Model Of Load Research Articles

Analytical Solution of Terzaghi’s Ground Arch Model for Loads on Circular Tunnels

Analytical Solution of Terzaghi’s Ground Arch Model for Loads on Circular Tunnels

Research on the Operation Optimization of Public Building Systems in Extremely Cold Areas Based on Flexible Loads

The heating energy consumption in public buildings in cold regions is notably significant, presenting substantial scope for energy savings and emission reductions. Flexible loads can actively participate in controlling the operation of the power grid, improving the energy utilization and the economy of the system. This study introduces flexible loads into the operation optimization of energy systems, establishing mathematical models for flexible thermal and electrical loads. A two-stage operation optimization method is proposed: the first stage simulates the starting and stopping control conditions of equipment at varying temperatures and times, selecting the optimal time period to regulate the thermal loads; the second stage employs a multi-objective particle swarm optimization algorithm to optimize the scheduling of the system’s electrical load. Finally, an empirical analysis is carried out in a public building in Shenyang City as an example, and the results indicate that optimal scheduling of flexible thermal and electrical loads reduces the daily operating cost of the energy supply system by RMB 124.12 and decreases carbon emissions by 22.7%.

Stochastic Scheduling of Grid-Connected Smart Energy Hubs Participating in the Day-Ahead Energy, Reactive Power and Reserve Markets

An Energy Hub (EH) is able to manage several types of energy at the same time by aggregating resources, storage devices, and responsive loads. Therefore, it is expected that energy efficiency is high. Hence, the optimal operation for smart EHs in energy (gas, electrical, and thermal) networks is discussed in this study based on their contribution to reactive power, the energy market, and day-ahead reservations. This scheme is presented in a smart bi-level optimization. In the upper level, the equations of linearized optimal power flow are used to minimize energy losses in the presented energy networks. The lower level considers the maximization of profits of smart EHs in the mentioned markets; it is based on the EH operational model of resource, responsive load, and storage devices, as well as the formulation of the reserve and flexible constraints. This paper uses the “Karush–Kuhn–Tucker” method for single-level model extraction. An “unscented transformation technique” is then applied in order to model the uncertainties associated with energy price, renewable energy, load, and energy consumed in mobile storage. The participation of hubs in the mentioned markets to improve their economic status and the technical status of the networks, modeling of the flexibility of the hubs, and using the unscented transformation method to model uncertainties are the innovations of this article. Finally, the extracted numerical results indicate the proposed model’s potential to improve EHs’ economic and flexibility status and the energy network’s performance compared to their load flow studies. As a result, energy loss, voltage, and temperature drop as operation indices are improved by 14.5%, 48.2%, and 46.2% compared to the load flow studies, in the case of 100% EH flexibility and their optimal economic situation extraction.

A Novel LCLC Parallel Resonant Circuit for High-Frequency Induction Heating Application

A Novel LCLC Parallel Resonant Circuit for High-Frequency Induction Heating Application

An Equivalent Model for Frequency Dynamic Analysis of Large Power Grids Based on Regulation Performance Weighting Method

With the construction of the UHV (Ultra High Voltage) AC/DC hybrid power grid and the large-scale access to renewable energy such as wind power, frequency dynamic fluctuation has become a prominent problem affecting the safe and stable operation of large power grids. The expansion of the scale of the power system makes it impossible to use traditional fine modeling to analyze the power system. In order to reduce the calculation scale and storage capacity of power system frequency dynamic simulation, it is necessary to make appropriate equivalent simplification of the external system, so the appropriate dynamic equivalent method is of great significance. This paper mainly studies the equivalent model suitable for frequency dynamic analysis of large power grids. Firstly, the typical models of generator set and load are simplified, and the parameters that have a great influence on frequency in the simplified model are obtained through characteristic analysis. Then, a dynamic aggregation method of generator governor and prime mover parameters and load parameters based on regulation performance weighting (the parameters of the generator or load are weighted and summed according to its regulation ability on the system) is proposed. This method is applied to the simulation example of the East China Power Grid. The simulation proves that the frequency of the East China Power Grid before and after equivalence can be consistent under four different faults, which verifies the effectiveness of the equivalent method proposed in this paper in the frequency dynamic analysis of large power grids.

Data-driven surrogate model for wind turbine damage equivalent load

Abstract. Aeroelastic simulations are employed to assess wind turbines in accordance with IEC standards in the time domain. These analyses enable the evaluation of fatigue and extreme loads experienced by wind turbine components. Such simulations are essential for several reasons, including but not limited to reducing safety margins in wind turbine component design by accounting for a wide range of uncertainties in wind and wave conditions and fulfilling the requirements of the digital twin, which necessitates a comprehensive set of simulations for calibration. Thus, it is essential to develop computationally efficient yet accurate models that can replace costly aeroelastic simulations and data processing. To address this challenge, we propose a data-driven approach to construct surrogate models for the damage equivalent load (DEL) based on aeroelastic simulation outputs. Our method provides a quick and efficient way to calculate DEL using wind input signals without the need for time-consuming aeroelastic simulations. Our study focuses on utilizing a sequential machine learning (ML) method to map wind speed time series to DEL. Additionally, we demonstrate the versatility of the developed and trained surrogate models by testing them on a wind turbine in the wake and applying transfer learning to enhance their predictive accuracy.

Identifying possible ways for adapting an open wagon for transporting containers

The object of this study is the processes of perception and redistribution of loads in the supporting structure of a open wagon loaded with containers, taking into account the new scheme of their fastening. For safe transportation of containers in an open wagon, it is suggested to use a removable module. This module works according to the principle of an intermediate adapter between the container and the open wagon body. Fastening of the module itself in the open wagon is carried out through the fitting stops, which are placed on the floor of the open wagon. Mathematical modeling was carried out to determine the longitudinal dynamic load acting on the container fixed according to the new scheme in the open wagon. To this end, a mathematical model was built that characterizes the longitudinal movements of the "open wagon - removable module - container" system. Determination of the accelerations that act on the supporting structure of the open wagon loaded with containers was also carried out by means of computer simulation. Verification of the formed model of the dynamic load of the open wagon was carried out according to the F-criterion. Also, as part of the study, a modal analysis of the load-bearing structure of a open wagon loaded with containers was carried out, which made it possible to assess its traffic safety. A feature of the results obtained as part of the research is that the proposed design of the removable module could be used not only for fastening containers but also for transportation of other types of cargo. The field of practical application of the results is railroad transport, including other transportation industries. The conditions for the practical use of the results are a symmetrical scheme of loading the body of a open wagon with containers. The results of this study will contribute to increasing the efficiency of container transportation and the profitability of railroad transport

Applying Multi-Task Deep Learning Methods in Electricity Load Forecasting Using Meteorological Factors

The steady rise in carbon emissions has significantly exacerbated the global climate crisis, posing a severe threat to ecosystems due to the greenhouse gas effect. As one of the most pressing challenges of our time, the need for an immediate transition to renewable energy is imperative to meet the carbon reduction targets set by the Paris Agreement. Buildings, as major contributors to global energy consumption, play a pivotal role in climate change. This study diverges from previous research by employing multi-task deep learning techniques to develop a predictive model for electricity load in commercial buildings, incorporating auxiliary tasks such as temperature and cloud coverage. Using real data from a commercial building in Taiwan, this study explores the effects of varying batch sizes (100, 125, 150, and 200) on the model’s performance. The findings reveal that the multi-task deep learning model consistently surpasses single-task models in predicting electricity load, demonstrating superior accuracy and stability. These insights are crucial for companies aiming to enhance energy efficiency and formulate effective renewable energy procurement strategies, contributing to broader sustainability efforts and aligning with global climate action goals.

Analysis of the tooth-root stress of external spur gears with high effective contact ratio

For spur gears with contact ratio close to 2, the extension of the contact interval resulting from loaded tooth deflections and local contact deformations may result in an effective contact ratio above 2. In these cases, the load is transmitted by at least two tooth-pairs, the maximum load and tooth-root stress decrease, and therefore the calculation methods of the gear rating Standards ISO and AGMA provide very conservative results. In this work, two models are applied to the calculation of the tooth-root stress of load-induced high contact ratio external gears: (i) an analytic model of load sharing, based on the minimum energy method, and (ii) a finite element model, which validates the results obtained from the previous model. Obtained values of the stress are compared with those provided by ISO and AGMA rating methods, which do not account for the stress reduction due to the higher contact ratio. A new modification coefficient is proposed to correct these conservative values, which allows the AGMA and ISO geometry factors to remain as no load-dependent factors and keep their actual calculation methods and significance.

A Hybrid Prediction Model for Short-Term Load Forecasting in Power Systems

Short-term load forecasting (STLF) plays a vital role in effective power system management by assisting power dispatch centers in developing generation plans and ensuring smooth system operation. This study introduces a novel hybrid prediction model called iSSA-LSSVM to tackle the STLF challenge. By integrating the Salp Swarm Algorithm (SSA) with Least Squares Support Vector Machines (LSSVM), the iSSA-LSSVM model significantly improves LSSVM's prediction accuracy. One of the key contributions is the model's ability to autonomously ne-tune LSSVM hyperparameters, eliminating the need for manual adjustments and optimizing performance. Modifying the SSA within iSSA-LSSVM enhances the original algorithm's exploration and exploitation capabilities, ensuring better search efficiency and precision. Using a dataset with four independent variables as input and electrical power output as the target variable, the model demonstrates superior predictive performance. Comparative analysis with three other models shows that iSSA-LSSVM achieves a lower Mean Square Error (MSE) and faster convergence. This improvement in accuracy and efficiency enhances STLF, allowing power dispatch centers to develop more precise generation plans and ensure more reliable power system operation.

How uncertainty in calibration data affects the modeling of non-point source pollutant loads in baseflow

Baseflow is a major transport pathway for non-point source (NPS) pollutants. Watershed water quality (WWQ) models calibrated by low-quality data may produce misleading predictions of baseflow NPS pollutant loads, resulting in poor management decisions. We evaluated how models of the baseflow nitrate loads in the Huron River basin, southwest of Lake Erie, were affected by uncertainty in the calibration data. Based on a five-year time series of daily streamflow, nitrate concentration, and specific conductance, two sets of “observed” baseflow nitrate load data that include uncertainty were estimated using various tracer-based and non-tracer-based hydrograph separation methods, in conjunction with assumptions regarding baseflow nitrate concentrations. We calibrated the Soil and Water Assessment Tool plus (SWAT+) model with the two “observed” data sets and used the Generalized Likelihood Uncertainty Estimation (GLUE) approach to quantify parameter and predictive uncertainties. The results showed that baseflow accounted for 26 %–34 % of the mean annual total streamflow (11.8 m3/s) and 8 %–37 % of the mean annual total nitrate load (14.3 kg·ha−1·year−1) in the Huron River basin. The baseflow and nitrate load estimates from the non-tracer-based methods resembled those from the tracer-based method but had greater uncertainty. The posterior parameter distributions, as well as the weighted means and 90 % prediction intervals of the simulated baseflow nitrate loads, exhibited minimal variation when different calibration data sets for SWAT+ and different threshold likelihood values for GLUE were used. Our analysis emphasizes the necessity of calibrating WWQ models with baseflow pollutant loads/concentrations when addressing water quality issues related to baseflow. It also demonstrates the feasibility of utilizing multiple non-tracer-based hydrograph separation methods to estimate baseflow NPS pollutant loads. These non-tracer-based methods offer a simplicity and broader applicability compared to tracer-based methods. This study has provided insights into how calibration data uncertainty impacts the modeling of NPS pollution in baseflow and highlights the practical value of non-tracer-based hydrograph separation methods.

On the applicability limits of the structurally orthotropic shell model in problems of calculating the buckling under axial compression of waffle cylindrical shells

The article considers the solution of the buckling problem of a waffle cylindrical shell under axial compression using the Euler (bifurcation) approach. Two models are used: a model based on the numerical integration method and a model based on the finite element method. The first model assumes that the stiffeners are structurally an orthotropic shell obeying the Kirchhoff-Love hypotheses, with the assumption of "smearing". The second model uses a tetrahedron element with ten nodes. The study is based on experimental data from tests of one of the waffle cylindrical shell samples under axial compression. The research results show that with an increase in the waffle shell rib thickness, a discrepancy between the calculation results of the two models is observed. With the optimal value of the k parameter, characterizing the ratio of the rib thickness to the rib pitch, equal to 0.035, such a discrepancy is estimated to be about 5%. At the same time, the calculation using the model of a structurally orthotropic shell leads to an underestimation of the critical load compared to the finite element model. This provides an estimate of the loss of stability of a waffle cylindrical shell with a safety margin. For parameter k between 0.02 and 0.05, the difference between the results from the two computational models for critical loads does not exceed 11%.

Factors affecting Kaposi’s sarcoma-associated herpesvirus transmission in rural Ugandan households, a longitudinal study

BackgroundWe report the impact of HIV infection within a household on oral Kaposi’s sarcoma-associated herpesvirus (KSHV) shedding.MethodsWe enrolled 469 individuals from 90 households. Mouthwash rinse samples collected at three monthly visits were analyzed for KSHV DNA using quantitative polymerase chain reaction (qPCR). Generalized linear mixed effects logistic models were applied to analyze factors associated with KSHV ever shedding, and among shedders, always versus intermittent shedding. Linear mixed effects models were applied to models of KSHV viral loads. Intraclass correlation coefficients (ICCs) were calculated to assess the contribution of household-level factors to variations in shedding probabilities. Hotspot analyses of geospatial feature clusters were calculated using Getis-Ord Gi* statistic and visualized using inverse distance weighted interpolation.ResultsAnalyses included 340 KSHV seropositive individuals, aged 3 + years, with qPCR results from 89 households. Forty households had 1 + persons living with HIV (PLWH), while 49 had none. Among participants, 149(44%) were KSHV ever shedders. Of 140 who shed KSHV at two or more visits, 34(24%) were always shedders. Increasing number of KSHV seropositive household members was significantly associated with ever shedding [Odds ratio(OR) (95% Confidence Interval(95%CI)):1.14(1.03,1.26);p = 0.013]. Among KSHV shedders, a statistically significant age-related trend was identified with 10–19 years being more likely to be always shedders (type III test p = 0.039) and to have higher viral loads (type III test p = 0.027). In addition, higher viral loads were significantly associated with increasing number of household members [coefficient(95%CI):0.06(0.01,0.12);p = 0.042], increasing number of KSHV seropositive members [coefficient(95%CI):0.08(0.01,0.15);p = 0.021], and living in households with 1 + PLWH [coefficient(95%CI):0.51(0.04,0.98);p = 0.033]. Always shedders exhibited higher viral loads than intermittent shedders [coefficient(95%CI):1.62(1.19,2.05);p < 0.001], and viral loads increased with the number of visits where KSHV DNA was detected in saliva (type III test p < 0.001). Household-level factors attributed for 19% of the variability in KSHV shedding (ICC:0.191;p = 0.010). Geospatial analysis indicated overlapping hotspots of households with more KSHV seropositive individuals and KSHV shedders, distinct from areas where PLWH were clustered.DiscussionKSHV oral shedding is influenced by multiple factors at the individual, household, and regional levels. To mitigate ongoing KSHV transmission a comprehensive understanding of factors contributing to oral KSHV reactivation and transmission within households is needed.

Study on the model for shock wave and bubble load of deep-sea explosion

The accurate estimation of deep-sea explosion loads is challenging due to the extreme conditions such as the ultra-hydrostatic pressure. The traditional Geers–Hunter model has acceptable accuracy in calculating underwater explosion load in conventional water depth, but it is not effective in deep-sea explosions. The model established by Zhang is applied to the underwater explosion load calculation in ultra-deep water. The model's accuracy is validated by both the deep-sea and the conventional explosion experimental results published and the Eulerian finite element method numerical results. The results show that, compared to the Geers–Hunter model, the present model can not only accurately calculate the conventional conditions, but also has better accuracy for calculating deep-sea explosion shock wave and bubble load, by providing more accurate shock wave attenuation and smoother connection between shock wave stage and bubble pulsation stage in deep sea.

Research on sedimentation characteristics of the end bend water extraction project of cascade reservoir backwater

Abstract The operation mode of upstream and downstream cascade power stations significantly impacts the river bed in the water intake section, leading to complex sediment deposition characteristics in the curved river section at the reservoir’s end. In this research, a standard physical model for bed load and suspended load at a scale of λl=λh=100 was conducted, combined with the operation mode of upstream and downstream power stations, to investigate the sediment deposition patterns in the intake section and to study the sediment deposition characteristics in the intake area. The experimental results reveal that bed load sediment predominantly accumulates on the right bank (convex bank) of the river, with no bed load sediment accumulation at the mushroom head layout of the water intake project on the left bank (concave bank). When the operation of upstream and downstream power stations is uncoordinated, suspended sediment settles in the water intake section, with no sediment accumulation in the local area of the mushroom head of the intake due to the disturbance effect. Safety sensitivity analysis indicates that the safety of the water intake project can be ensured.

Pilot turning behavior cognitive load analysis in simulated flight.

To identify the cognitive load of different turning tasks in simulated flight, a flight experiment was designed based on real "preliminary screening" training modules for pilots. Heart Rate Variability (HRV) and flight data were collected during the experiments using a flight simulator and a heart rate sensor bracelet. The turning behaviors in flight were classified into climbing turns, descending turns, and level flight turns. A recognition model for the cognitive load associated with these turning behaviors was developed using machine learning and deep learning algorithms. pnni_20, range_nni, rmssd, sdsd, nni_20, sd1, triangular_index indicators are negatively correlated with different turning load. The LSTM-Attention model excelled in recognizing turning tasks with varying cognitive load, achieving an F1 score of 0.9491. Specific HRV characteristics can be used to analyze cognitive load in different turn-ing tasks, and the LSTM-Attention model can provide references for future studies on the selection characteristics of pilot cognitive load, and offer guidance for pilot training, thus having significant implications for pilot training and flight safety.

Crack-tip cleavage/dislocation emission competition behaviors/mechanisms in magnesium: ALEFM prediction and atomic simulation

Structural properties and reliability of materials can be improved by increasing fracture toughness. At the atomic scale, the fracture is a material separation process, and the fracture toughness of materials is associated with the atomic-scale crack-tip behaviors/mechanisms. The crack-tip behaviors are relevant to the energy state of atoms in the system. Atomic thermal oscillation increases with increasing temperature, which may affect/alter the crack tip behaviors. This work is the first to investigate the temperature-dependent crack-tip cleavage/dislocation emitting competition in magnesium (Mg) using anisotropic linear elastic fracture mechanics theory, Density Functional Theory (DFT), and atomic simulation. Crack-tip behaviors are examined using a specially designed ‘K-field’ loads model. DFT calculations show that a single crystal system with lower entropy and higher Gibbs free energy implies stronger interatomic bonding that favors a higher KIc. Changes in the stress distribution initiate a brittle-ductile transition in crack-tip behavior. The ductile crack tip can be blunted by continuous crack-tip dislocations nucleation/slip, and the evolution of the ductile crack-tip geometry from sharp to semicircular structure significantly decreases the stress concentration at the crack tip. A new criterion of the crack-tip force vector is established, which reasonably explains the geometrical evolution of ductile crack tip where the angle θ between the crack plane and the slip plane is 0∘<θ<90∘ and θ=90∘. This work expands the atomic-scale brittle/ductile crack-tip behaviors/mechanisms of Mg, which provides a reference for crack-tip behavior analysis in engineering research.

Analytical model and stress behavior of consolidated load bearing geotextile tubes

Accurately predicting stress-strain characteristics is crucial to ensuring the regulated capacity and controlled deformation of the tubes during and after construction. However, research on the shear strength of geotextile tubes under surcharge loading, especially after dewatering, is insufficient. This study proposes an analytical model with a Stress-State Boundary (SSB) and Yield Function to comprehensively describe the stress-strain behavior of Load-Bearing Geotextile Tubes (LGTs). The SSB is designed to predict the initial state of stress in the infill soil prior to load application, while the Yield Function is formulated to express the shear stress path experienced by the LGT before fabric failure. The model considers various factors that affect LGT behavior, including diverse soil mechanical parameters, nonlinear fabric stiffness, initial tension due to self-weight and principal stress axes rotation. Results show that a decrease in Poisson's ratio corresponds to an increase in failure stress. Moreover, it was demonstrated that the axial failure strain can be influenced by the geotextile linear or nonlinear behavior. Notably, the study highlights that tube height and inclination angle significantly affect the geotextile's confining effect. Beyond theoretical contributions, the analytical model serves as a valuable tool for optimizing geotextile tube design and execution, contributing to project success and longevity through enhanced structural stability.

A review of resistance–capacitance thermal network model in urban building energy simulations

With the increasing population and urbanization promotion, energy consumption and carbon emissions have increased, and concern for inefficient energy use and deteriorating urban environments is growing. The prediction of building energy consumption and environmental evaluation, especially at large scales, are considered to be a major challenge confronting the research community. An outstanding strategy for mitigating energy consumption and carbon emissions resides in the field of energy modeling. As a simplified building energy modeling model, resistance–capacitance (RC) network model has the applications in fast predicting building energy consumption. Notably, in recent years, there has been an evident absence of thorough review endeavors related to RC model for urban building energy loads and climate. This review systematically explores the application of low-order models and reduction methods for urban or regional energy simulation starting from the typical RC model for building. In summation, the challenges associated with employing this model for urban building energy loads and climate can be succinctly summarized as follows: the absence of a unified platform for RC modeling; insufficient readily applicable urban datasets; the need for modeling tools that facilitate cross-platform analyses in conjunction with existing Geographic Information Systems (GIS) and urban thermal environment simulation research platforms, and the essential requirement for seamless integration with other complementary modules.

Factors affecting Kaposi's sarcoma-associated herpesvirus transmission in rural Ugandan households, a longitudinal study.

We report the impact of HIV infection within a household on oral Kaposi's sarcoma-associated herpesvirus (KSHV) shedding. We enrolled 469 individuals from 90 households. Mouthwash rinse samples collected at three monthly visits, were analyzed for KSHV DNA using quantitative polymerase chain reaction (qPCR). Generalized linear mixed effects logistic models were applied to analyze factors associated with KSHV ever shedding, and among shedders, always versus intermittent shedding. Linear mixed effects models were applied to models of KSHV viral loads. Intraclass correlation coefficients (ICCs) were calculated to assess the contribution of household-level factors to variations in shedding probabilities. Hotspot analyses of geospatial feature clusters were calculated using Getis-Ord Gi* statistic and visualized using inverse distance weighted interpolation. Analyses included 340 KSHV seropositive individuals, aged 3 + years, with qPCR results from 89 households. Forty households had 1 + persons living with HIV (PLWH), while 49 had none. Among participants, 149(44%) were KSHV ever shedders. Of 140 who shed KSHV at two or more visits, 34(24%) were always shedders. Increasing number of KSHV seropositive household members was significantly associated with ever shedding [Odds ratio(OR) (95% Confidence Interval(95%CI)):1.14(1.03,1.26);p = 0.013]. Among KSHV shedders, a statistically significant age-related trend was identified with 10-19 years being more likely to be always shedders (type III test p = 0.039) and to have higher viral loads (type III test p = 0.027). In addition, higher viral loads were significantly associated with increasing number of household members [coefficient(95%CI):0.06(0.01,0.12);p = 0.042], increasing number of KSHV seropositive members [coefficient(95%CI):0.08(0.01,0.15);p = 0.021], and living in households with 1 + PLWH [coefficient(95%CI):0.51(0.04,0.98);p = 0.033]. Always shedders exhibited higher viral loads than intermittent shedders [coefficient(95%CI):1.62(1.19,2.05);p < 0.001], and viral loads increased with the number of visits where KSHV DNA was detected in saliva (type III test p < 0.001). Household-level factors attributed for 19% of the variability in KSHV shedding (ICC:0.191;p = 0.010). Geospatial analysis indicated overlapping hotspots of households with more KSHV seropositive individuals and KSHV shedders, distinct from areas where PLWH were clustered. KSHV oral shedding is influenced by multiple factors at the individual, household, and regional levels. To mitigate ongoing KSHV transmission a comprehensive understanding of factors contributing to oral KSHV reactivation and transmission within households is needed.