Load Model Research Articles

СПРОЩЕНА ІМІТАЦІЙНА МОДЕЛЬ НАВАНТАЖЕННЯ В ЗАМКНЕНІЙ МЕРЕЖІ КОМУТАЦІЇ ПАКЕТІВ

An approach to simplified simulation modeling of packet switching network load has been developed. The simulated representation of network processes is based on a simplified mathematical model of the packet switching network, which is based on a dis-cretized description of the load states of the network nodes and corresponds to the repre-sentation of the current state of the network in the tasks of managing its load. Changes in the node's filling state are modeled as a process of reproduction and death, and in the full load model, two factors of change are taken into account: the node's exchange of load with network users and transit load flow passing through it. To study an artificially se-lected "part of the network functioning process" - an approach to transit load modeling in a load-locked network is proposed. The approach allows for significant simplifications and ensures the leveling of the negative impact of simplifications on "load closure" (en-sures the rule of constant amount of load in a closed network). The developed simulation model, firstly, creates the basis for the improvement of the complete model, and secondly, it is convenient for working out routing algorithms when analyzing them from the point of view of indicators of the ability to "influence" the load in the network. The obtained re-sults are generalized, but attention is focused on the example of satellite packet switching networks.

Research on the Support Performance of Internal Feedback Hydrostatic Thrust and Journal Bearing Considering Load Effect

This study aims to analyze the impact of uniform and eccentric load conditions on the performance of internal feedback hydrostatic thrust and journal bearing. Two distinct models are established: a three-degrees-of-freedom uniform load model and a five-degrees-of-freedom eccentric load model. The support stiffness, overturning stiffness, and flow rate for both thrust and journal bearings are calculated. Additionally, numerical analysis is conducted to examine the influence of oil film thickness, inlet pressure, and restrictor size on the operational characteristics of the bearings, revealing the interplay between an eccentric load and journal bearing speed. The validity of the theoretical algorithm is verified through finite element simulation. The research outcomes hold significant guiding implications for the design and application of internal feedback hydrostatic bearings.

Dynamic behaviour of bridges under critical conventional and regular trains: Review of some regulations included in EN 1991-2

In the field of structural analysis dedicated to the study of vibrations of high-speed railway bridges, one reference load model is the well-known HSLM-A, which limits of validity are stated in Eurocode EN 1991-2, Annex E. In a recent paper published in the Journal of Rail and Rapid Transit, the authors investigated the degree of coverage provided by HSLM-A to critical articulated trains. Now in the present article, the authors have extended those analyses to critical conventional and regular trains as well. This is an important aspect because HSLM-A as such is an articulated-type model, so it is of interest to understand how it deals with covering the various resonance phenomena generated by other train types. Therefore, the main goal of this work is to establish whether the conventional and regular trains that stem from the validity rules given in Annex E/EN 1991-2, produce vibratory effects that are duly covered by HSLM-A. Following the aforementioned validity rules, one first aspect analysed is the importance of near-to-integer wheelbase ratios in the coupled vibrations produced by conventional trains. Subsequently, seven realistic, conventional and regular high-speed train models have been synthesised; these models have been made publicly available in Mendeley Data, and comprise almost 3800 different sequences of axle loads. Finally, the response of simply-supported bridges has been analysed with a view to compare the seven synthesised models versus HSLM-A. The exceedance and required speed increase have been computed for both displacements and accelerations, in a comprehensive ensemble of spans and speeds. The results provide a diagnosis of the degree of coverage of HSLM-A with respect to those conventional and regular trains compliant with Annex E/EN 1991-2.

Heliostat Consortium: Gap Analysis on State of the Art in Wind Load Design

Abstract Wind loads are a major driver of heliostat cost. Standardized methods and tools are needed for a more detailed understanding of the static and dynamic loads of a heliostat design. This will enable cost reduction of wind-dependent heliostats to avoid unnecessarily conservative heliostat designs and increase field efficiency and reliability to reduce the risk of component failures due to high-wind events. Gaps related to wind load include lack of site characterization for wind measurements, insufficient critical load cases for heliostat design, Insufficient understanding of turbulence impacts on heliostat tracking error, lack of knowledge on wind load under various heliostat array configurations, and underexplored heliostat field wind load reduction and operating strategies. Recommended pathway forward is to develop wind load and site characterization guidelines for heliostat design and develop heliostat field wind load models with optical performance impacts.

Power generation–network–load–energy storage co-planning under uncertainty

With the aggregation of renewable energy in the power system, the uncertainty caused by the renewable energy affects the planning and operation of power systems. Meanwhile, the existing planning models fail to consider renewable energy uncertainty methods, specifically concerning renewable energy confidence and future possible scenarios; thus, a confidence-based scenario cluster method is presented. A novel generator, network, load, and energy storage (GNLS) co-planning model is proposed in the paper. First, a confidence-based scenario cluster is built, which can reflect uncertainties by clustering and analyzing wind, solar, and load. Second, the proposed model focuses on load and energy storage co-planning, and in addition, relevant flexible indices are used to assess the model. Finally, the GNLS co-planning model is built as a bi-level stochastic model on continuous time scales. The model is solved using the Benders decomposition algorithm. The method in this paper is validated using an IEEE RTS 24-bus and a real test system in China to demonstrate the reduction in renewable energy curtailment and optimization of economic factors in power system planning.

SIMPLIFIED SIMULATION MODEL OF LOAD IN A CLOSED PACKET SWITCHING NETWORK

An approach to simplified simulation modeling of packet switching network load has been developed. The simulated representation of network processes is based on a simplified mathematical model of the packet switching network, which is based on a discretized description of the load states of the network nodes and corresponds to the representation of the current state of the network in the tasks of managing its load. Changes in the node's filling state are modeled as a process of reproduction and death, and in the full load model, two factors of change are taken into account: the node's exchange of load with network users and transit load flow passing through it. To study an artificially selected "part of the network functioning process" - an approach to transit load modeling in a load-locked network is proposed. The approach allows for significant simplifications and ensures the leveling of the negative impact of simplifications on "load closure" (ensures the rule of constant amount of load in a closed network). The developed simulation model, firstly, creates the basis for the improvement of the complete model, and secondly, it is convenient for working out routing algorithms when analyzing them from the point of view of indicators of the ability to "influence" the load in the network. The obtained results are generalized, but attention is focused on the example of satellite packet switching networks.

Dynamic Response Study of Space Large-Span Structure under Stochastic Crowd-Loading Excitation

With the development of civil engineering, lightweight and high-strength materials, as well as large-span, low-frequency structural systems, are increasingly used. However, its self-oscillation frequency is often close to the stride frequency of pedestrians, which is easily affected by human activities. To study the effect of human activities on the dynamic response of structures, it is crucial to choose an appropriate anthropogenic load model. Considering the inter-subject and intra-subject variability of pedestrian walking parameters and induced forces in a crowd, we introduce the interaction rules between pedestrians based on the floor field cellular automata (FFCA). A stochastic crowd-loading model coupling walking parameters, induced forces between pedestrians, and induced forces between pedestrians and structures is proposed for simulating crowd-walking loads. The feasibility of the model is verified by comparing the measured response of a space large-span structure with the predicted response of the proposed stochastic crowd-loading model. The comfort level of the structure under different crowd densities was also evaluated based on the model. It was found that both random combinations of walking parameters and dynamic behaviors of pedestrians can cause significant differences in the structural response. Therefore, the crowd-loading model should consider the influence of pedestrian behavioral factors on the structural response.

Time-dependent synchronization factor of crowd rhythmic motion and its application on intelligent structural monitoring

The synchronization factor is an index to evaluate the level of consistency for crowd motion under rhythmic guidance, which is important for crowd load modeling as well as structural vibration monitoring. Most of the existing synchronization factors were defined based on the time history of the crowd movement over a long period of time, failing to consider the time-dependent characteristics of intra-subject variability. Moreover, these factors were generally based on the data that can only be measured under laboratory conditions, making them difficult to be applied on the monitoring of structures subjected to crowd excitation in real engineering practice. In this regard, this paper proposed a novel time-dependent synchronization factor for a crowd in rhythmic motion. By using multiple object tracking technology, the head movements of each person in the crowd were measured as the basis for calculating the newly proposed time-dependent synchronization factor, which is defined as the ratio between a representative value of the crowd movement time history and the average of the same values for each person in the crowd. A series of experiments were organized to study the characteristics and the applicability of the synchronization factor, whose relation with structural responses was clearly observed in the laboratory experiments as well as in a real structure. The results demonstrated that the newly proposed time-dependent synchronization factor is effective when applied on the intelligent operation and maintenance of the engineering structures.

Theoretical and experimental investigation of vibration-assisted scratching silicon

Interaction between tip and workpiece plays a crucial role in determining the scratching depth in Vibration-assisted Tip Based Nanofabrication (VTBN), which is more complicated than that of conventional scratching (without vibration-assisted). To establish the relationship between the scratching depth and machining parameters, a cubecorner indenter is employed to conduct VTBN procedure. The indenter is simplified as a cone to calculate the contact model in both 1D groove and 2D surface machining processes. An instantaneous normal load model is established based on the developed tip-workpiece contact model. Due to the variation of instantaneous contact area, effective contact area is put forward for the first time to predict the normal load. Effects of vibration frequency and amplitude on the normal load and surface quality are analyzed based on the proposed model. Experimental results show that higher frequency and larger amplitude will reduce the effective contact area. Vibration can increase the scratching depth and width without enlarging the cutting force. Furthermore, the inconsistency caused by scratching directions in conventional scratching is also overcome during VTBN.

Advancing infrastructure resilience: machine learning-based prediction of bridges’ rating factors under autonomous truck platoons

AbstractThe operational characteristics of freight shipment will significantly change after the implementation of Autonomous and Connected Trucks (ACT). This change will have a significant impact on freight mobility, transportation safety, and the sustainability of infrastructure. Truck platooning is an emerging truck configuration that is expected to become operational in the future due to the rapid advancements in connected vehicle technology and autonomous driving assistance. The platooning configuration enables trucks to be connected with themselves and the surrounding infrastructure. This arrangement has shown to be a promising solution to improve the vehicles’ fuel efficiency, reduce carbon dioxide emission, reduce traffic congestion, and improve transportation service. However, platooning may accelerate the damage accumulation of pavement and bridge structures due to the formation of multiple load axles within each platoon since those structures were not designed for such loads. According to AASHTO, bridges are designed based on a notional live load model comprised of one or two trucks per lane in conjunction with or separate from an applied uniform load (AASHTO, LRFD 2022). This damage, if accumulated, its repair would require billions of dollars from the government and would impede the movement of both people and goods. The potential damage to infrastructure may arise due to various factors such as the number of trucks in a platoon, gap spacing between trucks, and the type of trucks. This research work includes a thorough parametric study with 295,200 computer simulations using SAP 2000. The goal was to evaluate the effect of different truck platooning configurations on the load rating of existing bridges. The obtained results served as the dataset for training various machine learning models, including Random Tree, Random Forest, Multi-Layer Perceptron (MLP), Support Vector Regression (SVR), K-Nearest Neighbor (KNN), and Extreme Gradient Boosting (XGBoost). Results showed that Random Forest model performed the best, with the lowest prediction errors. The proposed machine learning model has shown its effectiveness in identifying optimal platooning configurations for bridge structures within the scope of the study. Graphical Abstract

A Time-History Contact Force Model of the Dynamic Load of AERORail Structures

This study proposes two curves that depict the vehicle–bridge contact force in a novel transportation system named AERORail, which is a lightweight cable-supported structure in which the rails and the prestressed cable form the load bearing system. Based on the contact force identified from a full-scale AERORail system, single and double-valley curves were obtained as the idealized contact force model for large- and small-span AERORail systems, respectively. This was achieved by utilizing the Bezier curves and the least squares method. The proposed curves were verified through a moving load model from a previous study under various spans and speeds. Moreover, the structural response of the AERORail structure under high-speed vehicle passing was explored using the idealized contact force model. The simulation results show that the proposed contact force model can predict the displacement response of 5 m and 15 m spans with a relative error of less than 5%, proving that the model can be used for dynamic analysis of AERORail.

Influence of Load Models and DG Operational Power Factor on Optimization of Shunt Capacitor Placement in Distribution Grids Considering Voltage Stability

The paper proposes a mathematical model of mixed-integer nonlinear programming (MINLP) to optimize the location and size of shunt capacitors in power distribution grids with distributed generation (DG), considering voltage stability constraints. At the same time, the paper also analyzes the influence of the load models, such as voltage-dependence load (ZIP load) and constant power load, together with DG’s operating power factor on the optimal solution. The objective function of the proposed optimization formulation is minimizing the total expenses, including the capital investment of capacitors and the expense incurred by network energy loss. This MINLP model includes constraints in the current and critical loading conditions, such as the system of power flow equations, nodal voltage limits, branch thermal limits, capacitor-related constraints, and restrictions of minimum security level. The proposed optimization model was evaluated on a modified IEEE 33-node distribution grid using the KNITRO commercial solver with the GAMS programming language. The calculation results show that the voltage stability constraints, load models, and operational power factor of the DG units have an essential influence on the optimal position and capacity of the shunt capacitors.

Global sensitivity analysis of the maximum live load and its applications

The design live loads are determined by the probability distribution of the maximum live load, which is influenced by the amplitudes and time intervals of various sustained and extraordinary loads. If the relative impact of different input variables on the maximum can be clarified, more targeted load surveys and modeling can be achieved. However, there is currently no global sensitivity analysis that simultaneously considers all input variables. In this study, the probability density function of the maximum live load is determined using the load coincidence principle and probability density evolution method. The relative entropy is employed as a measure for conducting a global sensitivity analysis across five common building occupancy types. The results indicate a significant imbalance in the impact of different input variables. The load amplitudes have a much greater effect than the time intervals. Among various load amplitudes, those related to the extraordinary loads often have the most significant impact. Regarding the time intervals, the occurrence intervals corresponding to the extraordinary loads caused by furniture stacking and normal crowding consistently have the least influence. For the time intervals with minimal impact, it is suggested to treat them as deterministic values in the live load modeling. This treatment has a negligible impact (not exceeding 10%) on the mean and upper fractile of the maximum, which are generally used for design in load codes.

A deep dive into enhancing frequency stability in integrated photovoltaic power grids

Voltage control strategies (VCS) and frequency stability analysis (FSA) are essential for power system reliability, particularly during high-load periods. Stable voltage and frequency levels prevent malfunction, power quality deterioration, and supply interruptions. Grid operators must skillfully manage VCS and FSA control to ensure system stability. Nonlinear loads, especially under transient conditions, significantly affect voltage stability (VS), introducing harmonics, waveform distortion, and stability complexities. Accurate modeling of these nonlinear loads is vital when traditional static load models fall short. Frequency fluctuations from power generation-demand imbalances require vigilant monitoring and regulation. Effective frequency control mechanisms are indispensable for preserving desired frequencies. Using a Western Algeria case study, this paper underscores FSA's significance in integrating photovoltaic (PV) systems into power grids. It addresses challenges from frequency fluctuations due to dynamic ZIP load profiles, emphasizing the importance of FSA for reliable grid operation. The study offers insights and practical approaches to enhance VS, FSA control, and energy management (EM), improving grid reliability and ensuring uninterrupted power supply. We must look into FSA's benefits in integrating PV systems to improve performance and lower grid interruptions. This includes looking into its control mechanisms and feedback systems.

Total suspended solid (TSS) pollution load model at the Jagir River Estuary, Surabaya, East Java

The research aims to create a TSS spatial distribution map model and a pollution load model at the Jagir River Estuary—a descriptive method for the last six years to describe the TSS pollution load model. Satellite image data processing uses SNAP software with the application of the Laili algorithm (2015). The distribution of TSS pollution values and loads still meets the quality standards for mangrove ecosystems. The average value of TSS distribution in 2018 - 2023, respectively, is 18,966 mg/L, 14,578 mg/L, 17,267 mg/L, 16.921 mg/L, and 15,013 mg/L with the highest TSS pollution load occurring in 2023 and the lowest in 2021 and 2022. The average pollution load from 6 research stations in 2019 – 2023 still does not exceed the capacity limit of the Jagir River to accommodate TSS accumulation. The TSS pollution load model in the Jagir River Estuary, Surabaya, East Java, follows a quadratic pattern with the regression equation y = -173.01X2 + 2339.20X - 2299.80 (R2 = 0.7674) with a peak TSS pollution load reaching 5,607.05 tons m3/month in January and lowest in August

Suspended sediment load modeling using Hydro-Climate variables and Machine learning

Machine learning (ML) models have the potential to improve the accuracy of Suspended Sediment Load (SSL) predictions. However, their ability to incorporate climate data to enhance SSL predictions remains underexplored. This study investigates the efficacy of ML models in modeling sediment transport using hydro-climate variables as input data. Boosted decision tree models, including Xgboost, LightGBM, and Catboost, were employed to estimate SSL using hydro-climate variables such as streamflow and precipitation, as well as maximum and minimum temperatures. The models were trained, optimized, and evaluated using either climate/streamflow data or a combination of both, encompassing 47 gauges across the contiguous United States (CONUS) between 1950 and 2022. The results demonstrate that streamflow data alone is insufficient for accurate SSL estimation in all rivers. Consequently, integrating climate data and streamflow data proved effective in predicting SSL at the daily scale across diverse climate zones with commendable accuracy. The models achieved an average Nash-Sutcliffe Efficiency (NSE) of > 0.66, Kling-Gupta Efficiency (KGE) of > 0.67, and Willmott Index (WI) of > 0.83 during the unseen (test) phase. These findings highlight the potential of ML models, particularly when combining climate data with streamflow, to enhance SSL predictions and improve our understanding of sediment transport dynamics.

Enhancing riverine load prediction of anthropogenic pollutants: Harnessing the potential of feed-forward backpropagation (FFBP) artificial neural network (ANN) models

Assessing riverine pollutant loads is a more realistic method for analysing point and non-point anthropogenic pollution sources throughout a watershed. This study compares numerous mathematical modelling strategies for estimating riverine loads based on the chosen water quality parameters: Biochemical Oxygen Demand (BOD), Chemical Oxygen Demand (COD), Suspended Solids (SS), and Ammoniacal Nitrogen (NH3–N). A riverine load model was developed by employing various input variables including river flow and pollutant concentration values collected at several monitoring sites. Among the mathematical modelling methods employed are artificial neural networks with feed-forward backpropagation algorithms and radial basis functions. The classical multiple linear regression (MLR) statistical model was used for the comparison. Four widely used statistical performance assessment metrics were adopted to evaluate the performance of the various developed models: the root mean square error (RMSE), mean absolute error (MAE), mean relative error (MRE), and coefficient of determination (R2). The considerable number of errors (with RMSE, MAE, and MRE) discovered in estimating riverine loads using the multiple linear regression (MLR) statistical model can be attributed to the nonlinear relationship between the independent variables (Q and Cx) and dependent variables (W). The feed-forward neural network model with a backpropagation algorithm and Bayesian regularisation training algorithm outperformed the radial basis neural network. This finding implies that, in addition to suspended sediment loads, riverine loads may be predicted using an artificial neural network using pollutant concentration (Cx) and river discharge (Q) as input variables. Other geographical and temporal fluctuation characteristics that may impact river water quality, on the other hand, may be incorporated as input variables to enhance riverine load prediction. Finally, riverine load analyses were successfully conducted to reduce the riverine load.

Structural analysis and application research for controlled strand length cutting blade of rotary guillotine cutter

In the tobacco processing stage leading to the production of cigarettes, there are strict technical requirements for the structure and proportion of different lengths of cut tobacco following the cutting process. Moreover, the lightweight design of the high-speed rotary-cutting blades is of significant importance during this stage. To better address issues related to the rational design of the blade structure in rotary-cutting cutters and the effective control of size proportion of cut tobacco, this paper establishes a CLSC blade design method featuring concave-convex edges and inter-edge grooves, and provides specific structural schemes. By introducing the structure of the cutting components of the cutter and the cutting process, and by analyzing the differences in layout features between RSEC and CLSC blade structures, as well as their relevance to tobacco cutting and shaping, this paper establishes the installation constraints and mechanical load model for cutter blades under rated conditions. Finite element methods were used to model and analyze both types of cutter blade structures, yielding static and modal analysis data. Furthermore, production verification was conducted on an SD EVO cutter, and data on the rate of different lengths of cut tobacco were obtained. The results show that the mass of the CLSC concave-convex edge cutter blade was reduced by 4.33%, and the static stiffness efficiency was increased by 2.37 times. Although the first-six natural frequencies of the CSLC blade showed degradation in modal analysis, it still met the equipment vibration usage condition. The production verification results indicated that the use of CLSC concave-convex edge cutter blades reduced the long cut rate by 2.48%, increased the medium cut rate by 2.29%, broken cut rate increased by 0.08%, and increased the short cut rate by 0.12%, with an overall reduction in the whole cut rate by 0.19%, achieving the objectives of reducing the long cut rate, controlling the whole cut rate, and not increasing the broken cut rate. The CLSC blades were able to operate continuously, stably, and safely on the rotary-cutting cutter machine. The research presented in this paper can provide a reference for the analysis and design of cutter blade structures in tobacco cutter machines.

Probability of Failure for Fixed Offshore Structures Determination: An Analysis of a Braced Monopod and 4-Legged Platforms with Respect to Industry Standards

The structural integrity is susceptible to a combination of statistical and engineering design uncertainties that may remain flexible as long as the structure can successfully manage the encountered load. There are many methods for determining the probability of failure (POF) in the oil and gas sector, but they all have particular constraints. Therefore, this study is a quantitative risk assessment to establish a more reliable method for calculating the POF value. This study chose and evaluated a sample of a braced monopod and 4 legged offshore structures for global non-linear analysis. The most reliable form of distribution was predetermined, and the appropriate integral equation was applied and computed based on the load and strength model conditions. The result of the testing of stress-strength interference for 12 directions and for 8 directions of wave impact in POF indicated that the platform remained intact and reliable compared to the L2 exposure level for the reliability target of POF recommended by ISO 19902, ISO 19901, and PETRONAS. Furthermore, this established that the applied integral equation provided a high degree of confidence in calculating the new POF. This newly created approach will enable the structural engineer to outline action items as part of the organisation’s risk management process.

Mobilizing railway track stability with nonionic cement asphalt mortar for floating sleeper mitigation – 2D/3D numerical investigation and full-scale testing verification

This study addresses the mitigation of the floating sleeper phenomenon in ballasted railway tracks through the implementation of Nonionic Cement Asphalt Mortar (CAM). Full-scale testing experiments evaluate track performance with and without CAM stabilization, revealing its efficacy in forming a robust membrane between ballast particles to reinforce the track-bed structure. Wheel load modeling, including static and dynamic, train movement modeling, roadbed pressure analysis, distribution of load, and track rigidity analysis. Life Cycle Cost Analysis (LCCA) establishes CAM's cost-effectiveness by significantly reducing settlement and ensuring long-term durability. Numerical simulations using ABAQUS demonstrate CAM's effectiveness in stabilizing deteriorated ballast. Furthermore, the simulations prove capable of detecting the floating sleeper phenomenon in the full-scale test bed, offering valuable insights into real-world applications. Both 2D and 3D modeling consistently show a more than 30% reduction in initial settlement and a 0.5 mm displacement in CAM-reinforced ballast compared to controls. Plastic displacement in new ballast is consistently 50% less in the initial 50,000 cycles, with ultimate plastic settlement reduced by 40%, indicating the robustness of CAM across different modeling dimensions. The dataset analysis identifies a floating sleeper issue, evidenced by pressure variations in deteriorated ballast. In the control and simulation sections, there is a pressure drop from 65 kPa to 28 kPa, while the reinforced section maintains stable pressures (50 kPa, 36.5 kPa, 10 kPa) with closely aligned predicted values. Despite pressure reduction in simulated zones, the dataset strongly supports concerns about the substantial pressure decrease due to the floating sleeper phenomenon. LCCA substantiates CAM's economic advantage, showcasing a 79.7% cost reduction over 60 years, endorsing CAM as a sustainable maintenance strategy. In conclusion, CAM stabilization effectively mitigates the floating sleeper effect, ensuring long-term track durability, and providing valuable contributions to sustainable railway infrastructure design and maintenance practices.