Load Model Research Articles

Uncertain pedestrian load modeling for structural vibration assessment in footbridge design

The request of material savings in bridge constructions leads to the development towards slender and lightweight structures, which are more sensitive to human-induced vibrations caused by walking pedestrians. The resulting accelerations do not endanger the structural safety, but can be perceived as unpleasant for the user and must therefore be restricted within the limit state of serviceability. For this purpose, the guidelines define acceleration intervals, called as “comfort levels” (CL), which evaluate the comfort of the pedestrians, given the maximum acceleration of the bridge. The model for human-induced loads in the design guideline is based on conservative and deterministic simplifications. In particular, the uncertainties in the human gait parameters are neglected. The main objective of this paper is to develop an uncertain load model for walking pedestrians based on Fourier series. The aleatory and epistemic uncertainties in the human gait parameters are quantified with appropriate uncertainty models relying on available data. In a dynamical finite element analysis, the model is used to simulate groups of pedestrians randomly walking over a bridge to calculate the resulting acceleration amplitudes. The approach is applied to a single span beam and to a real world footbridge using a 3D finite element model. The results are then evaluated within the CLs, enabling new assessment methods.

An Improved CNN-BILSTM Model for Power Load Prediction in Uncertain Power Systems

Power load prediction is fundamental for ensuring the reliability of power grid operation and the accuracy of power demand forecasting. However, the uncertainties stemming from power generation, such as wind speed and water flow, along with variations in electricity demand, present new challenges to existing power load prediction methods. In this paper, we propose an improved Convolutional Neural Network–Bidirectional Long Short-Term Memory (CNN-BILSTM) model for analyzing power load in systems affected by uncertain power conditions. Initially, we delineate the uncertainty characteristics inherent in real-world power systems and establish a data-driven power load model based on fluctuations in power source loads. Building upon this foundation, we design the CNN-BILSTM model, which comprises a convolutional neural network (CNN) module for extracting features from power data, along with a forward Long Short-Term Memory (LSTM) module and a reverse LSTM module. The two LSTM modules account for factors influencing forward and reverse power load timings in the entire power load data, thus enhancing model performance and data utilization efficiency. We further conduct comparative experiments to evaluate the effectiveness of the proposed CNN-BILSTM model. The experimental results demonstrate that CNN-BILSTM can effectively and more accurately predict power loads within power systems characterized by uncertain power generation and electricity demand. Consequently, it exhibits promising prospects for industrial applications.

Optimal Planning of PV Sources and D-STATCOM Devices with Network Reconfiguration Employing Modified Ant Lion Optimizer

This research emphasizes a meta-heuristic modified ant lion optimizer (MALO) optimization approach for the simultaneous utilization of DSTATCOM devices and distributed photovoltaic (PV) sources with network reconfiguration in a radial power distribution scheme. In a radial power distribution network with network reconfiguration, the majority of the research is based on constant power model analysis. However, it is noticed that load models have a substantial impact on the distributed PV sources and the DSTATCOM device’s optimal size and position. The effect of the constant power (CP) and polynomial (ZIP) with load growth load models for the simultaneous insertion of distributed PV sources and DSTATCOM devices with network reconfiguration is examined in this research work for power system planning. The penetration levels of distributed PV sources considered for the investigation are 25%, 50%, 75%, and 100%. The principal objective of this research is to reduce network total power losses, enhance the voltage magnitude profile at all buses, and reduce the overall operating cost while adhering to equality and inequality constraints. The proposed algorithm is verified on 118-node test systems. The investigation is carried out for planning network upgrading to a high-voltage distribution system (HVDS) on 317 nodes in the rural Bangalore Electricity Supply Company Limited (BESCOM) radial distribution scheme. The simulated results obtained with this method are validated with the BAT algorithm and techniques available in the literature. It is observed that in the IEEE 118-bus system, via the simultaneous placement and sizing of PV sources considering a 25% penetration level and DSTATCOM devices during network reconfiguration, the total power loss reduction is 41.47% and 42.98% for the constant power model and ZIP with the load growth model. For the 317-bus system, the total power loss reduction observed for 11 kV is 49.77% and 59.34% for the constant power model and ZIP model with load growth. Similarly, for the 22 kV system, the power loss reduction observed is 51.69% and 55.75% for the constant power model and ZIP with the load growth model.

Elevating sustainability with a multi-renewable hydrogen generation system empowered by machine learning and multi-objective optimization

The global energy landscape is rapidly shifting toward cleaner, lower-carbon electricity generation, necessitating a transition to alternate energy sources. Hydrogen, particularly green hydrogen, looks to be a significant solution for facilitating this transformation, as it is produced by water electrolysis with renewable energy sources such as solar irradiations, wind speed, and biomass residuals. Traditional energy systems are costly and produce energy slowly due to unpredictability in resource supply. To address this challenge, this work provides a novel technique that integrates a multi-renewable energy system using multi objective optimization algorithm to meets the machine learning-based forecasted load model. Several forecasting models, including Autoregressive Integrated Moving Average (ARIMA), Random Forest and Long Short-Term Memory Recurrent Neural Network (LSTM-RNN), are assessed for develop the statistical metrics values such as RMSE, MAE, and MAPE. The selected Non-Sorting Moth Flame Optimization (NSMFO) algorithm demonstrates technological prowess in efficiently achieving global optimization, particularly when handling multiple objective functions. This integrated method shows enormous promise in technological, economic, and environmental terms, emphasizing its ability to promote energy sustainability targets.

Digital twin‐driven online intelligent assessment of wind turbine gearbox

AbstractRemaining useful fatigue life monitoring of wind turbine drivetrains is important. However, the implementation of real‐time monitoring often faces efficiency and accuracy challenges. In order to resolve this, this paper proposes a vibration‐based damage monitoring digital twin (VBDM‐DT) that enables the online intelligent evaluation of wind turbine gearboxes, using gear tooth surface durability as an example fatigue mode. The VBDM‐DT integrates a random wind load model, a high‐fidelity dynamics model, and a fatigue damage model. The random wind load model takes the wind speed from the supervisory control and data acquisition (SCADA) as input to estimate the input torque of the drivetrain in real time. Simultaneously, VBDM‐DT uses the vibration signals from the condition monitoring system (CMS) to intelligently calibrate the dynamics model, allowing it to be continuously adjusted and optimized in response to actual vibrations. The fatigue damage model takes the real‐time dynamic loads estimated by the high‐fidelity dynamic model as input to achieve real‐time fatigue damage monitoring of key components in the wind turbine gearbox. Applying the VBDM‐DT model to a 2 MW wind turbine gearbox, the results indicate that the model provides satisfactory accuracy in estimating input loads and good adaptability in intelligent calibration of the dynamic model. Based on this model, the fatigue life estimation for gears and bearings is more credible and reliable.

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.

Design and Development of Mathematical and Thermal Load Modelling for Induction Heating Systems

Complex systems can be modelled and their performance can be anticipated with the help of current computers and software tools that are applicable in real-world situations. The IH system is capable of being modelled and put through analysis in two distinct domains. The modelling is compatible with the research and applications in their entirety, including the particular control circuits and converter systems under consideration. The use of electrical modelling makes it possible to analyse the features of the IH load and to examine the variations in the parameters in order to determine the impact of load variations. In order to gain an understanding of the load's flow and thermal distribution, thermal modelling is a crucial component of this study. For the purpose of analysing the performance of the coil in terms of flux and temperature from its comparable model, the COMSOL Multiphysics software that is utilised is utilised.

Assessment of the bearing capacity of variable profile piles in soil using static load model tests on a testing apparatus

The paper presents the results of the study of the proposed type of variable profile piles. The proposed type of piles is reinforced concrete driven piles segmented in length. Each subsequent section has a radial displacement along the axis of symmetry relative to the previous section. The positive effect of the performance of the proposed pile is to change the nature of the lateral contact of the pile with the soil, and to increase the drag of the soil. The conducted research is aimed at solving the problem related to the relatively low bearing capacity of traditional square section piles. The research was carried out by the method of model tests of piles on a test bench (tray) at a scale of 1:10. The tests were performed for variant pile types in comparison with a standard square section prismatic pile. The adopted dimensions of the pile model allow the use of this tray without the influence of its boundary conditions on the stress-strain state of the soil. A total of 42 tests were performed, 3 tests for each type of piles compared. Evaluation of pile bearing capacity was performed by static loading of pile models with vertical indentation load until failure. According to the results of the investigations, the resistance values of the compared pile types in the soil were obtained, as well as the dependence of bearing capacity changes on the section dimensions and on the rotation angle. According to the results, the optimal pile solution was selected. The bearing capacity of the proposed optimal pile solution exceeds the bearing capacity of the standard driven pile by 22 %. The results obtained allow us to conclude about the influence of the technological solution of the proposed pile type on its serviceability in soil conditions

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.

Reliability-driven time-of-use tariffs for efficient plug-in electric vehicle integration

Demand response (DR) plays a crucial role in balancing electricity supply and demand, but existing studies on price-based DR for plug-in electric vehicle (PEV) load management lack comprehensive considerations. Therefore, this study designs reliability-driven time-of-use (TOU) tariffs for the integration of PEVs, considering various factors such as random component failures, the stochastic characteristics of non-dispatchable components, and fluctuations in load patterns and uncertainties. The proposed framework employs advanced stochastic simulation models to assess generation facility reliability amidst extensive PEV deployment in three steps. First, individual components, including conventional generation capacity, renewable energy sources, and demand, as well as PEV load models, are developed. Then, a system reserve margin assessment is carried out via the generation of seasonal synthetic time series data. During this step, innovative techniques, including box-plot methods and expert rules, are used to simplify the representation of the system reserve margin at each hour. Finally, optimized TOU pricing schedules for PEVs are generated. These pricing hours are initially categorized as off-peak, mid-peak, or on-peak, streamlining scenario analysis and aligning PEV charging profiles with predefined pricing schedules for grid reliability evaluation. Case studies have shown that the proposed method can improve the reliability indices by effectively mitigating the impacts.

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.