Irregular Loading Research Articles

Damage analysis of OC4 jacket subjected to cyclic loading by peridynamic approach

Catastrophic structural failure caused by fatigue damage under cyclic loads can be avoided by identifying critical locations of the damage initiation and the fatigue life during the design stage. Peridynamic (PD) theory defines structure as a collection of material points with non-local bond interactions where the structural discontinuity due to fatigue represented by instantaneous bond breakage is estimated through cumulative decrement of the bond’s life at each load cycle. In this work, we model a reference jacket presented under the Offshore Code Comparison Collaboration Continuation project (OC4) through peridynamic beam formulation. Initially, the static deformations of the beam and deformations of the OC4 jacket under static, harmonic, and irregular point loads are validated with ABAQUS results in all six degrees of freedom. Thereby, the PD fatigue parameters of the jacket’s steel material are calibrated from the experimental data of the corresponding material with a trial simulation for fatigue damage analysis. Later, different load cases from regular waves interacting with the jacket are generated in the PD framework by adopting linear wave theory. Based on the PD cyclic energy release rate model, a comparative study of all load cases to identify critical damage locations with the failure load cycles for damage initiation and fracture is performed for the considered OC4 jacket.

Transfer of learning in convolutional neural networks for thermal image classification in Electrical Transformer Rooms

Overheating of power transformers, low-voltage panels, and medium-voltage components in Electric Transformer Rooms (ETRs) can result from various factors, such as contact issues, irregular loads, and other similar problems. Thermal imaging shows significant potential for detecting faults in power equipment. However, its effectiveness is hindered by the complex thermal patterns of faults and variability in equipment and environmental conditions, making accurate fault detection challenging. This paper aims to study the effectiveness of transfer learning architectures for automating equipment classification in ETRs. This work applies four transfer learning architectures: AlexNet, SqueezeNet, VGG19, and GoogLeNet. The findings of the testing phase demonstrated that the use of transfer learning by fine-tuning pre-trained convolutional neural networks was highly effective in the classification of thermal images captured from ETRs, with the models achieving accuracy rates between 86.98% and 100%, and F1-Scores between 86.79% and 100%.

Analysis of a Grid-Connected Solar PV System with Battery Energy Storage for Irregular Load Profile

In recent decades, Saudi Arabia has experienced a significant surge in energy consumption as a result of population growth and economic expansion. This has presented utility companies with the formidable challenge of upgrading their facilities and expanding their capacity to keep pace with future energy demands. In order to address this issue, there is an urgent need to implement energy-saving solutions such as energy storage systems (ESSs) and renewable energy sources, which can help to reduce demand during peak hours. To ensure optimal use of ESSs, it is crucial to integrate a load forecasting model with the ESS in order to control charging and discharging rates and schedules. The irregular load profile is a particularly significant consumer of energy, consuming approximately 2.5 GWh annually at the cost of USD 3 billion in Saudi Arabia. In light of this, this paper develops a load forecasting model for the irregular load profile with a high degree of accuracy: achieving 95%. One of the key applications of this model is load peak shaving. Given the region’s abundance of solar irradiation, the paper propose an integration of a solar PV system with a battery energy storage system (BESS) and analyzes various scenarios to determine the efficacy of the proposed approach. The results demonstrate significant savings when the proposed forecasting model is integrated with a BESS and PV system, with the potential to reduce monthly imported power by more than 22% during the summer season.

Evaluation of the Nearest and The Longest Shipping Routes in MT. Perla

Abstract Shipping routes are sea transportation from one port to another. There are loading and unloading activities in shipping routes. Loading and unloading activities are the activity of moving cargo from land to ship at the loading port, and moving cargo from ship to land at the unloading port. On ships with irregular routes, loading and unloading activities are random in terms of shipping route, amount of cargo, and length of time required. Evaluation is a process of collecting information to improve and determine quality and performance based on certain considerations and criteria (value and meaning) in implementing a program. This study will discuss the shipping route, amount of cargo, length of shipping time, loading and unloading. The research aimed to evaluate the nearest and the longest shipping routes in MT. Perla. The authors used a quantitative analysis research method where the data was processed and calculated by using Ms. Excel and SPSS was then used to evaluate shipping routes. The research results show that the evaluation of shipping routes for 12 months. Some factors hinder shipping routes, loading, and unloading activities. The researcher’s advice is for all parties to pay attention to the evaluation results and the factors causing those to avoid company losses and to improve the quality of shipping routes.

Nonlinear Slippage of Tensile Armor Layers of Unbonded Flexible Riser Subjected to Irregular Loads

The unbonded flexible riser has been increasingly applied in the ocean engineering industry to transport oil and gas resources from the seabed to offshore platforms. The slippage of helical layers, especially the tensile armor layers of unbonded flexible risers, contribute to the nonlinear hysteresis phenomenon, which is a research hotspot and difficulty. In this paper, on the basis of a typical eight-layer unbonded flexible riser model, the nonlinear slippage of a tensile armor layer and the corresponding nonlinear behavior of an unbonded flexible riser subjected to irregular external loads are studied by numerical modeling with detailed cross-sectional properties of the helical layers, and are verified through a theoretical method considering the coupled effect of the external loads on the unbonded flexible riser. Firstly, the balance equation of each layer considering the effect of external loads is established based on functional principles, and the overall theoretical model of the unbonded flexible riser is set up in consideration of the contact between adjacent layers. Secondly, the numerical modeling of each separate layer within the unbonded flexible riser, including the actual geometry of the carcass and pressure armor layer, is established, and solid elements are applied to all the interlayers, thus simulating the nonlinear contact and friction between and within interlayers. Finally, after verification through test data, the behavior of the unbonded flexible riser under the cyclic axial force, torsion, bending moment, and irregular external and internal pressure is studied. The results show that the tensile armor layer can slip under irregular loads. Additionally, some suggestions related to the analysis of unbonded flexible risers under irregular loads are drawn in the end.

Effects of dynamic load application, irregular wave conditions, and base flexibility on the structural response of a vertical pile platform

The dynamic structural response of a vertical pile port platform under the action of irregular wave loads considering soil-structure interaction is studied. Deck displacement and bending moment on the piles were evaluated for a set of structural, hydraulic, and geotechnical parameters. Static structural analyses with the maximum base shear produced by wave loads and dynamic time-history analyses are performed to study the effects of dynamic amplification on the structural response of the platform. Two types of waves are analyzed, regular and irregular, to analyze the amplification effects due to the superposition of a wave train of different periods in the case of irregular waves. Cases with fixed base support and non-linear spring-modeled soil are considered to evaluate the effect of considering a flexible base for the platform. Eight structures composed of a different number of piles and with different heights were analyzed. Six wave conditions with different characteristics are used, varying the wave period and mean water height. Two sandy and two clayey soil profiles are considered with different densities and consistency. The results show that resonance effects associated with dynamic loading and small-period wave superposition in cases of irregular waves significantly affect the response. Furthermore, considering a flexible base tends to decrease the response for short wave periods, concluding that, in most cases, for wave periods close to 5 s, considering a fixed base model overestimates the response.

Research on damage identification of jacket structure under wind and wave loads based on variational mode decomposition and chirplet transform

To conduct damage localization and damage quantification of jacket structure, a new damage identification approach based on variational mode decomposition (VMD) and chirplet transform (CT) is firstly introduced. Three kinds of environmental loads, namely, regular wave load, irregular wave load, and wind load, are set on the jacket structure to simulate the real environment of the jacket as much as possible. The acceleration response signals of each node of the damaged bar are extracted and analyzed by VMD and CT. The proposed method is used to analyze the time-frequency energy changes before and after damage for damage localization. Then, the time-frequency entropy of the damage location is further calculated for damage quantification. The feasibility of the introduced method is verified by setting different operating conditions, and the results show that the proposed method can locate and quantify the damage of the jacket under different environmental loads.

Dissipated Energy and Pore Pressure Generation Patterns in Sands and Non-Plastic Silts Subjected to Cyclic Loadings

The factors that influence dissipated energy and pore pressure generation patterns with respect to the cycle ratio and dissipated energy ratio were analyzed using the results of cyclic triaxial and cyclic direct simple shear tests. These analyses revealed several interesting differences in pore pressure generation patterns when related to cycle ratio than to dissipated energy ratio. Soils with different pore pressure generation patterns when plotted against the cycle ratio were found to have nearly identical pore pressure generation patterns when plotted against the dissipated energy ratio. The differences exist as the result of the fundamental differences between cycle ratio and dissipated energy ratio. The fundamental difference is that pore pressure generation is directly linked to the process of energy dissipation; it is not inherently linked to cycles of loading. Therefore, to achieve an understanding of the pore pressure response of a soil that is independent of the specifics of the applied loading, one needs to evaluate it in terms of energy dissipation, not in terms of cycles of loading, especially irregular loadings.

An optimization model for just-in-time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time

PurposeAlthough extensive research has been conducted on precast production, irregular component loading constraints have received little attention, resulting in limitations for transportation cost optimization. Traditional irregular component loading methods are based on past performance, which frequently wastes vehicle space. Additionally, real-time road conditions, precast component assembly times, and delivery vehicle waiting times due to equipment constraints at the construction site affect transportation time and overall transportation costs. Therefore, this paper aims to provide an optimization model for Just-In-Time (JIT) delivery of precast components considering 3D loading constraints, real-time road conditions and assembly time.Design/methodology/approachIn order to propose a JIT (just-in-time) delivery optimization model, the effects of the sizes of irregular precast components, the assembly time, and the loading methods are considered in the 3D loading constraint model. In addition, for JIT delivery, incorporating real-time road conditions in the transportation process is essential to mitigate delays in the delivery of precast components. The 3D precast component loading problem is solved by using a hybrid genetic algorithm which mixes the genetic algorithm and the simulated annealing algorithm.FindingsA real case study was used to validate the JIT delivery optimization model. The results indicated this study contributes to the optimization of strategies for loading irregular precast components and the reduction of transportation costs by 5.38%.Originality/valueThis study establishes a JIT delivery optimization model with the aim of reducing transportation costs by considering 3D loading constraints, real-time road conditions and assembly time. The irregular precast component is simplified into 3D bounding box and loaded with three-space division heuristic packing algorithm. In addition, the hybrid algorithm mixing the genetic algorithm and the simulated annealing algorithm is to solve the 3D container loading problem, which provides both global search capability and the ability to perform local searching. The JIT delivery optimization model can provide decision-makers with a more comprehensive and economical strategy for loading and transporting irregular precast components.

The Actin Cytoskeleton as a Regulator of Proteoglycan 4.

The superficial zone (SZ) of articular cartilage is responsible for distributing shear forces for optimal cartilage loading and contributes to joint lubrication through the production of proteoglycan 4 (PRG4). PRG4 plays a critical role in joint homeostasis and is chondroprotective. Normal PRG4 production is affected by inflammation and irregular mechanical loading in post-traumatic osteoarthritis (PTOA). THe SZ chondrocyte (SZC) phenotype, including PRG4 expression, is regulated by the actin cytoskeleton in vitro. There remains a limited understanding of the regulation of PRG4 by the actin cytoskeleton in native articular chondrocytes. The filamentous (F)-actin cytoskeleton is a potential node in crosstalk between mechanical stimulation and cytokine activation and the regulation of PRG4 in SZCs, therefore developing insights in the regulation of PRG4 by actin may identify molecular targets for novel PTOA therapies. A comprehensive literature search on PRG4 and the regulation of the SZC phenotype by actin organization was performed. PRG4 is strongly regulated by the actin cytoskeleton in isolated SZCs in vitro. Biochemical and mechanical stimuli have been characterized to regulate PRG4 and may converge upon actin cytoskeleton signaling. Actin-based regulation of PRG4 in native SZCs is not fully understood and requires further elucidation. Understanding the regulation of PRG4 by actin in SZCs requires an in vivo context to further potential of leveraging actin arrangement to arthritic therapeutics.

ESTIMATION OF FATIGUE CURVES USING ORTHOGONAL POLYNOMIALS

The article proposes a methodology for processing fatigue tests carried out to assess the fatigue curve of materials and structural elements, the most important design characteristic of the endurance, service life and reliability of aircraft products, transport engineering and other structures operating under conditions of both regular and irregular loading. The task of estimating the parameters of the fatigue curve is often complicated by the limited scope of fatigue tests, significant scattering of cyclic durability, uneven duplication of experiments at a given level of alternating stress amplitudes, the presence of censoring, especially at low levels, and other factors. which necessitates the use of the weighted least squares method. These circumstances lead to the violation of the conditions for using standard methods of regression analysis and the least squares method. This is especially true for the confidence assessment of the fatigue curve, the accuracy of which significantly depends on these factors. For this purpose, the article proposes to apply the procedure of orthogonalization of factor characteristics to estimate the parameters of the fatigue curve, which reduces the errors arising from standard approaches and makes it possible to obtain accurate confidence intervals for durability. A special feature of the technique is the use of orthogonal polynomials in statistical procedures, which makes it possible to modify the covariance matrix of estimates by converting it to a diagonal form, regardless of the spread of the weight parameters of the original data. These transformations make it possible to perform statistical procedures for point and confidence estimation when processing the results of fatigue tests. To test the methodology, a statistical analysis of a large volume (about 200 samples) of fatigue tests of samples with varying degrees of stress concentration from titanium alloy VT3-1 and aluminum alloy V-95 was performed, algorithms and computer programs were developed in the public domain.

Pore Pressure Generation of Liquefiable Soil Subjected to Irregular Cyclic Loading

Pore Pressure Generation of Liquefiable Soil Subjected to Irregular Cyclic Loading

Frequency Regulation of Microgrid with Renewable Sources Using Intelligent Adaptive Virtual Inertia Control Approach

Frequency control of small inertia microgrids (MGs) with amalgamation of renewable energies like wind and solar is a difficult job. Virtual inertia control (VIC) scheme is the concept of enhancing the MGs inertia employing storage elements. In the present study, an optimized fuzzy adaptive virtual inertia control strategy has been proposed for adaptive allocation of virtual inertia constant. To tune the suggested controllers, an improved golden jackal optimization (i-GJO) is projected in which priority is given to the better jackal’s during the search process, and also sine cosine adopted scaling factor is used to modify the jackal’s position throughout search process. The effectiveness of i-GJO method is evaluated by comparing the results with golden jackal optimization (GJO) and other similar recently proposed optimization algorithms. Then, proportional-integral-derivative controllers are tuned in VIC environment with fixed virtual inertia constant (KVT ) using proposed i-GJO technique, and the superiority of i-GJO over genetic algorithm, particle swarm optimization, and GJO is demonstrated. Finally, fuzzy based controller with constant KVT as well as fuzzy adaptive KVT has been designed using the proposed i-GJO technique. The sensitivity of the controller is also examined under irregular load changes and various rates of RES amalgamation.

Optimization of social welfare and mitigating privacy risks in P2P energy trading: Differential privacy for secure data reporting

With the growing demand for energy and the integration of distributed energy resources (DERs), Peer-to-Peer (P2P) energy trading has emerged as a promising approach that enables participants in the energy market to coordinate and trade energy. However, due to the intermittent generation from DERs and irregular load patterns, energy shortages can occur in the P2P market. To address this issue, an effective demand response-based energy allocation policy is proposed with usage-based billing in the P2P market that differentiates cooperative and non-cooperative buyers based on the supply-to-demand ratio. In general, monitoring buyers' demand and sellers' generation data is performed to formulate contracts, posing risks to participants' privacy. The utility grid requires this data for timely decision-making regarding load forecasting and demand response programs. However, real-time reporting of this data in the P2P market can inadvertently reveal participants' lifestyles, potentially leading to privacy breaches. To mitigate this issue, Differential Privacy (DP) is integrated to preserve participants' privacy. Extensive simulations are performed over the one-year dataset, demonstrating the proposed method's effectiveness that maximizes social welfare, ensures data privacy, and enhances participant satisfaction. The results indicate an average reduction in energy costs, ranging from 9.83% to 27.86% for all buyers, and an average increase in revenues, ranging from 5.42% to 10.69% for all sellers, compared to the previously proposed study and the grid, respectively. This research contributes to the advancement of P2P energy trading by providing an innovative approach that simultaneously addresses available energy, privacy concerns, satisfaction level, and economic benefits for participants.

ITRAQ-based proteomics reveals potential markers and treatment pathways for acute Achilles tendon rupture

BackgroundDue to its limited blood supply and irregular mechanical loading, the Achilles tendon is the most frequently ruptured tendon. Despite the rising incidence of acute Achilles tendon rupture (AATR), the optimal treatment remains controversial. Missed diagnoses and delayed treatments lead to poor outcomes and limited treatment options. This study aimed to identify potential biomarkers for diagnosing and developing therapies for AATR.MethodsWe employed the coupled isobaric tag for relative and absolute quantitation-liquid chromatography–electrospray ionization-tandem mass spectrometry approach to investigate protein expression in tissues from AATR patients. Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were conducted to identify differentially expressed proteins (DEPs) between AATR patients and healthy individuals. A protein–protein interaction (PPI) network of DEPs was constructed using the Search Tool for the Retrieval of Interacting Genes. The screened hub genes were selectively verified by immunohistochemical staining.ResultsWe identified 410 DEPs between AATR patients and controls. The DEPs were significantly enriched in GO terms such as the extracellular region, extracellular region part, and defense response, as well as KEGG pathways, including complement and coagulation cascades, focal adhesion, and regulation of actin cytoskeleton. The main hub nodes in the PPI network comprised fibronectin 1 (FN1), major histocompatibility complex, class I, B (HLA-B), filamin A (FLNA), heat shock 27-kDa protein 1 (HSPB1), heat shock protein family A member 5 (HSPA5), apolipoprotein A4 (APOA4), and myosin IC (MYO1C). Although APOA4 and collagens I, II, and III were detectable in healthy tendons, immunohistochemical staining confirmed higher expression of these proteins in the acutely ruptured Achilles tendon.ConclusionsOur findings lay a foundation for further molecular studies of AATR. Inflammation and age-related degeneration may contribute to the pathogenesis of AATR. Moreover, the identified DEPs could be potential biomarkers for AATR diagnosis and treatment.

Analysis of Survivability of Cast Railroad Parts under Operational Loading

A comparison of the patterns of development of fatigue cracks in the modeling of the operational loading process and the calculated pattern, according to the linear summation algorithm using the standard fatigue fracture kinetic diagram of the same material, is carried out. The material is the low-alloy steel type 20L. During the tests, the load on a curved railway track section was simulated. The parameters of the original fatigue fracture kinetic diagram of material were corrected to adapt it to the calculation under irregular loading of this type.

A physics-guided modelling method of artificial neural network for multiaxial fatigue life prediction under irregular loading.

To improve the generalization of the artificial neural network (ANN) model on the prediction of multiaxial irregular cases, a physics-guided modelling method is proposed with inspiration from the Basquin-Coffin-Manson equation. The method suggested using two neurons in the last hidden layer of the ANN model and constraining the sign of weight and bias value. In this way, the prior physical knowledge of fatigue life distribution is introduced into the ANN model, which resulted in a satisfactory performance on the life prediction of multiaxial loading cases and better extrapolation ability. Furthermore, the physics-guided ANN model can also provide satisfactory prediction on irregular cases with the training of only regular cases. Compared with the conventional model, the average relative error and root mean squared error (RMSE) of prediction decreased by 33.29% and 44.29%, respectively. It greatly broadens the application scenarios of neural networks on multiaxial fatigue life prediction. This article is part of the theme issue 'Physics-informed machine learning and its structural integrity applications (Part 1)'.

Design of Adaptive Sensor Coupling-Based Upper Limb 7-DOF Exoskeleton for Smooth Human Motion Tracking: ASC-EXO

The conventional sensor coupling of the sensor in an exoskeleton robot suffers from inaccurate human motion tracking due to irregular inertial loading on the sensor axis. It may cause sensor axis misalignment, performance degradation and damage the sensor permanently, especially in the multiple degrees of freedom (DOF) exoskeleton robot. In this paper, we proposed a novel design of an adaptive sensor coupling-based 7-DOF exoskeleton (ASC-EXO) for motion tracking and tele-manipulation. The adaptive sensor coupling mechanism has been designed based on the flexure structure cantilever with a slew ring, which suppresses irregular inertial loading on the sensor and is stiff enough along the sensor rotation in the z-axis and compliant on the other axes. The slew ring allows smooth joint rotation. Further, the sensor values are improved by a Butterworth filter. As a result, the adaptive sensor coupling suppressed the sensor noise 20 times more than the conventionally coupled sensor with the robot joint. Finally, we developed a prototype of the 7-DOF exoskeleton (ASC-EXO) using adaptive sensor coupling for smooth human motion tracking. The performance evaluation of adaptive sensor coupling and human motion tracking is presented. In addition, we demonstrated smooth control of the slave robot (KUKA) using the exoskeleton (ASC-EXO) as a master device.

Numerical Simulation Study on Vibration Reduction Effect of Flexible Cutting-Tooth Unit

Under the conditions of drilling in gravel-bearing and heterogeneous stratas, the movement and force of the PDC bit during drilling are highly unstable. Irregular impact loads often cause fatigue failures such as tooth fracture, tooth breakage and delamination of the composite sheet. Dynamic impact load is the main cause of fatigue failure of cutting-tooth, which seriously affects the rock-breaking performance of PDC bits. This paper proposes a flexible cutting tooth unit consisting of a central tooth, an elastic element, and a base. The technical concept of flexible-cutting rock breaking is to reduce the impact load amplitude suffered during the cutting process to a certain threshold range by setting elastic elements or reducing the support stiffness of the cutting tooth, so as to inhibit the expansion of micro defects caused by the impact dynamic load of cutting teeth and prolong the service life of drill bits. The finite-element models of flexible cutting teeth for rock cutting were established. The influence of cutting depth, front rake angle and stiffness of elastic elements on the cushioning and vibration-absorption effect of flexible cutting was analysed. The results show that flexible cutting can reduce the peak and average value of von Mises stress at the cutting edge. Under different cutting-depth conditions, the decline rates were 21.45–49.02% and 9.42–17.8%, respectively. Then, under different front-rake-angle conditions, the decline rates were 20.51–24.02% and 9.41–17.8%, respectively. There is a suitable stiffness of the elastic element, which makes the peak and average value of von Mises stress at the cutting edge of the flexible cutting-tooth unit perform better and the effect of improving the uneven stress distribution at the cutting edge better. Flexible cutting technology can effectively improve the adaptability of the PDC bit in heterogeneous formations and reduce the occurrence of abnormal failure of cutting teeth. The research results of this paper can provide theoretical support for the drilling speed of PDC bits in hard formations.

A novel short-term multi-energy load forecasting method for integrated energy system based on feature separation-fusion technology and improved CNN

Integrated energy system (IES) is an important way for energy structure transition and development. Considering the characteristics of large data volume, strong randomness, and multi-energy coupling of IES, this paper proposes a novel short-term multi-energy load forecasting method for IES based on feature separation-fusion technology and improved CNN. Firstly, based on the distribution pattern of pixels in static images, the irregular multi-energy load is reconstructed into 3D load pixel matrix, giving them certain correlation features in both horizontal and vertical directions respectively. Secondly, the feature separation-fusion technology is employed to differentially process distinct features based on their information value differences. Finally, the extracted features are combined and input into a multi-task learning framework with BiLSTM as the shared layer. The hard parameter sharing mechanism is employed to learn the IES multi-coupling information and extract temporal characteristics of the load sequence through BiLSTM. In particular, three different structures of fully connected neural network are designed as feature interpretation modules to accommodate the different prediction requirements of various loads. The simulation results show that the proposed model achieves a weighted mean accuracy of 98.01% during winter days, with an average standard deviation of relative error as low as 0.0242. Among all the contrast models, it exhibits better prediction accuracy and stable error distribution.