Network Flow Model Research Articles

Effects of Manufacturing Tolerances on Double-Wall Effusion Cooling

Abstract As aeroengine designers seek to raise turbine entry temperatures for greater thermal efficiencies, novel cooling schemes are required to ensure that components can survive in increasingly hotter environments. By utilizing a combination of impingement cooling, pin-fin cooling, and effusion cooling, double-wall effusion cooling is well equipped to achieve the high metal cooling effectiveness required for such challenges while keeping coolant consumption at an acceptably low level. However, this high performance can drop off within the variability of common manufacturing tolerances, which can also expose cooling schemes to issues such as hot gas ingestion. This paper uses an experimentally validated low-order flow network model (LOM) to assess the cooling performance of a double-wall effusion cooling scheme employed in a high-pressure turbine nozzle guide vane, subject to the variability of geometric parameters set by their manufacturing tolerances. The relative significance of each geometric parameter is examined by varying it individually and comparing the effects on the cooling performance. A Monte Carlo analysis is then conducted to assess the likelihood of performance variation for a baseline design. Finally, multiple optimization studies are conducted for the cooling scheme, with the simultaneous objectives of reducing coolant usage and maximizing the design tolerances to reduce manufacturing cost, all while maintaining acceptable metal cooling effectiveness and backflow margins.

Functional Impairment in Small Airways Associated With the Breathlessness Symptoms in Long-Coronavirus Disease.

This study aimed to determine the association between functional impairment in small airways and symptoms of dyspnea in patients with Long-coronavirus disease (COVID), using imaging and computational modeling analysis. Thirty-four patients with Long-COVID underwent thoracic computed tomography and hyperpolarized Xenon-129 magnetic resonance imaging (HP Xe MRI) scans. Twenty-two answered dyspnea-12 questionnaires. We used a computed tomography-based full-scale airway network (FAN) flow model to simulate pulmonary ventilation. The ventilation distribution projected on a coronal plane and the percentage lobar ventilation modeled in the FAN model were compared with the HP Xe MRI data. To assess the ventilation heterogeneity in small airways, we calculated the fractal dimensions of the impaired ventilation regions in the HP Xe MRI and FAN models. The ventilation distribution projected on a coronal plane showed an excellent resemblance between HP Xe MRI scans and FAN models (structure similarity index: 0.87 ± 0.04). In both the image and the model, the existence of large clustered ventilation defects was not identifiable regardless of dyspnea severity. The percentage lobar ventilation of the HP Xe MRI and FAN model showed a strong correlation (ρ = 0.63, P < 0.001). The difference in the fractal dimension of impaired ventilation zones between the low and high dyspnea-12 score groups was significant (HP Xe MRI: 1.97 [1.89 to 2.04] and 2.08 [2.06 to 2.14], P = 0.005; FAN: 2.60 [2.59 to 2.64] and 2.64 [2.63 to 2.65], P = 0.056). This study has identified a potential association of small airway functional impairment with breathlessness in Long-COVID, using fractal analysis of HP Xe MRI scans and FAN models.

Equitable distribution of perishable items in a food bank supply chain

In the United States, food banks play an important role in helping to reduce the rate of food insecurity by distributing donated food among the population in need. One of the challenges that food banks face is to equitably distribute food donations among their clients such that, ideally, each recipient receives the same amount of food. They aim to do so while minimizing waste that occurs due to spoilage and capacity limitations. Perishable food items present specific challenges since they are susceptible to spoilage and need to be distributed before their expiry dates. Based on our longstanding partnership with a large food bank located in the southeastern United States, we present a capacitated, multiperiod, multiproduct network flow model to help them equitably and effectively distribute perishable food donations among the food‐insecure population in their service region. The model is applied within the context of a case study and reveals managerial insights that would be useful to practitioners. Our findings show that although equity is one of the food bank's highest priorities, inequities cannot be eliminated completely. Given the inevitability of inequitable food allocations in practice, this paper provides food banks guidance on how to strategically control inequities using two approaches: ( i) by increasing the number of periods for which equity should be satisfied or ([Formula: see text]) by allowing deviations from a perfectly equitable distribution. The results show that modest deviations from perfect equity using either approach can lead to significant improvements in both the quality and quantity of food distributed and can also reduce food waste. While approach ([Formula: see text]) is preferable, the most desirable outcomes occur when both are applied simultaneously. We also find that county capacities inhibit a food bank's ability to achieve balance between equity and effectiveness when distributing perishables. Our framework provides food banks the flexibility to balance the trade‐off between effectiveness and equity based on their preferences.

Research on intelligent control theory and strategy of gas drainage pipe network based on graph theory

To address the issues related to the working conditions of the mine gas drainage system and achieve adaptive adjustment of negative pressure during gas drainage, this study proposes a gas drainage pipe network structure model based on graph theory principles. The model is built upon the air-gas mixed flow model of the pipe network. The boundary conditions of the pipe network are determined, and both the pipe network flow model and resistance model are constructed. An assignment iterative adjustment method is used to establish the pipe network solution model, enabling the solution of the gas drainage pipe network. The mathematical model for regulating and controlling the gas drainage pipe network is formulated, considering the objective function, decision variables, and constraint conditions based on the drainage parameters. The control model, along with the flow calculation model of the gas drainage pipe network system, is employed to calculate the optimal gas control scheme. This scheme enables remote intelligent control of the control valve opening on the ground, significantly improving the safety and efficiency of the gas drainage system. The engineering application of Liuzhuang Coal Mine demonstrates the successful realization of the rational distribution of negative pressure and intelligent dynamic control of the drainage pipe network. As a result, the total gas concentration in the drainage pipe network has increased by 12%, yielding favorable outcomes. The findings of this study have significant theoretical and practical implications for the design, development, and improvement of intelligent control systems for mine drainage pipe networks.

A Novel Multi-state Bogie System Reliability Evaluation Approach by Extended d-MC Model for High Speed Train

Abstract Bogie is a pivotal system and plays a critical part in the safety and reliability management of high speed rail. However, the available bogie system reliability analysis methods lack the consideration of multi-state characteristics, and the common multi-state reliability analysis methods, being an NP-hard problem, lead to a low efficiency. In order to overcome the mentioned drawbacks, this paper proposes a novel multi-state rail train bogie system reliability analysis approach based on the extended d-MC model. Three different function interactions within the bogie system are considered to build the multi-state bogie system flow network model. Meanwhile, an extended d-MC model is established to remove unnecessary d-MC candidates and duplicates, which greatly enhances the computing efficiency. The bogie system reliability is calculated, and the examples are provided. Numerical experiments are carried out for the different operational conditions of the bogie system and are used to testify the practicability of the method projected in this article, and is is found that the proposed method outperforms a newly developed method in solving the multi-state reliability problems.

Flow-models 2.0: Elephant flows modeling and detection with machine learning

This article presents the new version of the flow-models IP network flow modeling framework. The improved features include flow skipping and counting, flow filtering, IP address anonymization, and time series data calculation. The new version also enables simulation of the first packet mirroring feature and provides tools for modeling the detection of elephant flows. It includes examples of using the scikit-learn library to build machine learning models for elephant flow detection based on the first packet. Furthermore, it provides an anonymized flow dataset, enabling researchers to train and validate machine learning models for traffic analysis in a reproducible and comparable manner.

Artificial neural network model of non-Darcy MHD Sutterby hybrid nanofluid flow over a curved permeable surface: Solar energy applications

The conversion of solar radiation to thermal energy has recently much interest as the requirement for renewable heat and power grows. Due to their enhanced ability to promote heat transmission, nanofluids can significantly improve solar-thermal systems' efficiency. This section aims to study the heat transfer behavior of the Sutterby hybrid nanofluid flow of magnetohydrodynamics in the presence of a non-uniform heat source/sink and linear thermal radiation over a non-Darcy curved permeable surface. A novel implementation of an intelligent numerical computing solver based on multi-layer perceptron (MLP) feed-forward back-propagation artificial neural network (ANN) with the Levenberg-Marquard algorithm is provided in the current study. Data were gathered for the ANN model's testing, certification, and training. Established mathematical equations are nonlinear, which are resolved for velocity, the temperature in addition to the skin friction coefficient, and the rate of heat transfer by using the bvp4c with MATLAB solver. The ANN model selects data, constructs and trains a network, then evaluates its efficacy via mean square error. Graphs illustrate the impact of a wide range of physical factors on variables, including pressure, velocity, and temperature. In the entire study, the thermal energy improved by the SiO2 (silicon dioxide) - Au (gold) hybrid nanofluid than the SiO2-TiO2 (titanium dioxide) hybrid nanofluid. The higher internal heat generation/absorption parameter values increase the temperature.

Virtual Gas Turbines: A novel flow network solver formulation for the automated design-analysis of secondary air system

Abstract The complex and iterative workflow for designing the secondary air system (SAS) of a gas turbine engine still largely depends on human expertise and hence requires long lead times and incurs high design time-cost. This paper proposes an automated methodology to generate the whole-engine SAS flow network model from the engine geometry model and presents a convenient and inter-operable framework of the secondary air system modeller. The SAS modeller transforms the SAS cavities and flow paths into a 1D flow network model composed of nodes and links. The novel, object-oriented pre-processor embedded in the SAS modeller automatically assembles the conservation equations for all flow nodes and the loss correlations for all links. The twin-level, hierarchical SAS solver then solves the conservation equations of mass, momentum and energy supplemented with the correlations in the loss model library. The modelling swiftness, mathematical robustness and numerical stability of the present methodology are demonstrated through the results obtained from IP compressor rotor drum flow network model.

Solving the unit-load pre-marshalling problem in block stacking storage systems with multiple access directions

Block stacking storage systems are highly adaptable warehouse systems with low investment costs. With multiple, deep lanes they can achieve high storage densities, but accessing some unit loads can be time-consuming. The unit-load pre-marshalling problem sorts the unit loads in a block stacking storage system in off-peak time periods to prepare for upcoming orders. The goal is to find a minimum number of unit-load moves needed to sequence a storage bay in ascending order based on the retrieval priority group of each unit load. In this paper, we present two solution approaches for determining the minimum number of unit-load moves. We show that for storage bays with one access direction, it is possible to adapt existing, exact tree search procedures and lower bound heuristics from the container pre-marshalling problem. For multiple access directions, we develop a novel, two-step solution approach based on a network flow model and an A* algorithm with an adapted lower bound that is applicable for all possible access direction combinations. We further analyze the performance of the presented solutions in computational experiments for randomly generated problem instances and show that multiple access directions greatly reduce both the total access time of unit loads and the required sorting effort.

Mesoscopic interpretation of hydraulic fracture initiation and breakdown pressure using discrete element method

The relationship between initiation pressure and breakdown pressure, two vital parameters in hydraulic fracturing operations, requires further investigation, with the explicit mechanism underlying breakdown pressure yet to be revealed. This study presents an enhanced particle discrete element model with a regular-shaped borehole, wherein the pressure-updating equation of the pipe network flow model is improved to incorporate comprehensive fluid–solid coupling. Through simulation of the mesoscopic process of hydraulic fracturing initiation and propagation, the characteristic curve of borehole fluid pressure is obtained. By comparing the curves of borehole pressure and fluid flow into the fracture, the physical meaning of breakdown pressure is explained at the mesoscopic level. The results show that the breakdown pressure fundamentally arises from the unstable propagation of the fracture, with its numerical value corresponding to the borehole fluid pressure when the fluid flow into the fracture reaches the injection rate for the first time. Additionally, employing fluid–solid coupling modeling, the impacts of fluid viscosity, injection rate, and borehole diameter on breakdown pressure are investigated. Finally, the evolution of fracture morphology under different injection rates and elastic moduli is analyzed.

So2al-wa-Gwab: A New Arabic Question-Answering Dataset Trained on Answer Extraction Models

Question answering (QA) is the task of responding to questions posed by users automatically. A question-answering system is divided into three main components: question analysis, information retrieval, and answer extraction. This paper has focused only on the answer extraction part. In the past couple of years, many QA systems have been developed and become mature and ready for use in different languages. Nevertheless, the advancement of Arabic QA systems still faces different obstacles and a lack of relevant resources and tools for researchers. This paper presents the So2al-wa-Gwab dataset since the publicly available datasets include various faults, such as the use of machine translation to build the data, a short context size, and a small number of question-answer pairings. Thus, this new dataset avoids the aforementioned drawbacks. Furthermore, in this paper, we have trained three deep learning models, namely, Bi-Directional flow network (BiDAF), QA Network (QANet), and BERT model, and tested them on seven different datasets, thus providing a comprehensive comparison between existing Arabic QA datasets. The obtained results emphasize that machine-translated datasets fall back when compared with human-annotated data. Also, the QA task becomes harder as the context, from which to extract the answer, becomes larger.

Pore‐to‐mesoscale network modeling of heat transfer and fluid flow in packed beds with application to process design

AbstractA dual‐network model (DNM) representing the topological characteristics of both the pore space and solid fraction of a packed bed was developed to study coupled incompressible water flow and heat transport from the pore‐scale to mesoscale (μm‐cm) with the consideration of temperature‐dependent fluid viscosity. The DNM was validated and used to study the temperature and velocity at the pore scale and their effects on fluid flow and heat transfer. Then the pore volume of the DNM was varied to illustrate the effect of bed porosity on transport processes, quantifying the trade‐off between flow conditions and heat transfer. This work demonstrates the ability of the DNM to simulate pore‐scale fluid flow and heat transfer simultaneously, which can then be averaged over the entire simulation domain to approximate meso/macroscopic parameters efficiently in relation to the pore geometry.

Joint location optimization of charging stations and segments in the space-time-electricity network: An augmented Lagrangian relaxation and ADMM-based decomposition scheme

Electric vehicles that contribute to better air quality, less noise, and low-carbon emissions are a promising selection for sustainable transportation. However, the development of electric vehicles is impeded by various factors, including driving range anxiety, long recharging period, and insufficient charging facilities. As the charging-while-driving techniques gradually mature, electric vehicles can recharge on charging stations stationarily or charging lanes (segments) dynamically. Additional charging facilities should be constructed to improve the level of charging services. Under a predefined construction budget, the difficulty is how to determine their numbers and distributions. This study jointly locates charging stations and segments by maximizing the accessibility of electric vehicles. In space-time-electricity networks, we formulate a multi-commodity network flow model with location and routing. A decomposition scheme based on augmented Lagrangian relaxation and alternating direction method of multipliers is developed to tackle this problem. The standard and augmented Lagrangian relaxed problems are decomposed into many solvable subproblems. Numerical experiments are conducted in three transportation networks, showing that the proposed method can achieve good integrality gaps.

Influence of Intercity Network on Land Comprehensive Carrying Capacity: A Perspective of Population Flow

The world is experiencing the largest wave of urban growth in history. Maintaining the rapid growth of cities without causing land and resource shortages is a severe problem that must be solved urgently. With the rapid development of globalization and information technology, the meaning of land comprehensive carrying capacity presents new changes. It is no longer entirely dependent on local resources and is likely to benefit from intercity connections beyond urban boundaries. However, can an inter-city network be a non-local solution to sustain urban growth without increasing land pressure? To address this question, this study adopted 287 cities in China as the research object to describe the spatial carrying characteristics of land at the national level by constructing an evaluation index system for land comprehensive carrying capacity. Furthermore, we constructed a population flow network model through social network analysis to explore the influence of intercity network on land comprehensive carrying capacity. Our findings are as follows: (1) The regional differentiation characteristics of land comprehensive carrying capacity at the national scale are evident, and reveal a spatial pattern significantly related to the urban economic development level. (2) The weighted in-degree, weighted degree centrality, and betweenness centrality in the intercity network positively impact the land comprehensive carrying capacity, and land use efficiency has a partial mediating effect. (3) Land comprehensive carrying capacity can be determined by non-local factors rather than local factors. As an effective non-local channel, strengthening intercity population flow and network integration can flexibly manage urban land scarcity.

Hierarchical Network-Based Tracklets Data Association for Multiple Extended Target Tracking with Intermittent Measurements.

The key issue of multiple extended target tracking is to differentiate the origins of the measurements. The association of measurements with the possible origins within the target's extent is difficult, especially for occlusions or detection blind zones, which cause intermittent measurements. To solve this problem, a hierarchical network-based tracklet data association algorithm (ET-HT) is proposed. At the low association level, a min-cost network flow model based on the divided measurement sets is built to extract the possible tracklets. At the high association level, these tracklets are further associated with the final trajectories. The association is formulated as an integral programming problem for finding the maximum a posterior probability in the network flow model based on the tracklets. Moreover, the state of the extended target is calculated using the in-coordinate interval Kalman smoother. Simulation and experimental results show the superiority of the proposed ET-HT algorithm over JPDA- and RFS-based methods when measurements are intermittently unavailable.

Emission-concerned coordinated dispatching of electrified autonomous mobility-on-demand system and power system incorporating heterogeneous spatiotemporal scales

Electrified autonomous mobility-on-demand (E-AMoD) systems will play a crucial role in future cities, which couple the carbon emissions generated by the transportation system and the power system. This paper proposes a joint dispatching framework to facilitate city decarbonization by coordinating the fleet operator and power system operator. Given the substantial differences in spatial and temporal dispatching scales between the two systems, this framework effectively integrates micro vehicle-level service dispatching with macro fleet-level charging scheduling. Firstly, a novel bipartite matching model efficiently solves the short-term passenger serving and rebalancing dispatching at the vehicle level. The service dispatching results are then aggregated as spatial-temporal transition flows across different areas and time intervals. Incorporating these mobility constraints, the long-term charging-discharging scheduling at the fleet level is captured as an expanded network flow model and integrated with the power system dispatching model, which aims at renewable energy utilization and carbon emission mitigation. Finally, the fleet-level scheduling results are disaggregated to the serving-rebalancing-charging sequences of each vehicle, for practical implementation. To validate the proposed framework, numerical experiments are conducted on a large-scale real-world case. Multiple charging schedule scenarios, including heuristic charging, smart charging, and smart charging-discharging, are investigated under both centralized and distributed supplied power systems. (1) The framework proves to be an effective solution for addressing the differences in dispatching spatiotemporal scales, calculation efficiencies, and privacy concerns between power and traffic systems. (2) Through power-traffic coordination, the E-AMoD fleet demonstrates its ability to alleviate 8% of average renewable energy curtailment and reduce nearly 15% of the total emissions in a power system installed with renewable distributed generators. (3) Furthermore, the extent of emission mitigation effects is influenced by critical factors such as fleet size, renewable energy profile, and power system capacity. Overall, the E-AMoD system show great potential as mobile storage devices to provide spatial-temporal flexibility to the power system and contribute to city decarbonization.

A multi-road quasi network flow model for the vertical alignment optimization of a road network

ABSTRACT Minimizing earthwork costs is crucial in the optimization of vertical alignment. In this article, the previous quasi network flow model for the vertical alignment optimization of a single road is extended to a multi-road network. A ‘one-at-a-time’ method is first examined to solve the optimization problem and it is noted that this method is effectively what is currently used in practice. Two academic test problems are used to demonstrate that this method is not optimal. This leads to the development of three novel methods to solve the optimization problem: no-flow, with-flow and divide-and-conquer. It is noted that the with-flow method guarantees global optimality. All methods are compared on two real-world case studies. On small road networks, the with-flow method decreases costs by 7.4%, and achieves a 20 times speedup over the one-at-a-time method. On long road networks, the divide-and-conquer method is faster than all other methods without any notable cost difference. However, the divide-and-conquer method is an heuristic and cannot guarantee that it returns a global minimum.

Network periodic train timetabling with integrated stop planning and passenger routing: A periodic time–space network construct and ADMM algorithm

Designing a passenger-centric railway service is of great importance for railway companies to attract more passengers and increase their revenue. Focusing on the passenger-centric operational plans, this paper considers the problem of integrated periodic train timetabling and train stop planning in high speed railway networks. In view of the immediate dependency of passengers’ routes on the train timetable and stop plan, simultaneous passenger routing is included to directly shape the train stops and timetable in a passenger-friendly way. Besides travel time, ticket price is also considered in passenger utility to better depict passengers’ route choices in the railway network. A railway network-based time–space network with train stop-skip technique, periodic properties and detailed consideration of different train headways is designed, based on which a multi-commodity network flow model is developed. With a novel linearization technique of train capacity penalty term, the alternating direction method of multipliers (ADMM) is used to relax and decompose the track and train capacity constraints. Further, time deviation of regular train services is achieved by a new shortest path algorithm to design more flexible timetables, however its application is still limited to small instances due to computational efforts required. The algorithm is finally tested on the Shandong high-speed railway network in China, and demonstrates its capability of providing tighter lower bounds than Lagrangian relaxation method and generating good feasible solutions. Besides, the impacts of different cycle times on service quality and train cost are evaluated.

Multicommodity network flow model of a human resource allocation problem considering time periods

This article addresses the problem of finding work assignments for employees within a given time horizon in a company using a multicommodity network flow model. The problem of human resource allocation is defined by the actual manpower demands of different periods which may vary during different periods. The investigation focuses on when workers should be called in-house and for how long to satisfy demands, while also complying with labour standards and regulations. Additional targets may also be set up, such as minimising the overall number of labour, as well as meeting “comfort” expectations, i.e. the most even working time should be realised for every worker within the event horizon. The paper describes how the multicommodity network flow model is constructed and the corresponding MILP mathematical programming model is formulated in a simple situation where there is only one position for the labour. Finally, the article explains how to construct the multicommodity network flow model and the MILP model for the general case, where there are multiple positions for the labour requiring various skills and competences per position within the periods.

Customised bus route design with passenger-to-station assignment optimisation

As an emerging and innovative public transportation mode, the customised bus (CB) has drawn widespread attention. Existing studies of the CB mainly focus on issues regarding the bus station location, route design, timetabling, and fare setting, but rarely consider the passenger to bus station assignment problem which can significantly influence passengers’ benefit and the buses’ operating scheme. Thus, this study focuses on the customised bus routing problem with passenger-to-station assignment (CBRP-PSA) aiming to simultaneously minimise passengers’ CB service access cost and the bus route cost. A time-discretized multi-commodity network flow model is developed to jointly determine the CB routes, timetables, and passenger-to-station assignment by allowing split loads and mixed loads. Through the dualization, the developed model is decomposed into two solvable sub-problems, and a Lagrangian-based heuristic solution algorithm is proposed. The model and algorithm are implemented in illustrative, medium-scale, and large-scale transportation networks to demonstrate their effectiveness under different scenarios.