Network Flow Research Articles

Application of flow and heat transfer network analysis method to the core of a prismatic gas-cooled micro reactor

Application of flow and heat transfer network analysis method to the core of a prismatic gas-cooled micro reactor

A line balancing problem with parallel workers and cycle time minimization

We study the problem of balancing assembly lines with parallel workers, motivated by features observed at a third-party logistics provider. The assembly line differs from the two known types of line balancing problems in the literature since it determines simultaneously the line cycle time and the number of workers per station. Furthermore, each station can be occupied by more than one worker and a restriction on the minimum number of stations is imposed. This new type of line balancing problem minimizes the line cycle time, where two types of decisions must be made: how to group the tasks into stations and how many workers to assign to each station. We adapt mathematical models based on assembly line balancing problems with parallel stations. Furthermore, we propose a new model based on network flow formulations for the special case of lines with a serial task structure, frequently observed at the company. We perform an extensive computational study with realistic instances in order to compare the speed of solving different formulations, which is important as such a problem is at the operational level. We also provide a sensitivity analysis of key parameters aiming to better understand the trade-offs and provide meaningful managerial insights.

Network Flow of Campaign Discourses and News Frames: The Changing Fortunes of Twitter/X as a Conduit for Frame-Building

ABSTRACT Periodic changes continue to impact the fortunes of Twitter as a political communication and news-sharing platform, particularly among diverse individual and institutional African users. Yet few studies provide empirical insights into Twitter’s changing role in campaign communication and its influence on news frame-building across the region, mainly in Nigeria. This study bridges the gap by analysing n = 763 tweets to determine how the political campaign discourse strategies in the tweeting activities of two prominent candidates influenced the news frame-building practices of mainstream newspapers during the four months of Nigeria’s 2019 presidential campaign. Findings affirm that the Twittersphere served as a playground where candidates deployed diverse discourse frames correlating with political agendas to show their intent to integrate Twitter into their campaign communication strategies. Moreover, the candidates’ tweets contribute to the journalistic frame-building process, although the newspapers tend to focus more on frame-setting than frame-sending due to the dynamic nature of journalistic content production across individual news outlets.

An Active Learning Local Control Method for Optimal Power Flow in Low Voltage Distribution Networks Considering Missing Data

An Active Learning Local Control Method for Optimal Power Flow in Low Voltage Distribution Networks Considering Missing Data

Analyzing network flows of used EEE and e‐waste with platform data: Adding reuse into the EPR system for WEEE recycling in China

AbstractChina has established a nationwide formal e‐waste disassembly system based on the extended producer responsibility principle. However, the system mainly focuses on material recycling while excluding the reuse of used electrical and electronic equipment (used EEE) due to the complexity of flows and transactions during the reuse stage. Recently, emerging online platforms have played an increasingly crucial role in the value chain of reuse and recycling, significantly improving the visibility of flows in these activities. This paper aims to depict the spatial structure of the used EEE and e‐waste flow networks using a multi‐scale analysis framework. Using spatial analysis tools in complex network analysis, we characterize cross‐city reuse flows in China. A clear regional pattern of a hierarchical reuse network of notable regional hubs in certain regions in China is revealed in this analysis. The role of regulation is demonstrated in the comparison of the spatial flows in reuse and recycling. In conclusion, it is proposed that reuse should be included and emphasized in the management of waste electrical and electronic equipment in China to maximize the value of circularity.

Network reliability of a stochastic flow network by wrapping linear programming models into a Monte-Carlo simulation

A stochastic flow network (SFN) serves as a fundamental framework for real-life network-structured systems and various applications. Network reliability NRd is defined as the probability that an SFN can successfully send at least d units of demand from a source to a terminal. Current analytical algorithms for the network reliability evaluation are classified into an NP-hard problem. This limitation hinders the ability of decision-makers to monitor and manage decisions for an SFN flexibly and immediately. Therefore, this paper develops an algorithm to estimate network reliability by wrapping developed linear programming (LP) models based on minimal paths (MPs) into a Monte-Carlo simulation. The developed LP models present satisfaction of the demand d in the SFN in terms of minimal paths. The effectiveness and efficiency of the proposed algorithm are verified using a series of numerical investigations. Contributions are manifold: (1) an integrated model with the simulation and LP models is provided to estimate network reliability in terms of the MPs, thereby filling a crucial gap in existing research; (2) the scalability and efficiency of the proposed method are shown for the complex SFNs; (3) decision-making capabilities can be provided under real-time reliability predictions.

Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach

The macroscopic fundamental diagram (MFD) captures an orderly relationship among traffic flow, density, and speed at the network level. It is a simple yet powerful tool for modeling traffic dynamics in large urban networks with broad application in traffic control and management. However, empirically derived MFDs in urban regions require high-resolution traffic data from the network. Having the network flow and vehicular density estimated at the (granular) census tract level using vehicle probe data, we apply machine learning methods to predict the MFDs across U.S. urban areas and capture the impacts of location-specific input features on the network flow–density relationships at a large scale. The results show that, among the four tested machine learning approaches (Random Forest, XGBoost, Support Vector Machine, and Neural Network), XGBoost delivers the best performance in predicting network traffic flow based on vehicular density and location attributes. Using interaction Shapley Additive explanation (SHAP) values and partial correlation analysis, we examine the factors influencing MFD shapes across different locations. Our empirical findings reveal that across U.S. urban areas, network topology, transportation infrastructure, and land use are primary factors shaping MFD curves, while demand and trip-related factors play a lesser role. Specifically, higher ranking roads, centrality, and development levels correlate positively with network capacity and critical density, whereas negative associations are observed for network connectivity, mixed-use development, and road roughness levels.

Dynamics of a susceptible-infected-recovered model on complex networks with interregional migration.

We present a susceptible-infected-recovered model based on a dynamic flow network that describes the epidemic process on complex metapopulation networks. This model views population regions as interconnected nodes and describes the evolution of each region using a system of differential equations. The next-generation matrix method is used to derive the global basic reproduction number for three cases: a general network with homogeneous infection rates in all regions, a fully connected network, and a star network with heterogeneous infection and recovery rates. For the homogeneous case, we show that this global basic reproduction number is independent of the migration rates between regions. However, the rate of convergence of each region to an equilibrium state exhibits a much larger variance in random (Erdős-Rényi) networks compared to small-scale (Barabási-Albert) networks. For the general heterogeneous case, we report interesting results, namely that the global basic reproduction number decays exponentially with respect to the smallest nonzero Laplacian eigenvalue (algebraic connectivity). Furthermore, we demonstrate both analytically and numerically that as the network's algebraic connectivity increases, either by increasing the average node degree of each region or the global migration rate, the global basic reproduction number decreases and converges to the ratio of the average local infection rate to the average local recovery rate, meaning that the lower bound of the global basic reproduction rate does not equal the mean of local basic reproduction rates.

Unlocking responsive flexibility within local energy communities in the presence of grid-scale batteries

The transition towards a decentralized, decarbonized, and distributed energy infrastructure necessitates techno-economic initiatives to empower local energy communities (LECs) to achieve self-reliance and evolve into self-sustained electricity networks. It is crucial to underscore the significance of network resilience, especially in the context of local power generation, battery storage, and the radial topology of low-voltage (LV) networks. While contemporary LV networks have made significant attempts to integrate distributed energy resources (DERs), the notable deficiency lies in their lack of network redundancy, posing a substantial challenge in the occurrence of high-impact, low-probability (HILP) events. Therefore, to enhance LV network resilience and leverage its capability to withstand unexpected disruptions, the network operator needs to unlock the potential contributions of end-users within the active distribution networks (ADNs). In this paper, a comprehensive model is developed based on multi-temporal optimal power flow (MTOPF) for unbalanced LV networks addressing the technical issues in islanded microgrid operational planning. The contributions of the grid-scale batteries in forming islanded microgrids and the flexibility that can be provided by the end-users in the LEC have been considered in this paper. To demonstrate the performance of the proposed model, the simulation studies have been carried out on a part of medium and low voltage networks, consisting of network reconfiguration and load transferring capability to reduce the service interruptions during HILP events. The energy-not-served (ENS) is chosen as one of the key performance indicators (KPIs) in this study. With the unlocking flexibility potentials and contribution of the DERs, including grid-scale energy storage (GES) units and Photovoltaic (PV) panels, the ENS has been reduced from 700.8 kWh to 447.5 kWh by activating the local resources, proper switching action, and contribution of the flexible loads, for one of the severe HILP events, i.e., the main grid outage. In this case, the full load curtailment index is reduced from 180 to 106 client hours.

Flow reduction due to arterial catheterization during stroke treatment - A computational study using a distributed compartment model.

The effectiveness of various stroke treatments depends on the anatomical variability of the cerebral vasculature, particularly the collateral blood vessel network. Collaterals at the level of the Circle of Willis and distal collaterals, such as the leptomeningeal arteries, serve as alternative avenues of flow when the primary pathway is obstructed during an ischemic stroke. Stroke treatment typically involves catheterization of the primary pathway, and the potential risk of further flow reduction to the affected brain area during this treatment has not been previously investigated. To address this clinical question, we derived the lumped parameters for catheterized blood vessels and implemented a corresponding distributed compartment (0D) model. This 0D model was validated against an experimental model and benchmark test cases solved using a 1D model. Additionally, we compared various off-center catheter trajectories modeled using a 3D solver to this 0D model. The differences between them were minimal, validating the simplifying assumption of the central catheter placement in the 0D model. The 0D model was then used to simulate blood flows in realistic cerebral arterial networks with different collateralization characteristics. Ischemic strokes were modeled by occlusion of the M1 segment of the middle cerebral artery in these networks. Catheters of different diameters were inserted up to the obstructed segment and flow alterations in the network were calculated. Results showed up to 45% maximum blood flow reduction in the affected brain region. These findings suggest that catheterization during stroke treatment may have a further detrimental effect for some patients with poor collateralization.

Particle-based modeling and GPU-accelerated simulation of cellular blood flow

Computational modeling and simulation of cellular blood flow is highly desired for understanding blood microcirculation and blood-related diseases such as thrombosis and tumor, but it remains a challenging task primarily because blood in microvessels should be described as a dense suspension of different types of deformable cells. The focus of the present work is on the development of a particle-based and GPU-accelerated numerical method that is able to quickly simulate the various behaviors of deformable cells in three-dimensional arbitrarily complex geometries. We employ a two-fluid model to describe blood flow, incorporating the deformation and aggregation of cells. A smoothed dissipative particle dynamics is used to solve the two-fluid model, and a discrete microstructure model is applied for the cell deformation, as well as a Morse potential model for the cell aggregation. The heterogeneous CPU-GPU environment is established, where each GPU thread is dedicated to a particle, and the CPU is mainly responsible for loading and exporting data. Five test cases are conducted against analytical theory, experimental data, and previous numerical results, for pure fluid, cell deformation, cell aggregation, cell suspension and the cellular flow in a complex network, respectively. It is shown that the methodology can accurately predict various behaviors of cells, and the GPU is well suited for particle-based modeling. Especially for cellular blood flow, where calculating cellular forces is a compute-intensive and time-consuming task, the GPU offers exceptional parallel capabilities, significantly enhancing the simulation efficiency. The speedup is about 3.5 times faster than the CPU parallelization with 96 cores for the pure fluid, and this acceleration nearly reaches 20 times when cells are included in the simulations. Particularly, the calculations for deformation and aggregation forces demonstrate a substantial speedup, achieving the improvements of up to 120 and 640 times, respectively, compared to their serial counterparts. The present methodology can effectively integrate various behaviors of cells, and has the potential in simulating very large microvascular networks at organ levels.

Descriptive analysis of wide area network flow control internet traffic on Metro-E 100 Mbps campus network

QoS in computer networking is the capability to provide better service to network traffic over various technologies such as ethernet and IP networks. This paper presents a descriptive analysis of WAN flow control and internet traffic on a Metro-E campus network. Issues on network congestion and delay in network QoS where internet traffic is gradually increasing, resulting in bursts of network capacity that affect network QoS. The method implies 12 months data collection and analysis on protocol, bytes and packets inbound and correlation between parameters on the Metro-E 100 Mbps campus network. The result presents heavy-tailed distributions on an inbound packet kurtosis value of 347 and an outbound packet kurtosis value of 780. Bytes outbound and inbound are skewed at 122 and right at 17 respectively. The average amount of data inbound and outbound is 458.5 MB and 34.8 MB. Protocol 6 TCP presents the highest amount of -traffic and a weak positive correlation at 0.104 exists between the inbound and outbound packets and bytes on the network. The correlation coefficient's 95% confidence interval ranges between 0.096 and 0.111. This research is significant in the future deployment of traffic scheduling, policing, and shaping algorithms for QoS bandwidth management on the WAN Metro-E campus network.

Modeling of cryogenic heated-tube flow boiling experiments of hydrogen and helium with the Generalized Fluid System Simulation Program

Accurate modeling of cryogenic boiling heat transfer is vital for the development of extended-duration space missions. Such missions may require the transfer of cryogenic propellants from in-space storage depots or the cooling of nuclear reactors. Purdue University in collaboration with NASA has assembled a database of cryogenic flow boiling data points from steady-state heated-tube experiments dating back to 1959, which has been used to develop new flow boiling correlations specifically for cryogens. Computational models of several of these experiments have been constructed in the Generalized Fluid System Simulation Program (GFSSP), a network flow code developed at NASA’s Marshall Space Flight Center. The new Purdue-developed universal correlations cover the full boiling curve: onset of nucleate boiling, nucleate boiling, critical heat flux, and film boiling. These correlations have been coded into GFSSP user subroutines. The fluids modeled in this study are liquid hydrogen and liquid helium. Predictions of local wall temperature and pressure drop are presented and compared to the test data.

A Semantic Detection Method for Network Flows With Global and Generalized Nature

A Semantic Detection Method for Network Flows With Global and Generalized Nature

Analyzing the exothermic reactions of [formula omitted] nanoparticles with the Dufour effect and the influence of the heat generation and permeability past a porous moving surface

The heat and mass concept is analyzed through the properties of the Dufour number, chemical reactions, and the heat generation parameters on MHD incompressible nanofluids flow. The MATLAB software with the finite difference method (FDM) was employed to find and graphically present the system’s numerical solutions. This study finds an increased Dufour number enhancing the flow speed and hotness of the nanofluid. The chemical reactions released heat energy to the surrounding environment, escalating the nanofluid’s temperature and decreasing the concentration of the nanoparticles. The rate of permeability strengthens the flow velocity by improving the flow network. The increased heat generation parameter influenced the increase in nanofluids’ velocity and temperature. This study can be considered in some applications, such as designing heat exchangers, solar cells, climate changes, and separation process industries.

Resilient Control of Dynamic Flow Networks Subject to Stochastic Cyber-Physical Disruptions

Resilient Control of Dynamic Flow Networks Subject to Stochastic Cyber-Physical Disruptions

Singular value decomposition for single-phase flow and cluster identification in heterogeneous pore networks

Pore networks play a key role in understanding and quantifying flow and transport processes in complex porous media. Realistic pore-spaces may be characterized by singular regions, that is, isolated subnetworks that do not connect inlet and outlet, resulting from unconnected porosity or multiphase configurations. The robust identification of these features is critical for the characterization of network topology and for the solution of the set of linear equations of flow and transport. We propose a robust method based on singular value decomposition to solve for network flow and locate isolated subnetworks simultaneously. The performance of the method is demonstrated for pore networks of different complexity.

Features of Network Flow Systems for Decision Making with Motivation Accounting

The present work investigates the problem of considering the influence of motivation in discrete network flow decision-making systems. Motivation is the primary factor that drives people to achieve their goals and plays a key role in decision-making systems. A method is proposed by which this influence is accounted for using the arc flow function amplification or attenuation coefficients. These operations are in the class of generalized network flows (flows with gains and losses). Methods to determine both the maximal and minimal value of the generalized network flow with consideration of the motivation are proposed A numerical example of a decision-making system based on a generalized network flow with motivation accounting is described, which confirms the obtained theoretical results.

Solute transport characteristics of columnar volumetric contraction networks with mega column structure and aperture variability

Numerical simulations explore for the first time the role of mega columns and aperture variability on particle transport through mature volumetric contraction networks as informed by a unique synthesis of network propagation and maturity. Columnar fracture patterns are generated by updating a series of Voronoi centers to the midpoint of a generated polygon over many iterations, creating 250 network realizations. A DFN simulator solves for fluid flow and tracks conservative particle trajectories within each network. Dominant fracture attributes impacting fluid flow and solute transport in volumetric contraction networks are fracture orientation, density, and aperture/transmissivity. Ensemble plume snapshots generated by networks with equal fracture transmissivity define a baseline-level of dispersion that is solely attributed to network structure and connectivity. Longitudinal and transverse dispersion increase and the center of plume mass becomes delayed relative to the baseline case when fracture transmissivity is varied according to a lognormal distribution. The incorporation of highly-transmissive, large-aperture mega column fractures leads to plume snapshots with a more pronounced leading edge and an order of magnitude faster average breakthrough times. The breakthrough curves contain three peaks reflecting contrasting transport pathways in which particles are: (i) initially placed in mega column fractures and remain in these features until exiting the model domain, (ii) initially placed into small column fractures, incur additional time to migrate and enter a mega column fracture, and remain within those mega columns, and (iii) initially placed in small column fractures and remain in these fractures. Incorporating variability in fracture transmissivity for both small column and mega column fractures disrupts the binary distinction between small column and mega column fracture velocities and leads to dispersed breakthroughs over long time scales with a single peak. These results demonstrate that preferential flow paths emerge in volumetric contraction networks due to contracts in fracture transmissivity, not fracture connectivity as observed in tectonic networks.

A Fast History-Matching and Optimization Tool and Its Application to a Full Field with More than 1,000 Wells

Summary In this work, we study a waterflood field containing more than 1,000 wells, and the modern field management techniques with full-fidelity 3D geocellular reservoir models become computationally prohibitive. To overcome the difficulty, we developed a novel flow-network data-driven model—the general-purpose simulator-powered network (GPSNet) model—and used it for rapid history matching and optimization. GPSNet includes physics, such as mass conservation, multiphase flow, and phase changes, while maintaining a good level of efficiency. To build such a model, a cluster of 1D connections among well completion points is constructed and forms a flow network. Multiphase fluid flow is assumed to occur in each 1D connection, and the flow in the whole network is simulated by our in-house general-purpose simulator. Next, to effectively reduce the uncertainty, a hierarchical history-matching workflow is adopted to match the production data. Ensemble smoother with multiple data assimilation (ESMDA) plays an important role in reducing the error at each history-matching step. After that, the best-matched candidate is selected for numerical optimization to maximize field oil production with constraints satisfying field conditions. Excellent history-matching results have been achieved on the field level, and good matches have also been observed for key producers. It is also worth mentioning that the history-matching process took a mere 4 hours to finish 1,100 simulation jobs. The successful application of the GPSNet to this waterflood field demonstrates a promising workflow that can be used as a fast and reliable decision-making tool for reservoir management.