Network Flow Model Research Articles

An Online Feedback-Based Linearized Power Flow Model for Unbalanced Distribution Networks

The nonlinear and nonconvex nature of ac power flow challenges the analysis and optimization of unbalanced distribution networks. To tackle this issue, this paper proposes an online feedback-based linearized power flow model for unbalanced distribution networks with both wye-connected and delta-connected loads. The online feedback-based linearized model is grounded on the first-order Taylor expansion of the branch flow model (BFM), and updates the model parameters via online feedback by leveraging the instantaneous measurements of voltages and load consumption. Exploiting the connection structure of unbalanced radial distribution networks, we also provide a unified matrix-vector compact form of the model. The numerical tests on the IEEE 123-bus test system validate its accuracy and superiority compared with other offline BFM methods. Additionally, we apply the proposed model to the optimal power flow for voltage regulation, which further demonstrates its effectiveness.

Exploration of a new path to reduce air pollutant emissions in the sinter plant of steelworks

Tremendous air pollutant emissions have become an obstacle to curbing the green development of the steel industry. How to minimize the air pollutant emissions? In this study, the gas flow network model of the sinter plant was first established by using the dynamic material flow analysis method. Moreover, based on the circulation theory, the waste gas recycling of the sinter plant was constructed and the gas flow network model was optimized. Therefore, the waste gas and air pollutant emissions can be reduced. The results showed that the actual air consumption in the sinter plant was 79.40% higher than the theoretical value, with a serious waste of air resources. After waste gas recycling, the waste gas emissions from the production of per ton sinter were reduced by 49.54%, and the emissions of SO2, NOX, and soot were reduced by 4.5 g, 9.1 g, and 1.8 g, respectively.

Capacity Evaluation Method of Ship Terminal Area Based on Network Maximum Flow

With the development of modern military ship equipment, the airspace operation environment of shipboard aircraft is becoming increasingly complex. The objective and accurate evaluation of ship-terminal capacity is the basis of air traffic flow management for shipboard aircraft, and it is also the premise of improving the efficiency of airspace resources. First of all, this paper divides the airspace and ship terminal areas and realizes the route network planning for the arrival and departure of shipboard aircraft. Following this, according to the airspace characteristics of the ship terminal area and the operation process of the shipboard aircraft, an arrival-and-departure network flow model for shipboard aircraft is established by using capacity-limitation and flow-conservation conditions. Finally, simulated annealing (SA) is used to solve the maximum flow in the arrival-and-departure network flow model, and the capacity evaluation results of the ship terminal area are obtained. The results show that when the number of gates is ng ≥ 7, the bottleneck in the ship terminal area’s operation capacity is the deck runway. When 3 ≤ ng < 7, imbalanced take-off and landing tasks lead to a waste of runway resources, and when ng < 3, the number of gates becomes the bottleneck which limits the capacity. With the number of gates being reduced from seven to three, the capacity is reduced from twenty sorties per hour to six sorties per hour. The model and core idea proposed in this paper can not only quickly evaluate the capacity of the terminal area of ships but also provide a solid foundation for the development of future fleet groups and the full use of maritime airspace.

Combined carbon, hydrogen, and clumped isotope fractionations reveal differential reversibility of hydrogenotrophic methanogenesis in laboratory cultures

Stable isotope analysis has been widely used to aid the source identification of methane. However, the isotopic (13C/12C and D/H) and isotopologue (13CH3D and 12CH2D2) signatures of microbial methane in natural environments are often different from those in laboratory cultures in which methanogens are typically grown under optimal conditions. Growth phase and hydrogen (H2) concentration have been proposed as factors controlling the isotopic compositions of methane produced via hydrogenotrophic methanogenesis, but their effects on the relationship among carbon, hydrogen and doubly-substituted “clumped” isotopologue systems have not been assessed in a quantitative framework. Here we experimentally investigate the bulk (δ13C and δD) and clumped (Δ13CH3D) isotopologue compositions of methane produced by hyperthermophilic hydrogenotrophic (CO2-reducing) methanogens using batch and fed-batch systems at different growth phases and H2 mixing ratios (Methanocaldococcus bathoardescens at 82 or 60 °C and on 80 or 25 % H2; Methanothermobacter thermautotrophicus str. ΔH at 65 °C and on 20, 5 or 1.6 % H2). We observed a large range (from 18 to 63 ‰) of carbon isotope fractionations, with larger values observed during later growth phase, consistent with previous observations. In contrast, hydrogen isotope fractionations remained relatively constant at –317 ± 25 ‰. Linear growth was observed for experiments with M. bathoardescens, suggesting that dissolution of gaseous H2 into liquid media became the rate limit as cell density increased. Accordingly, the low (and undersaturated) dissolved H2 concentrations can explain the increased carbon isotope fractionations during the later growth phase. The δD and Δ13CH3D values indicated departure from equilibrium throughout experiments. As the cell density increased and dissolved H2 decreased, Δ13CH3D decreased (further departure from equilibrium), contrary to expectations from previous models. Our isotopologue flow network model reproduced the observed trends when the last H-addition step is less reversible relative to the first three H-addition steps (up to CH3-CoM). In this differential reversibility model, carbon, hydrogen and clumped isotopologue fractionations are largely controlled by the reversibility of the first three H-addition steps under high H2 concentrations; the last H-addition step becomes important under low H2. The magnitude of depletion and decreasing trend in Δ13CH3D values were reproduced when a large (≥6 ‰) secondary clumped kinetic isotope effect was considered in the model. This study highlights the advantage of combined bulk and clumped isotope analyses and the importance of physiological factors (growth phase) and energy availability (dissolved H2 concentration) when using isotope analyses to better understand methanogenic metabolisms and methane cycling processes.

Map equation centrality: community-aware centrality based on the map equation

To measure node importance, network scientists employ centrality scores that typically take a microscopic or macroscopic perspective, relying on node features or global network structure. However, traditional centrality measures such as degree centrality, betweenness centrality, or PageRank neglect the community structure found in real-world networks. To study node importance based on network flows from a mesoscopic perspective, we analytically derive a community-aware information-theoretic centrality score based on network flow and the coding principles behind the map equation: map equation centrality. Map equation centrality measures how much further we can compress the network’s modular description by not coding for random walker transitions to the respective node, using an adapted coding scheme and determining node importance from a network flow-based point of view. The information-theoretic centrality measure can be determined from a node’s local network context alone because changes to the coding scheme only affect other nodes in the same module. Map equation centrality is agnostic to the chosen network flow model and allows researchers to select the model that best reflects the dynamics of the process under study. Applied to synthetic networks, we highlight how our approach enables a more fine-grained differentiation between nodes than node-local or network-global measures. Predicting influential nodes for two different dynamical processes on real-world networks with traditional and other community-aware centrality measures, we find that activating nodes based on map equation centrality scores tends to create the largest cascades in a linear threshold model.

Train shunting with service scheduling in a high-speed railway depot

At high-speed railways, trains cover services during the day and are required to undergo maintenance at depots each night. A low-quality train schedule in the depot may result in delays in the availability of trains during the day which influences the reliability of the train timetables. Accordingly, this study examines the problem of train shunting with service scheduling in a depot where daily maintenance, cleaning operation, and safety operational requirements are considered. To cope with this complex problem, we first construct a two-layer time–space network in which each layer can only be used by trains traveling in the same direction. We then formulate the considered problem as a minimum-cost multi-commodity network flow model with incompatible arc sets and operational constraints. To solve the network flow problem, we present a Lagrangian relaxation heuristic. Finally, several computational experiments with practical data based on the Hefei–Nan depot and randomly generated data on trains’ arrival and departure times at the depot are conducted to confirm the effectiveness of our model and the efficiency of the proposed heuristics.

Utilizing partial flexibility to improve emergency department flow: Theory and implementation

AbstractEmergency departments (EDs) typically have multiple areas where patients of different acuity levels receive treatments. In practice, different areas often operate with fixed nurse staffing levels. When there are substantial imbalances in congestion among different areas, it could be beneficial to deviate from the original assignment and reassign nurses. However, reassignments typically are only feasible at the beginning of 8–12‐h shifts, providing partial flexibility in adjusting staffing levels. In this work, we propose a stochastic queueing network model of patient flow in the ED and study an associated fluid control problem to guide the reassignment decision for two types of nursing staff. We propose a heuristic solution approach and investigate its performance both analytically and using simulation. Analytical results and simulation experiments suggest a significant reduction of waiting times in parameter regimes relevant to the ED setting. We further implement the staffing approach at a large ED. This pilot study highlights several challenges of implementing operational interventions in the ED, including the difficulty of establishing a clean statistical environment in such setting. Despite these challenges, we find that guiding reassignment decisions using our approach is associated with significant improvements to patient flow including a reduction in average total ED length‐of‐stay of 1.7 h.

A Lagrangian relaxation approach based on a time-space-state network for railway crew scheduling

The Crew Scheduling Problem (CSP) is an important and difficult problem in railway crew management. In this paper, we focus on the railway crew scheduling problem with time window constraints caused by meal break rules. To address these complex crew scheduling rules, we construct a Time-Space-State Network (TSSN), formulate the studied problem as an initial network flow model and a strengthened network flow model. By exploring the characteristics of the TSSN and the improved model, we develop an efficient algorithm based on Lagrangian relaxation to get a valid lower bound. The latter algorithm is combined with solving a set covering model to get a good quality upper bound solution for the studied CSP problem. We compare the performance of our Lagrangian relaxation approach with a commercial solver (CPLEX) and Column Generation (CG) on several real-world instances based of Chinese high-speed railways. The computational experiments show that our method can solve the railway CSP in a very short computation time compared to both CPLEX and CG, while obtaining (near-)optimal solutions for all instances. We thus propose an innovative efficient and effective approach to improve the railway CSP with meal break constraints.

Artificial neural network modeling to examine spring turbulators influence on parabolic solar collector effectiveness with hybrid nanofluids

Numerical simulation and artificial neural network modeling of turbulent flow inside a pipe equipped with two spring turbulator samples with two different scales and a segmental cross-section have been investigated. Increased heat transfer rate (HTR) due to the use of a spring turbulator is predicted for the TiO2Cu-Water hybrid nanofluid based on the single-phase model, feed-forward artificial neural network (ANN) and fitting method. The role of Reynolds number (Re), scale and volume fraction (ϕ) on Nusselt number (Nu), pressure drop (ΔP), performance evaluation coefficient (PEC), solar collector efficiency (η), and the field synergy principle (FSP), compared to simple pipe, is considered using the finite volume method. The results show that increasing the spring turbulator scale increased the contact surface of the working fluid and the spring turbulator. As a result, the flow turbulence is increased, which leads to better mixing of the nanofluid as the operating fluid of the solar collector absorber pipe. Finally, ANN outputs and fitting results are compared, and it has been observed that the obtained ANN could predict the targets accurately.

Technological and Engineering design of a megawatt proton exchange membrane fuel cell system

For the large-scale application of Proton Exchange Membrane Fuel Cell (PEMFC) in the generation, multi-stack PEMFCs are usually adopted to meet high-power needs. The connection method among stacks, layout parameters, and operating parameters can greatly influence the performance and uniformity of system. A megawatt (MW) class PEMFC system with 20 stacks is proposed and modeled with a flow network model, a thermal model, and a PEMFC model. The temperature and voltage uniformity index (TUF and VUF) are defined to evaluate the system uniformity among stacks. The effect of several operating parameters, such as current density and hydrogen inlet pressure, and layout parameters such as spacing distance and the diameter of the pipe, on the stack voltage, system electricity efficiency, VUF and TUF are quantitatively compared. The results show that as the current density increases, the consistency of the system output decreases. The stack with the shortest gas transmission distance shows the best performance and the frictional loss is the main factor influencing the pressure differences among the inlet pressure of stacks. Under the inlet pressure of 2.5 atm, the voltage difference among stacks is within 0.15 V. From the perspective of overall cost and system output, a pipe diameter of 25 mm is more suitable for the design of the intake pipe in this system.

Расчет величины задела при строительстве лесовозных автомобильных дорог

Organizational and engineering models provide a formalized description of the construction process, including a list of operations, their sequence, description of the relationship between the operations, and the features of the work technology. Network models have the most complete mathematical description. However, a number of authors in their works point to serious shortcomings inherent in the network modeling of construction flow. The network model allows displaying accurately enough only the relationship of specialized traffic flows when the object is divided into sections equal in size to interchangeable grips. Such schedules are quite effective in planning and operational management of road works in a short planning period (decade or month). The adequacy of the real construction representation is violated when the network model is enlarged for long-term planning of the flow. The currently developed so-called generalized network models achieve process combination and continuity by introducing links between the operations, characterized by temporal parameters assuming any values, including negative values. However, the complexity of the mathematical description of these models still prevents their use in solving optimization problems. The research aims at developing a mathematical model and an algorithm for calculating the operational control of the groundwork volume during the construction of logging roads. A model of a complex object flow is proposed, where the flow operation time has a discrete nature. This condition is realized in the model by dividing the planned operation period of a complex flow into equal time intervals – scheduling intervals. The below described model of complex road construction flow can be presented on the basis of the scheduling task classification scheme and the analysis of existing economic and mathematical models and methods. The scheduling model developed for the purpose of determining the optimal groundwork for the road construction elements is a deterministic (at the 1st stage), discrete in time, engineering model with variable speeds of operations.

Pedestrian danger assessment under rainstorm-induced flood disaster for an artificial island

As extreme weather becomes more frequent and intense, cross-sea transportation infrastructures face a greater risk of flooding, which leads to economic loss and threats to life. The artificial island of the Hong Kong-Zhuhai-Macao Bridge is a crucial hub connecting the undersea tunnels and bridges. Heavy rainstorms could cause flooding of the artificial island, which threaten the safety of traffic and pedestrians on the island. It is significant for simulating flooding and assessing danger for pedestrians on islands, where people are difficult to rescue if flooding occurs. In this study, a coupled flood model is proposed to simulate the rainstorm-induced flood, and a stability model in floodwater is used to evaluate the danger degree of pedestrians on the artificial island. The drainage network and surface flow models are coupled to establish a flooding disaster model for the island. The results indicate that the turning of secondary roads on the island is more susceptible to flooding under extreme rainfall conditions. The map of pedestrian danger presents that the road turnings and main road are dangerous locations during the flood. The method proposed could offer technical support for safety management and flood prevention of cross-sea transportation infrastructures.

Optimization of Fuel Cell Stack Consistency Based on Multi-Model

With the proposal of cloud computing, fog computing, and edge computing, various simulation operations are greatly guaranteed, which benefits the multi-model operations of Matlab and CFD. This paper established the 1-D flow network model and 12 cm ∗ 8 cm 3-steady-state PEMFC model. Based on the experiment, the intake flow distribution of the cathode anode of 80 cells is simulated to obtain the maximum and minimum intake flow cell. The 3-D and steady-state single-cell model is used to calculate the cell’s performance, and the performance difference between the two cells is improved by optimizing the size structure of the single cell. The results show that the best version of the cell was obtained when the values of the width and depth were 1.1 mm and 0.8 mm, and the power density difference between the two cells decreased from 5.7% to 2.1%. The voltage difference at 1000 mA·cm−2 current density decreases from 0.065 to 0.035 V after optimization. The intake flow extreme difference of the reactor improved significantly, and C v was reduced by 48.7%.

Mission reliability–centered maintenance approach based on quality stochastic flow network for multistate manufacturing systems

Previous studies of reliability centered maintenance (RCM) rarely consider the maintenance quality for the operation condition monitoring of manufacturing system. Therefore, a quality-oriented maintenance approach for the multistate manufacturing system with the aid of mission reliability is proposed. First, connotations of the mission reliability and maintenance quality of the multistate manufacturing system are expounded on the basis of the operational mechanism. Second, a quality stochastic flow network (QSFN) model of the multistate manufacturing system is established, and a novel mission reliability model is presented. Third, a quality-oriented mission reliability–centered maintenance framework for multistate manufacturing systems is proposed, and the optimal integrated maintenance strategy is obtained by minimizing the total cost. Finally, an industrial example of subway flow receiver is presented to verify the proposed method. Results show that the proposed method can simultaneously balance the maintenance cost and maintenance quality of the multistate manufacturing system.

An interdependent network interdiction model for disrupting sex trafficking networks

We map the organized sex trafficking networks (STNs) into a network flow model and develop an interdependent network interdiction model (NIM) to disrupt such networks. Our model considers the interdependence between the information and physical victim networks and reformulates the problem using duality theory. Our NIM provides a clear strategy for interdicting STNs. Namely, it determines when to monitor (i.e., gather information) or arrest the criminals to maximize the impact of the interdiction operation and cripple the network. We tested our NIM using three federally prosecuted sex trafficking cases in the United States of different sizes and structural properties: hybrid, international transactional, and Tenancingo models. Our experiments show promising results regarding the effectiveness of our NIM in all network types. In addition to that, we tested our model with an increasingly higher fraction of missing information to account for the inherently hidden nature of illicit networks. Our study provides analytical insight regarding (1) the importance of building the case prior to the arrests of criminals in illicit networks and (2) deploying a holistic strategy to improve interdiction and enhance the fight against sex trafficking.

Sensitivity analysis on a network model of glymphatic flow.

Intracranial cerebrospinal and interstitial fluid (ISF) flow and solute transport have important clinical implications, but limited in vivo access to the brain interior leaves gaping holes in human understanding of the nature of these neurophysiological phenomena. Models can address some gaps, but only insofar as model inputs are accurate. We perform a sensitivity analysis using a Monte Carlo approach on a lumped-parameter network model of cerebrospinal and ISF in perivascular and extracellular spaces in the murine brain. We place bounds on model predictions given the uncertainty in input parameters. Péclet numbers for transport in penetrating perivascular spaces (PVSs) and within the parenchyma are separated by at least two orders of magnitude. Low permeability in penetrating PVSs requires unrealistically large driving pressure and/or results in poor perfusion and are deemed unlikely. The model is most sensitive to the permeability of penetrating PVSs, a parameter whose value is largely unknown, highlighting an important direction for future experiments. Until the value of the permeability of penetrating PVSs is more accurately measured, the uncertainty of any model that includes flow in penetrating PVSs is so large that absolute numbers have little meaning and practical application is limited.

Dynamic Network Flow Model for Power Grid Systemic Risk Assessment and Resilience Enhancement

Dynamic Network Flow Model for Power Grid Systemic Risk Assessment and Resilience Enhancement

Constructing a cell transmission model solution adhering fully to first-in-first-out conditions

Multi-commodity continuous flow network models, especially the cell transmission model (CTM) and the link transmission model, have been widely used in theory and practice for dynamic network loading (DNL), as well as for computing dynamic user equilibria (DUE). These models attempt to follow a first-in-first-out (FIFO) priority rule at various levels of approximation. However, previous studies have shown that these existing models and implementations do not fully ensure FIFO. Also, they allow deviations from FIFO that are not observed or permitted or physically possible in practice and these deviations can cumulate over time. A benchmark is needed for assessing the implications of FIFO violations in these models and methods, preferably over a wide range of instances. For this purpose, we propose here the first solution method that ensures perfect adherence to FIFO at the network-wide level.For clarity of exposition, the specific methods are presented here within the CTM framework. Variants of the principles presented here can be used to develop methods for ensuring FIFO for other DNL models. The proposed method is computationally intensive, and therefore suitable for small to medium size problems. As such, this method may not be applicable to large scale problems, which are considered fairly often in practice. The main value of the proposed method is therefore in the ability to produce a set of benchmark solutions, to evaluate the FIFO performance of other methods or the implications of FIFO violations in other methods. In addition, partial adoption of the ideas proposed here could lead to new efficient algorithms that approximate FIFO better than existing methods.

A Mathematical Model of a Valve-Controlled Bioreactor for Platelet Production

As blood donor numbers decrease, while demand for platelets increases, hospitals worldwide are becoming increasingly vulnerable to critical platelet shortages. Alternative methods of supplying platelets are therefore required. One approach is to engineer platelets in vitro in a bioreactor. To characterise such a system, we develop a mathematical model of a novel platelet bioreactor described in Shepherd et al. (Biomaterials, 2018, 182, 135–144). The bioreactor consists of upper and lower tube systems, with a cell-seeded porous collagen scaffold situated between them. Flow through the system is driven by gravity, and controlled by valves on each of the inlets and outlets. The bioreactor is long relative to its width, a feature which we exploit to derive a lubrication reduction of the Navier-Stokes equations for flow in the tube systems, coupled to Darcy flow through the porous scaffold. Flow in the tube systems and scaffold are coupled to form a network model for the bioreactor flow. We characterise the effect of geometrical parameters and valve configuration and synchronisation, on the fluxes through the bioreactor and shear stress experienced by cells in the scaffold. The simplicity of the model means that parameter sweeps take only seconds or minutes to perform, making the model a convenient tool for future bioreactor design optimisation.

Generalized network modelling of two-phase flow in a water-wet and mixed-wet reservoir sandstone: Uncertainty and validation with experimental data

We use a generalized pore network model in combination with image-based experiments to understand the parameters that control upscaled flow properties. The study is focued on water-flooding through a reservoir sandstone under water-wet and mixed-wet conditions. A set of sensitivity studies is presented to quantify the role of wettability, pore geometry, initial and boundary conditions as well as a selection of model parameters used in the computation of fluid volumes, curvatures and flow and electrical conductivities.We quantify the uncertainty in the model predictions, which match the measured relative permeability and capillary pressure within the uncertainty of the experiments. Our results show that contact angle, initial saturation, image quality and image processing algorithm are amongst the parameters which introduce the largest variance in the predictions of upscaled flow properties for both mixed-wet and water-wet conditions.