Variable Load Research Articles

Complex High‐Cyclic Loading in an Accumulation Model for Sand

ABSTRACTExperimental evidence indicates that multidimensional cyclic loading of soils causes larger accumulation of deformations than equivalent one‐dimensional loading. The response of sand to high‐cyclic loading with 10,000 cycles and up to four‐dimensional stress paths (i.e., four independent oscillating components) is examined in 120 triaxial and hollow cylinder tests in this work to extend these findings. With increasing number of oscillating stress components, the accumulation of permanent strains tends to increase. It is demonstrated that the definition of the multidimensional strain amplitude incorporated in the high‐cycle accumulation (HCA) model can account for this. The validation of the HCA model for complex cyclic loading is complemented by the simulation of model tests on monopile foundations of offshore wind turbines subjected to multidirectional cyclic loading, for which the consideration of spatially variable cyclic loading with nonconstant load amplitudes in the HCA model is discussed. For this purpose, an extension of the HCA model considering multiple strain amplitudes is presented.

Seasonal Variability of Human Enteric Viruses Discovered in Food Production Mussels (Mytilus galloprovincialis) Farmed in the Central Adriatic Sea (Italy).

Among the different naked and quasi-enveloped viruses, the hepatitis A virus (HAV), hepatitis E virus (HEV), and norovirus genogroups I and II (NoV GI and NoV GII) are considered the main microbiological noxae involved in foodborne outbreaks. Mussels can harbor pathogens in their tissues. In addition to epidemiological attention, marine water temperature changes are considered a crucial variable influencing viral loads. This study aimed to biomolecularly screen 1775 farmed mussels (Mytilus galloprovincialis) for viral ribonucleic acid (RNA) sequence detection (belonging to the HAV, HEV, and NoV GI and GII genogroups) in three different sampling periods (spring, summer, and winter), with the mussels collected from three farms located in the Central Adriatic Sea (Italy). The results showed that 10.42% of the screened animals harbored at least one type of pathogen RNA, more specifically, 5.35% HEV, 4.51% NoV GI, and 0.56% HAV. The highest genetic equivalent (GE) amounts were majorly observed in the winter season (NoV GI 1.0 × 103 GE/g and HEV 1.0 × 102 GE/g), resulting in statistical differences when compared to summer and spring (p-value: <0.001). The original data obtained serve to bring scientific attention to the possible influence of environmental and climatic aspects on viral loads, highlighting the crucial role played by biomolecular assays as preventive medicine tools.

Synergistic Control of Active Filter and Grid Forming Inverter for Power Quality Improvement

This paper addresses the challenges and opportunities associated with integrating grid-forming inverters (GFMs) into modern power systems, particularly in the presence of nonlinear loads. Nonlinear loads introduce significant harmonic distortions in the source voltage and current, leading to reduced power factor, increased losses, and an overall reduction in system performance. To mitigate these adverse effects, active filters are employed. The objective of this study is to investigate a synergistic approach to modeling and control in integrated power systems with GFMs, focusing on enhancing power quality and grid stability by reducing harmonic distortions through the use of voltage-source active filters. This research contributes to sustainability by supporting the reliable and efficient integration of renewable energy sources, thereby reducing dependency on fossil fuels and minimizing greenhouse gas emissions. Additionally, improving power quality and system efficiency helps reduce energy waste, which is crucial for achieving sustainable energy goals. Simulations are conducted on a 1000 kW GFM connected to a grid with a nonlinear variable load, demonstrating the system’s effectiveness in adapting to dynamic conditions, reducing harmonics, and promoting a stable, resilient, and sustainable power grid.

Long short‐term memory‐based forecasting of uncertain parameters in an islanded hybrid microgrid and its energy management using improved grey wolf optimization algorithm

AbstractAn islanded hybrid AC‐DC microgrid interconnects renewable energy sources, distributed generators, and energy storage, primarily for remote areas without grid access. Its reliability depends on variable renewable output and load demand, while an energy management system optimizes power scheduling and reduces costs. In the first phase of this paper, uncertainty parameters like day‐ahead power from renewable energy sources (RES) and load demand (LD) are forecasted using the long short‐term memory (LSTM) deep learning algorithm. The LSTM outperforms the artificial neural network (ANN) model in terms of mean square error (MSE) and prediction accuracy (R2) for both training and testing datasets. In the second phase, the forecasted RES power and LD are used for optimal distributed generator (DG) scheduling using the improved grey wolf optimization (IGWO) algorithm. The objective of energy management in an islanded hybrid microgrid (HMG) is to minimize daily operating costs by considering load demand and the bidding costs of energy sources and storage devices. Two operational scenarios are evaluated to minimize the operating costs and optimize battery life. The proposed method, validated with IEEE standard test systems, is compared against several metaheuristic techniques. Results demonstrate that the improved grey wolf optimization (IGWO) algorithm is more effective at reducing costs and provides faster optimal solutions.

Applications and analyses of ammonia slip control based on precise ammonia injection technologies guided by big data

Abstract After the implementation of ultra-low emission policies, the pollutant emissions from the coal-fired power generation systems in China have been further reduced, which creates more critical requirements for the control accuracies of denitrification systems using selective catalytic reduction technology and controls of ammonia slip. This article presents big data-based technologies for controlling ammonia slip, through precise ammonia injections, which were applied and demonstrated for a flue gas denitrification system of a Chinese coal-fired power plant. Through examinations and analyses of the basic operating conditions of the unit and parameters, such as NOx emission control efficiency, ammonia injection amount of the denitrification system, and non-uniformity of NOx concentrations in the denitrification zone were compared before and after the implementation of these technologies, the outcome proves that this artificial intelligence algorithm based on big data can effectively solve the automatic control problem of denitrification system under complex working conditions such as varied unit loads, day and night operation changed coal source. In addition to the effective controls of the ammonia slip of the system, the controls of NOx emission of the system become more stabilized, creating a successful example for wider applications of these precise ammonia injection control technologies in the future. Analyses show that NOx non-uniformities can be reduced by more than 50% under both stable and variable load conditions. NOx fluctuations at the unit outlet are tightly controlled within ±10 mg/Nm3 under variable load conditions and within ±5 mg/Nm3 under stable conditions. The average ammonia injection amounts under various load conditions have decreased by 15.7%.

Metabolic cross-feeding interactions modulate the dynamic community structure in microbial fuel cell under variable organic loading wastewaters.

The efficiency of microbial fuel cells (MFCs) in industrial wastewater treatment is profoundly influenced by the microbial community, which can be disrupted by variable industrial operations. Although microbial guilds linked to MFC performance under specific conditions have been identified, comprehensive knowledge of the convergent community structure and pathways of adaptation is lacking. Here, we developed a microbe-microbe interaction genome-scale metabolic model (mmGEM) based on metabolic cross-feeding to study the adaptation of microbial communities in MFCs treating sulfide-containing wastewater from a canned-pineapple factory. The metabolic model encompassed three major microbial guilds: sulfate-reducing bacteria (SRB), methanogens (MET), and sulfide-oxidizing bacteria (SOB). Our findings revealed a shift from an SOB-dominant to MET-dominant community as organic loading rates (OLRs) increased, along with a decline in MFC performance. The mmGEM accurately predicted microbial relative abundance at low OLRs (L-OLRs) and adaptation to high OLRs (H-OLRs). The simulations revealed constraints on SOB growth under H-OLRs due to reduced sulfate-sulfide (S) cycling and acetate cross-feeding with SRB. More cross-fed metabolites from SRB were diverted to MET, facilitating their competitive dominance. Assessing cross-feeding dynamics under varying OLRs enabled the execution of practical scenario-based simulations to explore the potential impact of elevated acidity levels on SOB growth and MFC performance. This work highlights the role of metabolic cross-feeding in shaping microbial community structure in response to high OLRs. The insights gained will inform the development of effective strategies for implementing MFC technology in real-world industrial environments.

Adaptive Reactive Power Management with Thyristor-Controlled Transformer and Fixed Capacitor

The objective of this article is to develop and analyse a thyristor-controlled transformer with a fixed capacitor for reactive power compensation in power systems. Reactive power compensation is crucial for enhancing the efficiency and stability of power systems by reducing power losses, improving voltage profiles, and minimizing equipment stress. Traditional compensation methods often rely on fixed capacitors, reactors, or static VAR compensators, but these systems lack the flexibility required for dynamic control of reactive power under varying load conditions. The proposed approach integrates a thyristor-controlled transformer with fixed capacitors, allowing for precise, real-time adjustment of reactive power flow. The novelty of this article lies in the hybrid configuration of the thyristor-controlled transformer and fixed capacitor, which provides a cost-effective and robust solution compared to conventional systems. Unlike traditional methods that depend solely on switching capacitors or reactors, the use of thyristors allows for fine-tuning of reactive power, offering improved performance under variable loading conditions without the need for complex control algorithms. This setup enhances the adaptability of reactive power management, thus maintaining optimal power factor and voltage regulation. The findings from the simulation and experimental results demonstrate significant improvements in power factor correction, voltage stabilization, and reduction in harmonic distortion. The proposed system exhibits a faster response time and greater control accuracy compared to existing compensation techniques. These advantages make the thyristor-controlled transformer with a fixed capacitor a promising alternative for power utilities seeking to enhance the operational efficiency and reliability of their networks. This article contributes to the advancement of reactive power compensation technologies, providing a scalable solution suitable for modern power system.

Development of near-optimal advanced control sequences for chiller plants with water-side economizers in U.S. Climates (ASHRAE RP-1661)

Various advanced control sequences for chiller plants with water-side economizers (WSE) have been proposed in literature, but the evaluation and optimization of those controls is limited. It is possible to maximize energy savings by selecting different sequences and related parameters based on the plant configuration, load, and climate. This paper addresses this gap by developing near-optimal advanced control sequences for chiller plants with WSEs. First, advanced control sequences for chiller plants with WSEs are categorized into condenser water, chilled water, and hybrid controls and representative sequences from each category are identified. Next, 504 different scenarios are optimized. These scenarios represent all possible combinations of two plant configurations, a constant or variable load profile, three advanced control sequences, and seven optimization parameter combinations in six climate zones. The results show the recommended near-optimal sequences can reduce energy consumption by up to 15% relative to the baseline depending on the configuration, load profile, and climate. Specifically, the CW-CHW sequence is recommended for the majority of systems because it is often the most energy efficient and/or reduces the runtime of chillers. The methodology in this paper provides practical guidance for achieving energy savings through near-optimal control of chiller plants with WSEs.

Study on control strategies of the MW-scale supercritical CO2 recompression Brayton cycle for lead-cooled fast reactor

The supercritical CO2 (sCO2) Brayton cycle for the lead-cooled fast reactor (LFR) is an ideal power generation system for the distributed energy supply in remote islands. To figure out the optimal operating modes of the MW-scale sCO2 LFR unit in different load demands, this work performs a dynamic simulation based on the developed control modules. A parametric analysis is conducted about the effects of rotational speed, split ratio, and main gas temperature on the dynamic characteristics. Control strategies are further studied based on the thermodynamic performance and safety under four variable temperature load regulation modes and three fixed temperature rapid load regulation modes. Results show that the tracking αopt mode has the highest average net efficiency (ηnetav) of 19.13 %. The tracking αsafe mode allows compressors to operate in the safest range by sacrificing a little ηnetav. Excellent load change rates are revealed by using the main circuit regulating valve control mode and the turbine bypass control mode, with −12.48 %Pe/min & 11.92 %Pe/min, and −8.25 %Pe/min & 9.70 %Pe/min, respectively. Though the load change rate of the RS control mode is −3.96 %Pe/min & 3.47 %Pe/min, its ηnetav is higher. Our work guides the efficient, flexible, and safe operation of the sCO2 LFR unit.

Research and clearance analysis on of steam twin-screw expander employed in indutrial waste heat recovery

In actual factories, screw expanders frequently operate under partial loads.To address the lack of theoretical research and the risks of errors in engineering design associated with steam pressure differential power generation under these conditions,a simulation of the twin-screw expander's working process was developed. The rotor profile was designed, geometric variables such as rotor mesh clearance were calculated, and models for leakage and heat transfer were established. A suction pressure correction model was proposed for the suction process under partial loads, along with correction parameters to simplify simulations.Experimental results demonstrate that the twin-screw expander operates stably under highly fluctuating conditions, demonstrating excellent variable load performance.As the load decreases, the loss of suction pressure reduces the expander's pressure differential, leading to lower leakage and improved efficiency. When the load is reduced from 100 % to 65 %, the volumetric efficiency increases from 78.87 % to 84.76 %, and the isentropic efficiency rises from 66.81 % to 74.25 %.Compared to experimental data, the flow calculation error of the suction pressure correction model presented in this study is controlled within 3 %.This model addresses performance calculations under partial loads, ensuring that theoretical models better align with real-world applications and supporting accurate selection of twin-screw expanders.This model enables appropriate selection of expanders to reduce pressure losses and enhance economic efficiency.

Numerical Simulation for Durability of a Viscoelastic Polymer Material Subjected to Variable Loadings Fatigue Based on Entropy Damage Criterion.

This study aims to explore the impact of load history on the premature failure of the viscoelastic polymer matrix in carbon-fiber-reinforced plastics (CFRPs) using a method based on the concept of fracture fatigue entropy (FFE). A user-defined subroutine (UMAT) developed by the authors in previous studies was incorporated to apply the FFE damage criterion using ABAQUS software. Several variable-amplitude load modes, including frequent load amplitude changes and intermittent interruptions, were designed based on the conventional linear damage accumulation method (Palmgren-Miner rule), and the fatigue life under these loadings was obtained via numerical simulations. The results show that both frequent amplitude changes and even brief pauses in loading can accelerate damage accumulation, leading to premature failure of the polymer matrix. In these scenarios, the fatigue life ranged from 33.6% to 91.9% of the predictions made using the Palmgren-Miner rule, which shows significant variation and highlights cases in which the predicted fatigue life falls far short of expectations. This study offers a more practical and reliable approach for predicting fatigue life under complex loading conditions. Since the accuracy of the FFE criterion has been comprehensively validated in previous studies, this research focuses on its application to predict failure under variable loading conditions.

Hysteresis Model of RC Column Considering Cumulative Damage Effect under Variable Axial Load

Hysteresis Model of RC Column Considering Cumulative Damage Effect under Variable Axial Load

Remaining Useful Life of the Rolling Bearings Prediction Method Based on Transfer Learning Integrated with CNN-GRU-MHA

To accurately predict the remaining useful life (RUL) of rolling bearings under limited data and fluctuating load conditions, we propose a new method for constructing health indicators (HI) and a transfer learning prediction framework, which integrates Convolutional Neural Networks (CNN), Gated Recurrent Units (GRU), and Multi-head attention (MHA). Firstly, we combined Convolutional Neural Networks (CNN) with Gated Recurrent Units (GRU) to fully extract temporal and spatial features from vibration signals. Then, the Multi-head attention mechanism (MHA) was added for weighted processing to improve the expression ability of the model. Finally, a new method for constructing Health indicators (HIs) was proposed in which the noise reduction and normalized vibration signals were taken as a HI, the L1 regularization method was added to avoid overfitting, and the model-based transfer learning method was used to realize the RUL prediction of bearings under small samples and variable load conditions. Experiments were conducted using the PHM2012 dataset from the FEMTO-ST research institute and XJTU-SY dataset. Three sets of 12 migration experiments were conducted under three different operating conditions on the PHM2012 dataset. The results show that the average RMSE of the proposed method was 0.0443, indicating high prediction accuracy under variable loads and small sample conditions. Three different operating conditions and two sets of four migration experiments were conducted on the XJTU-SY dataset, and the results show that the average RMSE of the proposed method was 0.0693, verifying the good generalization of the model under variable load conditions. In summary, the proposed HI construction method and prediction framework can effectively reduce the differences between features, with high stability and good generalizability.

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

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

Development of Assistance Level Adjustment Function for Variable Load on a Forearm-Supported Robotic Walker.

With the progression of an aging society, the importance of walking assistance technology has been increasing. The research and development of robotic walkers for individuals requiring walking support are advancing. However, there was a problem that the conventional constant support amount did not satisfy the propulsion force required for the walking speed that users wanted. In this study, in order to solve this problem, we propose an algorithm for determining the support amount to maintain the walking speed when the average walking speed of each user is set as the target speed. A robotic walker was developed by attaching BLDC motors to an actual walker, along with a control algorithm for assistance based on sampling-type PID control. The effectiveness of the assistance determination algorithm and the usefulness of the parameters were demonstrated through experiments using weights loaded on the forearm support and target speeds. Subsequently, subject experiments were conducted to verify the ability to maintain target speeds, and a questionnaire survey confirmed that the assistance did not interfere with actual walking. The proposed algorithm for determining the assistance levels demonstrated the ability to maintain target speeds and allowed for adjustments in the necessary level of assistance.

Experimental and theoretical assessment of the effects of electrical load variation on the operability of a small-scale Organic Rankine Cycle (ORC)-based unit equipped with a hermetic scroll expander

Scroll expanders are widely adopted in small-scale Organic-Rankine-Cycles-(ORCs)-units integrated with renewable energy sources for low power residential Combined Heat and Power Production (CHP). These expanders are often connected to variable direct loads without the possibility to impose its speed externally. Despite the effect of electric load variation on expander speed is known, a minor attention is focused on its impact on the plant operability and regulation. To fill this gap, a wide experimental analysis is carried out on a solar micro-cogenerative ORC-based power unit equipped with a 1 kW hermetic scroll expander connected with a variable electric load. The experimental behaviour of the ORC-unit was assessed for different values of load resistance (from 20 Ω up to 80 Ω). Results show that properly acting on the load resistance the ORC-power unit can be improved up to 50 % in terms of efficiency and power production. Moreover, if the expander is properly designed its self-regulatory capability allows to achieve similar performance of an equivalent speed-controlled expander tested on the same plant. Finally, a new theoretical model reproducing the impact of main operating quantities on expander behaviour was developed and experimentally validated paving the way to a novel model-based control approach.

Dynamic analysis for ultra-supercritical boiler water wall considering uneven heat flux distribution and combustion instability

The dynamic characteristics of water-cooled wall are of great significance to the evaluation of the safety and flexibility for the boiler during peak shaving process. However, few dynamic studies considered the uneven heat flux distribution on the water-cooled wall, let alone the variable heat load caused by the combustion instability. In this paper, a segmented dynamic model is developed for the water-cooled wall of a 660 MW ultra supercritical boiler. The model is validated under steady and dynamic working conditions, respectively. Then, the step responses of the steam temperature in different flow loops under different load conditions are comparatively studied under the disturbances of single-parameter changes. In the cases of 10 % step decrease of either the feedwater temperature, the heat load or the feedwater mass flowrate, the outlet steam temperature is influenced greatly by the step decreased feedwater temperature under 100 % BMCR condition, while it is impacted most by the step decreased feedwater mass flow under 50 % THA condition. Under these disturbances, the outlet steam temperature of the tube with higher heat loads has larger variation and longer response time. In addition, the simulated combustion instability by the sinusoidal fluctuating heat load on the water wall is employed to explore the influences of the flame fluctuation amplitude and frequency to the water wall. The maximum steam temperature rises with the increase in amplitude but drops with the frequency of flame fluctuation. The thresholds for the flame fluctuation amplitude and frequency are obtained under different load conditions.

Less is more: Optimisation of variable catalyst loading in CO[formula omitted] electroreduction

The electrochemical conversion of CO2 is a promising method of carbon-neutral chemical production. However, commercial realisation in aqueous electrolytes is challenging, due to competition with the hydrogen evolution reaction (HER), and the propensity for CO2 to participate in the carbonate equilibrium reactions. These two phenomena are linked through OH− ions, as both the by-product of the catalytic reactions and the culprit behind the parasitic carbonate reactions. By reducing the local catalyst loading where the CO2 concentration is low, the HER is decreased more than the reactions that are dependent on CO2 as a reactant. Therefore, it is possible to improve reaction selectivity and reactant utilisation while reducing the capital cost of catalyst. We demonstrate this theory through an analytical solution of a 1D flow electrolyser model. We extend this to a comprehensive model of a contemporary gas-diffusion electrode (GDE) setup. We find that the operation costs are dominated by the electrolyser power consumption and, to a lesser degree, the cost of CO2 and its recovery at the anode. We numerically obtain the catalyst loading profiles that maximise operating profit. The optimisation process reveals that profits are maximised for high gas flow rates, and consequently, low single pass conversions, where the CO2 concentration is as high as possible. However, when lower gas flow rates are used for practical reasons, variable catalyst loadings are shown to lead to significant operational improvements, especially in the production of higher C products that require a greater number of electrons transferred. The model is made freely available in MATLAB and its use is encouraged in determining the applicability of variable catalyst loading to future experimental setups.

Thermal performance of a novel hybrid heat pipe with composite heat transfer characteristics

Considering the capillary threshold of oscillating heat pipes (OHP), a novel hybrid heat pipe (HHP) was designed with channels with hydraulic diameters that alternately exceed and fall below this threshold, parallelly arrayed within a unified copper substrate. This technology attains a comprehensive heat transfer performance within the HHP, seamlessly transitioning from pure phase change heat transfer to hybrid pulsating flow heat transfer to effectively accommodate the escalating demands of heat load. Performance and visualization are used to analyze the operational characteristics of HHP. The results indicate that the HHP mainly transfers heat through the phase change in the large channels in lower heat input, while the working fluid in the small channels remains calm. Once the heat input is sufficient, the geyser boiling in the large channels and the liquid slug pulsating in the small channels jointly carry out heat transfer. Furthermore, the liquid content in the large and small channels can be adaptively adjusted to each other. The high-speed liquid in the small channels can be squeezed into the large channels to ensure the sufficient pulsating space and achieve efficient heat transfer under high heat input. In addition, the operation of the HHP can be significantly impacted by the filling ratio (FR). In the phase change stage, the FR of 47.6 % can be considered as the turning point. After transitioning to the hybrid pulsating flow stage, the HHP with low FR can get a better performance. This new type of heat pipe with gradually changing operation characteristics may be critical to solve thermal problems in variable heat load domain.

Research on typical operating conditions of hydrogen production system with off-grid wind power considering the characteristics of proton exchange membrane electrolysis cell

Hydrogen energy, with its abundant reserves, green and low-carbon characteristic, high energy density, diverse sources, and wide applications, is gradually becoming an important carrier in the global energy transformation and development. In this paper, the off-grid wind power hydrogen production system is considered as the research object, and the operating characteristics of a proton exchange membrane (PEM) electrolysis cell, including underload, overload, variable load, and start- stop are analyzed. On this basis, the characteristic extraction of wind power output data after noise reduction is carried out, and then the self-organizing mapping neural network algorithm is used for clustering to extract typical wind power output scenarios and perform weight distribution based on the statistical probability. The trend and fluctuation components are superimposed to generate the typical operating conditions of an off-grid PEM electrolytic hydrogen production system. The historical output data of an actual wind farm are used for the case study, and the results confirm the feasibility of the method proposed in this study for obtaining the typical conditions of off-grid wind power hydrogen production. The results provide a basis for studying the dynamic operation characteristics of PEM electrolytic hydrogen production systems, and the performance degradation mechanism of PEM electrolysis cells under fluctuating inputs.