Search Results (6,134)

Rotary tillage blades, as critical components of soil tillage machinery, encounter significant challenges in mountainous agricultural operations, where excessive wear and high energy consumption are persistent issues. To address these problems, this study proposes an integrated strategy combining structural optimization with surface reinforcement. A blade–soil interaction model based on Smoothed Particle Hydrodynamics (SPH) was developed to optimize blade geometry, reducing power consumption to 0.106 kW with a simulation error of only 2.83%. In parallel, Fe60–WC composite coatings containing 30%, 35%, and 40% WC were fabricated on 65Mn substrates using laser cladding. Microstructural analysis revealed significant grain refinement with increasing WC content, while tribological tests showed that the 35% WC coating blades exhibited superior wear resistance, with a mass loss of 1.9 mg, and a relatively low friction coefficient of 0.362. Field trials further confirmed that the blades resulted in a 45.75% reduction in average wear, after structural enhancement and the application of the optimized coating, with a measured loss of 2.259 g compared to the uncoated blades. These findings demonstrate the synergistic benefits of structural optimization and advanced surface engineering, providing an effective pathway to improve the durability and efficiency of rotary tillage blades in demanding field conditions. Full article

Valve internal leakage in thermal power stations exhibits a strong concealed nature. If it cannot be discovered and predicted of development trend in time, it will affect the safe and economical operation of plant equipment. This paper proposed an intelligent identification method for valve internal leakage that integrated an Improved Kepler Optimization Algorithm (IKOA) with Support Vector Regression (SVR). The Kepler Optimization Algorithm (KOA) was improved using the Sobol sequence and an adaptive Gaussian mutation strategy to achieve self-optimization of the key parameters in the SVR model. A multi-step sliding cross-validation method was employed to train the model, ultimately yielding the IKOA-SVR intelligent identification model for valve internal leakage quantification. Taking the main steam drain pipe valve as an example, a simulation case validation was carried out. The calculation example used Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE) and determination coefficient (R²) as performance evaluation metrics, and compared and analyzed the training and testing dataset using IKOA-SVR, KOA-SVR, Particle Swarm Optimization (PSO)-SVR, Random Search (RS)-SVR, Grid Search (GS)-SVR, and Bayesian Optimization (BO)-SVR methods, respectively. For the testing dataset, the MSE of IKOA-SVR is 0.65, RMSE is 0.81, MAE is 0.49, and MAPE is 0.0043, with the smallest values among the six methods. The R² of IKOA-SVR is 0.9998, with the largest value among the six methods. It indicated that IKOA-SVR can effectively solve problems such as getting stuck in local optima and overfitting during the optimization process. An Out-Of-Distribution (OOD) test was conducted for two scenarios: noise injection and Region-Holdout. The identification performance of all six methods decreased, with IKOA-SVR showing the smallest performance decline. The results show that IKOA-SVR has the strongest generalization ability and robustness, the best effect in improving fitting ability, the smallest identification error, the highest identification accuracy, and results closer to the actual value. The method presented in this paper provides an effective approach to solve the problem of intelligent identification of valve internal leakage in thermal power station. Full article

This study presents the geometric optimization of a Savonius-type VAWT with multi-element blade profiles using a full factorial design integrated with RSM. Two crucial geometric parameters, the blade twist angle ($\gamma$) and the aspect ratio ($AR$), were systematically varied to assess their influence on the power coefficient ($C_p$). Experimental measurements were performed in a controlled wind tunnel environment, and a second-order regression equation was used to model the resulting data. The optimization approach identified the combination of $\gamma$ and $AR$ that maximized $C_p$. The optimal configuration was achieved with a $\gamma$ of 30° and an $AR$ of 2.0, for which the experimentally measured power coefficient ($C_p$) reached a value of 0.2326. The results confirm that lower twist angles and higher aspect ratios enhance aerodynamic efficiency, reduce manufacturing complexity, and improve structural reliability. These findings highlight the potential of Savonius turbines as competitive solutions for small-scale energy harvesting in low-wind-speed environments. Moreover, the identified optimal configuration provides a basis for future work that focuses on scaling the design, integrating power transmission and electrical generation components, and validating performance under real operating conditions. Full article

In this paper, virtual synchronous generator (VSG) technology is innovatively introduced into the distributor-unified power flow controller (D-UPFC) control to simulate the power generation characteristics of the synchronous generator. Concepts such as inertia and damping in the synchronous generator are introduced into power electronic equipment to provide voltage and frequency support for the system. The VSG control system, which specifically includes the virtual governor, the virtual excitation regulator, and the construction of the VSG model, is designed first. Then, the overall control combining the VSG and the series converter in D-UPFC is discussed. Finally, based on the influence of moment of inertia and damping coefficient on the response parameters, a VSG parameter adaptive control strategy based on refined fuzzy control was proposed. The simulation shows that this strategy can effectively reduce the active overshot and frequency deviation in the dynamic process of the system, eliminate secondary oscillations, and improve the dynamic response capability. Full article

The blade is a core component of the gas turbine, and blade fouling is characterized by highly concealed failure modes in the early stages and significant destructive potential in later stages. To address the lack of intelligence in early warning systems for compressor fouling, this study proposes a data-driven approach combining a digital-twin-based dynamic simulation model with the Weibull Proportional Hazards Model (WPHM) algorithm to enable reliable fault early warning. A modular design methodology was first adopted to construct a digital gas turbine model of the gas–gas combined power system on a dynamic simulation platform. High-fidelity fault simulation data were then generated to represent both healthy and faulty operating conditions. Through data governance and uncertainty quantification, key parameters influencing compressor fouling were identified. The Pearson correlation coefficient was applied to screen the most sensitive indicators, ensuring effective input selection for the prognostic model. Using historical health data from the simulation platform, the WPHM algorithm was trained to learn degradation patterns and establish a baseline failure risk model. This trained WPHM was then deployed to monitor real-time performance trends and provide early warnings for compressor blade fouling. Validation results from multi-unit simulations show that the proposed method achieves a fault warning rate of 95.0%, demonstrating its effectiveness and readiness to meet practical engineering requirements. Full article

Cr content χ of 0.4 at% for a Cr doped Nd (1 at%): YAG laser rod (LR) gave a higher laser output (I_output) than that of 0.0, 0.7, and 1.0 at% in a specially designed compact solar-pumped laser (SPL) outdoors. I_outputs were measured as a function of an 808 nm pumping laser’s power indoors, changing the transmittance of the output coupler. From the obtained slope efficiencies, round-trip resonator losses Ls for the four χs were estimated, and the best-fit function L(χ) was derived. From the experimentally estimated Cr-to-Nd effective energy transfer efficiency η_Cr→Nd at the four χs, the best-fit function η_Cr→Nd(χ) was derived. Using L(χ), η_Cr→Nd(χ), and a wavelength λ- and χ-dependent absorption coefficient α(λ, χ), inferred from the literature, the power conversion efficiency η_power(χ) under 1 Sun was estimated. The estimated η_power(0.4) and η_power(0.7) were reproduced in experimentally deduced factors at the mode-matching efficiency η_mode = 0.19. The estimated maximum η_power(χ) appeared around χ = 0.2 at%, being 20% higher than that at χ = 0.4 at%. In addition to this, a composite LR (Cr, Nd: YAG core/Gd: YAG cladding) was found to achieve η_mode = 0.68 and η_power = 0.064, ranking among the highest-class SPL η_powers. Full article

Virtual synchronous generator (VSG) simulates the dynamic characteristics of synchronous generator, offering significant advantages in flexibly adjusting virtual inertia and damping parameters. However, their dynamic stability is susceptible to constraints such as control parameter design, grid disturbances, and the intermittent nature of distributed power sources. This study addresses the degradation of transient performance in traditional sliding mode control for VSG, caused by insufficient multi-parameter cooperative adaptation. It proposes an adaptive sliding mode control strategy based on radial basis function (RBF) neural networks. Through theoretical analysis of the influence mechanism of virtual inertia and damping coefficient perturbations on system stability, the RBF neural network achieves dynamic parameter decoupling and nonlinear mapping. Combined with an integral-type sliding surface to design a weight-adaptive convergence law, it effectively avoids local optima and ensures global stability. This strategy not only enables multi-parameter cooperative adaptive regulation of frequency fluctuations but also significantly enhances the system’s robustness under parameter perturbations. Simulation results demonstrate that compared to traditional control methods, the proposed strategy exhibits significant advantages in dynamic response speed and overshoot suppression. Full article

This study investigates the aerodynamic effect of Gurney flaps (GFs) of different heights on the performance of a Darrieus vertical axis wind turbine (VAWT). Through numerical simulations, the performance of a baseline airfoil is compared against configurations with GFs of 0.5%c, 1%c, and 1.5%c chord lengths across a range of tip-speed ratios (TSRs). Results identify the 0.5%c GF as the optimal configuration, providing consistent power enhancement across all tested conditions, unlike the taller flaps which showed inconsistent or negative effects. This optimal configuration achieved a peak power coefficient (C_p) of 0.366 at TSR = 2.0, a 3.73% improvement over the baseline, and critically, enhanced the low-speed power by 6.30% at TSR = 0.5, improving the turbine’s self-starting capability. Flow field analysis reveals a dual-benefit mechanism for this superior performance: at low TSRs, the GF delays flow separation during the upwind pass to increase lift, while at higher TSRs, it effectively manages the wake during the downwind pass to reduce drag and mitigate negative torque. The study concludes that the 0.5%c GF strikes an optimal balance between lift augmentation and drag. Full article

Accurately assessing the remaining useful life (RUL) is a significant challenge to the reliability of Silicon Carbide (SiC) MOSFETs and is crucial for their safe operation. Consequently, this paper proposes a novel data-driven prediction method that integrates Successive Variational Mode Decomposition (SVMD), the Sparrow Search Algorithm (SSA), and the Transformer model. The threshold voltage V_th is selected as the degradation parameter for prediction. Firstly, SVMD is utilized to decompose the original V_th data into a degradation trend component and several fluctuation components with different central frequencies, thereby providing a more precise feature for prediction models. Subsequently, based on the Transformer model, trend predictions are conducted on each intrinsic mode function (IMF) derived from SVMD, and these results are aggregated as the final predicted value of V_th. The hyperparameters of the Transformer are optimized using SSA to enhance prediction accuracy. Ultimately, a power cycling platform is constructed to acquire the dataset of the device, where the device is subjected to rated current and 80 °C junction temperature fluctuation stress during testing. Building upon this, the difference between the number of cycles when V_th reaches its upper limit and the current number of cycles is determined as the predicted RUL value. Results demonstrate that compared to both a single Transformer model and the SVMD-Transformer model, the proposed method achieves a higher coefficient of determination (R²) and a lower root mean square error (RMSE), indicating superior prediction performance. Full article

With the increasing penetration of renewable energy and the rising demand for power quality, the dynamic performance and accuracy of grid-voltage detection have become crucial for the control of grid-following devices such as dynamic voltage restorers (DVRs). However, the conventional moving average filter (MAF) in grid-voltage detection suffers from inherent limitations in dynamic response. To address this issue, this paper proposes a voltage-detection method, which is based on an adaptive variable-parameter filtering algorithm termed MAF-MATCH-V. First, a cascaded filter model is constructed by integrating a zero-pole matcher (MATCH) with the MAF. Frequency-domain analysis demonstrates that the MATCH compensates for the mid- and high-frequency magnitude attenuation and reduces the phase delay of the MAF, thereby accelerating the dynamic response while preserving its harmonic-rejection capability. Second, the influence of the matching coefficient on the time-domain response is investigated, and a time-varying adaptive strategy is designed to balance rapid disturbance recognition with steady-state convergence. Finally, experimental results under various non-ideal grid conditions demonstrate that the proposed method achieves superior overall performance compared with conventional approaches. Specifically, MAF-MATCH-V realizes millisecond-level event recognition and zero steady-state error convergence, making it a practical solution for the real-time control of grid-following equipment in modern power systems. Full article

In order to conveniently and efficiently determine the Permissible Exposure Limits (PELs) of organic chemicals in the workplace, this study employed Quantitative Structure–Activity Relationship (QSAR) modeling to predict properties related to occupational health and safety. The predictive study was conducted by correlating the PELs of 75 hydrocarbons and their oxygen-containing derivatives with the molecular structures of the organic compounds. Meanwhile, this study conducted a comprehensive and in-depth comparative analysis of the four developed predictive models. The sample set was partitioned using the Affinity Propagation (AP) clustering algorithm. Four characteristic molecular descriptors were selected by integrating the Genetic Algorithm (GA) with the variance inflation factor (VIF) value. Subsequently, the Multiple Linear Regression (MLR) model and two nonlinear models, namely the Support Vector Machine (SVM) and the Extreme Gradient Boosting (XGBoost), were developed and used for predictive comparison. Furthermore, the performance of the models was evaluated through both internal and external validation methods, and the Williams plots were constructed to define the model’s applicability domain. The results indicated that the XGBoost model achieved high performance, with a coefficient of determination (R²) of 0.9962 on the training set and 0.8892 on the testing set. The corresponding root mean square errors (RMSE) were 0.1012 and 0.6623 for the training and testing sets, respectively. The internal validation coefficient (Q²loo) was 0.8975, while the external validation coefficient (Q²ext) was 0.832. Moreover, the majority of the sample data (approximately 96%) fell within the application domain defined by ±3 times the standard residue-to-critical arm ratio, where h* = 0.2. This demonstrates that the XGBoost model exhibits excellent fitting capability, stability, and predictive power, thereby uncovering a significant nonlinear relationship between the molecular structure of compounds and the PELs. As outlined above, the utilization of the QSAR method for predicting the PELs of hydrocarbons and their oxygen-containing derivatives constitutes a highly effective approach. Full article

Lameness remains a significant welfare and productivity challenge in dairy farming, often underdiagnosed due to the limitations of conventional detection methods. Unlike most previous approaches to lameness detection that rely on a single-sensor or gait-based measurement, this study integrates four complementary data domains—behavioural, physiological, biochemical, and milk composition parameters—collected from 272 dairy cows during early lactation to enhance diagnostic accuracy and biological interpretability. The main objective of this study was to evaluate and compare the diagnostic classification performance of multiple machine learning (ML) algorithms trained on multimodal data collected at the time of clinical lameness diagnosis during early lactation, and to identify the most influential physiological and biochemical traits contributing to classification accuracy. Specifically, six algorithms—random forest (RF), neural network (NN), Ensemble, support vector machine (SVM), k-nearest neighbors (KNN), and logistic regression (LR)—were assessed. The input dataset integrated physiological parameters (e.g., water intake, body temperature), behavioural indicators (rumination time, activity), blood biochemical biomarkers (non-esterified fatty acids (NEFA), aspartate aminotransferase (AST), lactate dehydrogenase (LDH), gamma-glutamyl transferase (GGT)), and milk quality traits (fat, protein, lactose, temperature). Among all models, RF achieved the highest validation accuracy (97.04%), perfect validation specificity (100%), and the highest normalized Matthews correlation coefficient (nMCC = 0.94), as determined through Monte Carlo cross-validation on independent validation sets. Lame cows showed significantly elevated NEFA and body temperatures, reflecting enhanced lipid mobilization and inflammatory stress, alongside reduced water intake, milk protein, and lactose content, indicative of systemic energy imbalance and impaired mammary function. These physiological and biochemical deviations emphasize the multifactorial nature of lameness. Linear models like LR underperformed, likely due to their inability to capture the non-linear and interactive relationships among physiological, biochemical, and milk composition features, which were better represented by tree-based and neural models. Overall, the study demonstrates that combining sensor data with blood biomarkers and milk traits using advanced ML models provides a powerful, objective tool for the clinical classification of lameness, offering practical applications for precision livestock management by supporting early, data-driven decision-making to improve welfare and productivity on dairy farms. Full article

The present investigation conducts a comparative analysis of the nucleate pool boiling heat transfer performance of two dielectric fluids, a low-GWP hydrofluoroolefin-based fluid (commercially known as Opteon™ SF33, referred to hereafter as SF33) and a perfluoropolyether-based fluid with a high GWP (commercially known as Galden^R HT55, referred to hereafter as HT55) under atmospheric pressure conditions. Pool boiling experiments and visual observations were performed to assess essential performance parameters, such as critical heat flux, heat transfer coefficient, and boiling dynamics. SF33 exhibits enhanced heat transfer performance, achieving markedly higher heat transfer coefficient values at all the heat flux levels and attaining superior critical heat flux relative to HT55. The results show that SF33 provides a consistently higher heat transfer coefficient, reaching approximately 12 W/m²·K at maximum heat flux, compared to only 6 W/m²·K for HT55, representing nearly a 100% improvement. The visual observations indicated that reduced surface tension and increased latent heat of vaporization of SF33 facilitate more frequent bubble nucleation and smaller bubble departure, thereby enhancing its boiling performance. Properties of SF33 render it a superior candidate for high-performance cooling systems in data centers and power electronics. The study concludes that SF33 is a more efficient and adaptable fluid for next-generation cooling systems, providing superior heat dissipation and energy efficiency relative to HT55. Full article

Reducing energy consumption in buildings presents a challenge for the construction and architectural industries. Stakeholders in the building sector require innovative products and systems to reduce energy usage effectively. Building Energy Simulation (BES) tools are essential for understanding energy-related issues during the design phase. However, the existing BES tools are often complex and costly, making them inaccessible to many architects and engineers who lack the software expertise for integrating new systems into existing Building Energy Simulation frameworks. To address this gap, the authors of this article have developed a new tool that enables early-stage evaluation of building performance. Additionally, the tool includes Water Flow Glazing (WFG) as a construction element that is part of both the facade and the building’s heating and cooling system. The authors validated the methodology by comparing the results from the new tool with those from the commercial BES tool Indoor Climate and Energy IDA-ICE 5.0 in accordance with ASHRAE standards. The same cases were tested by comparing the indoor temperature of a room with the power absorbed by the water, as measured by both tools. A WFG facade can effectively help maintain comfortable room temperatures throughout both winter and summer while producing renewable thermal energy via water heat absorption. The accuracy of this tool was validated using the normalized root mean square error between results from the new tool and those from IDA-ICE 5.0, which remained below the maximum allowable error established by ASHRAE. Validation of the tool using an experimental prototype showed that a coefficient of determination (R²) of 0.91 can be achieved through iterative refinement between the model and measured data. Full article

Runoff simulation and forecasting is of great significance for flood control, disaster mitigation, and water resource management. Alpine regions are characterized by complex terrain, diverse precipitation patterns, and strong snow-and-ice melt influences, making accurate runoff simulation particularly challenging yet crucial. To enhance predictive capability and model applicability, this study takes the Upper Jinsha River as a case study and comparatively evaluates the performance of a physics-based hydrological model BTOP and the data-driven deep learning models LSTM and BiLSTM in runoff simulation and short-term forecasting. The results indicate that for daily-scale runoff simulation, the LSTM and BiLSTM models demonstrated superior simulation capabilities, achieving Nash–Sutcliffe efficiency coefficients (NSE) of 0.82/0.81 (Zhimenda Station) and 0.87/0.86 (Gangtuo Station) during the test period. These values are significantly better than those of the BTOP model, which achieved a validation NSE of 0.57 at Zhimenda and 0.62 at Gangtuo. However, the hydrology-based structure of the BTOP model endowed it with greater stability in water balance and long-term simulation. In short-term forecasting (1–7 d), LSTM and BiLSTM performed comparably, with the bidirectional architecture of BiLSTM offering no significant advantage. When it came to flood events, the data-driven models excelled at capturing peak timing and hydrograph shape, whereas the physical BTOP model demonstrated superior stability in flood peak magnitude. However, forecasts from the data-driven models also lacked hydrological consistency between upstream and downstream stations. In conclusion, the present study confirms that deep learning models achieve superior accuracy in runoff simulation compared to the physics-based BTOP model and effectively capture key flood characteristics, establishing their value as a powerful tool for hydrological applications in alpine regions. Full article