”) are shown using the example of mathematical modeling of technological processes of a pneumatic system of a grain seeder. It is proposed to apply the mechanics of two-phase gas-particle flow to the theoretical description of the processes of motion and distribution of solid particles in pneumatic systems. The results of the study establish the possibility of using these methods to analyze and optimize the pneumatic system of grain seeders. Practical and theoretical methods were used to determine the air flow and speed of movement of seeds of various crops in pneumatic systems of grain drills. The values of the seed resistance coefficients necessary for mathematical modeling of the technical processes of the pneumatic system are also presented. The research will make it possible to effectively simulate the operation of the pneumatic system of the sowing complex using the mechanics of two-phase flows and computational fluid dynamics methods.

Distributed Heterogeneous Flow Shop Scheduling Method for Dual-Carbon Goals

Distributed Heterogeneous Flow Shop Scheduling Method for Dual-Carbon Goals

Modeling the Movement of Heterogenous Flows of People as a Geometric Design Problem

Modeling the Movement of Heterogenous Flows of People as a Geometric Design Problem

Multiscale simulation of highly heterogeneous subsurface flow with physically adaptive 3D unstructured grids

The multiscale finite volume framework is extended with physical-based grid adaptation (MSFV-GA) for three-dimensional fully unstructured grids. The MSFV method produces large errors for highly heterogeneous and anisotropic problems on standard coarse grids generated based on geometrical features. Adding constraints based on the fine-scale physical properties to construct coarse grids can increase the accuracy and robustness of the MSFV solutions. A novel and automated coarse grid generation strategy adaptable to the reservoir heterogeneity for 3D unstructured grids is presented. Adaptive coarse grids are generated based on local variations of fine scale permeability field. The proposed procedure is very flexible respect to the local physical and geometrical limitations in coarse blocks. Multiscale coarse grid adaptation notably improves the basis functions calculations. The performance of the MSFV-GA in handling challenging problems involving high media contrasts or impermeable shale layers is assessed. The results are very promising and show that the MSFV-GA can accurately approximate complex physics problems. The presented 3D unstructured procedure can notably improve the applicability of the multiscale finite volume method and provide a promising framework for the next generation of reservoir flow simulators.

Learning to Detect Memory-related Vulnerabilities

Memory-related vulnerabilities can result in performance degradation or even program crashes, constituting severe threats to the security of modern software. Despite the promising results of deep learning (DL)-based vulnerability detectors, there exist three main limitations: (1) rich contextual program semantics related to vulnerabilities have not yet been fully modeled; (2) multi-granularity vulnerability features in hierarchical code structure are still hard to be captured; and (3) heterogeneous flow information is not well utilized. To address these limitations, in this article, we propose a novel DL-based approach, called MVD+ , to detect memory-related vulnerabilities at the statement-level. Specifically, it conducts both intraprocedural and interprocedural analysis to model vulnerability features, and adopts a hierarchical representation learning strategy, which performs syntax-aware neural embedding within statements and captures structured context information across statements based on a novel Flow-Sensitive Graph Neural Networks, to learn both syntactic and semantic features of vulnerable code. To demonstrate the performance, we conducted extensive experiments against eight state-of-the-art DL-based approaches as well as five well-known static analyzers on our constructed dataset with 6,879 vulnerabilities in 12 popular C/C++ applications. The experimental results confirmed that MVD+ can significantly outperform current state-of-the-art baselines and make a great trade-off between effectiveness and efficiency.

A self-learning knowledge-based MOEA/D for distributed heterogeneous assembly permutation flowshop scheduling with batch delivery

With the requirements of just-in-time and make-to-order, it is necessary to consider the batch delivery in distributed heterogeneous assembly flow shop scheduling as well as the minimization of inventory and tardiness costs. Hence, this work proposes a new mixed integer linear programming model and a self-learning knowledge-based multi-objective evolutionary algorithm based on decomposition. In this algorithm, 4 problem-specific properties are extracted and embedded in the designed solution representation, forward-reverse objective calculation and local search operators. A new hybrid initialization with six knowledge-based NEH heuristics and a random generation is developed to generate better initial solutions. A self-learning selection strategy is developed to coordinate the above knowledge-based local search operators. The experimental study, based on 900 small and 540 large instances, demonstrates that the designed components such as hybrid initialization and self-learning selection strategy are effective, and the proposed algorithm is superior to 6 existing algorithms in the literature in solving this new problem.

Mapping water scarcity risk in China with the consideration of spatially heterogeneous environmental flow requirement

Water is an indispensable raw material for industrial production across various economic activities. Extensive research indicates that local water scarcity may cause severe economic output losses, posing risking not only to the affected regions (referred to as local water scarcity risk, LWSR) but also to distant regions through trade networks (known as virtual water scarcity risk, VWSR). To assess water scarcity risk (WSR), a key metric used is the environmental flow requirement (EFR), which exhibits considerable spatial heterogeneity. However, previous studies have primarily employed a constant EFR value (approximately 80%), limiting the reliability of the WSR findings. To address this limitation, we propose a comprehensive framework that incorporates spatially heterogeneous EFR. Our analysis reveals that in 2017, the WSR in China amounted to over 3.9 trillion US dollars, consisting of 1.6 trillion dollars attributed to LWSR and 2.3 trillion dollars to VWSR. Through examination at provincial, sectoral, and trade-link levels, we identify specific WSR hotspots and present relevant policy implications. Additionally, by comparing our results with those obtained using the 80% EFR criterion, we uncover significant differences. These findings emphasize the importance of considering spatially heterogeneous EFR and contribute to more accurate evaluations of WSR.

Modeling and optimization of steady flow of natural gas and hydrogen mixtures in pipeline networks

We extend the canonical problems of simulation and optimization of steady-state gas flows in pipeline networks with compressors to the transport of mixtures of highly heterogeneous gases injected throughout a network. Our study is motivated by proposed projects to blend hydrogen generated using clean energy into existing natural gas pipeline systems as part of efforts to reduce the reliance of energy systems on fossil fuels. Flow in a pipe is related to endpoint pressures by a basic Weymouth equation model, with an ideal gas equation of state, where the wave speed depends on the hydrogen concentration. At vertices, in addition to mass balance, we also consider mixing of incoming flows of varying hydrogen concentrations. The problems of interest are the heterogeneous gas flow simulation (HGFS), which determines system pressures and flows given fixed boundary conditions and compressor settings, as well as the heterogeneous gas flow optimization (HGFO), which extremizes an objective by determining optimal boundary conditions and compressor settings. We examine conditions for uniqueness of solutions to the HGFS, as well as compare and contrast mixed-integer and continuous nonlinear programming formulations for the HGFO. We develop computational methods to solve both problems, and examine their performance using four test networks of increasing complexity.

Local factors and bionomic characteristics determining the occurrence of semiaquatic bugs in streams of Central Amazonia

Abstract Individual responses to changes in the environment by different species drive a better understanding of the dynamics of diversity and habitat filtering. Hypotheses based on functional and morphometric characteristics of the species are especially useful for accounting for different interpretations of biological responses. This study aimed to determine the responses of semiaquatic species of insects to habitat changes from the elaboration of hypotheses based on the importance of species traits facing environmental variations. We tested these hypotheses with maximum likelihood models to explain the occurrence of the species in streams of the central Amazon region. In a total of 17 collected Gerromorpha species, we used 14 to develop five predictive models of occurrence considering the bionomic characteristics of each species and their possible relationships with habitat changes in 33 streams in the central Amazon region. We used maximum likelihood models to assess the fit of the models to the observed occurrence, with environmental characteristics as covariates affecting the occurrence probability, and sampling effort affecting the detection probability. A total of five species exhibited changes in the probability of occurrence in streams related to an environmental condition (riparian forest disturbance, flow heterogeneity, and surface habitat heterogeneity). The probability of occurrence tended to reduce for four of the five species and increase for one with increase of environmental impacts. Furthermore, the sampling effort covariate did not affect the probability of detection in most species, indicating that the group showed a high detection. The use of functional characteristics of Gerromorpha species to develop ecological hypotheses proved relevant and indicated unexpected relationships, expanding our knowledge of this community of aquatic insects. Therefore, including the individual responses of organisms to environmental variations helps in studies on aquatic biodiversity, both for basic ecology and for conservation and bioassessment.

Large eddy simulation of particle hydrodynamic characteristics in a dense gas-particle bubbling fluidized bed

A subgrid scale particle kinetic turbulent energy model coupled with a second order moment model is developed to describe the particle hydrodynamic characteristics in dense gas-particle fluidized bed. Anisotropic behaviors of particle dispersions and interactions between gas and particle are fully revealed by means of two-phase Reynolds stresses transport eqs. A four-way coupling strategy for the consideration of particle-particle collisions is modeled by frictional granular temperature transport equation. Simulation results by large eddy simulation are validated by experimental measurement with acceptable agreement. Heterogenous flow structures, anisotropic characteristics of particle dispersions and wider-size distribution behaviors of bubbles are indicated. At the height of 0.2 m, the largest dominant frequency of porosity is 25.0 Hz at lateral 0.375 cm away from center, it is approximately 5.0 times larger than that of lateral 9.375 cm. Compared to 0.45 m, the standard deviations of 82.5 cm/s at lateral region is approximately 1.5 times larger than that of center region.

A Physics-Informed Spatial-Temporal Neural Network for Reservoir Simulation and Uncertainty Quantification

Summary Surrogate models play a vital role in reducing computational complexity and time burden for reservoir simulations. However, traditional surrogate models suffer from limitations in autonomous temporal information learning and restrictions in generalization potential, which is due to a lack of integration with physical knowledge. In response to these challenges, a physics-informed spatial-temporal neural network (PI-STNN) is proposed in this work, which incorporates flow theory into the loss function and uniquely integrates a deep convolutional encoder-decoder (DCED) with a convolutional long short-term memory (ConvLSTM) network. To demonstrate the robustness and generalization capabilities of the PI-STNN model, its performance was compared against both a purely data-driven model with the same neural network architecture and the renowned Fourier neural operator (FNO) in a comprehensive analysis. Besides, by adopting a transfer learning strategy, the trained PI-STNN model was adapted to the fractured flow fields to investigate the impact of natural fractures on its prediction accuracy. The results indicate that the PI-STNN not only excels in comparison with the purely data-driven model but also demonstrates a competitive edge over the FNO in reservoir simulation. Especially in strongly heterogeneous flow fields with fractures, the PI-STNN can still maintain high prediction accuracy. Building on this prediction accuracy, the PI-STNN model further offers a distinct advantage in efficiently performing uncertainty quantification, enabling rapid and comprehensive analysis of investment decisions in oil and gas development.

Aspiration efficiency and respiratory tract deposition of indoor suspended micro-particles during steady and transient breathings

The assessment of potential health risks from exposure to airborne contaminants and the inhalability of microparticles through human nasal breathing is crucial for the design of ventilation systems. In this study, using a standing computer-simulated person (CSP) directly linked to a numerical model of the respiratory tract, we investigated the aspiration efficiency (AE) of microparticles ranging between 1 and 80 μm under steady and transient breathing conditions. Two ventilation scenarios with displacement and mixed ventilation systems (DV and MV) were assumed for AE calculations. To determine the appropriate particle injection location, we investigated the position of highly inhaled particles corresponding to the breathing zone using a reverse particle-tracking simulation with reversed time progression. Additionally, the total and regional deposition fractions (TDF and RDF) of the inhaled microparticles on the respiratory wall surfaces were evaluated using the Lagrangian approach. The results indicate that the transient breathing cycle showed relatively higher AE and TDF rates compared to the assumed steady inhalation conditions, particularly for the size range 1–10 μm. An insignificant variation was observed in the AE and RDF results between different ventilation systems. However, microparticles in the MV room showed slightly higher AE than the DV system. Moreover, AE and penetration ratio were highly sensitive to the initially assumed particle density. For a comprehensive and accurate assessment of inhalation exposure to microparticles, we should predict the heterogeneous flow field formed around the human body and then analyze the AE and TDF in the respiratory tract during realistic transient breathing.

Моделирование эрозионного износа титанового сплава высокоскоростным потоком частиц

Introduction. Predicting solid particle erosion (SPE) in gaseous flow and managing its intensity is still a relevant problem in mechanical engineering. It requires the development of a general modeling methodology, which also depends upon many special cases studying various physical processes. Such studies should also include verification analysis, process parameters and model sensitivity studies. Mainly computational fluid dynamics and finite element analysis (and mesh-free methods such as smooth particle hydrodynamics or similar) are used to simulate the erosion process. Papers focused on CFD simulation of solid particle erosion of metal alloys are widely presented, but most of it is associated with relatively low or medium particle velocities (< 100–150 m/s) and is close to uniform diameter distribution. This paper presents a CFD study of Ti6Al4V titanium alloy SPE at relatively high particle velocities and sufficiently non-uniform unimodal particle diameter distribution. The paper also studies the turbulence model influence and particle shape effect which appears as a “shape factor” coefficient in the particle drag model. Methods. The heterogenous flow simulation was based on the Reynolds-averaged Navier-Stokes formulation, where the particles, according to Euler-Lagrange formulation, were simulated as mathematical points with corresponding properties. The influence of turbulence models, such as k-epsilon standard, RNG k-epsilon, and a relatively new Generalized equation k-omega (GEKO) model and its coefficients were also studied. Oka and DNV erosion models were also compared based on the general sample mass loss and more specific erosion intensity profile criterions. The simulation results were compared to the lab-scale experimental results. Results and discussion. It is shown that neither erosion intensity profile or sample mass loss do not depend upon the turbulence model choice or GEKO parameters variation. As expected, erosion is dependent on the erosion model and its coefficients. A notable influence of the shape factor is shown. As the drag coefficient increased due to the particle shape, the erosion intensity decreased and the erosive profile on the surface also changed due to the changing velocity and diameter distribution of the heterogenous flow. It is expected that such results would be useful not only for erosion prediction in all areas of mechanical engineering, but also for wear management in mechanical assemblies and shot peening / shot peen forming management and simulation.

Effects of connected automated vehicle on stability and energy consumption of heterogeneous traffic flow system

With the development of intelligent and interconnected traffic system, a convergence of traffic stream is anticipated in the foreseeable future, where both connected automated vehicle (CAV) and human driven vehicle (HDV) will coexist. In order to examine the effect of CAV on the overall stability and energy consumption of such a heterogeneous traffic system, we first take into account the interrelated perception of distance and speed by CAV to establish a macroscopic dynamic model through utilizing the full velocity difference (FVD) model. Subsequently, adopting the linear stability theory, we propose the linear stability condition for the model through using the small perturbation method, and the validity of the heterogeneous model is verified by comparing with the FVD model. Through nonlinear theoretical analysis, we further derive the KdV-Burgers equation, which captures the propagation characteristics of traffic density waves. Finally, by numerical simulation experiments through utilizing a macroscopic model of heterogeneous traffic flow, the effect of CAV permeability on the stability of density wave in heterogeneous traffic flow and the energy consumption of the traffic system is investigated. Subsequent analysis reveals emergent traffic phenomena. The experimental findings demonstrate that as CAV permeability increases, the ability to dampen the propagation of fluctuations in heterogeneous traffic flow gradually intensifies when giving system perturbation, leading to enhanced stability of the traffic system. Furthermore, higher initial traffic density renders the traffic system more susceptible to congestion, resulting in local clustering effect and stop-and-go traffic phenomenon. Remarkably, the total energy consumption of the heterogeneous traffic system exhibits a gradual decline with CAV permeability increasing. Further evidence has demonstrated the positive influence of CAV on heterogeneous traffic flow. This research contributes to providing theoretical guidance for future CAV applications, aiming to enhance urban road traffic efficiency and alleviate congestion.

State observation for heterogeneous quasilinear traffic flow system with disturbances

State observation for heterogeneous quasilinear traffic flow system with disturbances