Variable Speed Research Articles

Wind turbine condition monitoring dataset of Fraunhofer LBF

Fraunhofer wind turbine dataset contains monitoring data from a 750 W wind turbine, including accelerometers and tachometer, to capture structural response, bearing vibrations and rotational velocity. Additionally, temperatures, wind speed and wind direction have been measured, while weather conditions have been acquired from selected sources. Various damage scenarios, including mass imbalance, and aerodynamic imbalance as well as damages on bearings’ outer race, inner race and roller element have been implemented. The availability of time series data makes the dataset well suited for both machine learning and signal processing-based condition monitoring applications. The availability of heterogeneous sensors has created a dataset particularly suited for information fusion, data fusion, multi-sensor approaches, and holistic monitoring. Experiments were conducted in real-world conditions outside of a controlled laboratory environment, thereby introducing challenges such as variable rotor speed, noise, overloads, and other environmental factors. Consequently, the dataset is qualified for tasks involving uncertainty quantification and signal pre-processing. This document will detail the test equipment, experimental procedures, simulated damage cases and measurement parameters.

Analysis and Suppression of Oscillations in Doubly Fed Variable Speed Pumped Storage Hydropower Plants Considering the Water Conveyance System

The doubly fed variable speed pumped storage (DFVSPS) system is a hydraulically, mechanically, and electrically coupled system, and the characteristics of the components from the water conveyance system to the transmission line need to be fully considered in the oscillation analysis. Hence, the model of the water conveyance system is included to investigate the oscillation characteristics of the DFVSPS connecting to the grid via a series-compensated line. A small-signal state-space model of the DFVSPS system in the generation mode is first established. The oscillation characteristics of the DFVSPS are studied, and the dominant state variables for each oscillation mode are identified. The impact of system parameters on oscillations is further studied, and simulations are carried out to validate the accuracy of the model. The results indicate the oscillation mode of the DFVSPS comprises the electrical sub-synchronous oscillation (SSO) mode and the hydraulically, mechanically coupled low-frequency mechanical oscillation modes. When the series compensation level is high, the SSO becomes divergent, and the system is more likely to be unstable. Optimizing the rotor-side control parameters and the governor control parameters, sub-synchronous and low-frequency oscillations could be effectively suppressed, respectively. This study provides reference suggestions for the development and use of the future DFVSPS system.

Comparative Analysis of the MDORA and AODV Routing Protocols for Vehicles Operating at Constant and Variable Speeds

The primary focuses of investigation have been and continue to be strategies for traffic management at intersections and measures to enhance road safety. An essential remedy for this issue is a Vehicular Ad-hoc Network (VANET). At VANET, a variety of protocols are used to route communications between vehicles. At this research, we utilize the Multipath Dynamic Optimized Routing Algorithm (MDORA), which is an acronym for position-based maximum distance on-demand routing. By choosing the vehicle closest to the destination vehicle, this protocol has the advantage of choosing the next best jumping path, which lowers the number of hops. In two scenarios—vehicle travel at a dynamic speed and constant speed—this paper compares the MDORA and AODV protocols. three standards: Factors including the time spent on communication, the pace at which packets are delivered, and the time it takes for information to travel from one end to another, were used to assess how well the two protocols performed. Finally, a comparison of the number of missed packets between the MDORA and AODV protocols was done for moving vehicles at constant and dynamic speeds, The MDORA protocol can identify the optimal route even when cars are moving at varying speeds, resulting in minimal packet loss.

Assessing the Speed–Accuracy Tradeoff in Psychological Testing Using Experimental Manipulations

The speed–accuracy tradeoff (SAT), where increased response speed often leads to decreased accuracy, is well established in experimental psychology. However, its implications for psychological assessments, especially in high-stakes settings, remain less understood. This study presents an experimental approach to investigate the SAT within a high-stakes spatial ability assessment. By manipulating instructions in a within-subjects design to induce speed variations in a large sample ( N = 1,305) of applicants for an air traffic controller training program, we demonstrate the feasibility of manipulating working speed. Our findings confirm the presence of the SAT for most participants, suggesting that traditional ability scores may not fully reflect performance in high-stakes assessments. Importantly, we observed individual differences in the SAT, challenging the assumption of uniform SAT functions across test takers. These results highlight the complexity of interpreting high-stakes assessment outcomes and the influence of test conditions on performance dynamics. This study offers a valuable addition to the methodological toolkit for assessing the intraindividual relationship between speed and accuracy in psychological testing (including SAT research), providing a controlled approach while acknowledging the need to address potential confounders. Future research may apply this method across various cognitive domains, populations, and testing contexts to deepen our understanding of the SAT’s broader implications for psychological measurement.

Fault diagnosis of rolling bearing under complex working conditions based on time-frequency joint feature extraction-deep learning

The same independent distribution is not obeyed for the data collected under complex working conditions such as time-varying speed or loading, and the fault characteristic information is insufficient, resulting in low accuracy by using traditional methods. To solve the above problem, a fault diagnosis method based on time-frequency joint feature extraction combined with deep learning is proposed. Firstly, the original vibration signal is processed by variational mode decomposition (VMD) to obtain several intrinsic mode functions (IMFs), then the sensitive components are selected by calculating the steepness values of each IMF. Subsequently, the characteristic features of the selected sensitive component in time-domain, frequency-domain and time-frequency domain are calculated to form the time-frequency joint feature. The sparse attention mechanism (SAM) is combined with the advantages of recurrent neural network (RNN) and convolutional neural network (CNN) to form a hybrid deep learning model (SAM-RNN-DCNN). Finally, the time-frequency joint features are combined with the hybrid model for fault diagnosis. Experimental verifications are carried out by using data sets under variable rotational speed, variable load and strong noise interference, and the analysis results show that the proposed method has high diagnostic accuracy, good diagnostic performance and robustness under complex working conditions.

Directional wind turbine noise prediction plots and prevalence histograms for community consultation

In Australia, wind turbine noise is predicted using international standards and guidelines with a set of conservative assumptions. For example, wind turbine noise predictions using the ISO 9613-2 method are based on a scenario in which each receiver is simultaneously downwind of every wind turbine. As such, winds that are outside of the downwind direction range will result in lower wind turbine noise levels than predicted using ISO 9613-2. These predictions are also based on the assumption that each turbine is simultaneously generating their maximum noise emissions, whereas variations in winds speeds across the site will frequently result in individual turbines producing lower noise levels than assumed in the modelling. The net effect of these conservative assessment choices is that actual wind turbine noise levels would be lower than presented in noise assessments in many cases. Providing an indication of how frequently predicted noise levels would likely occur can assist community consultation and provide useful context to noise modelling data. This paper presents analysis utilising directional noise modelling to assess the effect of wind direction, combined with an analysis of historic wind records, to determine the range of predicted noise levels and how frequently they may occur.

Two-dimensional visualisation of a travelling sound wave over porous medium

Prior research has demonstrated a variation in sound speed and a wave bending in the air when an acoustic wavefront approaches a porous medium at a grazing angle. The impact is found to be explicitly dependent on the frequency of the sound and the nature of the adjacent absorber. The current study expands the investigations by using a distributed array of eight microphones mounted on a single-axis motorised mover. The measurement setup allowed for an extensive collection of impulse responses within a confined area near a horizontally placed porous panel. The sensors captured a frequency sweep every 10 millimetres on multiple horizontal planes above the air-material boundary. Moreover, the array configuration allowed for simultaneous capture of wave propagation both above and outside the projection area of the absorbing material. The progression of a travelling wavefront is reconstructed as an image-sequence-based 2D animation on four discrete vertical planes, 60 cm by 50 cm each, traversing or passing outside the porous material.

Temperature Analysis of Single busbar With Variation of Total Harmonic Distortion in Current

Typically, busbars are used to transmit and distribute currents in a bus duct system. The alarming use of nonlinear loads in the industrial sector or at residentials, such as arc welding, computers, ballast lighting, variable speed drives, and so on, has resulted in the generation of harmonics in current distortion, which are uncontrolled and thus increase heat generation within the system. The research conducted in this paper focuses on the prediction of the heat distribution as well as the analysis on operating temperature of a single busbar with compliance to the British National and International Standard (BS 159: 2014) using the Finite Element Method (FEM) in COMSOL Multiphysics software. The copper busbar dimension used for this research was 20mm x 6mm x 300mm, and the fundamental Root Mean Square (RMS) current was 419.1 A. The size of this busbars can withstand the maximum current of 430 A at a maximum operating temperature of 90°C, which complies with the standard requirement. The fundamental current is injected with variation of total harmonic distortion in current up to 55% with an interval of 5%. According to the findings, the operating temperature increases in direct proportion to the increase in total harmonic distortion with the current injections. With the presence of 55% of total harmonics in the current, the current was increased up to 57.73 A from the fundamental current, while the operating temperature was increased up to 140C from the fundamental temperature. The total harmonics in current produced by the nonlinear loads could affect the operating temperature of the busbars, and this continuous operation of current flow will affect the busbars' lifespan due to the occurrence of overheating.

Nonlinear unsteady aerodynamic forces prediction and aeroelastic analysis of wind-induced bridge response at multiple wind speeds: A deep learning-based reduced-order model

Machine learning-based aerodynamic reduced-order models (ROMs) combine high accuracy with extremely low computational costs, making them highly effective in predicting nonlinear and unsteady bridge aerodynamic forces. Although several machine learning-based nonlinear aerodynamic models have been developed, the majority are built on a single wind speed parameter. However, in nonlinear aerodynamic prediction and aeroelastic analysis of bridges, the variability in incoming wind speed significantly influences the computed results. A ROM relying solely on a single wind speed lacks the ability to accurately forecast the intricate dynamic behaviors arising from changes in wind speed. When the incoming wind speed changes, the model's prediction accuracy significantly decreases. Usually, it is necessary to establish a new database and train a new model, which not only increases time and cost but also greatly reduces the convenience of the ROM. Addressing this challenge, this study proposes a multiple-wind-speed (MWS) nonlinear unsteady bridge aerodynamic model based on the LSTM deep neural network. Taking the Taohuayu Yellow River Bridge in the Henan Province of China as an example, the modeling process of the proposed MWS-ROM is demonstrated, along with non-linear aerodynamic predictions of the deck under various conditions and aerodynamic-elastic analysis of the deck under different wind speeds. The research results show that the trained LSTM network can accurately predict the nonlinear aerodynamic forces of bridges under single and double degrees of freedom vibration conditions. The MWS-ROM performed well in predicting convergent vibrations at low wind speeds and limits cycle oscillations at high wind speeds, aligning closely with results from the CFD full-order model. Compared to CFD, the aerodynamic ROM based on the LSTM network significantly enhances computational efficiency, consequently boosting the convenience and efficiency of bridge flutter analysis. Additionally, the methodology proposed herein can be extended for wind-induced vibration control and response prediction in other types of deck sections.

Diurnal and Seasonal Variations of Meteor Speed and Arrival Angle Observed by Mengcheng Meteor Radar

AbstractMeteor speed is crucial in identifying key astronomical aspects of the meteoroid environment, including the influx of meteoric material and the distribution of meteoric radiants. This study investigates diurnal and seasonal variations of meteor speed from April 2014 to December 2023 observed by the Mengcheng meteor radar (MCMR; 33.4°N, 116.3°E). In addition to the expected diurnal variation of meteor speed, azimuth peak, zenith peak, and the azimuthal direction of maximum meteor speed due to the Earth's rotation, where the meteor speed peaks northward in the local morning and shifts clockwise, we find that the meteor speed distribution resembles a superposition of two Gaussian distributions, with the lower (higher) distribution mainly ranging from 20 to 40 (45–65) km/s. The two speed peaks are estimated using a double‐Gaussian fitting approach. In terms of seasonal variation, we find that (a) the low‐speed peak indicates a semi‐annual variation cycle, with maxima in June–July and December–January, and minima in February and August; (b) the high‐speed peak indicates an annual variation cycle, with maxima in September–December and minima in March. There is also a correlation between the seasonal variations in the meteor speed peaks and the occurrence of meteor showers. Statistical analysis provides a map of seasonal variations in the intensity, duration, and timing of meteor shower activities, which is supplemental to previous shorter‐duration studies using backscatter meteor radar observations. We also find notable shower events where the meteor counts exceed 45%–97% of the background count on corresponding event dates and speeds.

A gray-box model for unitary air conditioners developed with symbolic regression

In this paper, we present the development of a gray-box model for unitary air conditioning equipment that can be trained with as little as 5 data points with higher accuracy on test data. The model utilizes the same model inputs as is typical in building energy simulation, and is accurate. While black-box models require large data sets to deliver accurate results, white-box models require higher computational and engineering efforts along with detailed knowledge of the system, and are often difficult to obtain. The model presented here addresses a hybrid solution that is a steady-state, component-based, gray-box model that requires inputs from the source and sink fluids and rated performance of the specific piece of equipment, only. The basic physics of a vapor compression cycle are captured in individual component models for the heat exchangers, compressor, and expansion valve. These components are generalized to eliminate refrigerant-side inputs. A key addition is the development of correlations for the overall heat transfer coefficient times surface area (UA) obtained from Symbolic Regression (SR). The model successfully predicts the cooling capacity, coefficient of performance (COP), and sensible heat ratio (SHR) for three state-of-the-art variable speed, split-system, air conditioning systems with capacities of 12.3(3.5), 14(4), and 17.6(5) kW(tons), achieving a mean absolute percentage error (MAPE) of less than 3.4%. These results suggest that the gray-box model can be useful in predicting the performance of similar systems in the future, which could be valuable for energy management and optimization purposes.

Artificial Intelligence enabled self-powered sensing and wind energy harvesting system for bridges monitoring

With the advancement of society, the significance of Artificial Intelligence (AI) and big data in facilitating the intelligent development of bridges is increasing. Bridges function as crucial urban links, and ensuring their safe and stable operation is paramount. Therefore, this paper proposes an Artificial Intelligence enabled self-powered sensing wind energy harvesting system (WEHS) designed specifically for bridges. The system mainly consists of two electromagnetic generators (EMG1 and EMG2) and a rolling ball triboelectric nanogenerator (RB-TENG). WEHS achieves the recycling of ambient wind energy through the lower electromagnetic generator (EMG2). A single-channel triboelectric nanogenerator (RB-TENG) enables the sensing of wind speed within the system, while the top electromagnetic generator (EMG1) utilizes a single magnet with coils of different diameters to realize wind direction sensing. Reynolds wind tunnel that the WEHS has an average power output of 392mW and a power density of 414.86W/m3, which is adequate to power the sensor. The accuracy of wind speed and direction prediction based on the Artificial Intelligence GRU deep learning algorithm model is 96% and 99.04%, respectively. Furthermore, by implementing deep learning models for wind speed and direction, it becomes feasible to forecast the variations in wind direction and speed in the vicinity of bridges. The WEHS exhibits significant potential for bridges application and can contribute to developing AIoT-based smart bridges.

Inflammation and Cognitive Performance in Elite Athletes: A cross-sectional Study

Functional cognition is relevant for athletic success and interdependent with physical exercise, yet despite repeatedly demonstrated inflammatory responses to physical training, there are no studies addressing the relationship between cognition and inflammation in athletes. The aim of this study was to investigate the relationship between cognitive performance and selected inflammatory, and further physiological biomarkers in elite athletes. Data from 350 elite athletes regarding cognitive performance (processing speed, selective attention, working memory, cognitive flexibility), systemic inflammatory markers, metabolic hormones, growth factors, tissue damage markers, and micronutrients (e.g., ferritin, 25-OH-vitamin D), as well as physiological, subjective ratings of recovery and stress were analysed by correlative and multiple regression analyses. Results show that across all athletes variance in processing speed, selective attention, and working memory, could be best explained through a combination of metabolic hormones with physiological and psychological indicators of stress, and in cognitive flexibility through vitamin D levels. Only for the subgroup of athletes from closed-skill sports, the ratio TNF-α:IL-10 significantly contributed to explanation of variance in working memory and cognitive flexibility. In general, found correlations point to the importance of inflammatory balance and sufficient long-term nutrient supply for unaffected cognitive performance.

Deep sediment heterogeneity inferred using very low-frequency features from merchant shipsa).

The very low-frequency noise from merchant ships provides a good broadband sound source to study the deep layers of the seabed. The nested striations that characterize ship time-frequency spectrograms contain unique acoustic features corresponding to where the waveguide invariant β becomes infinite. In this dataset, these features occur at frequencies between 20 and 80 Hz, where pairs of modal group velocities become equal. The goal of this study is to identify these β = ∞ frequencies in ship noise spectrograms and use them to perform statistical inference for the deep layer sound speeds and thicknesses in the New England Mudpatch for a larger number of ships and acoustic arrays over a larger geographical region than previously studied. Marginal probability distributions of the data indicate that using singular points for a feature-based inversion yields an estimate of the sound speed and a limiting value for the thickness of the first deep layer. Heterogeneity is examined by correlating spatial variability of the deep layer sound speeds with ship tracks.

Position Sensor-Less SRG Variable Speed WECS and an SECS-Based Microgrid Under Nonideal Grid and Load Conditions

Position Sensor-Less SRG Variable Speed WECS and an SECS-Based Microgrid Under Nonideal Grid and Load Conditions

A new simulation approach of air source heat pump adopting the holistic process distributed parameter method and hybrid PID-bisection control algorithm

Air source heat pump (ASHP) faces problems of performance deterioration when operating at low ambient temperature due to the low compression ratio, high discharge temperature, frost accumulation, etc., and may even become nonfunctional at sub-zero-centigrade ambient temperature, requiring attention and tools for studying. This paper proposed a kind of holistic process distributed parameter simulation approach of ASHP system adopting the hybrid PID-bisection (PID: proportional-integral-derivative) control algorithm. Main components of the ASHP are modeled with the distributed parameter method. An adiabatic compression model of two-phase fluid based on thermodynamics is proposed. The PID-bisection control algorithm and variable speed integral PID & bisection control algorithm are proposed and applied to iterative computation of the model. A single/two-stage compression ASHP with an intercooler is simulated by this approach. The average deviation of simulation results of the model from experimental data is not more than 7 %, and the maximum deviation is not more than 18 %. For simulation of single-stage compression mode, the maximum error is not more than 4%. For simulation of two-stage compression mode under low-evaporating-temperature operating conditions, the maximum error is not more than 4 %. Computational speed of the ASHP model is significantly improved by using the PID-bisection control algorithm, and could be further accelerated by using the variable speed integral PID & bisection control algorithm. The proposed simulation approach is not only effective in sophisticated simulation of performance of single-stage and two-stage compression ASHP, but also potential for research on the optimization of the ASHP in cold regions.

A novel multi-excitation transient vibration framework for coupling three- and one-dimensional pumped storage hydropower shafting systems

In vibration models of shafting systems, the hydraulic excitation is difficult to characterize due to the complex and changeable hydraulic factors. Thus, hydropower units are not well understood in terms of their dynamics and stability control under transient processes. A hydraulic–mechanical–electric multi-excitation transient vibration calculation framework is developed for analyzing the relationship between shafting vibration and internal flow regimes. First, the boundary data from penstocks, tailraces, and hydro-turbine are interacted with using one-dimensional and three-dimensional (1D–3D) coupling; Second, user-defined function secondary development is applied to achieve two-stage guide vane closure and the runner's variable speed rotation; Third, based on the computational fluid dynamics results, a multi-excitation vibration model is established to analyze shafting system characteristics. There is less than 1.2% error between the algorithm and the field test in terms of speed peak values. Under braking or reverse pumping modes, various vortice clusters are generated in the blade channel as well as the cascade, blocking the flow passage and leading to the runner's unbalanced force. Three sudden increases in vibration amplitudes of the shafting system have occurred in the radial direction under load rejection, each corresponded to the runner's stall rotations. The change trend in axial vibration amplitudes, however, is closely related to the change in axial hydraulic thrust. Furthermore, in braking and reverse pumping conditions, the axis trajectory is more complex under the action of multiple coupling factors than when only hydraulic factors are considered.

Zynq FPGA for Hardware Co-simulation of Takagi-Sugeno Neuro-Fuzzy for MPPT Algorithm Incorporating Sensorless Wind Speed Estimation in Grid-Connected Wind System

This paper presents a hardware implementation on the Field Programmable Gate Array (FPGA) Zed-Board of a Maximum Power Point Tracking (MPPT) incorporating pitch angle control system and sensorless wind speed estimation using the Takagi-Sugeno (TS) Adaptive Neuro-Fuzzy Inference System (ANFIS). The described approach is applied specifically to a grid-connected Permanent Magnet Synchronous Generator-based Variable Speed Wind Turbine (VSWT). Firstly, the paper proposes an estimator-based TS-ANFIS model for real-time wind speed estimation, addressing challenges with conventional anemometers, such as precision, and susceptibility to adverse weather conditions. The estimated wind speed guides the calculation of an optimized mechanical speed for MPPT control. Secondly, an MPPT-based TS-ANFIS controller is introduced to achieve the maximum power point, integrating pitch angle control to prevent turbine failures in high wind speeds. Finally, the paper emphasizes the hardware implementation on the FPGA Zed-Board, leveraging its parallel processing capabilities to enhance control system quality by reducing sampling periods and loop delays. Validation includes simulations in Matlab/Simulink using the Xilinx system generator and hardware co-simulation on the FPGA Zed-Board. A comparative analysis highlights the contributions and advancements of the proposed models and controllers compared to recent schemes in the field. Indeed, the proposed MPPT-based TS-ANFIS approach effectively maximizes the power extracted from the VSWT, achieving an estimated average efficiency of 99.84%. In contrast, PID methods show average efficiencies of 92.63%. Additionally, compared to other published works, the proposed MPPT-based TS-ANFIS method demonstrates a rapid response time of 0.001s and lower static error at 0.02%. Furthermore, the proposed WSE-based TS-ANFIS model exhibits superior performance and effectiveness, yielding a root mean squared error (RMSE) of 0.0085381, a determination coefficient (R2) value of 0.99985, and a Pearson correlation coefficient (r) value of 0.99996. Moreover, the proposed hardware implementation of these approaches maintains lower power usage, operating at a high frequency of 80.38MHz and achieving a high throughput of 2,572.16 Mbps.

Three-dimensional simulation of cavitating flow in reciprocating positive displacement pumps with fluid-actuated valves

Abstract An in-house compressible three-dimensional (3D) finite volume Computational Fluid Dynamics (CFD) method, with statistical turbulence model and moving grid capabilities, is presented and applied to the suction stroke of a simplex plunger positive displacement pump. The approach utilizes a pressure-based implicit solution algorithm and a mass transfer cavitation model. Fluid-actuated valve dynamics are approximated using a fluid-structure interaction algorithm. The closed valve and early phase of valve opening are approximated by a permeable wall. A circular segment model is introduced, significantly reducing computing time. Experimental validation is performed by time-resolved pressure and flow rate measurements, as well as high-speed visualizations of the valve dynamics. A speed variation is conducted to investigate harmless advanced and erosive distinctive partial cavitation. The simulation reproduces the delay of cavity collapse, observed with increasing speed, and reveals distinctive void patterns related to the chamber pressure time progression. These void patterns are fundamental for understanding the cavitation dynamics and potential erosion risk in the system. Deviations from data remain in the flow phase subsequent to the collapse due to overestimated wave reflection at the inlet boundary in the suction pipe.

Collaboration and resource sharing in the multidepot time-dependent vehicle routing problem with time windows

Concerns about energy conservation and emission reduction have highlighted the importance of environmentally sound logistics networks in urban areas. These networks are deeply intertwined with urban traffic systems, where variations in transit speeds can significantly increase the energy consumption and carbon emissions of delivery vehicles, compromising the environmental sustainability of urban deliveries. To address this, we propose a multidepot time-dependent vehicle routing problem with time windows, enhancing route planning flexibility and resource configuration. Our approach begins with a route spatiotemporal decomposition method to estimate vehicle travel times and emissions based on varying vehicle speeds. We then develop a multiobjective mixed integer linear programming model that aims to minimize total operating costs, the number of vehicles, and carbon dioxide emissions. A hybrid heuristic algorithm combining spectral clustering, multiobjective ant colony optimization, and variable neighborhood search is proposed to solve the model. This algorithm incorporates collaboration and resource sharing strategies, a pheromone initialization mechanism, a novel heuristic operator that accounts for time dependency, and a self-adaptive update mechanism, enhancing both solution quality and algorithm convergence. We compare the performance of our algorithm with that of the CPLEX solver, multiobjective ant colony optimization, non-dominated sorting genetic algorithm-Ⅲ, and multiobjective particle swarm optimization. The results demonstrate the superior convergence, uniformity, and spread of our proposed algorithm. Furthermore, we apply our model and algorithm to a real-world case in Chongqing, China, analyzing optimized results for different time intervals and vehicle speeds. This study offers robust methodologies for theoretically and practically addressing the multidepot time-dependent vehicle routing problem with time windows, contributing to the development of economical, efficient, collaborative, and sustainable urban logistics networks.