Dynamic Flow Characteristics Research Articles

Temporal asynchrony analysis for dynamic operation of hydraulic-thermal-electricity multiple energy networks based on holomorphic embedding method

Analyzing the operational states of multiple energy networks (MEN) in multi-energy systems is crucial for ensuring system stability. The dynamic operational characteristics of different energy flows pose challenges for computational analysis. Traditional steady-state methods are inadequate for addressing the dynamics of MEN, especially when dealing with temporal discrepancies between hydraulic and thermal flows in thermal networks (TN) and the heterogeneity between TN and electrical networks. Therefore, this paper proposes a novel holomorphic embedding method (HEM) based on multi-stage decomposition method. The developed HEM constructs a time coefficient matrix and utilize inner-outer loop recursion to handle the time lag between thermal flow and hydraulic flow in the TN. Additionally, we reconstruct a holomorphic matrix, integrating hydraulic flow to bridge thermal and electric power flows, thereby improving the operational heterogeneity among different networks. Real-case simulations show that when the Taylor expansion order in HEM is equal to 4, the proposed method achieves a mere 1% discrepancy from actual operational data, enhancing computational efficiency by 60% compared to the Newton-Raphson method. Moreover, in this real-case, the TN exhibits a maximum delay response time of 180s compared to electrical networks. Exploiting this delay time effectively increases renewable energy generation within multi-energy systems by 961.58kW per day.

CFD-DEM simulation and experimental investigations on continuous hydraulic sampling of deep-sea polymetallic nodules

The physical sampling to grasp the distribution of deep-sea polymetallic nodules is essential for efficient mining. A hydraulic sampling scheme is proposed that balances low environmental disturbance, high sampling precision, broad coverage, and statistical data viability to address the limitations of existing methods. This paper utilizes a coupled approach of Computational Fluid Dynamics (CFD) and Discrete Element Method (DEM) to explore innovative solutions for precise and continuous hydraulic sampling of deep-sea polymetallic nodules. Focusing on the optimization of the double-row nozzle sampling mechanism design, simulations are conducted to analyze the dynamic characteristics of solid-liquid two-phase flow during the hydraulic sampling process under various nozzle configurations and operational water pressures of the pump. Laboratory experiments with the hydraulic sampling mechanism validate the accuracy of the simulation and the effectiveness of the optimization suggestions. The configuration is finalized with a front nozzle diameter of 10 × φ9 mm, a rear nozzle diameter of 10 × φ7 mm, and an initial working water pressure for the pump of 80 kPa. Subsequently, integrated driving-sampling experiments are conducted using a self-propelled tracked vehicle, validating the feasibility of the proposed configuration.

Flow instability in mixed-flow/axial-flow pump: A review of relationship between tip leakage flow distortion and rotating stall

Pumps represent a quintessential type of fluid machinery and play a pivotal role in global economic development, especially in the hydropower industry. However, as their application range and single unit capacity continue to increase, the occurrence of rotating stall phenomena caused by internal flow instability has become more frequent. This phenomenon is especially prominent in pumps with semi-open impellers, where the presence of tip leakage flow (TLF) instability accelerates the rotating stall phenomenon. Over the past few decades, extensive research has been conducted to investigate the characteristics of rotating stall in pumps. As a result, there is now a preliminary understanding of the phenomenon of rotating stall induced by tip leakage flow in pumps. However, a significant gap remains in fully comprehending the underlying mechanism of this phenomenon. This review comprehensively reviews the research progress made in understanding the relationship between pump tip leakage flow instability and rotating stall. It encompasses various aspects, including the formation mechanism of tip leakage flow, the structure of tip leakage vortex (TLV) under stall conditions, the correlation between dynamic characteristics of tip leakage flow and rotating stall, as well as methods for mitigating rotating stall through tip leakage suppression. Finally, some promising potential stall suppression method is discussed to provide some guidance for the fields of fluid machinery.

Exploring the Effects of Fissures on Hydraulic Parameters in Subsurface Flows from the Perspective of Energy Changes

Reynolds number (Re), pore water pressure (P), and water flow shear force (τ) are primary indicators reflecting the characteristics of subsurface flow. Exploring the calculation of these parameters will facilitate the understanding of the hydrodynamic characteristics in different subsurface flows and quantify their differences. Hence, we conducted a study to monitor soil water content, matrix potential, and pore water pressure in two typical soil profiles (with and without fissures). The distribution of Re, P, and τ in both matrix flow (MF) and preferential flow (PF) were calculated with an improved calculation method, focusing on their energy changes. Results showed that these hydrologic parameters are quite different between MF and PF. Re values in MF remained below 0.1, indicating lower water flow velocities, while the Re values ranged from 0.8 to 2 in PF, indicating higher flow velocities. The P values in PF was tens to hundreds of times higher than that in MF, which is mainly due to the rapid accumulation and leakage of water within soil fissures. Additionally, the larger hydraulic radius and gradient in PF also resulted in higher τ values in PF (2~6 N m−2) than in MF (0~1.5 N m−2). In PF, the pressure potential was the significant factor for τ, while τ in MF was dominated by the matrix potential and varies with the magnitude of the matrix potential gradient. This study suggests that Re, P, and τ could be considered as the major indexes to reflect dynamic characteristics of subsurface flow.

Dynamic Characteristics of the Long Runout Rock‐ice Avalanche at High Altitude—A Case from the Zelongnong Basin, Eastern Himalayan Syntaxis, China

AbstractRock‐ice avalanches have frequently occurred in the Eastern Himalayan Syntaxis region due to climate change and active tectonic movements. These events commonly trigger catastrophic geohazard chains, including debris flows, river blockages, and floods. This study focuses on the Zelongnong Basin, analyzing the geomorphic and dynamic characteristics of high‐altitude disasters. The basin exhibits typical vertical zonation, with disaster sources initiating at elevations exceeding 4000 m and runout distances reaching up to 10 km. The disaster chain movement involves complex dynamic effects, including impact disintegration, soil‐rock mixture arching, dynamic erosion, and debris deposition, enhancing understanding of the flow behavior and dynamic characteristics of rock‐ice avalanches. The presence of ice significantly increases mobility due to lubrication and frictional melting. In the disaster event of September 10, 2020, the maximum flow velocity and thickness reached 40 m/s and 43 m, respectively. Furthermore, continuous deformation of the Zelongnong glacier moraine was observed, with maximum cumulative deformations of 44.68 m in the distance direction and 25.96 m in the azimuth direction from March 25, 2022, to August 25, 2022. In the future, the risk of rock‐ice avalanches in the Eastern Himalayan Syntaxis region will remain extremely high, necessitating a focus on early warning and risk mitigation strategies for such basin disasters.

Road network traffic flow prediction: A personalized federated learning method based on client reputation

Accurate traffic flow prediction can provide effective decision-making support for traffic management, alleviate traffic congestion, and improve road traffic efficiency. Traffic flow data contains personal privacy information, such as vehicle trajectories, driving speed, etc. However, most existing research focuses on using all local data to jointly construct prediction models, facing data security and privacy issues. In response to these challenges, this paper presents a Personalized Federated Learning method based on Client Reputation for traffic flow prediction. This method avoids direct sharing of original data by performing model training and information aggregation locally, thereby better protecting user privacy. We initially constructed the foundational model by integrating Graph Neural Networks (GCNs) and Gated Recurrent Units (GRUs) for federated learning. GCNs are capable of capturing the spatial relationships between nodes in a road network, thereby extracting the spatial features of traffic flow. On the other hand, GRUs has advantages at handling sequential data, effectively capturing the dynamic temporal characteristics of traffic flow. Consequently, by combining GCNs and GRUs, we are able to build a model that captures both the spatial and temporal features of traffic flow. Subsequently, we employed a federated learning framework to train the model. In the training process, we propose two evaluation algorithms to evaluate the reputation of all clients participating in federated learning. Then, different weights are customized for each client through a personalized algorithm based on the client's reputation to improve its personalization. Finally, we validated the method on the License Plate Recognition (LPR) datasets collected in Changsha city, China. The experimental results indicate that the method can achieve good prediction performance and stability while protecting the privacy of all participants.

Nusselt and Reynolds numbers correlation for oscillatory flow of thermoacoustics over heated tube banks

The back-and-forth motion characteristic of the oscillatory flow brings complexity in predicting the heat transfer nature for thermoacoustic wave conditions. Investigating this is crucial as the flow forms the backbone for the thermodynamic cycles of its operation. Often, the steady approximation in calculating heat transfer leads to ambiguity in designing the system. This concern is addressed in this paper via an in-depth analysis of the fluid dynamic behavior and heat transfer characteristics of oscillatory flow within a thermoacoustic framework, spanning experimental works for Reynolds numbers ranging from 300 to 24,000. The investigation focused on a tube bank heat exchanger composed of nine tubes arranged in inline and staggered configurations. Experiments were conducted at constant tube surface temperatures treated at 40 °C and 80 °C, respectively. The distinguishing difference in heat transfer behaviors for oscillatory flow is visualized by the Computational Fluid Dynamics (CFD) models, employing the Reynolds-Averaged Navier Stokes equation with the k-omega turbulence shear stress transport model. The flow never leaves the system, and it cyclically crosses the tubes back and forth with a travel distance that depends on the drive ratio of the flow. The unique nature of the flow forms the foundation for the experimental findings that show a linear relationship between the Nusselt and Reynolds numbers regardless of the configuration of tube banks and tube bank temperature. The Pearson ruler regression analysis was conducted using Matlab R2022b and a Nusselt correlation, Nu = 0.000853RePr⅓, is proposed with a confidence level of 95 %. Notably, the correlation aligns with the constant value known as the Colburn-j factor, with a value of 0.00083. The small Colburn-j value is shown to be the influence of the log-mean temperature difference characteristic of oscillatory flow. It shows a consistent heat transfer process between the heated tubes and surrounding fluid, which is important to sustain the thermoacoustic effect in the system. For future thermoacoustic design, the use of steady heat transfer correlation should be avoided or at least used with caution as the comparative analysis with published works concluded that the Nusselt and Reynolds correlation for steady flow tends to overpredict the heat transfer by two to threefold.

Characteristics of debris flow dynamics and prediction of the hazardous area in Bangou Village, Yanqing District, Beijing, China

Abstract Debris flow is one of the most common types of geological disasters in China. Owing to the influence of topography, geomorphology, geological conditions, human activity, and rainfall debris flow disasters frequently occur in the mountainous areas of Beijing. The research on debris flow in the Beijing area focuses on rainfall and risk evaluation, material sources, and early warning and prevention of debris flow. However, there are few studies on the development characteristics of single-gully debris flow and the prediction of hazardous areas in the Beijing area. Therefore, we chose the debris flow of Bangou Village in Yanqing District of Beijing as the research object. We analyzed the recharge conditions in the ditch domain and predicted the extent of the hazardous area around the gully, providing suggestions for control measures. The dynamic reserves of the loose deposits in the debris flow gully, currently in the development stage, were estimated as 15.48 × 104 m3, representing four supply sources: artificial deposits, alluvium and diluvium, residual slope deposits, and collapse. The peak flow is 24.49 m3/s for a 10-year rainfall event, 27.64 m3/s for a 20-year rainfall event, 31.79 m3/s for a 50-year rainfall event, and 34.93 m3/s for a 100-year rainfall event. The total amounts of solids washed out by a debris flow from the preceding events are 0.70 × 104 m3, 0.79 × 104 m3, 0.91 × 104 m3, and 1.00 × 104 m3, respectively. The size of the debris flow is small, with a maximum hazardous area of 0.2810 km2. We conclude that a small debris flow outbreak in the Bangou Village gully is possible. We expect that the results of this study will provide basic information and help improve debris flow research in Beijing.

Phase transitions in a heterogeneous lattice hydrodynamic model involving both communication distance and memory time duration differences

An in-depth analysis of vehicle heterogeneity characteristics in intelligent heterogeneous traffic flows, involving connected autonomous vehicles and human-driven vehicles, is essential to reveal the evolutionary mechanism of congestion and to understand its interaction among traffic factors. It has significant theoretical value in guiding the future field deployment and testing of large-scale connected autonomous vehicles through the systematic analysis of the complex dynamic characteristics of intelligent heterogeneous traffic flows from a traffic engineering perspective, as well as traffic management and control in intelligent heterogeneous traffic flow environments. In this study, the lattice hydrodynamics model is applied to construct a corresponding traffic flow model for the differences of communication capability and historical information memory capability for heterogeneous vehicles. The roles of these heterogeneous properties and their interactions are investigated. Through linear stability analysis and nonlinear analysis, we in this study explore the influence of key traffic factors involving the differences of communication capability and historical information memory capability contributing to ameliorating intelligent heterogeneous traffic dynamics, and numerical simulations are carried out to verify the accuracy and reliability of theoretical analysis. An interesting discovery is that longer memory duration gets better, but the optimizing impact of memory duration on the system being marginally decreasing.

Analysis of pedestrian second crossing behavior based on physics-informed neural networks

Pedestrian two-stage crossings are common at large, busy signalized intersections with long crosswalks and high traffic volumes. This design aims to address pedestrian operation and safety by allowing navigation in two stages, negotiating each traffic direction separately. Understanding crosswalk behavior, especially during bidirectional interactions, is essential. This paper presents a two-stage pedestrian crossing model based on Physics-Informed Neural Networks (PINNs), incorporating fluid dynamics equations to determine characteristics such as speed, density, acceleration, and Reynolds number during crossings. The study shows that PINNs outperform traditional deep learning methods in calculating and predicting pedestrian fluid properties, achieving a mean squared error as low as 10–8. The model effectively captures dynamic pedestrian flow characteristics and provides insights into pedestrian behavior impacts. The results are significant for designing pedestrian facilities to ensure comfort and optimizing signal timing to enhance mobility and safety. Additionally, these findings can aid autonomous vehicles in better understanding pedestrian intentions in intelligent transportation systems.

DEM investigation on the dynamic characteristics of wall normal stress and particle flow during silo discharge

Violent fluctuations of wall normal stress and particle velocity during silo discharge have been frequently reported by researchers, yet a detailed particle-scale description of these phenomena is still lacking. In this work, the discharge of granular assembly from a flat-bottomed silo was investigated by performing three-dimensional (3D) Discrete Element Method (DEM) simulations. It is found that arc-shaped rupture surfaces, characterized by the lower solid volume fraction, stronger shear interaction between particles and higher particle rotating velocity, developed continuously in the bottom converging part of flowing zone. And the temporal evolution of particle velocity field in this converging part presented a swinging feature manifested by the alternate appearance of large particle velocity magnitude in the right- and left- side regions. Discrete Fourier transform results showed that the fluctuation of wall normal stress at the transition point contained two dominate frequencies. The lower frequency matched well with the dynamic evolution of the rupture surface joining the transition point, and the higher frequency was nearly the same as the fluctuating frequency of particle vertical velocity. Thus, our DEM simulation results suggested that for the silo discharge considered in this work, the periodic fluctuation of wall normal stress at the transition point was contributed by both the alternating flow pattern mechanism and the free-fall arch mechanism.

The spatio-temporal evolution of spatial structure and supply-demand relationships of the ecological network in the Yellow River Delta region of China

Rapid urbanization has caused unregulated transformation of land use/land cover, further exacerbating the imbalance in ecological supply-demand relationships. Constructing the ecological supply-demand network by integrating natural ecological processes and human ecological demands into a unified spatio-temporal framework and considering their internal dynamic flow characteristics is an effective approach to address this issue. However, there is currently a lack of evaluation methods for the coordination of ecological network supply-demand relationships, which could provide essential guidance and reference. This article takes Yellow River Delta region (YRDR) as an example and utilizes the integrated traditional ecological network paradigm and ecosystem services theory to simulate the ecological supply-demand network for the years 2000, 2010, and 2020 in the study area. Then, a novel evaluation index system for the ecological network supply-demand relationships was proposed from the perspectives of intensity, capacity, and equilibrium. Based on this, combined with a graph-based network structure evaluation system, it reveals the spatio-temporal evolution process of the spatial structure and supply-demand relationship of the ecological network in the study area. The study results showed that: (1) The quantity of elements in the ecological supply network and the demand network show opposite trends, leading to an exacerbation of the spatial imbalance in the arrangement of ecological supply-demand network elements. (2) Although the structural changes in the ecological supply network were significantly greater than those in the ecological demand network, the overall structure of the ecological supply-demand network gradually improved. (3) However, under the coupled influence of imbalanced change in the spatial distribution and connectivity of ecological elements, the overall coordination of the ecological supply-demand relationships within the YRDR's ecological network is gradually deteriorating. (4) The proposed optimization strategy, "Two zones, Three belts, Four corridors", for the ecological security pattern in YRDR can accommodate the ecological process cycle and human ecological demand. This study can provide scientific references for "harmonious coexistence between humans and nature" ecological protection and sustainable development planning under the background of rapid urbanization.

Investigations into dynamic variation characteristics of near-bottom boundary flows over a long term at strait areas

Dynamic processes at the bottom boundary layers are crucial for various marine applications such as marine energy exploration and environmental protection. This paper explored hydrodynamic characteristics of near-bottom boundary currents based on the continuous measured velocity data at a site near the bottom boundary layer in the Strait of Georgia. Previous studies on the bottom boundary layer encompasses various aspects, including physical, chemical, and biological dimensions. However, the mechanism underlying the hierarchical structure of flow velocity remain poorly understood. Thus, the power spectrum and wavelet analysis were used to analyze the significant period of the averaged velocity and tidal characteristics, and to calculate the thickness of near-bottom boundary layer. The results indicate that the current velocities at the boundary layer tend to flow in south-eastern and north-western directions. There is a significant period of 15 days in August and September. At a depth of 301 m, the vertical velocities are stratified, indicating different velocity layers. The boundary layer thickness varies from 5.23 to 14.74 m, as indicated by the vertical structural characteristics. The structural characteristics of the seabed boundary layer are conducive to deepening our understanding of sediment's incipient motion, transport, and sedimentation process. The findings provide a theoretical foundation for future studies on sediment movement and the numerical simulation analysis of the bottom boundary layer. It is of great significance for the study of wave flow interaction and marine dynamic structure, and can provide scientific support for the development and utilization of the strait area.

Numerical investigation on dynamic flow characteristics of methane condensation in microchannels

Microchannel condensers play an essential role in cryogenic two-phase heat management systems due to their efficient heat transfer characteristics. Thus, it is worth conducting an in-depth study on microscale condensation characteristics of cryogenic fluids. This paper delves into the flow condensation process of methane in microchannels. A two-dimensional transient model with high accuracy for cryogenic fluids has been developed by combining a self-defined program for the source term of the phase transition model. The model fully considers the boundary layer thickness and accurately explores the mesh accuracy. The complete condensation flow patterns are captured for various vapor quality, mass flux, and wall subcooling degrees. The injection flow is a unique flow regime for condensation in microchannels. The decrease in wall subcooling degree and increase in mass flux leads to the separation point at the neck of the injected flow moving towards the exit, while the annular flow region is expanding and the flow pattern transition is lagging. The mass flux improves the heat transfer coefficient more significantly at high vapor quality. During injection and bubble flow, the wall shear stress and local heat transfer coefficient are subject to bouncing and oscillations, which may induce fluctuations in the upstream annular flow. The prediction performance of six classical heat transfer correlations is evaluated. The results indicate that the Nie et al. correlation has the highest comprehensive prediction accuracy with MRD and MARD of -5.00 % and 15.83 %, respectively.

A Novel Cost-Effective Approach To Flow Visualization Using Particle Streak Angles

Comprehending the complexity of pipe flow dynamics is important for a variety of sectors, including environmental, industrial, and engineering research. Conventional techniques such as Particle Image Velocimetry (PIV) and Laser Doppler Velocimetry (LDV) have long been the heart of these kinds of investigations, but they are often quite expensive and complex. Our research offers a novel, affordable visualization method that aims to transform the way we examine and interpret flow dynamics in response to these challenges. Our method is developed to utilize a low-cost camera system with a simple laser setup to investigate fluid dynamics inside pipes. By introducing particles into the flow and manipulating the camera shutter speed in real-time under the laser, we created streaks capable of displaying complex flow patterns. We gathered precise information about streak angles using rigorous image processing, providing clarity in our insights into the flow dynamics of the particulate pipe flow. Notably, our strategy eliminates the need for expensive and sophisticated equipment while providing researchers and practitioners with a simple yet reliable alternative. The potential for improving our knowledge of pipe flow dynamics is shown by preliminary studies that demonstrate how well it captures dynamic flow characteristics. Our technique, which is easy to use and effective, promises fresh insights into fluid dynamics and opens up new avenues for pipe flow analysis, making it suitable for a broad spectrum of users.

The human craniospinal venous system and its influence on postural intracranial pressure: a review.

The cerebral and spinal venous systems have similar functions but unique anatomical and physiological properties. CSF occupies space in the cranial and spinal vaults, is continuously produced, and has many roles, including maintaining a favorable environment for CNS structures. The influence of the cerebrospinal venous system on CSF dynamics has been theorized since the 1940s. Newer studies suggest venous outflow pattern alterations in response to changes in body position. However, the relationship of postural cerebrospinal venous outflow shifts with and their influence on CSF homeostasis is not well understood. The authors searched the published literature related to the anatomy and function of vertebral venous plexus (VVP), CSF, and positional cerebral venous flow characteristics. A comprehensive collection of literature was compiled and reviewed, and the relationship between cerebrospinal and venous system changes and alterations in body positions, with an emphasis on the craniocervical system, is discussed. The VVP is a network of valveless veins extending from the sacrum to the cranium that are interconnected with the cranial dural sinuses. The internal VVP occupies space within the extradural spinal canal and functions to return spinal venous blood to the heart, but it has additional properties, including the capability of bidirectional venous flow, an intraspinal dilatory capacity, and a role in cerebral venous outflow. When one rises to the upright position, CSF shifts toward the spinal canal and force vectors change, leading to reduced intracranial CSF pressure; simultaneously, cerebral venous outflow shifts from the jugular vein to the VVP outflow pathway. The venous outflow shift mechanism and its purpose are poorly understood. The authors review the known physiology of the system, identify gaps in knowledge to direct future research, and propose an interpretation of these data, concluding that position-dependent CSF and cerebrospinal venous shifts are part of a complementary positional craniospinal pressure regulation system that must be kept in balance for optimal CNS function. Current knowledge of the cerebrospinal venous anatomy, dynamic flow characteristics in response to gravity, and the venous system's influence on CSF suggests that the VVP plays a role in influencing CSF pressure, and the authors hypothesize that it plays a role in supporting intracranial pressure in the upright body posture. Further research is needed to better characterize the functional relationship of the VVP to CSF dynamics as well as identify potentially related disease states.

Impacts of connected autonomous vehicle platoon breakdown on highway

Connected Autonomous Vehicles (CAVs) are seen as an opportunity to improve traffic efficiency and safety. However, further studies are needed to prove such positive effects. Particularly, there is a lack of quantitative research on the impacts of CAV breakdown in mixed traffic flow conditions (i.e., CAVs and Human Driven Vehicles, HDVs). The aim of this research is to explore the impacts generated by the breakdown of the leading vehicle of a CAV platoon in mixed traffic conditions, which in turn generates CAV platoon breakdown (i.e., platoon dispersion), by considering several CAV market penetration rates (MPRs) and platoon size. In this perspective, a control algorithm (“avoidance algorithm”) for modelling CAVs and HDVs behaviour to avoid obstacles (i.e., breakdown CAV platoon) has been developed and tested on some simulation scenarios in order to derive key traffic flow parameters. The dynamic characteristics of traffic flow with CAV platoon breakdown have been explored for both low and high traffic flow volumes. Finally, potential conflicts, congestions, as well as energy consumption and CO2 emissions resulting from the breakdown of CAV platoons in mixed traffic streams have been assessed and discussed. Results suggest that (1) CAV platoon breakdown can reduce the traffic capacity by about 20 %; (2) higher CAV MPRs are more suitable for enhancing highway safety even in breakdown conditions, decreasing energy consumption, and reducing CO2 emissions; (3) platoon size should be limited to 4, since larger sizes affect traffic safety, increases vehicle average delay time as well as energy consumption. The obtained results provide useful insights for transportation planners and transport infrastructure management companies to design and apply policies aimed at improving driving conditions, traffic quality, and safety on highways.

Optimizing gate control coordination signal for urban traffic network boundaries using multi-agent deep reinforcement learning

The macro-aggregated dynamic characteristics of urban network traffic flow encapsulated embodied in macroscopic fundamental diagram theory provide a concise perspective for network-level traffic control. However, accurately translating inter-regional macro-traffic flow control into fine-grained control at each intersection along the boundary remains challenging. To address this issue, a regional boundary intersections collaborative control method is proposed, leveraging the model-free data-driven advantages of multi-agent deep reinforcement learning. First, the proposed model develops a real-time communication module using a bi-directional long short-term memory model and an attention mechanism, enabling intersections to capture spatiotemporal features of the boundary traffic flow. Second, the feedback reward mechanism of the boundary intersection agent is meticulously constructed by considering the traffic state of the protected region, the boundary control pressure, and the traffic pressure of the intersection. The proposed model fosters cooperation at boundary intersections and delicately balances their macro-micro control contradictions based on mastering the boundary state information of the road network. Data-driven boundary gating control can fine-regulate the traffic flow distribution of the network from the perspective of macro-micro to promote its operation efficiency. The simulation experiments verify the effectiveness of the proposed multi-agent signal collaborative control method.

STC-PSSA: A New Model of Traffic Flow Forecasting Based on Spatiotemporal Convolution and Probabilistic Sparse Self-Attention

Traffic flow forecasting is the foundation of the dynamic control and application of intelligent transportation systems (ITS). It is also of significant practical value in alleviating road congestion. Given the periodic and dynamic changes in traffic flow and the spatiotemporal coupling interaction of complex road networks, traffic flow forecasting is challenging and rarely yields satisfactory prediction results. To capture the dynamic spatiotemporal characteristics of traffic flow, a new model of traffic flow forecasting based on spatiotemporal convolution and probabilistic sparse self-attention (STC-PSSA) is proposed. It consists of a spatiotemporal graph convolution network (ST-GCN) module, a spatiotemporal convolution module (ST-Conv), and a probabilistic sparse attention module (PSSA). ST-GCN consists of the gated temporal convolutional network (G-TCN) and the graph convolution network (GCN), which are used to capture the temporal dependence and spatial correlation of the traffic flow, respectively. Multiple ST-GCNs are stacked to handle spatial features at various time levels. The ST-Conv captures intricate temporal dependence at the same location and dynamic spatial features at neighboring locations simultaneously. The PSSA combines dynamic spatiotemporal features and performs long-term forecasting efficiently. The experimental results demonstrate that the STC-PSSA model can accurately extract the dynamic spatiotemporal characteristics of traffic flow and outperforms the popular baseline methods in forecasting accuracy.

A complete dynamic modeling of two-by-one gas-steam combined cycle unit for coordinated control design

The high penetration of renewable energy can potentially change the role of combined cycle gas turbines (CCGT) from load suppliers to flexible suppliers. However, the slow process in the dynamic modeling of the CCGT complete system restricts the design of its best flexible operation strategy. Therefore, this paper adopts the composite modeling method to establish a complete nonlinear dynamic model of a 2 × 1 CCGT including two gas turbines, two three-pressure heat recovery steam generators (HRSG), one steam turbine, and one heat exchanger. This model can reflect the dynamic characteristics of pressure and flow in the bottom cycle due to the changes in operating mode, which can guide the design of the flexible operation control strategy of the 2 × 1 CCGT. To verify the model’s accuracy, the simulated data were compared with the field data from a 900 MW cycle with the same configuration. The results show that the established model can accurately reflect the changing trend of variables during mode switching. The range errors of essential variables such as gas turbine (GT) power is 3.8 %, GT outlet temperature is 1.4 %, high-pressure drum pressure is 4.5 %, and the range errors of other variables are below 5 %, proving the fidelity of the established control model. The dynamic model established in this paper will be the basis for model-based controller design which can handle multi-objective optimization and constraints in flexible operations. Moreover, this model will be used to guide the design of multi-mode control and energy management strategies.