Search Results (778)

Pump-turbines often experience performance deterioration under high-load conditions beyond their best efficiency point, while the underlying flow mechanisms remain insufficiently understood. In this study, we investigate the relationship between internal flow structures and energy performance in a pump-turbine operating at a rated head of 202 m over a range of guide vane openings. Energy losses are evaluated using an average kinetic energy-based method and compared with an entropy production approach. A threshold-independent rigid vorticity method is adopted for vortex identification, and a streamline-based coordinate system is introduced for spatial quantification of energy loss and blade loading. The results show that hydraulic losses are mainly concentrated in the draft tube (66–75%) and runner (25–30%) under high-load conditions. A coupled vortex system formed by separation vortices and horseshoe vortices governs localized dissipation in the runner. In the draft tube, a columnar vortex rope generates strong shear layers that dominate energy loss in the cone and elbow regions. At high flow rates, negative incidence induces pressure-side separation, forming negative torque regions that reduce net runner torque and lead to output power deterioration. These findings highlight the dominant role of coupled vortex structures and pressure redistribution in performance degradation under high-load operation. Full article

Triboelectric nanogenerators (TENGs) are promising for harvesting low-frequency mechanical energy, but rotating TENGs (R-TENGs) driven by low-speed water flow remain constrained by limited driving torque, sliding-contact losses, and rotating-system stability. Here, a three-dimensional (3D) riveted complementary-structure rotating triboelectric nanogenerator (RCSR-TENG) is proposed for low-speed water-flow energy harvesting. A semi-analytical formulation incorporating a force-dependent real-contact fraction is developed to describe the coupled relationships among output voltage, transferred charge, rotation angle, and contact force. Because the contact parameters were not independently calibrated, the formulation is used for sensitivity and trend analysis rather than as a quantitatively validated predictive model. For the single prototype tested for each configuration, at 1000 rpm under the fixed effective measurement load of 9 MΩ, the RCSR-TENG produced a peak output power of 544 μW, compared with 304 μW for the flat R-TENG, representing an increase of approximately 79%. The same RCSR-TENG prototype maintained a stable voltage amplitude of over 150,000 rotation cycles. When coupled to a fully passive flapping-foil collector in a 0.55 m s⁻¹ water flow, the system generated periodic electrical output with a peak area-normalized power exceeding 5000 μW m⁻². These results demonstrate the structural-performance advantage of the riveted complementary design and its proof-of-concept applicability to low-speed water-flow energy harvesting. Full article

Accurate air conditioning (AC) load prediction under extreme weather conditions is critical for power grid stability and energy management. While tree-based ensemble methods such as XGBoost, LightGBM, and Gradient Boosting have become the dominant paradigm in short-term load forecasting, their effectiveness for weather-sensitive AC load prediction—particularly during extreme weather events—remains insufficiently examined. This study presents a systematic comparison of four machine learning models—Support Vector Regression with RBF kernel (SVR-RBF), LightGBM, XGBoost, and Gradient Boosting—for daily AC load estimation conditioned on measured same-day meteorological and calendar features. Based on five years of processed load and weather data from a major city in Southwest China, we construct 31 features and evaluate model performance across four scenarios: normal days, weather-extreme days, high-load P85, and high-load P90. Hyperparameters are selected within the first four years by expanding-window validation, and the fifth year is held out for testing. Our results reveal that SVR-RBF achieves an overall $R^2$ of 0.9772, substantially outperforming LightGBM (0.9091), XGBoost (0.8972), and Gradient Boosting (0.9094); paired moving-block bootstrap intervals for the tree-minus-SVR MAE differences exclude zero. The advantage of SVR-RBF is most pronounced under extreme conditions: on weather-extreme days, SVR-RBF attains $R^2=0.8316$ versus $R^2=0.39$ for the best tree model. This pattern is consistent with the smooth U-shaped temperature–load relationship captured by the RBF kernel. Additional trend-sensitivity analysis shows that annual load growth and target-level extrapolation also explain a substantial part of the tree-model degradation. Furthermore, we quantify the performance limitation of the restricted feature set: while normal-day estimation achieves $R^2=0.984$, high-load P90 days reach $R^2=0.843$. These findings support SVR-RBF as a strong baseline for measured-weather conditional AC load estimation while emphasizing that temporal shift and training-domain coverage must be considered when interpreting model differences. For energy-system applications, the lower errors under high-load and weather-extreme conditions are relevant to peak-demand assessment, reserve planning, and demand-side management. Full article

This pilot in vitro study explored the tensile retention of lithium disilicate and monolithic zirconia crowns cemented onto zirconia one-piece implants using two resin cements and two cementation protocols. In addition, the relationship between micro-computed tomography (micro-CT)-derived cement layer characteristics and retention was explored for lithium disilicate crowns. Thirty-two implant–crown assemblies were prepared using 16 lithium disilicate and 16 zirconia crowns. Specimens were cemented with either an adhesive resin cement (Panavia V5) or a self-adhesive resin cement (SpeedCem Plus) using two protocols: conventional apical-half cementation (AH) and an abutment-assisted apical-half protocol (A-AH). Cement thickness and porosity for lithium disilicate crowns were obtained from a previously published micro-CT analysis of the same specimens; no micro-CT measurements were available for the zirconia specimens. Tensile pull-out testing was performed using a universal testing machine. The primary outcome was the maximum recorded force at the first observed mechanical failure, irrespective of the mode of that failure, so that all 32 specimens contributed a value. Failure occurred by crown debonding in 27 specimens, by crown fracture in 4 and by implant fracture in 1. For the primary outcome, the maximum recorded force was lower for lithium disilicate than for zirconia crowns (medians 347.20 versus 596.05 N; exact Mann–Whitney p = 0.017) and lower with the A-AH than with the AH protocol (medians 304.24 versus 614.38 N; p < 0.001), whereas the difference between the two resin cements was not statistically significant (medians 438.88 versus 550.83 N; p = 0.210). The highest observed mean maximum load was recorded for zirconia crowns cemented with Panavia V5 using the AH protocol (729.9 ± 237.7 N), whereas the lowest observed mean maximum load was recorded for lithium disilicate crowns cemented with Panavia V5 using the A-AH protocol (219.7 ± 105.1 N). In a secondary, cause-specific exploratory analysis restricted to crown debonding (27 events, 5 specimens censored at fracture), Cox proportional hazards regression on the applied-force scale gave hazard ratios of 3.75 (95% CI 1.40–10.01) for lithium disilicate versus zirconia, 6.47 (2.39–17.53) for A-AH versus AH and 1.82 (0.76–4.39) for Panavia V5 versus SpeedCem Plus. For lithium disilicate crowns, exploratory factorial ANOVA indicated that cementation protocol was associated with differences in cement thickness (p = 0.035), while cement type was associated with differences in porosity (p < 0.001). All 16 lithium disilicate cement thickness observations lay between 253.29 and 254.96 µm, a total span of 1.67 µm. Within that extremely restricted range, a univariable exploratory Cox model expressed per 0.1 µm gave a hazard ratio for debonding of 1.24 (95% CI 1.03–1.48; p = 0.024); this is an unadjusted association across a range that is itself associated with cementation protocol, and it does not demonstrate a clinically meaningful or independent effect of cement thickness. No association was detected for total porosity (0.959 per percentage point, 0.717–1.283); that interval is wide and indicates absence of evidence rather than evidence of no association. Within the limitations of this pilot in vitro study—four specimens per subgroup, wide confidence intervals and no adjustment for multiplicity—the findings suggest that crown material and cementation protocol may be associated with retention patterns. They are exploratory and hypothesis-generating and require confirmation in larger, independently powered studies. Full article

In the context of building a novel power system and with the increasing penetration of renewable energy, hydropower units are increasingly required to operate under wide-range operation (WRO) conditions. However, under the opening mode, the unit’s primary frequency regulation (PFR) performance is significantly affected by head and load fluctuations. This poses a risk of failing grid assessment requirements. To enhance the PFR performance, this study first establishes a nonlinear simulation model of the hydro-turbine regulation system (HTRS) for PFR and conducts a simulation analysis of PFR performance under varying head, load, and frequency deviation conditions. Subsequently, based on the simulation results, an operating-condition-dependent feedforward–feedback control strategy is proposed. This strategy utilizes a BP neural network (BPNN) to establish the head-power-opening (H-P-Y) mapping relationship, calculates the feedforward opening command in real time, and superimposes it with the PID feedback correction to form the total guide vane opening (GVO) setpoint. Performance comparisons through multi-condition simulations within the ranges of 70–100 m head, 10–90%P_r load, and 0.05–0.15 Hz frequency deviation demonstrate that the proposed strategy ensures that all selected assessment indices meet PFR compliance standards across all tested conditions. This study provides a feasible pathway for improving the PFR performance of hydropower units under WRO in the opening mode. Full article

The loss factor is an important index for evaluating the damping capacity of non-Newtonian viscous polymers used in viscous damping walls (VDWs). The modulus ratio method (MRM) and the tangent of the loss angle (TLA) are commonly used to calculate this parameter. However, deviations may arise when using the MRM or TLA to evaluate the damping capacity of a polymer exhibiting the power-law effect. To resolve this problem, a simplified equation for the loss factor that considers the power-law effect (SELF-PL) was proposed based on its energy definition. Then, a series of dynamic sandwich-type shear (DSTS) tests was conducted to measure the loss factor of a polymer (Oppanol-polyisobutylene B12, abbreviated as PIB-B12) for VDWs. Based on the DSTS test data, the accuracy of the SELF-PL was verified, and the error of the MRM caused by the power-law effect was investigated. Finally, an error equation for the MRM was derived to quantify its applicable range. The strain rate amplitude is found to be the most influential factor affecting a power-law material. The loss factor of PIB-B12 decreases with increasing loading frequency, while it increases with increasing strain amplitude. The relative error of the MRM exhibits a roughly linear relationship with the power-law exponent. Full article

In this paper, a fault diagnosis method based on temperature rise detection is proposed for power converters in switched reluctance motor drive systems used in electrical transportation equipment. First, the total power losses of all power devices are calculated and recorded under different operating conditions in both healthy and faulty states. A finite-element electrothermal model is then established to characterize the relationship between fault-induced power-loss redistribution and variations in the temperature rise of the converter devices. Based on the power-loss analysis, temperature rise is used as a key characteristic, and a corresponding fault diagnosis method is proposed. To account for the influence of operating conditions on the diagnostic criterion, three independent backpropagation neural network (BPNN) models are developed to predict fault-specific temperature-rise thresholds using rotor speed, load torque, and ambient temperature as inputs. During diagnosis, the real-time temperature evolution of the power diodes is compared with the selected thresholds to detect power converter faults. Finally, experimental results demonstrate the validity of the proposed fault diagnosis method. Full article

Rapid growth in artificial intelligence, machine learning, and high-performance computing has substantially increased data centre rack power densities, resulting in higher heat generation and more demanding cooling requirements. As water remains widely used in many cooling systems, understanding the relationship between cooling technologies and water consumption is essential for improving cooling efficiency and sustainability. This paper presents a survey of reported water usage effectiveness (WUE) across 83 data centre entries, providing a combined dataset that links WUE with heat-rejection categories. The reported data shows that 23 of these data centres exceed 0.4 L/kWh, which is a sustainability target specified by the Climate Neutral Data Centre Pact for new data centres in water-stressed regions using potable water. Dry facilities employing closed-loop liquid cooling require essentially no water, while evaporative systems typically report water usage effectiveness values up to 2.5 L/kWh. Reported WUE is a facility-level operational metric, set by the proportion of the IT heat load rejected by evaporation, which depends on the heat-rejection topology, ambient wet-bulb conditions, and operating set points. A higher server-side heat transfer coefficient permits a higher coolant supply temperature for a given chip temperature limit, widening the range of ambient conditions under which heat can be rejected without evaporative assistance. Server-side heat transfer is therefore an enabling condition for low WUE rather than a determinant of it. One-dimensional heat transfer models are developed to estimate heat transfer coefficients for different server-level cooling mechanisms widely used for cooling servers within data centres, including air cooling, single-phase immersion cooling, direct liquid cooling, and two-phase immersion cooling. Air cooling, with the lowest heat transfer coefficient, remains widely used in small-scale facilities, whereas direct liquid cooling and two-phase immersion cooling achieve coefficients up to three orders of magnitude higher and are increasingly deployed in high-density installations. These coefficients are used to derive an equivalent evaporative water demand, an upper-bound estimate of the water that would be evaporated in rejecting the heat each mechanism removes; it shares the units of reported WUE but describes thermal capability rather than facility water consumption. Full article

Accurate identification of customer transformer relationships in distribution sub-zones is a fundamental prerequisite for the refined management of low-voltage distribution networks and the integration of distributed generation sources. Addressing current issues such as missing records, non-standard wiring and unclear boundaries between multiple sub-zones, this paper proposes an identification method based on the Power Admittance Regression Algorithm (PARA). Based on the fundamental laws of electrical circuits, this method constructs a regressible model of the linear relationship between the total admittance at the transformer end and the admittances at each consumer end. By utilising electrical data collected simultaneously from smart metres and distribution transformer terminals, it formulates the identification of consumer transformer relationships as a problem of minimising regression residuals. For three typical operating conditions—pure residential load, mixed residential and commercial load, and photovoltaic connection at the feeder terminus—constrained least-squares regression models and binary regression models incorporating PV variables were established respectively; ridge regression regularisation was introduced to suppress multicollinearity and enhance model robustness. Simulation tests were conducted using a dataset comprising 150 consecutive time sections and 70 test nodes (of which 60 were customers within the local substation area and 10 were interference nodes from other substation areas) for validation. The results indicate that, under the three conditions described above, in engineering simulations accounting for three-phase imbalance, random perturbations in line parameters and measurement noise, the average accuracy of this method, as determined by 100 Monte Carlo simulations, was 86.2 percent, 92.8 percent and 93.1 percent respectively, with standard deviations ranging from 1.6% to 1.9%, thereby validating its effectiveness and superiority in scenarios involving complex load structures and the integration of renewable energy. As this work is based on simulation data, further online validation using actual feeder data from electricity consumption data acquisition systems is required. Full article

This study focuses on reducing harmonic distortion in residential electrical systems caused by the use of nonlinear household appliances. A combined prediction and control framework is proposed to estimate and reduce total harmonic distortion (THD) under different operating conditions. Measurements were collected from common domestic appliances, along with environmental factors such as temperature and humidity. An auto-optimized neighborhood fuzzy rough set (AO-NFRS) method is used to identify important input features. These features are then used in a physics-informed machine learning model to predict THD. Based on the predicted values, a Bayesian-optimized ANFIS controller is applied to decide the suitable filtering mode in real time. The results show that the proposed method improves prediction accuracy and reduces harmonic distortion compared to existing methods. It also provides stable filter switching under changing load conditions. The study demonstrates that combining measurement data, physical relationships, and adaptive control can improve power quality in residential systems. Full article

In industrialized countries, existing regulations generally require the use of renewable energy to the greatest feasible extent. A major difficulty with renewable sources is the inherent fluctuation in their power output due to the main source character. By now, one of the best technologies capable of providing rapid compensation for these fluctuations is hydroelectric power. Hydropower plants, those equipped with hydraulic turbines with fixed blades (e.g., Francis, propeller) are typically designed to operate close to their best efficiency point (BEP) with acceptable load limits in the vicinity due to vibrations and pressure pulsations. Usually, the swirling flow exiting the runner is tailored for peak overall efficiency, which minimizes energy losses in the draft tube cone. When operating away from the design point, draft tube cone losses increase abruptly, and pronounce flow instabilities arise (e.g., vortex rope). This study proposes a new method to control such instabilities that inject a radial-axial water jet into the draft tube cone. Compared with conventional axial water jet injection, the radial-axial jet requires a lower additional flow rate while still effectively suppressing hydraulic instabilities in the draft tube cone. The carried-out analysis was done numerically by using Ansys Fluent 2023 R2. The performed 3D unsteady numerical simulations were carried out to examine the internal flow behavior and evaluate the effect of the radial-axial water jet injection on the unsteady behavior of the flow unsteadiness. Finally, the paper quantifies the relationship between the draft tube pressure fluctuation amplitude and the auxiliary flow rate needed to mitigate these instabilities. Full article

In constructing power system load flow mapping relationships using machine learning algorithms, the fundamental prerequisite for ensuring the computational accuracy and generalization performance of the mapping model is the availability of a suitably sized, well-distributed, and high-quality labeled sample set that can be supplied in a precursory and efficient manner. Here, the quality of a load flow sample set is defined concretely by three concurrent properties, as follows: physical consistency (satisfaction of Kirchhoff’s and Ohm’s laws), representative coverage of the operational state space, and low inter-sample redundancy. Currently, both online and offline techniques for power flow samples are incapable of efficiently providing large-scale, high-quality power flow sample sets in this sense. To address this, the paper proposes a method for generating power flow sample sets that integrates a physical model of the power grid. This method encompasses the following: non-iterative, high-speed generation techniques for massive load flow samples; partitioned generation and multi-region splicing techniques for large power grid load flow samples; and the design of capacity requirements and quality technical indicators for load flow sample set production. Analytical results demonstrate that the proposed method can efficiently produce high-quality power flow sample sets of appropriate capacity based on actual needs. Case studies on the IEEE 9-bus and 39-bus systems show that sample generation is about 19 and 32 times faster than the whole-network Newton–Raphson method, respectively, for 100,000 samples, and the voltage-band capacity design requires only about 12.6% of the samples needed by uniform sampling for equal boundary-condition coverage, at a comparable learning error. Full article

Silver oxide–aluminum (AgO-Al) batteries are widely used as power sources for underwater vehicles, where direct current/direct current (DC/DC) converters play a critical role in supplying low-voltage loads. Accurate reliability assessment of DC/DC converters is essential for ensuring successful missions. Existing studies rarely consider the constraints between key performance parameters, as well as the load-dependent characteristics of measurement errors, limiting their applicability to DC/DC converters. To address these issues, a probabilistic model describing the relationship between operating load and measurement error is proposed, with particular emphasis on the load-dependent stochastic characteristics of measurement errors. Based on this relationship, load-dependent stochastic models for both the power-on and power-off voltages are developed by jointly considering unit-to-unit variability and random measurement errors. The model parameters are estimated by maximum likelihood estimation. A failure criterion incorporating the operational constraints between the power-on and power-off voltages is established according to the practical requirements of DC/DC converters. A reliability assessment method considering the dependence between the power-on and power-off voltages is provided according to the practical requirements. Based on test data, the results show that the Kolmogorov–Smirnov statistic of the proposed method is at least 86% lower than the compared methods, indicating a more accurate reliability assessment. Full article

Microgears enable mechanical power transmission, speed reduction, motion conversion, and synchronization in compact devices ranging from microelectromechanical systems to miniature robots and optically driven micromachines. Their behavior cannot, however, be inferred by geometrically scaling conventional gears alone. As size decreases, relative manufacturing errors, surface forces, friction, adhesion, environmental sensitivity, and metrological uncertainty become increasingly important, while torque capacity and stored kinetic energy decrease rapidly. This critical review integrates the design, manufacture, tribology, and system-level performance of microgears and microgear trains. It first clarifies dimensional terminology and derives the principal scaling relationships. It then compares external, internal, planetary, worm, bevel, compliant, and reconfigurable transmission architectures; evaluates silicon micromachining, electroforming, micro powder injection molding, microforming, micro-electrical discharge machining, ultrashort-pulse laser ablation, and additive microfabrication; and examines adhesion, friction, wear, lubrication, and environmental effects. Particular attention is paid to transmission efficiency, starting torque, backlash, transmission error, lifetime, and the influence of the measuring instrument on the observed response. The literature remains strongly weighted toward manufacturability and isolated components, whereas reproducible, loaded, system-level tests are comparatively scarce. On this basis, the review proposes a unified hierarchy of validation, a minimum functional test matrix, and scale-aware design indicators. The central conclusion is that successful microgear transmissions require concurrent design of geometry, process, surface condition, environment, load path, and measurement strategy. Full article

Traditional crack monitoring methods, relying on discrete point sensors, cannot capture the full spatiotemporal evolution of cracks. To address this limitation, this paper presents a distributed online monitoring approach using ultra-weak Fiber Bragg Grating (UWFBG) array sensing technology. A 16 m full-scale π-beam was instrumented with a grating array strain sensing system and tested under progressive mid-span loading until failure. The array successfully detected crack initiation at 848.7 kN (0.9P1) and tracked the transformation from L-shaped to U-shaped cracks, yielding a final crack count of 90 with a maximum width of 1.21 mm and length of 246.5 cm at 1791.7 kN. The strain–load curves exhibited a clear linear-to-nonlinear transition and continuous slope increase, closely matching manual observations. Quantitative correlation analysis further established a strong linear relationship between UWFBG peak strains and manually measured crack widths, with the fitting equation $\mathsf{\epsilon }=7918·\mathrm{w}-110$ and a coefficient of determination R² = 0.971, providing a specimen-specific basis for strain-based crack severity estimation that requires in-situ calibration before field application. The UWFBG array maintained stable signal acquisition throughout the entire loading process, offering superior data continuity and measurement range compared to resistive strain gauges, which suffered progressive data loss after cracking. The results demonstrate that the proposed method can provide real-time, full-field strain mapping and quantitative crack evolution monitoring, offering a powerful tool for bridge health assessment. Full article