Loading Sequence Research Articles

Multi-axial fatigue of high-strength concrete: Model-enabled interpretation of punch-through shear test response

This paper aims to fill the gap in multi-axial fatigue characterization of concrete by presenting a comprehensive numerical analysis of data from the punch-through shear test (PTST), which allows control over combined shear and compression loads in the tested ligament. To accurately reproduce the degradation process in this multi-axial configuration, a material model must capture two key effects: (i) dissipative behavior at the inter-aggregate level during subcritical pulsating loading and (ii) fatigue-induced tri-axial stress redistribution in the test ligament. The recently published MS1 model by the authors effectively incorporates these features, thereby enabling a deeper interpretation of the experimental data. The former effect is seamlessly integrated into the model using the Lemaitre-type fatigue hypothesis, facilitating the direct derivation of general evolution equations. The latter effect is efficiently represented through the microplane homogenization framework. The model’s predictions for PTST response under monotonic shear loading with varying levels of radial confinement align well with experimental results. Similarly, the simulated fatigue response accurately reproduces the experimentally observed S-N (Wöhler) curves. Further studies demonstrate the model’s ability to predict the effect of fatigue loading sequence. A thermodynamically-based formulation provides an energetic interpretation of this effect, offering a quantitative breakdown of energy dissipation attributed to individual dissipative mechanisms over the lifetime of the material. The studies show that damage-induced dissipation remains almost constant regardless of different fatigue loading histories.

Crack Propagation Tests for Load Sequences Developed Using Different Flight Parameters of a Trainer Aircraft

Abstract Military aircraft are subjected to highly variable and unpredictable loads due to diverse mission profiles, armament configurations, and individual piloting styles. This variability complicates the definition of precise load spectra, particularly in cases where data loss occurs due to Flight Data Recorder (FDR) malfunctions or data mishandling. This paper investigates the use of different flight parameters, such as load factor (nz ), barometric height (Hb ), and horizontal velocity (Vp ), to define load sequences for the PZL-130 “Orlik” TC-II military trainer aircraft. These sequences were then used to evaluate crack propagation using Compact Tension (CT) specimens. The results show that the incorporation of additional flight parameters improves the accuracy of crack propagation predictions when compared to direct strain measurements. This study highlights the potential of using available flight data to develop reliable load spectra for fatigue life estimation in military aircraft, even when direct load measurements are not financially feasible.

A hybrid load forecasting system based on data augmentation and ensemble learning under limited feature availability

Accurate power load forecasting is an important part of power system operation planning, it can ensure the stable operation of power systems and improve the efficiency of energy utilization. The power load is affected by many factors including temperature, season, population density, and so on, however due to privacy protection and other reasons, it is difficult to obtain some characteristic information that affects the load. The lack of characteristic data will reduce the accuracy of load forecasting and the generalization ability. To solve it, a new hybrid load forecasting framework is proposed, which is composed of two subsystems: a data preprocessing system and a high-precision forecasting system. Based on the load sequence itself, subsystem 1 obtains the trend data and denoising data by variational mode decomposition method, obtains the indicator variable for the weekend according to the one-hot encoding, and also introduces the electricity price data, thus obtaining the 4-dimensional extended data. Subsystem 2 constructs a hybrid prediction model by synthesizing various models, including deep learning and machine learning models, to forecast the expanded data. Finally, the multi-objective JAYA algorithm based on tent chaotic mapping and cross-perturbation strategy is used to ensemble the prediction results of the sub-models. To verify the superiority of the proposed forecasting framework, we conducted experiments using four sets of load data from New South Wales, Australia. The experimental results show that the average absolute percentage error of the hybrid framework on the four data sets are MAPEMarch=0.8070, MAPEJune=0.8296, MAPESeptember=0.7238 and MAPEDecember=0.7709, which are significantly lower than other models and provide a basis for power system scheduling management.

Multiple Load Forecasting of Integrated Renewable Energy System Based on TCN-FECAM-Informer

In order to solve the problem of complex coupling characteristics between multivariate load sequences and the difficulty in accurate multiple load forecasting for integrated renewable energy systems (IRESs), which include low-carbon emission renewable energy sources, in this paper, the TCN-FECAM-Informer multivariate load forecasting model is proposed. First, the maximum information coefficient (MIC) is used to correlate the multivariate loads with the weather factors to filter the appropriate features. Then, effective information of the screened features is extracted and the frequency sequence is constructed using the frequency-enhanced channel attention mechanism (FECAM)-improved temporal convolutional network (TCN). Finally, the processed feature sequences are sent to the Informer network for multivariate load forecasting. Experiments are conducted with measured load data from the IRES of Arizona State University, and the experimental results show that the TCN and FECAM can greatly improve the multivariate load prediction accuracy and, at the same time, demonstrate the superiority of the Informer network, which is dominated by the attentional mechanism, compared with recurrent neural networks in multivariate load prediction.

Development of a doubly-fed compound control for hybrid ground source heat pump systems in hot summer and cold winter areas

In hot summer and cold winter areas, the impact of soil thermal accumulation is significant even for hybrid ground source heat pump systems (HGSHPs), especially after a long-term operation. To alleviate the energy performance degradation caused by soil temperature rise, a detailed temperature field heat transfer model of ground heat exchangers (GHXs) has been established and a doubly-fed compound control for HGSHPs is developed and integrated into a central controller module. The central controller can automatically control the loop flow distribution of units and achieve group activation of GHXs based on the load sequences forecasting feedforward and system variables feedback processes. Five control modes are included in the central controller, and the energy performances and soil temperature rise distribution of the HGSHPs with different control modes for fifteen years are compared. The results indicated that the HGSHPs performs best in terms of energy performance and soil thermal alleviation with Control Unit 13. Compared to the Normal control mode, the mean water temperature of GHXs, energy consumption of the chiller and heat pump decreased by 1.6 °C, 5.9 %, and 15.5 %, respectively, and the units COP increased by 11.3 %.

Excellent properties of NaF and NaBr induced DNA/gold nanoparticle conjugation system: Better stability, shorter modified time, and higher loading capacity

The functionalization of gold nanoparticle (AuNP) is the key procedure for the biochemical and biomedical application. The conventional salt-aging method requires the stepwise additions of NaCl and excessive thiolated DNA, mainly due to the poor tolerance of the DNA/AuNP mixture toward NaCl. Herein, we found that NaF is capable of improving the stability for the modification of AuNP with different bases of DNA sequences (poly A/T/C/G), and allows for adding up with a high concentration of 200 mM at one time, which greatly reduces the total modification time to 0.5–1 h. Intriguingly, the introduction of NaBr effectively increases the DNA loading capacity. Besides the advantages of NaF and NaBr, the modification performance is improved via the introduction of the oligo A/T spacer for the G-rich DNA sequences. Furthermore, this method shows the superiority to another two methods (pH 3-based and salt-aging) in terms of the loading capacity or sequence components.

Oocytes Vitrification Using Automated Equipment Based on Microfluidic Chip.

Oocyte vitrification has a wide range of applications in assisted reproduction and fertility preservation. It requires precise cryoprotectant agents (CPAs) loading and removal sequences to alleviate osmotic shock, which requires manual manipulation by an embryologist. In this study, a microfluidic system was developed to facilitate the precise adjustment of the CPA concentration around the oocyte by linear loading and removal of CPA. In addition, the microfluidic-based automated vitrification (MAV) device combines CPA loading/removal process, with vitrification process, thereby achieving automated oocyte vitrification. Oocytes were vitrified by Cryotop/QC manual method and MAV method. The results showed that the survival, cleavage, and blastocyst rates of oocytes were 80.44, 54.17, and 32.95% for the MAV method, which were significantly higher than Cryotop manual method (73.35, 43.73, and 23.67%) (p < 0.05). In MAV, solution injection rate during CPA loading/removal process was designed as a 1-segment, 2-segment, and 4-segment function. Accordingly, three concave loading and convex removal protocols were adopted to vitrify oocytes. Oocytes vitrified using the 4-segment function group exhibited increased survival (86.18%), cleavage (63.29%), and blastocyst (45.58%) rates compared to those vitrified using the 1-segment and 2-segment groups. The oocytes vitrification with the highest concentration of CPA, denoted as VS1-TS1, exhibited the highest survival rate after rewarming (86.18%). In contrast, the VS3-TS3 group, characterized by a CPA concentration half that of VS1-TS1, exhibited lower survival (74.14%) and cleavage (59.31%) rates, but displayed the higher blastocyst rate (50.79%) following oocyte activation. Our study demonstrates potential of the MAV device for oocyte or embryo vitrification.

Extrapolation Framework and Characteristic Analysis of Load Spectrum for Agriculture General Power Machinery

As a crucial step in food production, tillage and land preparation play a pivotal role in achieving sustainable crop production and improving the soil environment. However, accurate assessment of the load that agricultural machinery implements during the operation process has always been a vexing problem that needs urgent solutions. In this paper, an extrapolation and reconstruction framework for the time-domain load is constructed based on the probability-weighted moments (PWM) estimation and the peaks-over-threshold function, and the load spectrum is obtained for agriculture general power machinery. Firstly, the load acquisition system was developed, the traction resistance and output torque of the tractor were measured, and the collected load signals were preprocessed. Next, the mean excess function and PWM estimation are introduced to select the optimal threshold and generalized Pareto distribution (GPD) fitting parameters and the extreme load distribution that exceeds the threshold range is fitted. The extreme points in the original data are replaced by generating new extreme points that follow the GPD distribution, and the extrapolation of the load spectrum is achieved. Finally, the real extrapolated load spectrum was validated based on statistical characteristics and rainflow counting analysis, and the correlation coefficient between the fitting data and the extreme load samples was greater than 0.99. It can retain the load sequence characteristics of the original load to a great extent, truly reflecting the load state during the operation of agricultural machinery. Meanwhile, the characteristics of the load spectrum can be accurately obtained, such as extreme, mean, and amplitude values, and the real load during deep loosening and rotary tillage are accurately described. The values provide more authentic and reliable data support for the subsequent selection of optimal operating parameters, reliability design of the power transmission system, and the life assessment of the agricultural implements.

A prediction method for new titanium alloy welded structure fatigue crack growth rate

ABSTRACT For the fatigue life prediction of titanium alloy welded structures used in deep-sea submersibles, a modified model considering the welding characteristics and the load sequence effect is established in this paper. Based on the modified model, the fatigue crack propagation behaviour of the new titanium alloy welded structure under different loading conditions is predicted. The comparison between the prediction results and the test data is analyzed. The prediction ability of the modified model over the conventional model is comparatively analyzed. The results show that the modified model has a strong ability to predict the fatigue crack propagation behaviour of new titanium alloy welded structures under various load spectra.

Hydrogen storage planning robust to year‐round net load fluctuation

AbstractLong‐term hydrogen storage systems are considered a solution to the long‐term supply imbalance caused by different seasonal characteristics in renewable energy output and load. However, most existing planning models assume the whole year‐round net load series is known in advance and the optimal operation sequence of long‐term storage is derived accordingly. This assumption underestimates the necessary long‐term storage capacity because of the difficulty of accurate fine‐grained long‐term forecasting. This work proposes a long‐term hydrogen storage planning framework that is robust to year‐round net load fluctuation. The daily average component from the historical net load series is first derived to formulate the long‐term operation scenario set. On this basis, a robust planning model is developed where the trend of long‐term storage operation is forced to be identical for all long‐term operation scenarios and not to respond to one specific scenario with full information of the net load sequence. The results show that the planning results given by the proposed framework are more robust in real‐time operation with less load shedding.

Experimental behaviour and fatigue life prediction of bonded joints subjected to variable amplitude fatigue

The variable amplitude fatigue behaviour of adhesive joints has not been extensively considered in literature and considerable knowledge gaps regarding its underlying mechanisms still exist, hindering accurate life prediction. This study investigates the effect of loading sequence and number of load transitions on single lap joints (SLJs) bonded with a toughened epoxy adhesive. Life prediction is also conducted using established cumulative rules: Palmgren–Miner (PM), Broutman and Sahu (BS) and, Hashin and Rotem (HR). To overcome their limitations, a modified Cortan–Dolan approach is proposed. For this, SLJs were subjected to single transition (low-to-high and high-to-low load transitions at different stages of the fatigue life and with different magnitudes) and multi-block loading spectra with different numbers of transitions.Results showed an extension of the fatigue life for high-to-low transitions. This was attributed to a combination of load interaction effects, as evidenced by an elastoplastic numerical simulation, and non-linear damage accumulation. Conversely, low-to-high transitions exhibited a damaging effect. For single transition spectra, BS and HR models were seen to improve prediction over PM by indirectly considering these effects. The proposed approach further improved this prediction. Multi-block results showed no significant influence of the number of load transitions and were satisfactorily predicted using PM.

Short-term natural gas load forecasting based on EL-VMD-Transformer-ResLSTM

Due to changes in urban residents’ consumption habits and lifestyles, accurately predicting natural gas consumption has become increasingly important. To address this issue, this paper proposes a forecasting model that combines Ensemble Learning (EL), Variational Mode Decomposition (VMD), Transformer, and LSTM. First, XGBoost, CatBoost, and LightGBM are used as base learners in the ensemble learning framework, with the predictions generated by the ensemble model integrated into the original dataset. Next, the VMD method is employed to decompose the natural gas load sequence into several intrinsic mode functions (IMFs), effectively extracting the inherent features of the natural gas load sequence. Finally, the data is input into the Transformer-ResLSTM network for prediction. This network replaces the original Transformer decoder structure with an LSTM network and fully connected layers, creating a new decoder structure. Additionally, a residual connection mechanism is introduced in both the encoder of the Transformer network and the new decoder structure. Experimental results show that, compared to traditional models such as ARIMA, Transformer, GRU, and LSTM, the proposed hybrid model significantly improves prediction accuracy, reducing MSE by 92–98% and MAE by 74–83%. In summary, this method demonstrates significant potential and practical value in enhancing the accuracy of natural gas load forecasting.

Finite element modeling of the glass sealing process for solid oxide cell stacks

The sealing process for solid oxide fuel cell (SOFC) and solid oxide electrolysis cell (SOEC) stacks is a vital step in the assembly and manufacturing of these systems. The cell seal and stack seal serve to prevent leakage and the mixing of air with hydrogen and steam during operation. Good seals are critical for reporting accurate cell and stack performance and durability, as leaks can mask degradations and manifest as performance improvement. Predictive modeling of seal materials during sealing processes for SOFC and SOEC stacks has been largely unexplored and could provide design and manufacturing insights for these systems. In this work, finite element modeling was conducted to simulate the assembly and stack sealing of planar cells to capture the densification, viscous flow, and crystallization behavior of the G18 glass ceramic seal material during full scale stack fabrication. The Skorohod-Olevsky viscous sintering material model was implemented in the ANSYS finite element program and modified to account for crystallization effects to simulate the response of the G18 seal material. The effects of initial cell/stack curvature were captured via preliminary stack manufacturing simulations, and its influence on stack sealing quality was studied. Effects of compressive loading sequence and configuration were investigated.

Ultra-short-term Load Forecasting Based on XGBoost-BiGRU

High-precision load forecasting serves as the foundation for power grid scheduling planning and safe economic operation. In scenarios where only historical power load data is available without other external information, fully exploiting meaningful features from the temporal load sequence is crucial for improving the accuracy of load forecasting. Therefore, an ultra-short-term load forecasting method that combines eXtreme gradient boosting (XGBoost) and bidirectional gated recurrent unit (BiGRU) is proposed in this paper. Considering various factors that affect loads, a candidate feature set is established, which includes temporal information and historical loads. XGBoost is used to select the features that contribute significantly to load forecasting, forming an optimal feature set. These optimal features are then used as inputs to the BiGRU, and the bayesian optimization algorithm is applied to optimize the network hyperparameters. Then the load forecasting model for the next 15 minutes based on BiGRU is generated by training iteratively. The proposed XGBoost-BiGRU method is validated on real load data from a province in China. Experimental results demonstrate that the method can effectively avoid the impact of redundant features, improving both prediction accuracy and efficiency. The research has significant importance for guiding real-time supply-demand balance calculations and scheduling in power grids.

Economic Load Forecasting based on IPSO-VMD-DBiLSTM Network

Accurate short-term load forecasting is vital for the stable operation of power systems. Given the highly volatile and nonlinear nature of load sequences, we propose a novel Deep Bidirectional Long Short-Term Memory (DBiLSTM) network incorporating Variational Mode Decomposition (VMD) and an attention mechanism to address the aforementioned challenges. In this study, the model's hyperparameters are optimized using an Improved Particle Swarm Optimization (IPSO) technique. Notably, IPSO employs a nonlinearly decreasing inertia weight to overcome the drawbacks of premature convergence and local optima.

Optimal planning for integrated electricity and heat systems using CNN-BiLSTM-Attention network forecasts

This paper investigates optimal planning of integrated electricity and heat systems (IEHS) based on convolutional neural network-bidirectional long short term memory with attention mechanism (CNN-BiLSTM-Attention) network forecasts. Firstly, CNN and BiLSTM are employed to extract the spatio-temporal features of IEHS data, and the attention mechanism automatically assigns corresponding weights to BiLSTM to distinguish the importance of different time load sequences, then a combined CNN-BiLSTM-Attention network is constructed, which allows for a more accurate load forecasting. Furthermore, we establish optimal planning strategy to improve efficient operation and energy efficiency of the IEHS, in which the upper-level planning model with the optimization objective of minimizing comprehensive operation costs is formulated, the lower-level efficiency model aiming to balance revenue and cost is considered, then genetic algorithm and iterative solution strategy of Cplex solver are applied to optimize the bi-level objective functions. Simulation results show that the proposed CNN-BiLSTM-Attention model obtained smaller RMSE, MAE and MAPE compared to several other forecasting models. In addition, the optimization method could obtain global optimal solution of the bi-level objective functions, which improves the energy utilization efficiency of the IEHS.

Effect of loading sequence on the ultra-low-cycle fatigue performance of all-steel BRBs

Buckling-restrained braces (BRBs) are intended to protect the primary structure of buildings by absorbing most earthquake input energy through the plastic deformation of the steel cores. The ultra-low-cycle fatigue performance of BRBs is crucial for their seismic performance as energy dissipators. Despite the plenty of research on the ultra-low-cycle fatigue behavior of steel members, it remains susceptible if arbitrary loading paths have a major effect on the ultra-low-cycle fatigue life or cumulative plastic deformation (CPD) capacity of BRBs. This paper introduces an experimental campaign of nine supposedly identical BRB specimens subjected to three different loading protocols of similar average strain amplitudes. For each loading protocol, the test was repeated for three times to account for possible uncertainty. The results show that the effect of loading protocol is trivial given the similar average strain amplitudes throughout the loading. The Miner's assumption that the loading sequence has no effect on the fatigue life of BRBs is supported by the test results, however, the proper choice of the Coffin-Masson model's parameters is crucial. The skeleton ratio, which is derived from decomposing the hysteresis curve into a skeleton part and a Bauschinger part and has been used to account for the loading path effects, is shown to be insensitive to varied loading protocols by the test results.

Power quality validation in micro off-grid daily load using modular differential, LSTM deep, and probability statistics models processing NWP-data

Load corrections with respect to power quality (PQ) after the first pre-estimate of Renewable Energy (RE) power consumption must ensure system-tolerant performance without malfunctions. First, acceptable daily load sequences for the attached equipment are combined and determined according to the RE potential and charge states in accommodation to user needs and normal operation. The main motivation is a consequent day-to-day verification of algorithmically scheduled power consumption tasks in the proposed two-stage optimisation according to the system resources and user needs. Statistical artificial intelligence (AI) is employed, as local atmospheric turbulences with terrain obstacles and unexpected user activity result in various operational states in real microsystems. A new unconventional neurocomputing strategy, called Differential Learning (DfL), was applied in the modelling and prediction of the high dynamical PQ parameters in an experimental RE based system according to input-output training data, without an exact specification of its behaviour. The DfL models were compared with recent deep and machine learning techniques. Prediction models were formed after an initial detection of adequate daily training intervals. The AI models are finally tested to process the complete 24-hour forecast series of related input variables used in learning, to estimate the PQ target output at the corresponding times.

OCAE-based feature extraction and cluster analysis of high-energy-consuming plant loads

Currently, in power systems and energy management, the extraction and clustering of load features play a critical role. It can support the planning, scheduling and equipment design of power systems, holding significant theoretical importance and practical benefits. Addressing the complexity of high-dimensional daily load data poses challenges in effectively capturing the intricate features of load sequence data and achieving optimal clustering outcomes. This paper proposes an optimal convolutional autoencoder (OCAE) model designed for good feature extraction. First, a large number of test comparisons demonstrate that the OCAE effectively mines deep feature data from load sequences and exhibits strong data reconstruction capabilities. Second, the OCAE model is compared with other autoencoders to validate its superior feature extraction performance. Finally, the deep feature information extracted by the OCAE encoder is utilized in the k-means clustering algorithm for cluster analysis and is quantitatively evaluated against other methods using Davies–Bouldin index (DBI) and silhouette coefficient (SC). The experimental results confirm the superiority of OCAE joint k-means clustering over improved 1D-CAE joint k-means clustering, with a reduction of approximately 12.3 % in DBI and an improvement of approximately 11.1 % in SC. The outstanding clustering outcomes achieved through OCAE joint k-means clustering have significant scientific implications for power system dispatch planning.

When wavelet decomposition meets external attention: a lightweight cloud server load prediction model

Load prediction tasks aim to predict the dynamic trend of future load based on historical performance sequences, which are crucial for cloud platforms to make timely and reasonable task scheduling. However, existing prediction models are limited while capturing complicated temporal patterns from the load sequences. Besides, the frequently adopted global weighting strategy (e.g., the self-attention mechanism) in temporal modeling schemes has quadratic computational complexity, hindering the immediate response of cloud servers in complex real-time scenarios. To address the above limitations, we propose a Wavelet decomposition-enhanced External Transformer (WETformer) to provide accurate yet efficient load prediction for cloud servers. Specifically, we first incorporate discrete wavelet transform to progressively extract long-term trends, highlighting the intrinsic attributes of temporal sequences. Then, we propose a lightweight multi-head External Attention (EA) mechanism to simultaneously consider the inter-element relationships within load sequences and the correlations across different sequences. Such an external component has linear computational complexity, mitigating the encoding redundancy prevalent and enhancing prediction efficiency. Extensive experiments conducted on Alibaba Cloud’s cluster tracking dataset demonstrate that WETformer achieves superior prediction accuracy and the shortest inference time compared to several state-of-the-art baseline methods.