Load Variations Research Articles

CALCULATION OF STATIC DEFORMATION OF AXISYMMETRIC SHELLS OF ROTATION WITH DIFFERENTIAL MODEL

In the paper differential equations of static geometrically nonlinear deformation of axisymmetric shell of rotation are obtained. The resolving functions are projections of vectors in the global coordinate system. The equations allow describing any geometry of meridian (breaks, curvature jumps), large deformations, changing of shell thicknesses during deformation, also cross shears characteristic for thick shells. For the numerical solution, the approach based on the finite difference method is applied, which is realized in the own software package for the calculation of the mechanics of spatial rod systems – DARSYS. The calculations of test problems of the internal pressure inflation of cylindrical, spherical, elliptical, conical shells, as well as a combined conicalcylindrical shell with a meridian break are presented. Graphs of convergence of displacements at the reference points as a function of mesh density and under load variation are given, and deformed meridian configurations are plotted. The solutions obtained in ANSYS by different finite elements of Shell type were used as a reference for comparison. APDL scripts for parametric calculations of the test problems are given in the text of the paper. The proposed approach to the calculation of static deformation of shells of rotation has shown good agreement with finite element modeling in ANSYS (including thick shells) and in the future will be extended to the modeling of dynamic deformation and the possibility of solving coupled problems of interaction with liquid or gas. The given equations of the axisymmetric shell are a special case of the general equations, the development and application of which are beyond the scope of this paper, and the obtained solution results are the first stage of testing the developed complex approach to the calculation of static and dynamic deformation of shells, alternative to finite-element modeling.

MODIFIED FUZZY SPEED CONTROLLER OF INDUCTION MOTOR DRIVE USING EXTENDED KALMAN FILTERING

This paper focuses on application of fuzzy logic to enhance induction motor drive performance in case where the measured stator currents are distorted by Gaussian noise. For different load torques, the fixed proportional-integral (PI) speed controller provides an undesired drive performance for both transient and steady-state responses. At first, load torque is computed thanks to extended-Kalman-filtered stator currents. Next, load magnitude is employed to adjust the proportional gain and integral constant time of the fuzzy logic (FL) proportional-integral (PI) speed controller. Simulations of drive using two controllers: FL-PI and fixed PI ones, are carried out in different cases of stator current noise and load variation. Performance evaluations indicate that the FL-PI speed controller reduces the assessed indices and increases robustness to noise and load variation.

Dynamic simulation analysis of aircraft engine planetary gear system

The Geared Turbofan (GTF) engine is used for aircraft propulsion with fan drive, characterized by its complex structure and operation in challenging environmental conditions. During its operation, variations in gear meshing and loads on the transmission shaft exacerbate vibration within the transmission system, leading to a decline in engine performance. This paper established three-dimensional solid models of the X-type aviation engine planetary gear system, input shaft, and output shaft, and employed ADAMS for multibody dynamics analysis, achieving simulation of engine operational vibrations. Modal and harmonic response analyses were conducted using ANSYS to observe and analyze critical parameters such as vibration displacement, vibration acceleration, gear meshing forces, modal resonance, and harmonic resonance, evaluating the impact of vibration amplitude, frequency, and their effects on system performance and longevity, providing guidance for vibration control and optimization of aviation engine systems.

Order Reduction Adaptive Current Tracking Control with Proportional–Integral-Type Filter for MAGLEV Applications

The proposed adaptive controller robustly tracks the coil current to its desired trajectory, regulating the target position of the magnetic levitation (MAGLEV) systems under the modeling errors and load variations. This paper makes the following two contributions. First, the order reduction proportional–integral filter replaces the conventional low-pass filter for the current measurement without the signal distortions in the high-frequency operation modes. Second, the proposed technique consists of the proportional-type feedback control and parameter adaptation mechanism estimating the DC component of the uncertain disturbances, involving only one design parameter. The practical advantages of the proposed technique are validated by the numerical simulations based on MATLAB/Simulink (ver. 2017b) considering the nonlinear dynamics of the MAGLEV systems and modeling errors.

DEFT: A Dynamic Environmental Filtering and Thresholding Algorithm for Adaptive Headlamp Control Using Ride Height Sensors

DEFT (Dynamic Environmental Filtering and Thresholding) algorithm is proposed to optimize vehicle height control and improve the performance of Adaptive Headlamp Systems. The DEFT algorithm is designed to enhance the reliability and stability of headlamp control systems by dynamically adapting to various driving conditions and environmental changes in real time. To detect and stabilize irregular fluctuations in vehicle height due to load variations, this study integratesreal-time data acquisition based on Hall sensors, dynamic boundary setting using linear interpolation, and signal stabilization by applying Kalman and Median filters with hysteresis. These components work together to suppress control signal instability caused by noise and abnormal signals, thereby reinforcing the reliability and consistency of vehicle height control. In particular, the hysteresis function reduces unnecessary signal fluctuations near threshold values, which not only extends the control system’s lifespan but also ensures stable operation. Experimental results demonstrate that the DEFT algorithm overcomes the limitations of conventional variable-resistance sensors and significantly enhances adaptive headlamp control performance under various driving conditions. This study presents a high-reliability solution for real-time vehicle height adjustment within ADASs (Advanced Driver Assistance Systems) and demonstrates the potential for application in diverse vehicle control systems.

An efficient design methodology for a tri-state multiplier circuit in carbon nanotube technology

Abstract This study delves into the computational aspects of ternary logic and the use of carbon nanotube field-effect transistors (CNTFETs) to develop an energy-efficient and robust ternary multiplier (TMUL). Leveraging the exceptional qualities of CNTFETs, such as balanced electron and hole mobility and easy modulation of threshold voltage, the research aims to achieve the desired designs. An innovative design method is employed, recommending a reduced count of logic gates for achieving necessary logic levels. These gates are then utilized to manage the activation and deactivation of the primary transistors within the TMUL cell to convey the intended logics to the outputs. Moreover, the suggested design is focused on a single-V DD , enhancing compatibility with the goals of a multi-valued logic platform. The proposed circuit is validated using Synopsis HSPICE simulator and Stanford’s standard 32-nm CNTFET model file. Comparative analysis with existing TMUL designs demonstrates a 25.43% decrease in average power consumption, a 42.24% reduction in power-delay product (PDP), and a 24.69% decrease in energy-delay product (EDP). The design undergoes thorough simulations under various conditions including load variations and process, voltage, and temperature (PVT) fluctuations to confirm its reliability and robustness.

Defining within-host SARS-CoV-2 RNA viral load kinetics during acute COVID-19 infection within different respiratory compartments and their respective associations with host infectiousness: a protocol for a systematic review and meta-analysis

IntroductionUnderstanding how RNA viral load changes (viral load kinetics) during acute infection in SARS-CoV-2 can help to identify when and which patients are most infectious. We seek to summarise existing...

Self-adaptability parallel active filter to load variations study

Self-adaptability parallel active filter to load variations study

Flexible regulation of airport air-conditioning systems: Impact of cooling load variations on temperature stability

The integration of renewable energy sources has led to notable supply-demand imbalances due to their intermittent nature. Air-conditioning systems, as significant energy consumers with considerable flexibility, play a crucial role in integrating renewable energy and regulating power systems. This paper delves into the capability of air-conditioning systems in airports to flexibly adjust, particularly examining how varying cooling loads impact indoor temperatures. The energy flexibility of the air-conditioning system is evaluated based on three fundamental cooling load variations (incremental, translational, and discrete), and several evaluation parameters are defined. For areas with smaller cooling load fluctuations, there is a greater adjustment margin, but long adjustment periods should be avoided. For areas with larger fluctuations, the original cooling load curve should be considered in the adjustment strategy. The simulation results show that advancing or delaying the supply by 1 hour during peak cooling load periods can ensure indoor temperature stability, and step-type cooling load curves do not adversely affect indoor comfort levels. Practical case studies further demonstrate that utilizing water storage as an active regulation strategy can reduce operational costs by 26%, and with flexible adjustments, this reduction can increase to 35-54%. The study offers a framework to optimize air-conditioning flexibility in airports, facilitating better renewable energy integration and grid stability, which holds significant potential to reduce costs and improve energy efficiency, thus contributing to sustainable energy management.

Experimental study of hydrodynamic effect of loading and longitudinal variations of the center of gravity on the performance of a single-step craft in calm water

Performance of stepped planing vessels is notably influenced by loading and longitudinal position of the center of gravity. These factors directly affect trim angle, rise, total resistance due to motion, and overall hydrodynamic efficiency. This study experimentally investigates a 2.5 m Fridsma series model with 20° deadrise angle, length-to-width ratio of 5, step distance of 28% of the overall length from the transom, and step height of 4% of the model's width. It involves testing the model with two different weights: 63 and 76.6 kg, corresponding to load coefficients of CΔ=0.5 and CΔ=0.61. Center of gravity is placed at 0.8 m (32%), 0.85 m (34%), and 0.9 m (36%) from the transom. Experiments are conducted at speeds of 1, 2, 3, 4, 5, 6.5, and 8 m/s, which correspond to beam Froude numbers of 0.45, 0.9, 1.35, 1.8, 2.26, 2.9, and 3.6, respectively. The results indicate that increasing the load from 63 to 76.6 kg leads to approximately 12% increase in static trim across all center of gravity positions. The final trim is shown to increase by around 15% at all tested speeds. Additionally, load variations result in 20% rise in the rise up and total resistance. The model achieves optimal performance with load coefficient CΔ=0.61 and center of gravity at 36% of the overall length from the transom, showing the lowest resistance to weight ratio. Overall, the findings reveal that optimizing internal load distribution and LCG placement can enhance the boat's hydrodynamic efficiency, resulting in improved speed, stability, and fuel economy under different operational conditions.

Load adaptive multi-threshold scheduling to concurrently improvise transmission time and wait time performance of C-RAN

Low-latency requirements of 5G networks are one of the fundamental needs to support delay-sensitive applications. Uplink scheduling mechanism at wired midhaul of Centralized Radio Access Network (C-RAN) architecture significantly affects the transmission speed and delay performance of the network. Inadequate performance of conventional scheduling algorithms necessitates improvements to attain these requirements. In this paper, load adaptive scheduling with single threshold (LAS-ST) and load adaptive scheduling with multi-threshold (LAS-MT) algorithms on C-RAN architecture are proposed to reduce transmission time and wait time parameters concurrently. The proposed algorithms have been compared with two conventional scheduling algorithms, multi-scheduling domain (MSD) & single scheduling domain (SSD) and applied at fronthaul and midhaul of C-RAN for its comprehensive analysis. MSD and SSD are widely used in Ethernet Passive Optical Network (EPON) architectures. The results confirm that the proposed multi-threshold scheduling algorithm performs best in meeting the objectives of the studycompared to the existing scheduling algorithms in terms of transmission time and wait time by adapting to conditions. Further, resource utilization and resource availability are observed for several transmission cycles (TCs). In addition, data load variation and variation in number of available resources is analysed and its effect on transmission time and wait time is studied.

Spatiotemporal Variation in Aerosol Optical Depth and Its Potential Effects on Snowmelt in High Mountain Asia from 2004 to 2023

Light-absorbing particles, which are vital components of aerosols, can cause significant snow albedo darkening and accelerate melting. However, restricted by the poor quality of remote sensing-based aerosol products in High Mountain Asia (HMA), previous studies have seldom reported the long-term pattern of aerosols. In this study, we analyzed the spatial and temporal distribution characteristics of AOD in HMA and surrounding areas using Moderate Resolution Imaging Spectroradiometer and Ozone Monitoring Instrument data from 2004 to 2023. The Mann‒Kendall test was applied to analyze the temporal trend and abrupt changes in AOD, while Rotated Empirical Orthogonal Function was used to identify subregions and investigate spatiotemporal variations. Moreover, random forest and XGBoost-Shap models were employed to quantify the contributions of the aerosols to changes in snow albedo and melting. The results indicate that the annual (monthly) average highest and lowest AOD occurred in 2021 (April) and 2022 (September) between 2004 and 2023, respectively. The AOD first increased and then decreased during our study period and an abrupt decline was detected in 2013. The REOF model revealed three regions in HMA (northern, southwestern, and southeastern parts) with strong variations in AOD load, which are strongly correlated with atmospheric circulation and monsoon driving. Specifically, REOF1, REOF2, and REOF3 are primarily associated with frequent dust events during springtime atmospheric circulation and anthropogenic emission transport during the monsoon season. Aerosol types were divided into four types, BC aerosol, DUST aerosol, MIX aerosol, and clean conditions, whose proportions were 16.7%, 16.1%, 6.6%, and 60.6%, respectively. The clean conditions constituted the main aerosol type in the region. The AOD notably decreased snow albedo (17.8%) and increased snowmelt (9.0%); we highlight the contribution of AOD to the intensification of snowmelt. These results could provide guidance for further studies on the relationship between snowmelt and AOD.

Modal analysis of PE pipeline under seabed dynamic pressure

This paper presents a modal analysis conducted under seabed dynamic pressure conditions. The polyethylene (PE) pipeline was approximated as a thin-walled long cylindrical shell. The impact of pressure on the pipeline structure was considered, the study investigated the circumferential dynamic characteristics. The natural characteristics of the structure were solved using the energy method. To validate the model, an experimental testing system was established to measure the natural frequencies of the polyethylene (PE) pipeline, capturing the first three natural frequencies and mode shapes of the structure. Additionally, we conduct a simulation analysis in COMSOL. The results showed that the discrepancies among the experimental, theoretical, and simulation results were within 6%, confirming the accuracy of the model. Building on this foundation, the simulation considered the impact of fluid-structure interaction on the natural frequencies of the cylindrical shell. Subsequently, the effects of pipe radius, pipe thickness, pipe length, variations in external load, and dimensionless parameters on the modal characteristics of the structure were further investigated. The comparison of simulation and theoretical results showed errors within 5%, further validating the reliability of the simulation outcomes.

Monte Carlo simulation of community microgrid operation: Business prospects in the Brazilian regulatory framework

Microgrids offer an innovative solution to modern energy challenges, providing enhanced reliability and resilience to distribution systems while accommodating the growing presence of distributed energy resources (DER). However, realizing the potential benefits of microgrids requires a comprehensive assessment of their complex technical and economic aspects. This paper introduces a dedicated techno-economic evaluation model for community microgrids (CMGs) while considering a consumer-centric internal market. Uncertainties arising from load variations and intermittent generation are addressed via Monte Carlo simulation (MCS). The proposed internal market model aligns with the Brazilian regulatory framework and enables currently unavailable innovations, such as a community internal market; however, the model can be adapted to the context of any system. We demonstrate the capabilities of the methodology by presenting a case study involving a real microgrid, where economic benefits are shared among all consumers. This study, derived from a research and development (R&D) project, underscores its practical relevance in advancing cleaner and more sustainable energy systems.

Valid time shifting ensemble Kalman filter (VTS-EnKF) for dust storm forecasting

Abstract. Dust storms pose significant risks to health and property, necessitating accurate forecasting for preventive measures. Despite advancements, dust models grapple with uncertainties arising from emission and transport processes. Data assimilation addresses these by integrating observations to rectify model error, enhancing forecast precision. The ensemble Kalman filter (EnKF) is a widely used assimilation algorithm that effectively optimize model states, particularly in terms of intensity adjustment. However, the EnKF's efficacy is challenged by position errors between modeled and observed dust features, especially under substantial position errors. This study introduces the valid time shifting ensemble Kalman filter (VTS-EnKF), which combines stochastic EnKF with a valid time shifting mechanism. By recruiting additional ensemble members from neighboring valid times, this method not only accommodates variations in dust load but also explicitly accounts for positional uncertainties. Consequently, the enlarged ensemble better represents both the intensity and positional errors, thereby optimizing the utilization of observational data. The proposed VTS-EnKF was evaluated against two severe dust storm cases from spring 2021, demonstrating that position errors notably deteriorated forecast performance in terms of root mean square error (RMSE) and normalized mean bias (NMB), impeding the EnKF's effective assimilation. Conversely, the VTS-EnKF improved both the analysis and forecast accuracy compared to the conventional EnKF. Additionally, to provide a more rigorous assessment of its performance, experiments were conducted using fewer ensemble members and different time intervals.

Four Quadrant Operation of Induction Motor Using H-Bridge in Harsh and Noxious Environment Field with Dust, Voltage Fluctuation and Major Load Variations

The investigation and findings show that the H Bridge variable frequency drive is the optimum choice for resolving intrinsic motor problems and energy conservation. Variable frequency drives outperform other strategies like soft starters because they offer numerous benefits over other techniques, including greater versatility and control over the motor's starting and stopping capabilities. Dynamic torque control, overload protection, and power reduction during idle are other essential aspects of variable frequency drives. Conserving energy to the greatest extent possible is one strategy to avoid energy shortages. It has been reported that 75% of the electrical energy generated globally is used by motors. It has been noted that the main problem with motors is that they use a lot of current to start. As a result, high inrush current produces excessive heat, reducing the lifespan of electrical equipment and using more electricity. Reducing the initial inrush current to a manageable level is advised. By using an induction motor and the H Bridge, this current can be decreased. H Bridge uses a technique known as variable speed drive, which is often referred to as variable torque drive, to control the speed and torque of AC induction motors. Pulse width modulation is used to monitor the motor's voltage and torque in this occurrence. Although there are several methods to reduce the motor's current, such as by employing soft starts, variable torque drives are more advantageous. The strong starting torque and flexible speed control of DC motors make them useful in a range of applications. However, because of the cost and commutation, there are a few significant problems. As a result, the PWM-powered H bridge inverter extends the life of machinery and provides electricity to electrical appliances in response to demand. The fact that it is an energy-saving gadget, however, is by far its greatest advantage. This is especially important in a country like India, where the energy crisis has halted economic growth.

Fractional order pole fixed second order generalized integrator based control for grid connected solar photovoltaic system

AbstractIn this article, the development of multi‐functional grid connected solar photovoltaic (PV) system using improved Fractional order Control theory‐based Pole fixed Second Order Generalized Integrator (FO‐PFSOGI) is proposed. The fractional order (FO) control technique offers an advantage to adjust the fixed structure of the integer order and provide additional degree of freedom to achieve accurate response during the system operation. The FO‐PFSOGI control technique is used to extricate fundamental constituent of the non‐sinusoidal load current. The PV system is competent of feeding the local load requisite and may also inject surplus power into the grid. The voltage source converter (VSC) utilized in the grid‐connected system can also be operated as power quality compensator, taking care of harmonics, unbalancing and excess reactive power demand of the local load. The feasibility of multifunctional operations of the converter is established in this article, thus achieving maximum utilization of the power electronics used in the system and reducing the overall cost. A performance comparison of the developed control technique is presented with the conventional techniques in terms of harmonic compensation, weight convergence and computational complexity. The implementation of the controller is modest and extracts fundamental quantities without any phase delay under different loading conditions. The developed system is validated using both simulation and experimental study. A scaled down experimental setup of grid‐connected PV system is implemented in the laboratory and real time performance of the FO‐PFSOGI is demonstrated using variations in system load and PV irradiance.

A power grid topology detection method based on edge graph attention neural network

Against the backdrop of growing power demand and market liberalization, operators optimize power transmission and system load balance through network reconfiguration by switching lines. However, the complex node connections and dynamic load variations exceed the capabilities of traditional algorithms, and data noise may cause detection errors, affecting grid dispatch and stability. To address this issue, a solution is proposed based on the Edge Graph Attention Neural Network (EGAT) model, providing an in-depth analysis of the reconfigured grid structure. The model employs a multi-head attention mechanism to integrate node and edge features from different layers, enhancing feature fusion and extracting critical topological information, thereby improving the accuracy and robustness of grid topology detection after reconfiguration. The method was tested on IEEE 14-bus, IEEE 39-bus, and IEEE 118-bus systems, achieving detection accuracies of 92.30 %, 90.14 %, and 87.25 %, respectively, significantly outperforming other neural network methods. However, the complexity of the model may result in additional computational overhead.

Design and validation of an electroencephalogram‐supported approach to tracking real‐time cognitive load variations for adaptive video‐based learning

Cognitive load is a critical internal state associated with learners' learning process and significantly influences learning outcomes. With the worldwide popularity of video‐based learning (VBL), tracking real‐time cognitive load variations becomes more and more important for the timely provision of adaptive learning support during the learning process. This study proposed and validated an electroencephalogram (EEG)‐supported approach to tracking real‐time cognitive load variations during continuous VBL. We recruited 108 healthy adult participants to watch a specially designed video lecture with a sequence of interconnected slides of equal length. EEG signals were continuously recorded throughout the session. The video lecture was designed with varying levels of content difficulty (ie, rated from 1 to 5) across slides and was narrated at three different speeds (ie, slow, normal and fast) to induce cognitive load variations. For each slide, the cognitive load was quantified using both subjective ratings (ie, self‐reported difficulty) and an EEG‐derived measure (ie, alpha power). Through linear mixed model analysis, we demonstrated the feasibility of using alpha power to track real‐time cognitive load variations during the continuous VBL process after controlling the effect of mental fatigue. This study provides a foundation for developing learning enhancement technologies that enable the timely provision of adaptive learning support in VBL. Practitioner notesWhat is already known about this topic Video‐based learning has become a prevailing learning method for the current generation. Tracking the internal learning state of learners is essential for the timely provision of adaptive learning support during the video‐based learning process. Cognitive load is a critical aspect of internal learning state. While EEG has been proven to be valuable in assessing average cognitive load of a task, few studies have investigated the feasibility of utilizing EEG to track real‐time cognitive load variations in a task. What this paper adds An EEG‐supported approach was proposed to track real‐time cognitive load variations in video‐based learning. A high consistency was found between subjective ratings and EEG‐derived measure of cognitive load. The presence of mental fatigue exerted a significant impact on EEG‐derived measure of cognitive load. Implications for practice and/or policy Generative AI can be leveraged to facilitate mass production of lectures required in the approach. Real‐time tracking of cognitive load variations in video‐based learning enables the timely provision of adaptive learning supports. Additional research is warranted to mitigate the effect of mental fatigue on real‐time tracking of cognitive load variations.

A Comprehensive Study of Voltage Swell and Sag in Power Distribution Systems: Characteristics, Causes, Effects, and Mitigation Strategies

Voltage variations, such as swells and sags, are prevalent disturbances in power distribution systems, posing significant challenges to system reliability and equipment performance. This research paper provides a comprehensive study of voltage swell and sag phenomena, encompassing their characteristics, causes, effects, mitigation strategies, and case studies. The study explores the magnitude, duration, and frequency of voltage variations, delving into the underlying factors contributing to their occurrence, including switching operations, faults, and load variations. Moreover, the paper examines the detrimental effects of voltage variations on electrical equipment and industrial processes, highlighting the importance of understanding and mitigating these disturbances. Various mitigation techniques, such as voltage regulation, uninterruptible power supplies (UPS), and dynamic voltage restorers (DVRs), are evaluated for their effectiveness in minimizing the impact of voltage variations. Additionally, the paper explores the use of PWM-based Distribution Static Synchronous Compensator (DSTATCOM) as a promising solution for mitigating voltage swell and sag issues, discussing principles, design considerations, control strategies, and performance evaluation. Through case studies, simulation results, and modeling approaches using MATLAB/Simulink, the research paper offers valuable insights into the complex landscape of voltage swell and sag in power distribution systems, equipping researchers and practitioners with knowledge to address these challenges effectively.