Search Results (245)

Reliable in situ temperature monitoring is essential for the safe operation of liquid-nitrogen-cooled superconducting cables, yet conventional electrical sensors are often difficult to scale to multi-point deployment in cryogenic, high-current environments. This work presents a fiber Bragg grating (FBG) sensing solution for in situ temperature monitoring of superconducting cables in liquid nitrogen. An FBG array packaged with a polyimide-coated fiber inside a 3 mm stainless-steel tube is deployed along the cable centerline to provide multi-point temperature measurements of the cable core. The system is validated under liquid-nitrogen immersion with a 2000 A current turn-on/turn-off test, with a 1 Hz update rate and a steady-state temperature fluctuation within ±0.1 °C. Experimental results show a continuous temperature decrease during liquid-nitrogen cooling, followed by a cryogenic plateau, during which a spatially consistent 0.6–0.7 °C current-induced temperature rise is observed across multiple sensing points in the present 2000 A turn-on/turn-off test, followed by recovery after current shutoff. Small-amplitude fluctuations during the plateau are attributed to packaging-dependent thermal coupling between the centerline-deployed sensor and the cable core. These results indicate that the proposed FBG-based approach enables reliable cryogenic thermometry for superconducting cables in liquid nitrogen and provides a practical tool for in situ operational condition assessment. Full article

Flexural stiffness identification of prestressed concrete beams plays an important role in evaluating the mechanical performance and damage condition of bridge structures and has become a critical research direction in bridge health monitoring. Accordingly, this paper presented a Physics-Informed Neural Network (PINN)-based method for flexural stiffness identification. In the physical modeling framework, point source forces in the beam-column equation (BCE) were represented by approximating the Dirac delta function with Gaussian functions. This strategy alleviated the convergence issue of the loss function caused by singular behavior and enabled the formulation of a unified governing equation for multi-point loading scenarios. To eliminate the long-term deflection caused by non-load-related factors and self-weight, the BCE was expressed in incremental form. The resulting nondimensional equation was adopted as the target constraint for PINN training to alleviate multi-scale challenges. Furthermore, the residual-based adaptive refinement (RAR) strategy was incorporated during network training to improve computational efficiency and identification accuracy. The proposed method was validated through nine numerical cases without linear relationships and three experimental cases. The results indicate that, even with limited measurement data and under the tested noise levels, the proposed framework can achieve satisfactory flexural stiffness identification under the tested loading conditions. This suggests that the proposed method has promising potential for flexural stiffness identification and may be useful in bridge structural health monitoring under sparse-data conditions. Full article

Fractional Langevin models are found to be useful in the study of physical phenomena such as diffusion processes, gait variability, etc. Langevin equations involving different fractional–order operators and boundary conditions have been addressed by many researchers. In this article, we formulate a new Langevin model consisting of a coupled system of Riemann–Liouville and Hadamard–type nonlinear fractional differential equations and coupled multipoint–integral boundary conditions. We present the existence and Ulam–Hyers stability criteria for solutions of the given model problem. Our study is based on the tools of the fixed–point theory. Numerical examples with graphical representations of solutions are offered to demonstrate the application of the obtained results. Our work is novel and useful in the given configuration, and specializes to some new results. Full article

Accurately quantifying vertical sediment transport rates in large seaward rivers is vital for estimating basin-scale water and sediment fluxes and assessing riverbed evolution. Traditional multi-point velocity and suspended sediment concentration (SSC) measurements are costly and slow, hindering long-term online monitoring. Bidirectional flows in tidal reaches further exacerbate this challenge. We propose a physics-constrained support vector machine (SVM) inversion method to estimate vertical sediment transport rates from single-point measurements. Constrained by modified logarithmic velocity and Rouse suspended sediment concentration profiles, it quantitatively relates single-point hydraulic variables to key parameters governing vertical distributions. Lower Yangtze River tidal reach field data validate the hybrid model’s successful reconstruction of vertical distributions. It accurately captures transient sediment responses across maximum flood and ebb. Inverted transport rates match measurements closely (RMSE = 0.085, NSE = 0.969, PBIAS = 2.50%) and exhibit strong cross-site generalization. Sensitivity analysis identifies 0.4 times the water depth above the riverbed as the optimal single-point sensor position. Although currently validated only in the lower Yangtze River, this low-cost, reliable method supports local basin management, flood control, and disaster mitigation by enabling continuous sediment flux monitoring. However, applying it to other river or estuarine systems may require recalibration or retraining to adapt to different local conditions. Full article

This study addresses the critical need for sustainable energy by optimizing diaphragm-type piezoelectric elements for efficient waste vibration energy harvesting. Traditional experimental optimization of complex, non-linear design parameters including applied load, tapper diameter, and support structures is often resource-intensive and time-consuming. To overcome these limitations, we developed a novel hybrid machine learning framework that seamlessly integrates an Artificial Neural Network (ANN) with a Modified Grey Wolf Optimization (mGWO) algorithm. The ANN was rigorously trained on experimental data using Bayesian Regularization, establishing itself as a robust and high-fidelity surrogate model capable of accurately predicting voltage output based on diverse input parameters, evidenced by an R-value close to 1. This predictive model subsequently served as the fitness function for the mGWO algorithm, which incorporated a non-linear control parameter to efficiently explore the multi-dimensional design space and effectively balance exploration with exploitation. The framework successfully identified the optimal configuration for maximizing voltage output, predicting a theoretical maximum of approximately 70.67 V. This optimal setup notably involved a high applied load of 100 N, the 6CA multi-pointed tapper configuration, and the three-support boundary condition, which is consistent with the experimentally validated results. The computational findings demonstrated excellent agreement with empirical results while providing significantly higher resolution for design insights. This validated, predictive tool offers a substantial advancement for the future scaling and design optimization of piezoelectric energy harvesters, minimizing the need for extensive physical prototyping and ensuring efficient stress transfer without mechanical failure. Full article

Generator faults are among the most expensive events in utility-scale wind turbines, and the remaining useful life (RUL) of a generator is strongly influenced by long-term thermal loading on windings and bearings. Although wind farms continuously log multi-point generator temperatures and operating variables via SCADA, these data are rarely converted into an actionable, quantitative RUL trajectory that can be used directly for maintenance planning. This study proposes a field-oriented RUL estimation framework that transforms multi-year SCADA records into degradation-focused indicators and converts them into a physically plausible, decision-ready RUL curve. First, SCADA data are cleaned and filtered by operating conditions, and temperature rises relative to ambient are extracted. Next, abnormal operation is detected and summarised using an abnormal operation index (AOI), and thermal severity indicators are aggregated into a health index (HI) that reflects both proximity to engineering limits and signal variability. The HI is then mapped to lifetime consumption to update an effective age relative to the generator’s designed lifetime, followed by smoothing and monotonicity enforcement to ensure a stable, non-increasing RUL trajectory. Field validation shows a highly smooth RUL profile (98.2%) and a near-linear long-term decreasing trend ($R^2=0.985$). The results demonstrate that SCADA temperature–operation data can support reliable online generator RUL prognostic monitoring without the need for additional sensors. Full article

The article considers nonlinear fractional differential equations with cotangent derivative. The boundary conditions are multipoint and integral specified, and the nonlinear terms are in Orlicz function spaces. Several existence theorems for solutions of such boundary value problems are obtained by different fixed-point methods. Illustrative examples serve to illustrate the theoretical parts. Full article

Multi-point array thin-film thermocouples have strong potential for high-precision, wide-range temperature monitoring in applications such as aircraft engine thermal condition assessment and industrial process control. However, conventional single-point thin-film thermocouples cannot satisfy the distributed measurement requirements of large-area temperature fields, and the accuracy of multi-point arrays is often degraded by coupling effects among sensing nodes, which hinders their engineering deployment. In this work, a multi-point array thin-film thermocouple is fabricated via precision welding, and an insulating layer is deposited on the sensor surface using electrospray atomization to establish a multi-point temperature-sensing hardware system. To compensate for coupling-induced deviations, a deep learning–based calibration method is developed: measurements from the array and reference thermocouples are synchronously collected to build the dataset, outliers are removed using the interquartile range (IQR) method, and a three-hidden-layer multilayer perceptron (MLP) is trained for each node independently using the Adam optimizer (learning rate 0.001) with an 8:2 train–test split. Performance is quantified by MAE, MSE, and R², and the results show that the proposed approach markedly reduces measurement errors and improves the accuracy of the array thermocouples, demonstrating reliable performance and practical applicability for precise large-area temperature-field monitoring. Full article

Port-area hydrogen refueling stations face low-frequency but high-consequence events when high-pressure leaks ignite as jet fires in wind-exposed, constrained environments. This study develops a consequence-based framework coupling theoretical screening, CFD combustion analysis, and hazard zoning to support separation-distance setting and emergency planning. A jet-fire model estimates flame-impingement distances for multiple leak diameters, and a weighted multi-point radiation model predicts heat-flux fields, from which lethal and irreversible-injury zones are delineated using thresholds of 7 and 5 kW/m², respectively. To move beyond wind-free screening, steady reacting-flow CFD is conducted for a representative release under four ambient conditions, with 4.34 m/s adopted as the representative wind speed for the windy cases based on Ningbo Port conditions. Validation against a visible-flame correlation defined by T ≥ 1573 K shows a deviation of 6.99%. Results show that radiation footprints expand markedly with diameter, with lethal and injury distances scaling approximately linearly within the studied range. Under wind, near-ground hot-plume extents defined by T ≥ 388 K and T ≥ 582 K depend strongly on wind direction and station geometry, whereas visible flame length is less sensitive. Additional sensitivity analyses indicate that the quasi-steady results are weakly affected by the selected ignition snapshot, while inclined releases modify projected plume/flame extents without altering the main engineering interpretation of the baseline case. The results support theory-based preliminary screening, but wind direction should be explicitly considered in exclusion-zone definition. Full article

In this paper, we study a nonlinear system of sequential Caputo fractional differential equations equipped with coupled mixed multi-point boundary conditions. In particular, the boundary conditions involve the values of the unknown functions at the endpoints expressed as linear combinations of their values at several interior points, forming a closed system of relations. The existence of solutions is established by applying the Leray–Schauder alternative, while uniqueness is proved using Banach’s contraction principle. In addition, we investigate the Hyers–Ulam stability of the proposed system. Several examples are included to demonstrate the applicability of the theoretical results. Some special cases of the general problem are also discussed. Full article

The range-extended sprayer can effectively balance the requirements of economy and power performance, which represents the development and transformation trend of intelligent plant protection machinery in the future. To more intuitively and reliably explore the energy variation rules of the range-extended sprayer under different energy management strategies (EMSs) and achieve optimal fuel economy, a co-simulation platform for energy management of the range-extended sprayer under multi-condition cyclic operation was established based on AVL-CRUISE and MATLAB Simulink. Meanwhile, a fuzzy control-based EMS optimized by the particle swarm optimization (PSO) algorithm was proposed. Simulation results show that the comprehensive fuel consumption of the PSO-optimized fuzzy control EMS is 3.68 kg; compared with the conventional fuzzy control strategy, its fuel economy is improved by 4.90%, and by 8.23% compared with the multi-point power following strategy. Subsequently, an energy management test platform for the range-extended sprayer was built, and experimental verification was carried out. The platform test results indicate that the electricity difference between the platform test and the simulation test is 0.38%, and the fuel consumption difference is 1.6%, both within a reasonable range. This further verifies the reliability of the simulation platform for the improved energy management model and the feasibility of the proposed EMSs. The research content and results provide theoretical basis and technical support for the optimization of EMSs and the joint simulation method of energy management for range-extended sprayers. Full article

Ultrafine hematite particles (<10 μm), commonly generated in beneficiation circuits, exhibit poor flocculation and slow settling, posing challenges for solid–liquid separation. This study investigates the influence of the anionic polyacrylamide (APAM) molecular weight on ultrafine hematite flocculation under controlled laboratory conditions, combining macroscopic experiments with molecular dynamics simulations (MDSs). Sedimentation tests show that the APAM molecular weight strongly affects settling kinetics, supernatant clarity, and floc structure, with the settling rate, flocculation-stage reaction time, supernatant turbidity, and underflow concentration exhibiting a non-monotonic trend and optimal performance at seven million. Under this condition, particles aggregate most efficiently, achieving a turbidity of 182 NTU, an underflow concentration of 51.5%, and the largest compact flocs, averaging 379.8 μm with a fractal dimension of 1.71. Higher molecular weights (≥9 million) induce chain coiling, reduce floc compactness, increase water retention, and impair settling. MDS indicates that polymer–surface interactions improve with an increasing polymerisation degree only up to an intermediate chain length; a polymerisation degree of 30 exhibits the most favourable extended–flexible conformation, maximal surface enrichment, strongest coordination between carboxyl groups and surface Fe atoms, lowest adsorption energy, and fastest adsorption kinetics. The functional-group distribution and hydrogen-bond analyses show that –NH₂ and –COO^– groups dominate interfacial interactions, with a polymerisation degree of 30 yielding the highest density of interfacial hydrogen bonds. By correlating macroscopic experiments with molecular-scale observations, this work provides mechanistic insight into how the APAM chain length governs ultrafine hematite flocculation, highlighting the role of polymer conformation and multipoint adsorption in controlling the settling performance. Full article

The widespread use of gasoline blended with 10% ethanol (E10) has raised questions regarding engine performance and emissions under conditions where ethanol supply may be disrupted, and pure gasoline (E0) is temporarily used instead. This study experimentally investigates the effects of E0 and E10 fuels on fuel consumption and exhaust emissions in spark-ignition engines equipped with two different fuel supply systems: multi-point fuel injection (MPI) and carburetion (CARB). Chassis dynamometer tests were performed on two passenger vehicles under steady-state part-load conditions at vehicle speeds of 60, 90, and 120 km/h, as well as during full-throttle operation. E0 and E10 were tested separately under identical operating points. Fuel consumption, brake-specific fuel consumption, air–fuel ratio, and exhaust gas components (CO, CO₂, HC, O₂) were measured and analysed. The results show that the MPI-equipped vehicle exhibited consistently lower fuel consumption when operating on E0 compared to E10, primarily due to the lower volumetric heating value of ethanol. In contrast, the carbureted engine demonstrated a stronger sensitivity to fuel composition, with E10 leading to leaner mixture formation and pronounced changes in fuel consumption and emissions. CO and HC emissions were significantly lower in the MPI engine, mainly due to closed-loop stoichiometric control combined with the presence of a three-way catalytic converter, while E10 substantially reduced these emissions in the carbureted engine. CO and HC emissions were significantly lower in the MPI configuration, mainly due to closed-loop stoichiometric control combined with the presence of a three-way catalytic converter. In the carbureted configuration, E10 substantially reduced CO and HC emissions compared to E0, primarily as a result of leaner mixture formation. Overall, the findings indicate that modern MPI engines are less sensitive to whether the supplied fuel is E10 and E0, whereas carbureted engines may show notable changes in performance and emissions under the same operating conditions. Full article

Modern buildings in subtropical and humid regions face growing challenges regarding energy consumption and indoor climate comfort. Traditional air conditioning and dehumidification systems are often inefficient, energy-intensive, and difficult to automate for real-time adaptation to fluctuating environments. The water curtain wall (WCW) leverages passive evaporative cooling and potential condensation dehumidification to deliver high energy efficiency and robust indoor microclimate regulation. Yet, its large-scale adoption depends on reliable automation, multi-point environmental sensing, and modular engineering that ensure stability, adaptability, and easy maintenance. The results of this study demonstrate a next-generation WCW system integrating multi-sensor feedback and dynamic control and a full cycle of engineering verification, operational analysis, and optimization for real-world deployment. Full article

Springback control in Multi-Point Stretch Forming (MPSF) is significantly hindered by the computational intensity of Finite Element Analysis (FEA) and the limited predictive robustness of traditional regression methods. This study develops a hybrid GWO-CatBoost model acting as a data-driven surrogate for MPSF simulations by integrating the Grey Wolf Optimizer (GWO) with the CatBoost algorithm for high-precision springback forecasting. An FEA model of the MPSF process was initially validated through experimental comparison under a representative working condition to assess modeling accuracy. A comprehensive dataset comprising 1200 scenarios was generated via a full factorial design, incorporating key variables: curvature radius, sheet thickness, cushion thickness, and pre-stretching rate. In this study, the GWO was employed to perform automated hyperparameter tuning for CatBoost by optimizing the learning rate, tree depth, and number of iterations, thereby enabling accurate modeling of the complex nonlinear relationship between process inputs and numerical springback values. Numerical evaluations demonstrate that the GWO-CatBoost model outperforms GWO-XGBoost and GWO-Random Forest benchmarks, achieving a Coefficient of Determination (R²) of 0.9293, a root mean square error (RMSE) of 0.0274 mm and mean absolute error (MAE) of 0.0189 mm. Sensitivity analysis identifies sheet thickness as the dominant factor (46% contribution), with cushion thickness as the secondary driver (23%). This predictive framework serves as a computationally efficient auxiliary surrogate, designed to assist iterative finite element analyses and support process optimization in the manufacture of complex-curved panels. Full article