Search Results (40,359)

This study examines the residual stress induced by manufacturing and its effect on failure in thermosetting unidirectional composites under quasi-static loading, using Finite Element-based computational models. During the curing process, the composite material develops residual stress fields due to various phenomena. These stress fields are predicted using a constitutive viscoelastic model and subsequently initialized within a damage-driven Phase Field model. Structural tensors are used to modify the stress-based failure criteria to account for inherent transverse isotropy. This influence is incorporated into the crack phase field evolution equation, enabling a modular framework that retains all residual stress information through a heat-transfer analogy. The proposed coupled computational model is validated through a representative numerical case study involving L-shaped composite parts. The findings reveal that cure-induced residual stresses, in conjunction with discontinuities, play a critical role in matrix cracking and significantly affect the structural load-carrying capacity. The proposed coupled numerical approach provides an initial estimation of the influence of manufacturing defects and streamlines the optimization of cure profiles to enhance manufacturing quality. Among the investigated curing strategies, the three-dwell cure cycle emerged as the most effective solution. Full article

Surface waterbodies are heavily exposed to pollutants caused by natural disasters and human activities. Empowering sensor technologies in water quality monitoring, sufficient measurements have become available to develop machine learning (ML) models. Numerous ML models have quickly been adopted to predict water quality indicators in various surface waterbodies. This paper reviews 78 recent articles from 2022 to October 2024, categorizing water quality models utilizing ML into three groups: Point-to-Point (P2P), which estimates the current target value based on other measurements at the same time point; Sequence-to-Point (S2P), which utilizes previous time series data to predict the target value at one time point ahead; and Sequence-to-Sequence (S2S), which uses previous time series data to forecast sequential target values in the future. The ML models used in each group are classified and compared according to water quality indicators, data availability, and model performance. Widely used strategies for improving performance, including feature engineering, hyperparameter tuning, and transfer learning, are recognized and described to enhance model effectiveness. The interpretability limitations of ML applications are discussed. This review provides a perspective on emerging ML for surface water quality models. Full article

With the new target set by the International Maritime Organization (IMO) of zero net emissions of atmospheric gases from maritime vessels by 2050, studies of methods that improve the efficiency of vessels have become highly relevant. One promising method is air injection, which creates a lubricating film between the hull and water, reducing the total resistance. Despite the potential of air injection, there is a lack of studies defining the correlation between key parameters (such as air layer thickness, injection angle, vessel speed, and the number of nozzles) in the method efficiency. Therefore, this study aimed to assess the method’s efficiency through a parametric analysis. The study utilized the OpenFOAM software to analyze the air injection method in the Duisburg Test Case (DTC) hull, a 1:59 scaled container ship. The numerical solution used finite volumes to discretize the conservation equations, RANS (Reynolds-Averaged Navier–Stokes) in the momentum equation, and $\kappa$-$\omega$ SST in the turbulence model. The optimum configuration achieved 14.13% net power savings, while the worst configuration increased the power consumption instead. An analysis of variance (ANOVA) confirmed the relationship between parameters and effectiveness. Therefore, the results showed the importance of adjusting the method’s parameters. Full article

The strain data acquired from structural health monitoring (SHM) systems of large-span bridges are often contaminated by a mixture of temperature-induced and vehicle-induced strain components, thereby complicating the assessment of bridge health. Existing approaches for isolating temperature-induced strains predominantly rely on statistical temperature–strain models, which can be significantly influenced by arbitrarily chosen parameters, thereby undermining the accuracy of the results. Additionally, signal processing techniques, including empirical mode decomposition (EMD) and others, frequently yield unstable outcomes when confronted with nonlinear strain signals. In response to these challenges, this study proposes a novel temperature-induced strain separation technique based on improved blind source separation (BSS), termed the Temperature-Separate Second-Order Blind Identification (TS-SOBI) method. Numerical verification using a finite element (FE) bridge model that considers both temperature loads and vehicle loads confirms the effectiveness of TS-SOBI in accurately separating temperature-induced strain components. Furthermore, real strain data from the SHM system of a long-span bridge are utilized to validate the application of TS-SOBI in practical engineering scenarios. By evaluating the remaining strain components after applying the TS-SOBI method, a clearer understanding of changes in the bridge’s loading conditions is achieved. The investigation of TS-SOBI introduces a novel perspective for mitigating temperature effects in SHM applications for bridges. Full article

Suspended particulate matter (SPM) is an indispensable component of water environments. Its fate and transport involve various physical and biogeochemical cycles. This paper provides a comprehensive review of SPM dynamics by integrating insights from biogeochemical processes, spatiotemporal observation techniques, and numerical modeling approaches. It also explores methods for diagnosing SPM-mediated biogeochemical processes, such as the flocculation kinetics test and organic matter composition analysis. Advances in remote sensing, in situ monitoring, and high-resolution retrieval algorithms are discussed, highlighting their significance in detecting and quantifying SPM concentrations across varying spatial and temporal scales. Furthermore, this review examines integrated models that incorporate population balance equations on the basis of flocculation kinetics into multi-dimensional sediment transport models. The results from this study provide valuable insights into SPM dynamics, ultimately enhancing our knowledge of SPM behavior and transport in water environments. However, uncertainties remain due to limited field data on flocculation kinetics and the need for parameter optimization in numerical models. Addressing these gaps through enhanced fieldwork and model refinement will significantly improve our ability to predict and manage SPM dynamics, which is critical for sustainable aquatic ecosystem management in an era of rapid environmental change. Full article

The triply periodic minimal surface (TPMS) is receiving much interest among researchers. The advantage of using this TPMS structure is the ability to design a structure based on engineering need. In the present context, experimental measurement was conducted and compared with numerical models using a foam porous medium and TPMS porous structure, leading to an accurate calibration of the model. A porous medium, metal foam, was heated experimentally at the bottom, and forced convection was investigated for different heating conditions. Then, the porous foam was replaced with a TPMS, and the experiment was repeated under similar conditions. The experimental data were compared with the numerical model using COMSOL software. Besides the model’s accuracy, the TPMS showed a uniform heating condition contrary to the metal foam case. At a later stage, the numerical model was used to investigate the importance of flow direction (two flow directions) in cooling hot surfaces. The first flow was parallel to the hot surface, and the second perpendicular to the hot surface. The TPMS structure was located on the top of the hot surface and acted as a fin in both cases. The Nusselt number exceeded 80 in the presence of the TPMS. As the porosity of the TPMS decreases below 0.7, a more considerable pressure drop is observed. The performance evaluation criterion was found to be greater than 70 when the porosity of the TPMS structure was 0.8. Full article

Background/Objectives: Investigating the importance and potential causal effects of serum lipid biomarkers in the management of hypertension is vital, as these factors positively impact the prevention and control of cardiovascular disease (CVD). Methods: We surveyed 3373 urban residents using longitudinal data from the CHARLS database, collected between 2015 and 2020. Pearson correlation methods were employed to explore the relationships among the numerical variables. A logistic regression model was utilized to identify the risk factors for hypertension. The dose–effect relationship between serum lipids and BP was assessed using restricted cubic splines (RCS). Additionally, piecewise structural equation modeling (PiecewiseSEM) was conducted to further elucidate the direct and indirect pathways involving individual body indices, serum lipids, and PA on BP responses at different levels of physical activity (PA). Results: The four serum lipids showed significant differences between hypertensive and non-hypertensive residents (p < 0.05). All lipids, except for HDL cholesterol, demonstrated extremely significant positive correlations with both systolic blood pressure (SBP) and diastolic blood pressure (DBP) (p < 0.001). All serum lipid variables were significantly associated with the incidence of hypertension. Specifically, triglycerides (bl_tg), HDL (bl_hdl), and low-density lipoprotein LDL cholesterol were identified as significant risk factors, with odds ratios (ORs) of 1.56 (95% CI: 1.33–1.85, p < 0.001), 1.16 (95% CI: 1.02–1.33, p < 0.05), and 1.62 (95% CI: 1.23–2.15, p < 0.001), respectively. Conversely, cholesterol (bl_cho) was a protective factor for hypertension, with an OR of 0.60 (95% CI: 0.42–0.82, p < 0.01). PA showed weak relationships with blood pressure (BP); however, PA levels had significant effects, particularly at low PA levels. The four serum lipids had the most mediating effect on BP, especially under low PA level conditions, while PA exhibited a partly weak mediating effect on BP, particularly under high PA level conditions. Conclusions: Serum lipids have significant nonlinear relationships with BP and PA levels exert different influences on BP. The significant mediating effects of serum lipids and the weak mediating effects of PA on individual body indices related to SBP and DBP demonstrate significant differences across varying levels of PA, highlighting the importance of low PA levels in hypertension management. This study could provide valuable recommendations and guidance in these areas. Full article

Soiling in PV modules is one of the biggest issues affecting performance and economic losses in PV power plants; thus, it is essential to supervise and forecast soiling profiles and establish the best cleaning program. This paper analyzes different methods for soiling modeling in Large Grid-Connected PV Plants and discusses the different factors influencing soiling. Analytical models from environmental conditions are discussed in detail, comparing the proposed model by the authors (SOMOSclean) with another three relevant models from the literature (Kimber, HSU, and Toth), applying them to 16 PV power plants in Spain (total capacity of 727 MWp). Uncertainty between models and sensors is also measured, presenting the numerical results for a period of 2 years. While simpler models may offer straightforward implementation, they often fail to capture the full complexity of soiling dynamics, leading to increased RMSE error. Full article

The aim of this study is to introduce and evaluate a dual filter that combines Radial Basis Function neural networks and Kalman filters to enhance the accuracy of numerical wave prediction models. Unlike the existing methods, which focus solely on systematic errors, the proposed framework concurrently targets both systematic and non-systematic parts of forecast errors, significantly reducing the bias and variability in significant wave height predictions. The produced filter is self-adaptive, identifying optimal Radial Basis Function network configurations through an automated process involving various network parameters tuning. The produced computational system is assessed using a time-window procedure applied across divergent time periods and regions in the Aegean Sea and the Pacific Ocean. The results reveal a consistent performance, outperforming classic Kalman filters with an average reduction of 53% in bias and 28% in RMSE, underlining the dual filter’s potential as a robust post-processing tool for environmental simulations. Full article

The motor gear system (MGS) is recognized for its potential in enhancing transmission efficiency and optimizing space utilization. However, the system is subjected to challenges, notably the occurrence of abnormal vibrations. These issues stem from the dynamic interaction between the motor and gears, the presence of nonlinear factors in gear system, and the impact of gear faults, all of which contribute to complex vibration patterns. Traditional dynamic models have been found to be inadequate in effectively addressing the complexities associated with electromechanical coupling problems in MGS. To address these limitations, a comprehensive analysis approach is proposed in this paper, which is grounded in the development of an electromechanical coupling model. This method involves establishing a coupled dynamic model of the motor and gear system, integrating numerical simulations, and experimental validations to thoroughly analyze the vibration characteristics of the system. Through this multifaceted methodology, a detailed analysis of the system’s vibration characteristics is conducted. The results indicate that internal excitations from tooth root cracks not only directly affect dynamic characteristics of the gear transmission system (GTS) but also indirectly influence dynamic behavior of the motor, which offers valuable insights into modeling integrated MGS and provides significant solutions for fault diagnosis within these systems. Full article

Structural damage identification based on structural health monitoring (SHM) data and machine learning (ML) is currently a rapidly developing research area in structural engineering. Traditional machine learning techniques rely heavily on feature extraction, where weak feature extraction can lead to suboptimal features and poor classification performance. In contrast, ML-based methods, particularly deep learning approaches like convolutional neural networks (CNNs), automatically extract relevant features from raw data, improving the accuracy and adaptability of the damage identification process. This study developed a time–frequency-based data-driven approach aiming to improve the effectiveness of traditional data-driven structural damage identification approaches for large complex structures. Firstly, the structural acceleration signals in the time domain were converted into two-dimensional images via the Gram angle difference field (GADF). Subsequently, the characteristic feature in the image data was studied by convolutional neural networks (CNNs) to predict the structural damage conditions. An experimental study on a scale model of a cable-stayed bridge was conducted to identify the damage of stay cables under the moving vehicle load on the main girders. The CNN was employed to extract the characteristic features from the time-varying monitoring data of vehicle–bridge interactions. The CNN parameters were optimized to conduct the structural damage classification task. The performance of the proposed method was evaluated by comparing it with various traditional pre-trained networks. The effect of environmental noise on the prediction accuracy was also investigated. The numerical results show that the ResNet model has the best performance in terms of damage identification accuracy and convergence speed, achieving higher accuracy and faster convergence compared to the other four traditional networks. The method can accurately identify damage on bridges using insufficient sensors on the bridge deck, which has valuable potential for application to real-world bridges with monitoring data. As the Signal-to-Noise Ratio (SNR) decreases from 20 dB to 2.5 dB, the prediction accuracy of ResNet decreases from 86.63% to 62.5%, which demonstrates the robustness and reliability in identifying structural damage. Full article

In this paper, the influence of the structure of the bottom of the ladle with ceramic dam or set of dams on the mixing process was assessed, determining the mixing time required to achieve the level of 95% chemical homogenization. The 0.1 scale water model was used for the physical experiments. The numerical simulations were carried out in the Ansys-Fluent 12.1 software for a 1:1 scale ladle and the behavior of hot metal—nitrogen system. The research focused on three issues, i.e., the influence of the flow rate of technical gas, the influence of the position of the top injection lance, and the influence of the type of dam mounted in the ladle bottom. Finally, the use of a semi-circle dam or set of dams in the ladle bottom together with the top lance being set to a lower depth resulted in a significant reduction in the total mixing time of the liquid metal by 42% and 50%, respectively, without increasing the nitrogen flow rate. Full article

This study employs a novel physics-informed neural network (PINN) approach, the standard explicit finite difference method (EFDM) and unconditionally positivity preserving FDM to tackle the one-dimensional Sine–Gordon equation (SGE). Two test problems with known analytical solutions are investigated to demonstrate the effectiveness of these techniques. While the three employed approaches demonstrate strong agreement, our analysis reveals that the EFDM results are in the best agreement with the analytical solutions. Given the consistent agreement between the numerical results from the EFDM, unconditionally positivity preserving FDM and PINN approach and the analytical solutions, all three methods are recommended as competitive options. The solution techniques employed in this study can be a valuable asset for present and future model developers engaged in various nonlinear physical wave phenomena, such as propagation of solitons in optical fibers. Full article

Background: Low back pain has frequently been mentioned as the most common sort of chronic pain, and numerous studies have confirmed its influence on the health-related quality of life (HRQoL). Despite a great deal of research demonstrating the important part that psychological factors play in explaining HRQoL, a therapeutic setting that prioritizes the physical domain still predominates. For this reason, the aim of this study is to assess the relationship between age, pain intensity, pain catastrophizing, depression, anxiety, pain-related anxiety, chronic pain acceptance and the psychological and physical dimensions of HRQoL in patients with chronic low back pain (CLBP). Methods: Data were collected from 201 patients with CLBP using sociodemographic data, the SF-36 Health Status Questionnaire (SF-36), the Hospital Anxiety and Depression Scale (HADS), the Pain Anxiety Symptoms Scale Short Form 20 (PASS-20), the Pain Catastrophizing Scale (PCS), the Chronic Pain Acceptance Questionnaire (CPAQ-8) and the Numeric Pain Rating Scale (NRS). The linear regression model for the dependent variable of Physical Health (SF-36 PhyH) was statistically significant (F (7, 201) = 38.951, p < 0.05), explaining 57.6% of the variance regarding the Physical Health dimension of HRQL in patients with CLBP. Results: The linear regression model for the dependent variable of Psychological Health (SF-36 PsyH) was statistically significant (F (7, 200) = 39.049, p < 0.05), explaining 57.7% of the variance regarding the Psychological Health dimension of HRQL in patients with CLBP. Conclusions: The findings of this study confirm that age, pain intensity, depression, pain-related anxiety and chronic pain acceptance are significant predictors of the physical dimension of HRQoL, while pain intensity, anxiety and depression proved to be significant predictors of the psychological dimension of HRQoL in patients with CLBP. Full article

In this paper, the problem of preview optimal control for second-order nonlinear systems for marine vessels is discussed on a fully actuated dynamic model. First, starting from a kinematic and dynamic model of a three-degrees-of-freedom (DOF) marine vessel, we derive a fully actuated second-order dynamic model that involves only the ship’s position and yaw angle. Subsequently, through the higher-order systems methodology, the nonlinear terms in the system were eliminated, transforming the system into a one-order parameterized linear system. Next, we designed an internal model compensator for the reference signal and constructed a new augmented error system based on this compensator. Then, using optimal control theory, we designed the optimal preview controller for the parameterized linear system and the corresponding feedback parameter matrices, which led to the preview controller for the original second-order nonlinear system. Finally, a numerical simulation indicates that the controller designed in this paper is highly effective. Full article