Flow Routing Research Articles

Methodology for Structured Data-Path Implementation in VLSI Physical Design: A Case Study

State-of-the-art modern microprocessor and domain-specific accelerator designs are dominated by data-paths composed of regular structures, also known as bit-slices. Random logic placement and routing techniques may not result in an optimal layout for these data-path-dominated designs. As a result, implementation tools such as Cadence’s Innovus include a Structured Data-Path (SDP) feature that allows data-path placement to be completely customized by constraining the placement engine. A relative placement file is used to provide these constraints to the tool. However, the tool neither extracts nor automatically places the regular data-path structures. In other words, the relative placement file is not automatically generated. In this paper, we propose a semi-automated method for extracting bit-slices from the Innovus SDP flow. It has been demonstrated that the proposed method results in 17% less density or use for a pixel buffer design. At the same time, the other performance metrics are unchanged when compared to the traditional place and route flow.

Evaluation of narrowed weld pool shapes and their effect on resulting potential defects during deep penetration laser beam welding

This study presents mechanisms of the evolution of a narrowed region in the weld pool center during deep penetration laser beam welding. In numerous numerical studies presented in this study, it was also found that the local reduction of the weld pool size can cause detrimental effects on the melt flow behavior and the resulting properties of the welds. A particularly large influence of this effect was identified in three aspects. First, the local variation of the solidification sequence of the weld pool causes an increase in the hot-cracking susceptibility due to a locally delayed solidification. Second, it was proven that a change in the local length and width of the weld pool is associated with an adverse impact on the potential flow routes of the molten material that induces stronger local variations of its solidification. Thus, the element mixing, e.g., during the welding with filler materials, is blocked. This leads to a nonhomogeneous chemical composition of the final weld and can cause undesired effects on the final material properties. Finally, another observed effect is related to the reduced ability of process pores to reach the top surface. As this type of porosity is usually produced around the keyhole tip, the change of the fluid flow regime above this area plays a significant role in determining the final path of the pores until the premature solidification in the middle of the weld pool captures them. This study summarizes mainly numerical results that were supported by selected experimental validation results.

Intermodal Green p-Hub Median Problem with Incomplete Hub-Network

In the literature, hub-networks have often been modeled such as only one mode is considered for all transportation. Also, the consolidated demand flows are assumed to be transferred directly between each origin-destination hub pairs. The previous assumptions impose restrictions on the practical applications of such hub-networks. In fact, various transport modes are usually retained for freight transport, and intermodal terminals (e.g., rail terminals) may not realistically be fully connected. Thus, to assist decision makers, we investigate if the appropriate use of more eco-friendly transportation modes in incomplete networks may contribute to the accomplishment of the significant global reduction goals in carbon emissions. In this paper, we study the intermodal green p-hub median problem with incomplete hub-network. For each p located hub nodes, the hub-network is connected by at most q hub-links. The objective is to minimize the total transportation-based CO2 emission costs incurred through the road- and rail-transportation of each o-d demand flows. We present a MILP formulation for the studied problem and propose a novel genetic algorithm to solve it. A penalty cost is considered on solutions where train capacity is exceeded. Additionally, we present a best-path construction heuristic to generate the initial population. Furthermore, we develop a demand flows routing heuristic to efficiently determine the partition of demand flows in the incomplete road-rail network. And we implement novel crossover and mutation operators to produce new off-springs. Extensive computational experiments show that the proposed solution approach outperforms the exact solver CPLEX. Also, we perform a comparison between the unimodal and intermodal cases, and offer a discussion on the tuning of freight trains.

Robustness improvement strategy of cyber-physical systems with weak interdependency

As technologies are advancing at a rapid pace, the robustness of cyber-physical systems becomes increasingly critical. This paper presents a methodological framework for enhancing the robustness of cyber-physical systems in different failure scenarios. In particular, an AC power flow model and routing scheme are combined to model cyber-physical systems. A weak interdependency is then introduced to accurately characterize cascading failures. Different improvement strategies are finally proposed by comparing their influences on cyber-physical systems from both structural and functional perspectives. For validating the performance of the proposed framework, a case study has been conducted on several simulations. The results have shown that the key node protection strategy can alleviate the impacts of initial failures, while interdependency adjustment strategies can mitigate cascading failures. Specifically, the robustness of using the betweenness-based protection strategy has increased by 60%. The application of the performance degree based interdependency adjustment strategy has still resulted in improving robustness by 24% even if the proportion of node removals is 35%.

ASPECTS OF PROVIDING ECOLOGICAL EFFICIENCY OF THE NORTHERN SEA ROUTE CONTAINER TRANSPORT SYSTEM

One of the important tasks of environment conservation, such as reduction of greenhouse gas emissions by commercial fleet, which is under special consideration of the International Maritime Organization (IMO) and becoming a basis of world trade policy, is considered in the paper. The analysis of the container transportation system in world trade demonstrates its growing role. The dynamics and trends of the modern container fleet for the period of 2015-2020 confirm the thesis of the growing importance of the container system in world trade. The volumes of greenhouse gas emissions and contributions by the tanker, bulk carrier and container segments of the world merchant fleet are considered, the conclusion about the low environmental efficiency of the container fleet toward the volume of cargo flow is presented. Options and consequences of including the Northern Sea Route in the global logistics network to reduce greenhouse gas emissions are proposed. Based on early studies, the comparative calculations linking variation of ship speed with reduction of carbon dioxide emissions for four types of container ships on the routes including the Northern Sea Route and through the Suez Canal are given. Estimates for the mentioned reduction due to shortening the route between Europe and Asia are proposed. The practical applicability of the calculated estimates and their impact on the overall environmental friendliness of transport corridors in the case of more active use of the Northern Sea Route as a transit route for the goods flow from the Asia- Pacific region is discussed.

Exploring Human Mobility for Multi-Pattern Passenger Prediction: A Graph Learning Framework

Traffic flow prediction is an integral part of an intelligent transportation system and thus fundamental for various traffic-related applications. Buses are an indispensable way of moving for urban residents with fixed routes and schedules, which leads to latent travel regularity. However, human mobility patterns, specifically the complex relationships between bus passengers, are deeply hidden in this fixed mobility mode. Although many models exist to predict traffic flow, human mobility patterns have not been well explored in this regard. To reduce this research gap and learn human mobility knowledge from this fixed travel behaviors, we propose a multi-pattern passenger flow prediction framework, MPGCN, based on Graph Convolutional Network (GCN). Firstly, we construct a novel sharing-stop network to model relationships between passengers based on bus record data. Then, we employ GCN to extract features from the graph by learning useful topology information and introduce a deep clustering method to recognize mobility patterns hidden in bus passengers. Furthermore, to fully utilize Spatio-temporal information, we propose GCN2Flow to predict passenger flow based on various mobility patterns. To the best of our knowledge, this paper is the first work to adopt a multipattern approach to predict the bus passenger flow from graph learning. We design a case study for optimizing routes. Extensive experiments upon a real-world bus dataset demonstrate that MPGCN has potential efficacy in passenger flow prediction and route optimization.

Maximum Flow Routing Strategy With Dynamic Link Allocation for Space Information Networks Under Transceiver Constraints

In this paper, we investigate the maximum flow routing strategy with dynamic link allocation under a constraint on the number of transceivers for space information networks (SINs). Specifically, the time-expanded graph (TEG) is exploited to characterize the dynamic topology of SINs. Furthermore, although there exist multiple feasible links for SINs, only a limited number of them can be actually established due to the constraint on the number of transceivers. Traditionally the established link is fixed within one time interval in the TEG. In order to fully exploit the resource of multiple feasible links, we divide each time interval in the TEG into multiple fine-grained time periods and design the maximum flow routing strategy by jointly optimizing both the fine-grained time period duration and the link allocation as well as the amount of data transmitted on each transmission link and the amount of data stored in each caching link. This problem can be formulated as a mixed-integer quadratic program (MIQP), which is difficult to solve. To overcome this difficulty, we transform the MIQP into an equivalent mixed-integer linear program (MILP), which can be effectively solved by existing methods. Simulation results show that the proposed dynamic link allocation strategy can significantly outperform the fixed link allocation strategy within each time interval.

Microfluidic Approaches for Affinity-Based Exosome Separation.

As a subspecies of extracellular vesicles (EVs), exosomes have provided promising results in diagnostic and theranostic applications in recent years. The nanometer-sized exosomes can be extracted by liquid biopsy from almost all body fluids, making them especially suitable for mainly non-invasive point-of-care (POC) applications. To achieve this, exosomes must first be separated from the respective biofluid. Impurities with similar properties, heterogeneity of exosome characteristics, and time-related biofouling complicate the separation. This practical review presents the state-of-the-art methods available for the separation of exosomes. Furthermore, it is shown how new separation methods can be developed. A particular focus lies on the fabrication and design of microfluidic devices using highly selective affinity separation. Due to their compactness, quick analysis time and portable form factor, these microfluidic devices are particularly suitable to deliver fast and reliable results for POC applications. For these devices, new manufacturing methods (e.g., laminating, replica molding and 3D printing) that use low-cost materials and do not require clean rooms are presented. Additionally, special flow routes and patterns that increase contact surfaces, as well as residence time, and thus improve affinity purification are displayed. Finally, various analyses are shown that can be used to evaluate the separation results of a newly developed device. Overall, this review paper provides a toolbox for developing new microfluidic affinity devices for exosome separation.

GenES-MDA: A generic open-source software package to solve inverse problems via the Ensemble Smoother with Multiple Data Assimilation

Ensemble Kalman filter methods have been successfully applied for data assimilation and parameter estimation through inverse modeling in various scientific fields. We have developed a new generic software package for the solution of inverse problems implementing the Ensemble Smoother with Multiple Data Assimilation (genES-MDA). It is an open-source, platform-independent Python-based program. Its aim is to facilitate the management and configuration of the ES-MDA through several programming tools that help in the preparation of the different steps of ES-MDA. genES-MDA has a flexible workflow that can be easily adapted for the implementation of different variants of the ensemble Kalman filter and for the solution of generic inverse problems. This paper presents a description of the package and some application examples. genES-MDA has been tested in three synthetic case studies: the solution of the reverse flow routing for the estimation of the inflow hydrograph to a river reach using observed water levels and a calibrated forward model of the river system, the identification of a hydraulic conductivity field using piezometric observations and a known forward flow model, and the estimation of the release history of a contaminant spill in an aquifer from measured concentration data and a known flow and transport model. The results of all these tests have demonstrated the flexibility of genES-MDA and its capabilities to efficiently solve different types of inverse problems.

Activity flow mapping over probabilistic functional connectivity.

Emerging evidence indicates that activity flow over resting-state network topology allows the prediction of task activations. However, previous studies have mainly adopted static, linear functional connectivity (FC) estimates as activity flow routes. It is unclear whether an intrinsic network topology that captures the dynamic nature of FC can be a better representation of activity flow routes. Moreover, the effects of between- versus within-network connections and tight versus loose (using rest baseline) task contrasts on the prediction of task-evoked activity across brain systems remain largely unknown. In this study, we first propose a probabilistic FC estimation derived from a dynamic framework as a new activity flow route. Subsequently, activity flow mapping was tested using between- and within-network connections separately for each region as well as using a set of tight task contrasts. Our results showed that probabilistic FC routes substantially improved individual-level activity flow prediction. Although it provided better group-level prediction, the multiple regression approach was more dependent on the length of data points at the individual-level prediction. Regardless of FC type, we consistently observed that between-network connections showed a relatively higher prediction performance in higher-order cognitive control than in primary sensorimotor systems. Furthermore, cognitive control systems exhibit a remarkable increase in prediction accuracy with tight task contrasts and a decrease in sensorimotor systems. This work demonstrates that probabilistic FC estimates are promising routes for activity flow mapping and also uncovers divergent influences of connectional topology and task contrasts on activity flow prediction across brain systems with different functional hierarchies.

An integrated GPU-accelerated modeling framework for high-resolution simulations of rural and urban flash floods

This paper presents an integrated modeling framework aiming at accurate predictions of flood hazard from heavy rainfalls. The accuracy of such predictions generally depends on the complexity and resolution of the employed model components. We propose an integration of complementary models in one framework that facilitates GPUs to improve accuracy and simulation time. The spatially distributed runoff model integrates surface flow routing based on the full shallow water equations, infiltration based on the Green–Ampt equation, and interception. In urban areas, the runoff model is coupled with the Storm Water Management Model (SWMM). The integrated model is validated and tested on laboratory, rural and urban scenarios with regards to accuracy and computational efficiency. The GPU acceleration yields speedups of 1000 times compared to a CPU implementation and enables the coupled simulation of flash floods at 1 m resolution for an urban area of 200 km2 in realtime.

The Role of a Detachment Fault in the Spatial Distribution of Ore-Bearing Paleofluid Flows in the Central Kolyma Region: A Nonconventional Approach to Predictive Metallogenic Modeling

—The Central Kolyma region is the main gold-bearing part of the Verkhoyansk–Kolyma fold-and-thrust belt. Analysis of the developed geodynamic models of fold and thrust belt formation mechanisms, the Verkhoyansk–Kolyma belt in particular, suggests the leading role of subhorizontal movements on the detachment zone (decollement) at the base of an orogen as the “sole,” on which nappes detached at an early stage and with which major reverse strike-slip listric faults were directly associated at the collisional stage. In our opinion, the role of a detachment fault, the most important regional structure, is obviously underestimated in predictive metallogenic models. The detachment fault zone is complicated by transverse NE-trending faults, where its thickness and the fluid permeability can occur. The paper proposes a variant that links previously discovered gold deposits and occurrences in five gold mineralization strips along the inferred paleofluid flow routes. Here, the paleofluid flow route is the horizontal projection of the most probable migration pathway of released fluids from their generation zone to the ore deposition zone, which is drawn across the largest ore accumulations.

Effects of the Digital Elevation Model and Hydrological Processing Algorithms on the Geomorphological Parameterization

Hydrological cycle research requires a detailed analysis of the involved parameters to understand watershed behavior comprehensively. In recent decades, both Geographic Information Systems (GIS) and Digital Elevation Models (DEMs) were implemented and took a substantial role in watershed geomorphological parameterization; however, the variability of these instruments remains a challenge, together with high-resolution DEMs being unavailable, requiring digital processing to improve resolution. This research aims to merge DEMs and evaluate GIS geoprocessing algorithms to determine drainage networks and the geomorphological parametrization of a semiarid watershed. DEMs with resolutions of 1.5, 5, 12.5, and 30 m, the Jenson/Domingue (J/D) and Wang/Liu (W/L) fill algorithms; and D8, D, KRA, and MFD flow routing algorithms were used. One of the research findings proved that the divergences of the drainage networks are mainly attributed to filling algorithms and not flow routing algorithms; the shifts between the networks obtained in the processes reach horizontal distances up to 300 m. Since the water movement within the watershed depends on geomorphological characteristics, it is suggested that DEM-based hydrological studies specify both the resolution and the algorithms used in the parametrization to validate the rigidity of the research, improving estimate areas of high hydrological risk.

Hub location problem considering spoke links with incentive-dependent capacities

This study proposes a hub-and-spoke (HS) network in which the capacity of a spoke link depends on the incentive provided to the transporters. The incentives influence the capacities of the spoke links, which in turn affect the hub selections and flow assignment. A mixed integer convex programming model is developed to optimize the proposed HS network. The model minimizes total transport cost by locating p hubs, routing flows between origin–destination (OD) pairs and determining the incentives on spoke links. A customized outer-approximation algorithm is presented to provide exact solutions to the developed model, which is difficult to solve optimally within acceptable computational times for large-scale instances. An approximation algorithm based on the stepwise linearization method is then developed to provide suboptimal solutions. The developed model and algorithms are validated in computational experiments with different problem scales. The experiments demonstrate that the proposed HS network tends to route the flows on more spoke links, balance the transport costs on the potential paths between OD pairs and serve more demands with lower transport costs, when compared with the HS network with fixed capacities.

Power-Efficient Data Collection Scheme for AUV-Assisted Magnetic Induction and Acoustic Hybrid Internet of Underwater Things

Power efficiency is a big concern in the Internet of Underwater Things (IoUT). The power consumption of underwater acoustic communications is typically in the scale of watts, which may drain the battery of underwater devices quickly. Whereas, the power consumption of underwater magnetic induction (MI) wireless communications is in the scale of milliwatt. Therefore, this article devotes to combine the underwater MI and acoustic communications to form a power-efficient underwater hybrid wireless network. Specifically, we investigate the power-efficient autonomous underwater vehicle (AUV) data collection schemes in an underwater MI and acoustic hybrid sensor network. We propose an alternating anchor nodes selection and flow routing (AANSFR) AUV data collection method, which alternately optimizes the AUV path planning and network data flow routing. The simulation results show that the proposed hybrid data collection scheme can significantly prolong the lifespan of underwater sensor networks.

Unmanned Aircraft System (UAS) Structure-From-Motion (SfM) for Monitoring the Changed Flow Paths and Wetness in Minerotrophic Peatland Restoration

Peatland restoration aims to achieve pristine water pathway conditions to recover dispersed wetness, water quality, biodiversity and carbon sequestration. Restoration monitoring needs new methods for understanding the spatial effects of restoration in peatlands. We introduce an approach using high-resolution data produced with an unmanned aircraft system (UAS) and supported by the available light detection and ranging (LiDAR) data to reveal the hydrological impacts of elevation changes in peatlands due to restoration. The impacts were assessed by analyzing flow accumulation and the SAGA Wetness Index (SWI). UAS campaigns were implemented at two boreal minerotrophic peatland sites in degraded and restored states. Simultaneously, the control campaigns mapped pristine sites to reveal the method sensitivity of external factors. The results revealed that the data accuracy is sufficient for describing the primary elevation changes caused by excavation. The cell-wise root mean square error in elevation was on average 48 mm when two pristine UAS campaigns were compared with each other, and 98 mm when each UAS campaign was compared with the LiDAR data. Furthermore, spatial patterns of more subtle peat swelling and subsidence were found. The restorations were assessed as successful, as dispersing the flows increased the mean wetness by 2.9–6.9%, while the absolute changes at the pristine sites were 0.4–2.4%. The wetness also became more evenly distributed as the standard deviation decreased by 13–15% (a 3.1–3.6% change for pristine). The total length of the main flow routes increased by 25–37% (a 3.1–8.1% change for pristine), representing the increased dispersion and convolution of flow. The validity of the method was supported by the field-determined soil water content (SWC), which showed a statistically significant correlation (R2 = 0.26–0.42) for the restoration sites but not for the control sites, possibly due to their upslope catchment areas being too small. Despite the uncertainties related to the heterogenic soil properties and complex groundwater interactions, we conclude the method to have potential for estimating changed flow paths and wetness following peatland restoration.

FORMATION OF COMPETITIVE ADVANTAGES IN THE FIELD OF WATER TRANSPORT, TRANSPORT INFRASTRUCTURE AND EFFICIENT USE OF TRANSIT POTENTIAL IN THE POST-WAR PERIOD

Topicality. The levers and conditions for the formation of competitive advantages in the Black and Azov Seas in the postwar period will be radically changed. This, in turn, will change the routes of traffic flows and their structure, which will inversely affect the level of competitiveness of water transport in Ukraine. This necessitates a detailed study of the problem of formation of competitive advantages in the field of water transport, transport infrastructure and effective use of transit potential in the postwar period. Aim and tasks. Based on the study of the peculiarities of the formation of competitive advantages in the field of water transport, restoration and development of transport infrastructure in the postwar period to propose a strategy for restructuring the transit potential on new, modern principles and develop a set of measures and directions of restructuring and, in such a way as to avoid the problems inherent in water transport in the pre-war period and based on the short-term post-war restructuring, to ensure the long-term development of the water sector, its infrastructure and, with the effective use of transit potential, to form the preconditions for economic development of regions and the country as a whole. Research results. Peculiarities of formation of competitive advantages in the field of water transport, restoration and development of transport infrastructure in the postwar period are investigated, proposed strategy to ensure competitive advantages in the field of water transport for the medium and long term: "not costs but investments". This strategy based on the urgent tasks of post-war restructuring due to the growing level of competitiveness of the water industry of Ukraine should provide the preconditions for long-term development of the water industry, its infrastructure and, with the effective use of transit potential. This should be facilitated by such an orientation of the urgent tasks of recovery of the water sector, in order to further avoid the problems inherent in the pre-war period. A set of measures has proposed to implement this strategy. Conclusion. A strategy for restructuring the transit potential proposed, which provides an opportunity to get rid of the problems that hindered the development of efficient transportation of goods by water transport in the pre-war period. The specified strategy allows to form consequential connection of performance of urgent tasks of branch so that to increase efficiency of performance of tasks of the following stages and as a whole, to promote increase of competitiveness of water transport. To implement the proposed strategy, a set of measures has been developed, which, aimed at solving short-term problems, in strategic terms allow realizing the potential of the industry's competitiveness, which, in turn, will create preconditions for economic development. An example is the proposal to create an information system for maritime risk control and automatic notification in the event of a possible increase in the level of danger, which, in turn, will form the image of a safe level of navigation in previously mined areas. In general, the results of the study can further used both for scientific purposes and for practical purposes.

Release process identification of non-instantaneous point source pollution in rivers via reverse flow and pollution routing

Source identification is fundamental for managing sudden river water pollution; however, it is a challenging task. Although numerous studies have investigated this issue, most involve optimization or statistical models for instantaneous pollution and do not consider the reverse propagation and release processes. Herein, we propose an approach for identifying the release process of non-instantaneous point source pollution in rivers, based on reverse flow and pollution routing. The identification approach can trace the historical trajectory of pollutants and their release processes, providing the necessary information for treating accidental pollution. The effectiveness and efficiency of the proposed approach were tested and demonstrated using hypothetical and real-world river cases. The results indicated that the approach identified the release process with high accuracy, and second-round identification using the ensemble Kalman filter could generally improve the identification results from the reverse routing model. This approach was feasible in different cases of observation error, although the error considerably reduced its accuracy. The identification results were also found to be substantially influenced by release duration, with a shorter release time corresponding to an inferior identification result. Nevertheless, the approach worked well in real-world river cases and was generally not affected by the release location, pollutant diffusion, or river geomorphology. In addition, the new approach has advantages in computational efficiency and applicability over traditional methods.

Recording surface runoff in the field: a simple detector made of polypropylene

Surface runoff is an extremely important hydrologic flowpath since it transports soil and pollutants. For this reason, low cost methods are needed to detect such process in the field. In the present paper, we present a simple surface runoff detector made of polypropylene. Its low cost along with its high repeatibility allow for the use of a high number of detectors in the field to elucidate spatial variability as well as to trace overland flow route. Simple demonstrations of the method are provided. With this simple detector, more knowledge can be obtained on this important hydrologic pathway which is related to soil erosion and transport of a number of potential harmful substances to water bodies.

Impact research of well layout schemes and fracture parameters on heat production performance of enhanced geothermal system considering water cooling effect

The fluid flow route in an enhanced geothermal system (EGS) is jointly determined by well layout and fracture network, which affects the fluid flow and heat transmission process. To obtain an efficient EGS, it is critical to explore the best well layout and fracture parameter. In this study, combination effects of well layout scheme and fracturing networks are researched. A thermal-hydraulic-mechanical coupling model, accounting for the thermal shock impact of cool water on high temperature rock, is built to evaluate the heat production performance, which takes production temperature, production thermal power and reservoir recovery rate as indicators. Results indicate that addition of symmetrical injection wells could lengthen EGS's operating life and boost production thermal power. Connection between injection-production wells should be as close to the main direction of the fracture as possible to improve fluid flow rate, accelerate heat transfer, and increase heat recovery rate. Furthermore, Morris based sensitivity analyses are performed to identify the key variables influencing EGS production performance. It reveals that the most crucial parameters are injection pressure and fracture opening, which should be raised in practice as much as allowed on the assumption that the production temperature meets the threshold.