Loading Sequence Research Articles

A Two-Step Approach for Evaluating the Dynamic Ultimate Load Capacity of Ship Structures

One important parameter for evaluating the safety and reliability of a ship is o the dynamic ultimate load capacity of ship structures. Because of the importance of this parameter, its determination is essential. In this paper, a novel “two-step” approach for determining the dynamic ultimate load capacity of ship structures is proposed. The main idea of two-step approach is to determine the dynamic ultimate load capacity based on the static ultimate load capacity after accounting for impacts that cause strain on the ship structures. This approach is based on nonlinear finite element method. Here, taking stiffened plate as a case study, the practical application of thus two-step approach is discussed in detail. The results of this approach reveal that the static ultimate load capacity decreases by less than 3% after a stiffened plate is subjected to an impact load whose amplitude corresponds to the dynamic ultimate load capacity. Then, the influence of the impact duration on the failure mode and the effect of the impact load cycles and the impact load sequence on the dynamic ultimate load capacity of the stiffened plate were investigated. Finally, the applicability of the two-step approach to a hull girder is demonstrated. The two-step approach and the conclusions presented in this paper can provide guidance for the evaluation of dynamic ultimate load capacity.

Experimental investigation and phenomenological modeling of fatigue crack growth in X80 pipeline steel under random loading

Ocean structures are subjected to random environmental loads during their service life. Under the action of random loads, fatigue crack growth within pipeline steel exhibits transient behaviors such as retardation or acceleration. This poses significant challenges in predicting the fatigue life of ocean structures under random loadings. To establish a predictive model for fatigue crack growth in X80 pipeline steel under random loadings, this study first conducts fatigue crack growth experiments under constant amplitude and random loading conditions. Then, based on deep learning methods and the fracture yield zone theory, a phenomenological model is developed to predict fatigue crack growth in X80 pipeline steel under random loading conditions. The results show that under random loading conditions, the fatigue crack growth curve of X80 pipeline steel maintains a relatively clear logarithmic-linear feature during the stable crack growth stage. However, due to the continuous variation of stress ratio with loading sequence, the fatigue crack growth curve exhibits strong fluctuation characteristics; The phenomenological model established in this study for predicting fatigue crack growth under random loading conditions can relatively accurately reflect the overall trend of fatigue crack growth under random loadings, with a maximum prediction error of less than 10%.

Short-term power load forecasting based on AC-BiLSTM model

The practice of ultra-short-term power load forecasting serves as a critical strategy for enabling rapid response and real-time dispatch in power systems. By improving the accuracy of load forecasting, both the safety of power systems and the efficiency of electricity usage can be significantly enhanced. Addressing the challenges posed by the non-linear and temporal characteristics of grid load data, this study introduces a novel ultra-short-term power load forecasting model, integrating Convolutional Neural Networks (CNN), Bidirectional Long Short-Term Memory networks (BiLSTM), and an Attention mechanism, referred to as the AC-BiLSTM model. This innovative approach harnesses the power of CNN and BiLSTM to extract spatio-temporal features of load data, while the Attention mechanism allocates optimal weights to the hidden states of the BiLSTM model, thereby amplifying crucial historical load sequence data and minimizing information loss. The final output of the model is then determined through a fully connected layer. To validate the efficacy of this approach, an empirical study was conducted using real load data from a specific region. The results, obtained from two contrasting experimental scenarios, demonstrate a significant enhancement in forecasting accuracy. This finding underscores the potential of the AC-BiLSTM model as a reliable tool for both strategic planning and maintaining operational stability in power systems.

Pre-trained transformer model as a surrogate in multiscale computational homogenization framework for elastoplastic composite materials subjected to generic loading paths

A composite material typically exhibits complex behavior at the engineer scale, arising from the interactions between its underlying constituent phases, as well as the competitions between micro-processes. It is generally a daunting task to develop an engineering model to adequately capture the essential micro mechanisms that propagate onto the macro scale. To this end, the multiscale computational homogenization (FE2) method enables a consistent coupling across length scales, to give results that compare well with direct numerical simulations having the full micro-structural details, without the need for any constitutive assumptions nor calibrations at the macro scale. Despite its predictive capabilities, the typical computational homogenization method is still computationally too expensive for most practical problems, as the coupling between micro and macro scales are solved simultaneously during its numerical implementation. In this presentation, focusing on the elastoplastic behavior of fiber-reinforced composite, we address this bottleneck with an offline development of a microscopic surrogate model for a given micro-structure, to be incorporated into a standard nonlinear FE framework, for rapid online implementations at the macro scale. For the offline training phase, we adopt the transformer-based architecture within a pre-training and fine-tuning framework. The proposed pre-trained transformer model is capable of parallelizing computations to effectively capture global dependencies within the strain-stress data sequences. To reduce the data generation cost, a constructed source representative volume element (RVE) having a single central heterogeneity with an identical volume fraction with the target RVE is utilized, to rapidly generate a huge source dataset for a pre-training process. The performance of the surrogate model is first demonstrated by comparing its predictions under random loading paths against the reference homogenized RVE responses. Next, the surrogate model is incorporated into a macro FE framework, and its predictive capabilities illustrated via the generic loading of two specimens with different microstructures, each having a different loading–unloading path. Finally, a chunking method is discussed as a potential remedy for managing very long load sequences.

A hybrid virtual–real traffic simulation approach to reproducing the spatiotemporal distribution of bridge loads

AbstractCurrent traffic simulation approaches analyze vehicle loads and load effects from a statistical perspective; however, they fail to reproduce the spatiotemporal distribution of bridge loads and resultant load effects at every moment, hindering real‐time bridge health management. This paper proposes a two‐step hybrid virtual–real traffic simulation (HvrTS) approach to reproduce the spatiotemporal distribution of bridge loads. Vehicle load sequences are first identified using computer vision based on traffic video from two surveillance cameras installed along the bridge. Next, traffic microsimulation is optimized with the identified vehicle load sequences from the two cameras serving as the known input and validated output. Using a cable‐stayed bridge under free‐flowing traffic, the HvrTS approach achieved a weighted mean square matching error of ≤0.3 m for the vehicle longitudinal location and a matching error of ≤7% for the vehicle lane position, whereas with congested traffic, the matching errors were much higher due to the inherent complexities and challenges associated with reproducing vehicle behavior in heavily congested situations. The traffic load effects calculated via HvrTS presented excellent spatiotemporal matching with those measured by a structural health monitoring system, especially in free‐flow traffic conditions. Applications on a continuous rigid frame girder bridge further validate these findings. Hence, the proposed HvrTS approach can overcome the challenge of spatiotemporal matching between vehicle loads and load effects in field monitoring.

An ultra-short-term forecasting method for multivariate loads of user-level integrated energy systems in a microscopic perspective: based on multi-energy spatio-temporal coupling and dual-attention mechanism

An ultra-short-term multivariate load forecasting method under a microscopic perspective is proposed to address the characteristics of user-level integrated energy systems (UIES), which are small in scale and have large load fluctuations. Firstly, the spatio-temporal correlation of users’ energy use behavior within the UIES is analyzed, and a multivariate load input feature set in the form of a class image is constructed based on the various types of load units. Secondly, in order to maintain the feature independence and temporal integrity of each load during the feature extraction process, a deep neural network architecture with spatio-temporal coupling characteristics is designed. Among them, the multi-channel parallel convolutional neural network (MCNN) performs independent spatial feature extraction of the 2D load component pixel images at each moment in time, and feature fusion of various types of load features in high dimensional space. A bidirectional long short-term memory network (BiLSTM) is used as a feature sharing layer to perform temporal feature extraction on the fused load sequences. In addition, a spatial attention layer and a temporal attention layer are designed in this paper for the original input load pixel images and the fused load sequences, respectively, so that the model can better capture the important information. Finally, a multi-task learning approach based on the hard sharing mechanism achieves joint prediction of each load. The measured load data of a UIES is analyzed as an example to verify the superiority of the method proposed in this paper.

A double load spectrum extrapolation for drill pipe of rotary drilling rig considering load sequence

The load spectrum of rotary drilling rig’s drill pipe is used for drill pipe design, simulated fatigue testing, and reliability assessment. However, the proper extrapolation of the drill pipe double load is challenging, especially to meet the requirements of both load sequence and load strengthening. In this paper, an extrapolation technique for a double load of drill pipe is proposed. The time sequence rainflow counting technique is proposed to keep the torque and pressure sequence. Meanwhile, the individual distributions of the loads are accurately estimated. Finally, the torque and pressure are simulated and extrapolated by the percentile extrapolation method. The results show that the proposed extrapolation method for maintaining the load sequence enriches the dynamics and diversity of the loads while ensuring the load sequence.

New investigation of delamination using the VCCT method to predict the damage in bonded composite repair plates subjected to tensile load

PurposeThis study provides an analysis of patch repair for cracked aircraft structures. Delamination is a type of damage that affects the patch's behavior. The purpose of this study is to assess the influence of delamination on repair performance.Design/methodology/approachAn analytical and numerical study using the finite element method was conducted for a cracked plate repaired with a patch containing a pre-existing delamination defect. The method for defining the contact pair surfaces and modeling the delamination interaction within the patch interface is specified using the virtual crack closure technique (VCCT) approach.FindingsThe efficiency of the repair is measured in terms of the J-integral. The effects of delamination initiation, mechanical loading, crack length and patch stacking sequences are presented. It is noted that in mode I, delamination propagation is only significant at node A. The numerical results are in good agreement with those of the analytical solution found in the literature. It is observed that the patch's behavior is strongly dependent on loading, crack size and stacking sequences in terms of reducing the structure's lifespan, especially in the presence of delamination.Originality/valueThe numerical modeling presented by the VCCT approach is highly valuable for studying delamination evolution. The influence of loading, crack size and stacking sequences on repair performance is discussed in this work.

Deformation and failure analysis of heterogeneous slope using nonlinear spatial probabilistic finite element method

<p>Slope failures in hilly terrain impact the social and economic balance of the community. The major reasons for these slope failures are steeper slopes, climate factors, seismic activity, nearby excavations, and construction. Natural slopes show significant heterogeneity due to the inherent randomness in material properties and geometric nonlinearities. Effective slope stability analysis solutions can be achieved by incorporating probabilistic approaches. We present a comprehensive method to develop and analyze a heterogeneous two-dimensional slope model, utilizing a non-linear-spatial-probabilistic-finite element method under a plane strain condition. The developed slope model encompasses geometrical and material nonlinearity with a uniform random distribution over the space. Also, the present slope model integrates the Mohr-Coulomb's constitutive model for elastoplastic analysis to capture more realistic and complex behavior. A benchmark soil slope problem was modeled using the spatial probabilistic finite element method, comprising all six material properties with uniform spatial uncertainties. These material properties are elastic modulus, unit weight, cohesion, friction angle, and dilation angle. During the numerical simulation, the detailed deformations, stress patterns, strain patterns, potential pre-failure zone, and failure characteristics of heterogeneous slopes were achieved under self-weight and step loading sequences. Nodal failure and probability of nodal failure were introduced as two novel quantitative parameters for more insights into failure investigations. The testbench slope model was subjected to self-weight load and external 100-step loading sequences with a loading increment of -0.1 kN/m. The percentage probability of nodal failure was obtained at 40.46% considering uniformly distributed material uncertainties with a 10% coefficient of variation. The developed testbench slope model was also simulated for different values of the coefficient of variation (ranging from 0% to 50%) and comparatively investigated. The detailed deformation patterns, thorough profiles of stresses-strains, failure zones, and failure characteristics provided valuable insights into geotechnical engineering practices.</p>

Life tests of membrane-type throttle flow dividers-adders

Increasing the reliability of the hydraulic drive of technological systems of machines is one of the main technical tasks. The article is devoted to a description of the life tests of membrane-type throttle dividers-adders of flows. The paper presents a schematic hydraulic diagram of an experimental stand for life tests of flow dividers-adders and describes the principle of operation of the stand for life tests of flow dividers. A comparison is made of the resources of two throttle flow dividers-adders of the membrane type DSP-01.000 DSP-05.00.000. The features of the test procedure and the loading sequence are described. The article represents a special contribution to the development of new technical solutions in the field of hydraulic drive control and can serve as the basis for further research in this area.

Probabilistic structural integrity assessment of a floating offshore wind turbine under variable amplitude loading

The objective of the present study is to assess the structural integrity of a semisubmersible-type floating offshore wind turbine probabilistically based on fracture mechanics. Although floating offshore wind turbines have been historically much more expensive than their fixed bottom peers, the levelised cost of energy of floating offshore wind turbines has decreased dramatically due to increasing wind turbine size, higher capacity factors, innovative designs, and optimal operational strategies. However, there is still much to be done to bring the cost down, especially from the operational and maintenance standpoint. In this regard, a damage-tolerant approach is a strong alternative to a safe-life design approach that leads to overdesigning. The present study assesses the structural integrity of a semisubmersible-type floating platform with an initial crack assumption using a probabilistic framework, resulting in the quantification of the uncertainty involved in the fatigue crack growth under variable amplitude loading. The present study outcomes can then be used to optimise the structural design, inspection, and maintenance plan. The cycle-by-cycle crack growth is simulated using the modified Paris’ law, which allows accounting for the overload-induced retardation on the crack propagation. Furthermore, the factors influencing crack growth are subjected to a great deal of uncertainty. The present study focuses on the variability caused by the load sequence and interaction effect. To this end, Monte Carlo Simulation is performed to quantify the uncertainty associated with the crack growth by first considering the load sequence effect and then the overload-induced retardation effect.

BILSTM-SimAM: An improved algorithm for short-term electric load forecasting based on multi-feature.

With the growing number of user-side resources connected to the distribution system, an occasional imbalance between the distribution side and the user side arises, making short-term power load forecasting technology crucial for addressing this issue. To strengthen the capability of load multi-feature extraction and improve the accuracy of electric load forecasting, we have constructed a novel BILSTM-SimAM network model. First, the entirely non-recursive Variational Mode Decomposition (VMD) signal processing technique is applied to decompose the raw data into Intrinsic Mode Functions (IMF) with significant regularity. This effectively reduces noise in the load sequence and preserves high-frequency data features, making the data more suitable for subsequent feature extraction. Second, a convolutional neural network (CNN) mode incorporates Dropout function to prevent model overfitting, this improves recognition accuracy and accelerates convergence. Finally, the model combines a Bidirectional Long Short-Term Memory (BILSTM) network with a simple parameter-free attention mechanism (SimAM). This combination allows for the extraction of multi-feature from the load data while emphasizing the feature information of key historical time points, further enhancing the model's prediction accuracy. The results indicate that the R2 of the BILSTM-SimAM algorithm model reaches 97.8%, surpassing mainstream models such as Transformer, MLP, and Prophet by 2.0%, 2.7%, and 3.6%, respectively. Additionally, the remaining error metrics also show a reduction, confirming the validity and feasibility of the method proposed.

RISK OF BIOAEROSOLS IN SMALL AND POORLY VENTILATED INDOOR PLACES WITH LOW CONCURRENT USE

Natural cross ventilation has become one of the most effective and sustainable strategies to address respiratory pandemics in confined spaces. Air intake through windows, doors and other openings improves the Indoor Air Quality (IAQ) of a classroom, decreases the likelihood of respiratory disease transmission when social distancing of about 1.5 m is observed, and is affordable in terms of energy consumption. In this study, a comparison between types of cross ventilation from CO2 concentration, number of people and window and door surfaces of a school in a Mediterranean climate is carried out for 3 different cases of openings. For this purpose, CO2 levels were measured in two similar and annexed classrooms with different window and door openings (0%, 33% y 66%) with 2 loading and unloading sequences. Two types of cross ventilation were analyzed: cross ventilation with window and door in the same direction or direct (CDIR), and diagonal cross ventilation (CDIA), in a time period of 3 h. in December 2022. The results show an interaction between CO2 levels and the type of ventilation. Diagonal cross ventilation was more effective in terms of air renewal (ACH) than direct ventilation, in smaller opening ranges (33%), with direct cross ventilation being more effective globally and in larger opening ranges (66%), under the conditions of this case study. Keywords: natural ventilation; cross ventilation; indoor air quality; carbon dioxide (CO2) concentration; classroom.

Residual life assessment of deep rolled railway axles considering the effect of process parameters

Deep rolling is an efficient technique to increase the fatigue life of railway axles. The application introduces compressive residual stresses into the area near the surface, which are the most effective positive influence on fatigue behaviour and remaining service life in presence of a crack. This paper investigates the remaining service life of deep rolled railway axles. Previous research studies are used as basis for this work whereas, firstly, an elaborated crack growth model for common railway axle steels was developed. This model is based on experimental crack propagation results and can consider crack closure as well as load sequence effects and local residual stress conditions. Secondly, a numerical deep rolling simulation model validated with experimental residual stress measurements to determine the introduced residual stress state and to investigate the influence of various process parameters. In this study, the comprehensive outcome of both studies is connected in order to evaluate and compare the residual life of railway axles with the presence of different deep rolling-induced residual stress distributions in-depth. The crack growth study involves combinations of the deep rolling force as one of the most influencing process parameters, load scenarios as well as initial crack sizes and finally discusses the comprehensive results.

Effect of metal loading sequences in CO2 methanation activity on samarium-doped ceria supported bimetallic catalysts

The La/Ni/SDC catalyst from one pot microwave-heating gives higher methanation activity than La–Ni/SDC and Ni–La/SDC from stepwise heating.

Quantifying the immediate post-implantation strain field of cadaveric tibiae implanted with cementless tibial trays: A time-elapsed micro-CT and digital volume correlation analysis during stair descent

Primary stability, the mechanical fixation between implant and bone prior to osseointegration, is crucial for the long-term success of cementless tibial trays. However, little is known about the mechanical interplay between the implant and bone internally, as experimental studies quantifying internal strain are limited. This study employed digital volume correlation (DVC) to quantify the immediate post-implantation strain field of five cadaveric tibiae implanted with a commercially available cementless titanium tibial tray (Attune, DePuy Synthes). The tibiae were subjected to a five-step loading sequence (0–2.5 bodyweight, BW) replicating stair descent, with concomitant time-elapsed micro-CT imaging. With progressive loads, increased compression of trabecular bone was quantified, with the highest strains directly under the posterior region of the tibial tray implant, dissipating with increasing distance from the bone-implant interface. After load removal of the last load step (2.5BW), residual strains were observed in all of the five tibiae, with residual strains confined within 3.14 mm from the bone-implant interface. The residual strain is reflective of the observed initial migration of cementless tibial trays reported in clinical studies. The presence of strains above the yield strain of bone accepted in literature suggests that inelastic properties should be included within finite element models of the initial mechanical environment. This study provides a means to experimentally quantify the internal strain distribution of human tibia with cementless trays, increasing the understanding of the mechanical interaction between bone and implant.

Dynamic resilient modulus of subgrade silty clay for heavy-haul railway: an experimental investigation and the predicted method

A large-scale series of cyclic triaxial tests were conducted to explore the evolution of the dynamic resilient modulus of silty clay for the heavy-haul railway subgrade. A novel loading sequence for measuring the dynamic resilient modulus was established, which characterized the dynamic stress state of the subgrade induced by the heavy-haul train load. In the experimental investigation, the deviatoric stresses, confining stress, initial moisture content, and compaction degree were considered as variables, and the effects of the aforementioned variables were evaluated quantitatively. The experimental results showed that the dynamic resilient modulus was negatively related to deviatoric stresses and initial moisture content, where the average decreased rates were 14.65% and 27.79% with the increase in deviatoric stresses from 60 kPa to 150 kPa and increase in the initial moisture content from 9.8% to 15.8%, respectively. Furthermore, the dynamic resilient modulus was positively related to confining stress and compaction degree, where the average increased rates were 23.25% and 27.48% with the increase in confining stress from 20 kPa to 60 kPa and increase in compaction degree from 0.91 to 0.95. To provide a better application, the two high-accuracy predicted methods were established through the empirical model and artificial neural network approach including the aforementioned variables. This study can provide useful guidelines for the effective and safe design of the heavy-haul railway subgrade filled with silty clay.

Customer baseline load estimation for virtual power plants in demand response: An attention mechanism-based generative adversarial networks approach

The virtual power plant (VPP) that aggregates demand-side resources, is a new type of entity to participate in the electricity market and demand response (DR) program. Accurate customer baseline load (CBL) estimation is critical for DR implementation, especially the financial settlement in incentive-based DR. However, this is a challenging task as CBLs cannot be measured and are not equal to actual loads when DR events occur. Moreover, VPPs with different aggregation scales form heterogeneous electricity customers, which increases the difficulty of CBL estimation. In order to address this challenge, this paper proposes a novel deep learning-based CBL estimation method for varied types of electricity customers with different load levels. Specifically, we first transform the CBL estimation problem into a time-series missing data imputation issue, by regarding actual load sequences as CBL sequences with missing data, during DR periods. Then, we propose an attention mechanism-based neural network model to learn load patterns and characteristics of various CBLs, and also create the DR mask to avoid the disturbance of actual loads of DR periods on CBL’s normal pattern. Further, we develop the generative adversarial networks (GAN)-based data imputation framework to produce the corresponding complete CBL sequence according to the actual load sequence, and then recover the missing values accordingly. Finally, comprehensive case studies are conducted based on public datasets, and our proposed method outperforms all benchmarks, where the mean and standard deviation of its estimation percentage error are 5.85% and 1.74%, respectively. This validates the effectiveness and superiority of the proposed method.

РАСЧЕТНАЯ МОДЕЛЬ ДЕФОРМИРОВАНИЯ ГРУНТОВОГО ОСНОВАНИЯ ВЫСОТНОГО ЗДАНИЯ С УЧЕТОМ ПРЕДЫСТОРИИ ЗАГРУЖЕНИЯ

While designing high-rise buildings, it is necessary to take into account the influence of stages of their erection, this is the sequence of loading, on the change in physical-mechanical properties of soils. The calculation of deformations of subgrade is made with due regard for the change in soils properties comparing with the initial (natural) state. On the basis of the results of experimental studies, the authors substantiate the necessity for introducing the new parameter which makes it possible to characterize the mechanical condition of the soil at any stage of loading and can be used for creating calculation models. An analytical diagram of soil deformation in coordinates «σ1–ε1» for triaxial compression (where σ1 – vertical stresses (deviator), ε1 – linear deformations under the triaxial compression) is adopted as this parameter. The procedure for constructing transformed diagrams of the soil state under the long performance triaxial loading is described. The procedure obtained was approbated when calculating deformations of the foundation base of the high-rise building with due regard for the influence of staging of construction and rheological properties of soils on the change in the base rigidity and, as a result, on the redistribution of stresses among separate elements of the system “subgrade –foundation – upper part of the building.

Modeling and solving the fuel distribution problem with unloading precedence and loading sequence considerations

AbstractThis paper presents a real-world liquid fuel distribution problem involving a heterogeneous fleet of multi-compartment vehicles servicing a set of orders of different fuels. Two new features are introduced that affect significantly the performance of the fuel distribution process in terms of safety and efficiency: (i) loading trucks so that the payload remains balanced throughout each phase of a delivery route, and (ii) sequencing the requests for loading trucks at the depot loading facilities. A Mixed Integer Programming formulation is presented and an Adaptive Large Neighbourhood Search algorithm with various novel features is developed and benchmarked. A new loading model is formed and solved for allocating the ordered items (fuel) to vehicle compartments, as part of constructing/repairing delivery routes. The computational performance of the proposed solution approach has been tested on a series of benchmark problems. Moreover, a series of experiments were performed in order to assess the effect of the balanced loading constraints on the traveled distance. The results indicate that the effect of this type of constraint on the total traveled distance is kept at a reasonable level, reaching a maximum increase of 4.37%. The computational tools presented in this work may accommodate the dispatchers’ work in producing efficient and safe delivery routes while managing efficiently any potential bottleneck in the truck loading facilities.