Search Results (288)

In the literature on rotating machinery, many articles discuss the analysis of various rotor and bearing defects, including both sliding and rolling bearings. Defects in the rotor supporting system are investigated much less frequently. In rotor-bearing-supporting structure systems, where there are couplings between the individual sub-systems, damage to the supporting structure can significantly impact the dynamic properties of the entire machine. The authors of this article have, therefore, focused on analysing the defects that can occur in the supporting system of the rotor and bearings. This article presents the results of a numerical analysis of two common defects in the supporting structure: cracks in the bolted joints attaching the machine body to the foundation and a decrease in foundation stiffness. The research object was a test rig that accurately reproduced the dynamic phenomena occurring in rotating machinery, such as vapour and gas turbines. In the numerical model of the rotating machine, a three-dimensional linear model of the supporting structure was combined with a beam model of the rotor line via a nonlinear fluid film-bearing model. The developed model allowed for the analysis of two different failures in the supporting system over a wide range of rotational speeds. The calculations showed that damage to the supporting structure can significantly impact the dynamic characteristics of the entire rotating machine. Full article

This study delves into a market characterized by vertical product differentiation. Product qualities are represented on a one-dimensional interval scale. The research investigates the equilibrium within a monopoly scenario, considering a production cost that is strictly convex. The monopoly offers a strategy comprising various quality–price combinations, with consumer choices determining profits. The analysis involves a comparison between two analogous models: one with a continuous range of consumers and the other with a finite number of consumers. The study explores disparities in the potential for market failure between these two settings. Notably, numerical illustrations underscore these divergences in both market contexts. Full article

Rare-earth-based permanent magnets (PMs) have a vital role in numerous sustainable energy systems, such as electrical machines (EMs). However, their production can greatly harm the environment and their supply chain monopoly presents economic threats. Alternative materials are emerging, but the use of rare-earth PMs remains dominant due to their exceptional performance. Damage to magnet structure can cause loss of performance and efficiency, and propagation of cracks in PMs can result in breaking. In this context, prolonging the service life of PMs and ensuring that they remain damage-free and suitable for re-use is important both for sustainability reasons and cost management. This paper presents a new harmonic content diagnosis and motor performance analysis caused by various magnet structure defects or faults, such as cracked or broken magnets. The proposed method is used for modeling the successive physical failure of the magnet structure in the form of crack formation, crack growth, and magnet breakage. A surface-mounted permanent magnet synchronous motor (PMSM) is studied using simulation in Ansys Maxwell software (Version 2023), and different cracks and PM faults are modeled using the two-dimensional finite element method (FEM). The frequency domain simulation results demonstrate the influence of magnet cracks and their propagation on EM performance measures, such as stator current, distribution of magnetic flux density, back EMF, flux linkage, losses, and efficiency. The results show strong potential for application in health monitoring systems, which could be used to reduce the occurrence of in-service failures, thus reducing the usage of rare-earth magnet materials as well as cost. Full article

This paper investigates, experimentally and numerically, the shear strengthening of Normal Concrete (NC) beams using post-tensioning steel bars and Engineered Cementitious Composite (ECC) reinforced with chemically cured Palm Fronds (PFs). The benefits of strain-hardening ECC and the tensile strength of PFs cured with 6% wt Alkali NaOH solution beside post-tensioned bars have been employed herein. Seven full-scale Reinforced Concrete (RC) beams were fabricated and experimented with under three-point loading until failure. The test parameters include the strengthening technique, type, and configuration of the material used for strengthening. The strengthening process has been implemented through two techniques: Externally Bonded Reinforcement (EBR) and Near-Surface Mounted (NSM) Reinforcement. The strengthening materials have been configured and placed in horizontal, vertical, and inclined positions. The effectiveness of the strengthening methods has been evaluated by examining their cracking propagations, load-deflection responses, collapse modes, elastic stiffness, and absorbed energy. It was found that the proposed strengthening systems could significantly control the crack pattern and failure mode, and they could enhance the ultimate load amplitude up to 37% and 50% for NSM ECC with PFs and EBR post-tensioning steel bars, respectively. Nonlinear three-dimensional finite element models of the tested beams were developed and validated with the test data, where it was found that finite element models predict the structural performance of tested beams with a maximum error of only 2%. Full article

Wind turbine rolling bearings are crucial components for ensuring the reliability and stability of wind power systems. Their failure can lead to significant economic losses and equipment downtime. Therefore, the accurate diagnosis of bearing faults is of great importance. Although existing deep learning fault diagnosis methods have achieved certain results, they still face limitations such as inadequate feature extraction capabilities, insufficient generalization to complex working conditions, and ineffective multi-scale feature capture. To address these issues, this paper proposes an advanced fault diagnosis method named the two-stream feature fusion convolutional neural network (TSFFResNet-Net). Firstly, the proposed method combines the advantages of one-dimensional convolutional neural networks (1D-ResNet) and two-dimensional convolutional neural networks (2D-ResNet). It transforms one-dimensional vibration signals into two-dimensional images through the empirical wavelet transform (EWT) method. Then, parallel convolutional kernels in 1D-ResNet and 2D-ResNet are used to extract multi-scale features, respectively. Next, the Convolutional Block Attention Module (CBAM) is introduced to enhance the network’s ability to capture key features by focusing on important features in specific channels or spatial areas. After feature fusion, CBAM is introduced again to further enhance the effect of feature fusion, ensuring that the features extracted by different network branches can be effectively integrated, ultimately providing more accurate input features for the classification task of the fully connected layer. The experimental results demonstrate that the proposed method outperforms other traditional methods and advanced convolutional neural network models on different datasets. Compared with convolutional neural network models such as LeNet-5, AlexNet, and ResNet, the proposed method achieves a significantly higher accuracy on the test set, with a stable accuracy of over 99%. Compared with other models, it shows better generalization and stability, effectively improving the overall performance of rolling bearing vibration signal fault diagnosis. The method provides an effective solution for the intelligent fault diagnosis of wind turbine rolling bearings. Full article

The reliable operation of scroll compressors is crucial for the efficiency of rotating machinery and refrigeration systems. To address the need for efficient and accurate fault diagnosis in scroll compressor technology under varying operating states, diverse failure modes, and different operating conditions, a multi-branch convolutional neural network fault diagnosis method (SSG-Net) has been developed. This method is based on the Swin Transformer, the Global Attention Mechanism (GAM), and the ResNet architecture. Initially, the one-dimensional time-series signal is converted into a two-dimensional image using the Short-Time Fourier Transform, thereby enriching the feature set for deep learning analysis. Subsequently, the method integrates the window attention mechanism of the Swin Transformer, the 2D convolution of GAM attention, and the shallow ResNet’s two-dimensional convolution feature extraction branch network. This integration further optimizes the feature extraction process, enhancing the accuracy of fault feature recognition and sensitivity to data variability. Consequently, by combining the global and local features extracted from these three branch networks, the model significantly improves feature representation capability and robustness. Finally, experimental results on scroll compressor datasets and the CWRU dataset demonstrate diagnostic accuracies of 97.44% and 99.78%, respectively. These results surpass existing comparative models and confirm the model’s superior recognition precision and rapid convergence capabilities in complex fault environments. Full article

Background/Objectives: Dental implants have emerged as a modern solution for edentulous jaws, showing high success rates. However, the implant’s success often hinges on the patient’s bone quality and quantity, leading to higher failure rates in poor bone sites. To address this issue, short implants have become a viable alternative to traditional approaches like bone sinus lifting. Among these, Bicon^® short implants with a plateau design are popular for their increased surface area, offering potential advantages over threaded implants. Despite their promise, the variability in patient-specific bone quality remains a critical factor influencing implant success and bone turnover regulated by bone strains. Excessive strains can lead to bone loss and implant failure according to Frost’s “Mechanostat” theory. To better understand the implant biomechanical environment, numerical simulation (FEA) is invaluable for correlating implant and bone parameters with strain fields in adjacent bone. The goal was to establish key relationships between short implant geometry, bone quality and quantity, and strain levels in the adjacent bone of patient-dependent elasticity to mitigate the risk of implant failure by avoiding pathological strains. Methods: Nine Bicon Integra-CP™ implants were chosen. Using CT scans, three-dimensional models of the posterior maxilla were created in Solidworks 2022 software to represent the most challenging scenario with minimal available bone, and the implant models were positioned in the jaw with the implant apex supported by the sinus cortical bone. Outer dimensions of the maxilla segment models were determined based on a prior convergence test. Implants and abutments were considered as a single unit made of titanium alloy. The bone segments simulated types III/IV bone by different cancellous bone elasticities and by variable cortical bone elasticity moduli selected based on an experimental data range. Both implants and bone were treated as linearly elastic and isotropic materials. Boundary conditions were restraining the disto-mesial and cranial surfaces of the bone segments. The bone–implant assemblies were subjected to oblique loads, and the bone’s first principal strain fields were analyzed. Maximum strain values were compared with the “minimum effective strain pathological” threshold of 3000 microstrain to assess the implant prognosis. Results: Physiological strains ranging from 490 to 3000 microstrain were observed in the crestal cortical bone, with no excessive strains detected at the implant neck area across different implant dimensions and cortical bone elasticity. In cancellous bone, maximum strains were observed at the first fin tip and were influenced by the implant diameter and length, as well as bone quality and cortical bone elasticity. In the spectrum of modeled bone elasticity and implant dimensions, increasing implant diameter from 4.5 to 6.0 mm resulted in a reduction in maximum strains by 34% to 52%, depending on bone type and cortical bone elasticity. Similarly, increasing implant length from 5.0 to 8.0 mm led to a reduction in maximum strains by 15% to 37%. Additionally, a two-fold reduction in cancellous bone elasticity modulus (type IV vs. III) corresponded to an increase in maximum strains by 16% to 59%. Also, maximum strains increased by 86% to 129% due to a decrease in patient-dependent cortical bone elasticity from the softest to the most rigid bone. Conclusions: The findings have practical implications for dental practitioners planning short finned implants in the posterior maxilla. In cases where the quality of cortical bone is uncertain and bone height is insufficient, wider 6.0 mm diameter implants should be preferred to mitigate the risk of pathological strains. Further investigations of cortical bone architecture and elasticity in the posterior maxilla are recommended to develop comprehensive clinical recommendations considering bone volume and quality limitations. Such research can potentially enable the placement of narrower implants in cases of insufficient bone. Full article

Extrusion-based polymer three-dimensional (3D) printing, specifically fused deposition modeling (FDM), has been garnering increasing interest from industry, as well as from the research and academic communities, due to its low cost, high speed, and process simplicity. However, bed adhesion failure remains an obstacle to diversifying the materials and expanding the industrial applications of the FDM 3D-printing process. Therefore, this study focused on an investigation of the surface treatment methods for aluminum (Al) foil and their applications to 3D printer beds to enhance the bed adhesion of a 3D-printed polymer filament. Two methods of etching with sodium hydroxide and anodization with phosphoric acid were individually used for the surface treatment of the Al foil beds and then compared with an untreated foil. The etching process removed the oxide layer from the Al foil and increased its surface roughness, while the anodizing process enhanced the amount of hydroxide functional groups and contributed to the formation of nano-holes. As a result, the surface-anodized aluminum foil exhibited a higher affinity and bonding strength with the 3D-printed polymers compared with the etched and pristine foils. Through the increase in the success rate in 3D printing with various polymers, it became evident that utilizing surface-treated Al foil as a 3D printer bed presents an economical solution to addressing bed adhesion failure. Full article

Entrapped air pockets can cause failure in water distribution systems if air valves have not been appropriately designed for expelling air during filling manoeuvres performed by water utilities. One-dimensional mathematical models recently developed for studying this phenomenon do not consider the effect of blocking columns inside water pipelines. This research presents the development of a mathematical model for analysing the filling process in a pipeline with an undulating profile with various air valves, including blocking columns during starting-up water installations. The results show how different air pocket pressure peaks can be produced over transient events, which need to be analysed to ensure a successful procedure that guarantees pipeline safety during the pressure surge occurrence. In this study, an experimental set-up is analysed to observe the behaviour of two blocking columns during filling by comparing the air pocket pressure pulses. Full article

Sandbars are commonly encountered in coastal environments, acting as natural protections during storm events. However, the sandbar response to waves and possible shear failure is poorly understood. In this research, a two–dimensional numerical model is settled to simulate the wave-induced sandbar soil dynamics and instability mechanism. The model, which is based upon the Reynolds-averaged Navier–Stokes (RANS) equations and Biot’s consolidation theory, is validated using available experiments. Parametric studies are then conducted to appraise the impact of the wave parameters and soil properties on soil dynamics. Results indicate that the vertical distribution of the maximum vertical effective stress in the sandbar is different from that in the flat seabed, which decreases rapidly along the soil depth and then increases gradually. The impact of soil permeability and saturation on the vertical effective stress distribution around the sandbar also differ from that in the flat seabed. Unlike the flat seabed, the vertical distribution of shear stress in the sandbar increases with an increasing wave period. The sandbar soil shear failure potential is discussed based upon the Mohr–Coulomb criterion. Results show that the range of shear failure around the sandbar is wider and the depth is deeper when the wave trough arrives. Full article

Existing methods for calculating the ultimate support pressure of tunnel faces do not consider the control of seepage flow. Therefore, a model for calculating the ultimate support pressure under seepage conditions was established based on a two-dimensional water head distribution model and the upper bound theorem of limit analysis. The reliability of this method was verified through comparisons with other studies. Subsequently, the influence of water level and tunnel face water pressure coefficient on stability was analyzed. The results indicate that the ultimate support pressure is linearly positively correlated with the water level and tunnel face water pressure coefficient; as the water level increases and the water pressure coefficient decreases, the failure area extends and enlarges. Finally, an existing seepage flow calculation formula was introduced, and a method for calculating the ultimate support pressure based on seepage control was proposed. The appropriate tunnel face water pressure coefficient is determined through the seepage flow calculation formula, and the corresponding ultimate support pressure is then calculated. The results demonstrate that this method can provide better theoretical guidance for seepage control in tunnel faces in practical engineering. Full article

Under atmospheric pressure, partial discharge initiated by free metallic particles has consistently been a significant factor leading to failures in high-voltage electrical equipment. Simulating the propagation of negative streamer discharge in N₂/O₂ mixtures contributes to a better understanding of the occurrence and evolution of partial discharge, optimizing the insulation performance of electrical equipment. In this study, a two-dimensional plasma fluid dynamics model coupled with the current module was employed to simulate the evolution process of negative streamer discharge caused by one free metallic particle under a suspended potential at 220 kV applied voltage conditions. Simulation results indicated that the discharge process could be divided into two distinct stages: In the first stage, the electron ionization region detached from the electrode surface and propagated independently. During this stage, the corona discharge on the negative electrode surface provided seed electrons crucial for the subsequent development of negative corona discharge. The applied electric field played a dominant role in the propagation of the electron region, especially in the electron avalanche region. In the second stage, space charge gradually took over, causing distortion in the spatial field, particularly generating a substantial electric field gradient near the negative electrode surface, forming an ionization pattern dominated by ionization near the negative electrode surface. These simulation results contribute to a comprehensive understanding of the complex dynamic process of negative streamer discharge initiated by free metallic particles, providing essential insights for optimizing the design of electrical equipment and insulation systems. Full article

Predicting student outcomes is an essential task and a central challenge among artificial intelligence-based personalised learning applications. Despite several studies exploring student performance prediction, there is a notable lack of comprehensive and comparative research that methodically evaluates and compares multiple machine learning models alongside deep learning architectures. In response, our research provides a comprehensive comparison to evaluate and improve ten different machine learning and deep learning models, either well-established or cutting-edge techniques, namely, random forest, decision tree, support vector machine, K-nearest neighbours classifier, logistic regression, linear regression, and state-of-the-art extreme gradient boosting (XGBoost), as well as a fully connected feed-forward neural network, a convolutional neural network, and a gradient-boosted neural network. We implemented and fine-tuned these models using Python 3.9.5. With a keen emphasis on prediction accuracy and model performance optimisation, we evaluate these methodologies across two benchmark public student datasets. We employ a dual evaluation approach, utilising both k-fold cross-validation and holdout methods, to comprehensively assess the models’ performance. Our research focuses primarily on predicting student outcomes in final examinations by determining their success or failure. Moreover, we explore the importance of feature selection using the ubiquitous Lasso for dimensionality reduction to improve model efficiency, prevent overfitting, and examine its impact on prediction accuracy for each model, both with and without Lasso. This study provides valuable guidance for selecting and deploying predictive models for tabular data classification like student outcome prediction, which seeks to utilise data-driven insights for personalised education. Full article

Friction-type bolted joints are widely used in both the civil and aerospace industries. Uncontrolled excessive bolt clamping force can cause damage to the laminated fiber-reinforced polymeric (FRP) composite through the thickness and damage the joint before applying the service loads. The effect of the friction coefficient (between 0 and 0.3), bolt clearance, joint type, and other parameters on failure modes and the maximum bolt clamping force of the carbon FRP lapped joint is studied. A three-dimensional finite element (FE) model consisting of a bolt, a washer, a laminate FRP composite plate, and steel plates was developed for the simulation of the double- (3DD) and single (3DS)-lapped bolted joint. The FE model was validated by using experimental results and was able to predict the experimental results by a difference of between 2.2 and 6.7%. The joint capacity of the clamping force was found to be greatly increased by adopting the double lap technique, which involves placing an FRP composite plate between two steel plates. Also, it was recommended to use an internal washer diameter less than or equal to the FRP composite plate hole diameter since a larger washer clearance can produce higher contact pressure and reduce the resistance by 22%. In addition, reducing the bolt head diameter can lead to a 65% reduction in the 3DS joint clamping strength. Full article

Nonlinear soil–pile–structure interaction (SPSI) phenomena are known to play a vital role in the response of bottom-fixed marine structures. For such structures, these phenomena are commonly considered by the imposition of p-y, τ-z, and q-z springs, representing the lateral and axial shaft and axial base soil resistances, respectively. The importance of each resistance mechanism depends on the type of foundation system, with only very limited studies investigating their roles in the response of piled marine structures, such as jetties. Within this context, this study presents numerical three-dimensional pushover analysis results for two marine jetties, a smaller model with four piles and a larger model supported by twenty-four piles. SPSI effects are considered through p-y, τ-z, and q-z springs, the behaviours of which are determined by following commonly employed procedures. The structures’ responses are investigated under the influence of various assumptions regarding the behaviours of springs, as well as steel plasticity. The current investigation underscores the substantial influence of the axial soil–pile interaction on the response of the jetty, particularly in terms of its failure mode. Moreover, it demonstrates the importance of incorporating p-y springs, even though the choice between their linear or nonlinear constitutive behaviour is found to be less critical. Finally, the study concludes that the behaviours of the springs significantly affect the system’s ductility and the degree of steel yielding in the piles, while also highlighting the unconservative influence of neglecting SPSI phenomena. Full article