Search Results (2,993)

Driven by the global energy transition and the dual carbon goals, developing low-carbon and zero-carbon alternative fuels has become a core issue for sustainable development in the internal combustion engine sector. Ammonia is a promising zero-carbon fuel with broad application prospects. However, its inherent combustion characteristics, including slow flame propagation, high ignition energy, and narrow flammable range, limit its use in internal combustion engines, necessitating the addition of auxiliary fuels. To address this issue, this paper proposes a composite injection technology combining “ammonia duct injection + hydrogen cylinder direct injection.” This technology utilizes highly reactive hydrogen to promote ammonia combustion, compensating for ammonia’s shortcomings and enabling efficient and smooth engine operation. This study, based on bench testing, investigated the effects of hydrogen direct injection timing (180, 170, 160, 150, 140°, 130, 120 °CA BTDC), hydrogen direct injection pressure (4, 5, 6, 7, 8 MPa) on the combustion and emissions of the ammonia–hydrogen engine. Under hydrogen direct injection timing and hydrogen direct injection pressure conditions, the hydrogen mixture ratios are 10%, 20%, 30%, 40%, and 50%, respectively. Test results indicate that hydrogen injection timing that is too early or too late prevents the formation of an optimal hydrogen layered state within the cylinder, leading to prolonged flame development period and CA10-90. The peak HRR also exhibits a trend of first increasing and then decreasing as the hydrogen direct injection timing is delayed. Increasing the hydrogen direct injection pressure to 8 MPa enhances the initial kinetic energy of the hydrogen jet, intensifies the gas flow within the cylinder, and shortens the CA0-10 and CA10-90, respectively. Under five different hydrogen direct injection ratios, the CA10-90 is shortened by 9.71%, 11.44%, 13.29%, 9.09%, and 13.42%, respectively, improving the combustion stability of the ammonia–hydrogen engine. Full article

The Practical Byzantine Fault Tolerance (PBFT) algorithm, while fundamental to consortium blockchains, suffers from performance degradation and vulnerability of leader nodes in large-scale scenarios. Existing improvements often prioritize performance while lacking systematic consideration of the structural characteristics of the nodes and network coverage. In this paper, a new network topology-aware Byzantine fault-tolerant algorithm NetTopoBFT is proposed for the supply chain and other application scenarios that require strict transaction finality but moderate throughput. Firstly, it innovatively combines the weighted signed network with the consortium chain, constructs a two-layer Bayesian smoothing node evaluation model, and evaluates the nodes through the two-dimensional evaluation of ‘behavioral reputation plus structural importance’. Then, to reduce the risk of being attacked, it uses Verifiable Random Function (VRF) to decide the leader. Furthermore, it uses a duplicate coverage-driven waitlisting mechanism to enhance the robustness and connectivity of the system. Theoretical analysis and experiment results show that NetTopoBFT significantly improves the quality of consensus nodes under the premise of guaranteeing decentralization, realizes the simultaneous optimization of communication overhead, security and network coverage. It provides a new idea for designing consensus mechanism of consortium blockchains. Full article

Floods are among the most destructive natural hazards, with damages expected to intensify under climate change and socio-economic pressures. Effective reservoir operation remains a critical yet challenging strategy for mitigating downstream impacts, as operators must navigate nonlinear system dynamics, uncertain inflow forecasts, and trade-offs between competing objectives. This study proposes a novel end-to-end data-driven framework that integrates process-based hydraulic simulations, a Transformer-based surrogate model for flood damage prediction, and reinforcement learning (RL) for reservoir gate operation optimization. The framework is demonstrated using the Coralville Reservoir (Iowa, USA) and two major historical flood events (2008 and 2013). Hydraulic and impact simulations with HEC-RAS and HEC-FIA were used to generate training data, enabling the development of a Transformer model that accurately predicts time-varying flood damages. This surrogate is coupled with a Transformer-enhanced Deep Q-Network (DQN) to derive adaptive gate operation strategies. Results show that the RL-derived optimal policy reduces both peak and time-integrated damages compared to expert and zero-opening benchmarks, while maintaining smooth and feasible operations. Comparative analysis with a genetic algorithm (GA) highlights the robustness of the RL framework, particularly its ability to generalize across uncertain inflows and varying initial storage conditions. Importantly, the adaptive RL policy trained on perturbed synthetic inflows transferred effectively to the hydrologically distinct 2013 event, and fine-tuning achieved near-identical performance to the event-specific optimal policy. These findings highlight the capability of the proposed framework to provide adaptive, transferable, and computationally efficient tools for flood-resilient reservoir operation. Full article

Space robots are vital for in-orbit maintenance of large satellites, but dense payloads and complex surface structures pose challenges for safe crawling operations. This study proposes an improved trajectory planning framework for three-dimensional satellite surfaces. In the path search stage, the traditional A* algorithm is enhanced with traction cost, reflecting surface adhesion, and proximity cost, ensuring collision avoidance. The resulting comprehensive cost function integrates path length, safety, and feasibility, producing paths more consistent with real mobility constraints. In the smoothing stage, cubic B-spline curves refine the discrete path, with real-time collision detection embedded in the optimization of control points to prevent trajectory penetration. Simulations show that the method achieves millisecond-level planning, with path length reduced by 6.82% and trajectory smoothness significantly improved, eliminating the phenomenon of sharp turns with folded corners. The approach ensures continuous, stable, and collision-free movement of space robots, highlighting its potential for reliable in-orbit operations. Full article

Aiming at the speed chattering problem caused by high-frequency square wave injection in permanent magnet synchronous motors (PMSMs) during low-speed operation (200–500 r/min), this study intends to improve the rotor position estimation accuracy of sensorless control systems as well as the system’s ability to resist harmonic interference and sudden load changes. The goal is to enhance the control performance of traditional control schemes in this scenario and meet the requirement of stable low-speed operation of the motor. First, the study analyzes the harmonic error propagation mechanism of high-frequency square wave injection and finds that the traditional PI phase-locked loop (PI-PLL) is susceptible to high-order harmonic interference during demodulation, which in turn leads to position estimation errors and periodic speed fluctuations. Therefore, the extended state observer phase-locked loop (ESO-PLL) is adopted to replace the traditional PI-PLL. A third-order extended state observer (ESO) is used to uniformly regard the system’s unmodeled dynamics, external load disturbances, and harmonic interference as “total disturbances”, realizing real-time estimation and compensation of disturbances, and quickly suppressing the impacts of harmonic errors and sudden load changes. Meanwhile, a dynamic pole placement strategy for the speed loop is designed to adaptively adjust the controller’s damping ratio and bandwidth parameters according to the motor’s operating states (loaded/unloaded, steady-state/transient): large poles are used in the start-up phase to accelerate response, small poles are switched in the steady-state phase to reduce errors, and a smooth attenuation function is used in the transition phase to achieve stable parameter transition, balancing the system’s dynamic response and steady-state accuracy. In addition, high-frequency square wave voltage signals are injected into the dq axes of the rotating coordinate system, and effective rotor position information is extracted by combining signal demodulation with ESO-PLL to realize decoupling of high-frequency response currents. Verification through MATLAB/Simulink simulation experiments shows that the improved strategy exhibits significant advantages in the low-speed range of 200–300 r/min: in the scenario where the speed transitions from 200 r/min to 300 r/min with sudden load changes, the position estimation curve of ESO-PLL basically overlaps with the actual curve, while the PI-PLL shows obvious deviations; in the start-up and speed switching phases, dynamic pole placement enables the motor to respond quickly without overshoot and no obvious speed fluctuations, whereas the traditional fixed-pole PI control has problems of response lag or overshoot. In conclusion, the “ESO-PLL + dynamic pole placement” cooperative control strategy proposed in this study effectively solves the problems of harmonic interference and load disturbance caused by high-frequency square wave injection in the low-speed range and significantly improves the accuracy and robustness of PMSM sensorless control. This strategy requires no additional hardware cost and achieves performance improvement only through algorithm optimization. It can be directly applied to PMSM control systems that require stable low-speed operation, providing a reliable solution for the promotion of sensorless control technology in low-speed precision fields. Full article

The accurate forecasting of solar power generation is becoming increasingly important in the context of renewable energy integration and intelligent energy management. The variability of solar radiation, caused by changing meteorological conditions and diurnal cycles, complicates the planning and control of photovoltaic systems and may lead to imbalances in supply and demand. This study aims to identify the most effective exponential smoothing approach for real-world PV power forecasting using actual hourly generation data from a 9 MW solar power plant in the Kyiv region, Ukraine. Four exponential smoothing techniques are analysed: Classic, a Modified classic adapted to daily generation patterns, Holt’s linear trend method, and the Holt–Winters seasonal method. The models were implemented in Microsoft Excel (Microsoft 365, version 2408) using real measurement data collected over six months. Forecasts were generated one hour ahead, and optimal smoothing constants were identified via RMSE minimisation using the Solver Add-in. Substantial differences in forecasting accuracy were observed. The Classic simple exponential smoothing model performed worst, with an RMSE of 1413.58 kW and nMAE of 9.22%. Holt’s method improved trend responsiveness (RMSE = 1052.79 kW, nMAE = 5.96%), but still lacked seasonality modelling. Holt–Winters, which incorporates both trend and seasonality, achieved a strong balance (RMSE = 1031.00 kW, nMAE = 3.7%). The best performance was observed with the modified simple exponential smoothing method, which captured the daily cycle more effectively (RMSE = 166.45 kW, nMAE = 0.84%). These results pertain to a one-step-ahead evaluation on a single plant and an extended validation window; accuracy is dependent on meteorological conditions, with larger errors during rapid cloud transi. The study identifies forecasting models that combine high accuracy with structural simplicity, intuitive implementation, and minimal parameter tuning—features that make them well-suited for integration into lightweight real-time energy control systems, despite not being evaluated in terms of runtime or memory usage. The modified simple exponential smoothing model, in particular, offers a high degree of precision and interpretability, supporting its integration into operational PV forecasting tools. Full article

Facility agriculture cultivation is the main production form of the vegetable industry in the world. As an important vegetable crop, hot peppers are easily threatened by many diseases in a facility microclimate environment. Traditional disease detection methods are time-consuming and allow the disease to proliferate, so timely detection and inhibition of disease development have become the focus of global agricultural practice. This article proposed a generalizable and explainable machine learning model for hot pepper damping-off in intensive seedling production under the condition of ensuring the high accuracy of the model. Through Kalman filter smoothing, SMOTE-ENN unbalanced sample processing, feature selection and other data preprocessing methods, 19 baseline models were developed for prediction in this article. After statistical testing of the results, Bayesian Optimization algorithm was used to perform hyperparameter tuning for the best five models with performance, and the Extreme Random Trees model (ET) most suitable for this research scenario was determined. The F₁-score of this model is 0.9734, and the AUC value is 0.9969 for predicting the severity of hot pepper damping-off, and the explainable analysis is carried out by SHAP (SHapley Additive exPlanations). According to the results, the hierarchical management strategies under different severities are interpreted. Combined with the front-end visualization interface deployed by the model, it is helpful for farmers to know the development trend of the disease in advance and accurately regulate the environmental factors of seedling raising, and this is of great significance for disease prevention and control and to reduce the impact of diseases on hot pepper growth and development. Full article

During transmission line faults, the pre-insertion resistors in circuit breakers accumulate heat and lead to thermal explosion during repeated closing. The risk of thermal explosion can be reduced if the pre-insertion resistor temperature can be accurately predicted. This study proposes a method for predicting the pre-insertion resistor temperature to optimize the cooling time. The overfitting problem is more serious for models using traditional loss functions. To solve this problem, deep learning models based on a new loss function, the rational smoothing loss, are used to predict the temperature of pre-insertion resistors. The rational smoothing loss, inspired by the kernel function, dynamically adjusts the error versus gradient and incorporates constraints for regularization. The coati optimization algorithm with Ornstein–Uhlenbeck mutation optimizes the rational smoothing loss parameters. The results demonstrate that models using rational smoothing loss significantly outperform those with traditional loss functions, showing reductions of 77.97% in mean absolute error and 93.72% in mean square error, reducing the mean absolute error to 0.29 K. Additionally, the prediction curves exhibit remarkable smoothness, indicating the rational smoothing loss’s robustness against overfitting. The accurate prediction of pre-insertion resistor temperature is crucial for safely operating circuit breakers and technically supporting cooling time optimization. Full article

Tandem cylinders, widely used in heat exchangers, water storage units, and electronic cooling, require optimized flow and heat transfer to enhance engineering performance. However, the combined effects of various factors in tandem configurations remain insufficiently explored. This study proposes an innovative approach that integrates multiple parameters to systematically investigate the influence of surface pattern characteristics and rotational speed on the fluid dynamics and heat transfer performance of tandem cylinders. Numerical simulations are conducted to evaluate the effects of various pattern dimensions (w/D = 0.12–0.18), surface shapes (square, triangular, and dimpled grooves), rotational speeds (|Ω| ≤ 1), and frequencies (N = 2–10) on fluid flow and heat transfer efficiency at Re = 200. The study aims to establish the relationship between the complexity of the coupling effects of the considered parameters and the heat transfer behavior as well as fluid dynamic variations. The results demonstrate that, under stationary conditions, triangular grooves exhibit larger vortex structures compared to square grooves. When a positive rotation is applied, coupled with increases in w/D and N, square grooves develop a separation vortex at the front. Furthermore, the square and dimpled grooves exhibit significant phase control capabilities in the time evolution of lift and drag forces. Under conditions of w/D = 0.12 and w/D = 0.18, the C_L of the upstream cylinder decreases by 17.2% and 20.8%, respectively, compared to the standard smooth cylinder. Moreover, the drag coefficient C_D of the downstream cylinder is reduced to half of the initial value of the upstream cylinder. As the surface amplitude increases, the C_D of the smooth cylinder surpasses that of the other groove types, with an approximate increase of 8.8%. Notably, at Ω = −1, the downstream square-grooved cylinder’s C_L is approximately 12.9% lower than that of other groove types, with an additional 6.86% reduction in amplitude during counterclockwise rotation. When N increases to 10, the of the upstream square-grooved cylinder at w/D = 0.18 decreases sharply by 20.9%. Conversely, the upstream dimpled-groove cylinder significantly enhances at w/D = 0.14 and N = 4. However, the upstream triangular-groove cylinder achieves optimal stability at w/D ≥ 0.16. Moreover, at w/D = 0.18 and N = 6, square grooves show the most significant enhancement in vortex mixing, with an increase of approximately 42.7%. Simultaneously, the local recirculation zones in dimpled grooves at w/D = 0.14 and N = 6 induce complex and geometry-dependent heat transfer behaviors. Under rotational conditions, triangular and dimpled grooves exhibit superior heat transfer performance at N = 6 and w/D = 0.18, with TPI values exceeding those of square grooves by 33.8% and 28.4%, respectively. A potential underlying mechanism is revealed, where groove geometry enhances vortex effects and heat transfer. Interestingly, this study proposes a correlation that reveals the relationship between the averaged Nusselt number and groove area, rotational speed, and frequency. These findings provide theoretical insights for designing high-efficiency heat exchangers and open up new avenues for optimizing the performance of fluid dynamic systems. Full article

Intelligent surface vehicles, including unmanned surface vehicles (USVs) and autonomous surface vehicles (ASVs), have gained significant attention from both academic and industrial communities. However, shipboard maritime images captured under hazy weather conditions inevitably suffer from a blurred, distorted appearance. Low-quality maritime images can lead to negative effects on high-level computer vision tasks, such as object detection, recognition and tracking, etc. To avoid the negative influence of low-quality maritime images, it is necessary to develop a visual perception enhancement method for intelligent surface vehicles. To generate satisfactory haze-free maritime images, we propose development of a novel transmission map estimation and refinement framework. In this work, the coarse transmission map is obtained by the weighted fusion of transmission maps generated by dark channel prior (DCP)- and luminance-based estimation methods. To refine the transmission map, we take the segmented smooth feature of the transmission map into account. A joint variational framework with total generalized variation (TGV) and relative total variation (RTV) regularizers is accordingly proposed. The joint variational framework is effectively solved by an alternating-direction numerical algorithm, which decomposes the original nonconvex nonsmooth optimization problem into several subproblems. Each subproblem could be efficiently and easily handled using the existing optimization algorithm. Finally, comprehensive experiments are conducted on synthetic and realistic maritime images. The imaging results have illustrated that our method can outperform or achieve comparable results with other competing dehazing methods. The promoted visual perception is beneficial to improve navigation safety for intelligent surface vehicles under hazy weather conditions. Full article

The efficient and non-destructive inspection of the chlorophyll content of grey jujube leaf is of great significance for its growth surveillance and nutritional diagnosis. Near-infrared spectroscopy combined with chemometric methods provides an effective approach to achieve this goal. This study took grey jujube leaves as the research object, systematically collected near-infrared spectral data in the range of 4000–10,000 cm⁻¹, and simultaneously measured their soil and plant analyzer development (SPAD) value as a reference index for chlorophyll content. Through various pretreatment and their combination methods on the original spectrum—smooth, standard normal variable transformation (SNV), first derivative (FD), second derivative (SD), smooth + first derivative (Smooth + FD), smooth + second derivative (Smooth + SD), standard normal variable transformation + first derivative (SNV + FD), standard normal variable transformation + second derivative (SNV + SD)—the effects of different methods on the quality of the spectrum and its correlation with SPAD value were compared. The competitive adaptive reweighted sampling algorithm (CARS) was adopted to extract the characteristic wavelength, aiming to reduce data dimensionality and optimize model input. Both BP neural network and RBF neural network prediction models were established, and the model performance under different training functions was compared. The results indicate that after Smooth + FD pretreatment, followed by CARS screening of the characteristic wavelength, the BP neural network model trained using the LBFGS algorithm demonstrated the best performance, with its coefficient of determination (R²) of 0.87 (training set) and 0.85 (validation set), root mean square error (RMSE) of 1.36 (training set) and 1.35 (validation set), and residual prediction deviation (RPD) of 2.81 (training set) and 2.56 (validation set) showing good prediction accuracy and robustness. Research indicates that by combining near-infrared spectroscopy with feature extraction and machine learning methods, the rapid and non-destructive inspection of the grey jujube leaf SPAD value can be achieved, providing reliable technical support for the real-time monitoring of the nutritional status of jujube trees. Full article

This study aims to enhance total pressure probe performance in transonic axial compressors using passive flow control via circular grooves. Simulations in ANSYS CFX were performed on six probe configurations, one smooth baseline and five with groove depths of 0.1 to 0.5 mm, across Mach numbers 0.3 to 0.86. The 0.1 mm grooved probe showed optimal results, reducing the drag coefficient from 15.23 to 14.33 and the lift from 0.0169 to 0.0042. A spanwise analysis from the hub to tip (55–95%) confirmed improved flow uniformity, while a streamwise analysis (zones 0–2) showed steadier downstream pressure and reduced wake-induced distortion. The 0.1 mm groove also minimized the shock strength and flow separation near blade tips. Results confirm that micro-grooving at 0.1 mm significantly stabilizes measurements and enhances aerodynamic efficiency, offering a practical optimization strategy for high-speed compressor applications. Full article

Horizontal annular flow channels are widely applied in various fields, including thermal engineering, drilling engineering, and food engineering. Investigating their internal flow patterns is crucial for optimizing pipeline design, selecting appropriate equipment, and understanding the sedimentation and migration modes of multiphase flows within annular geometries. In practical engineering applications, the operational conditions of annular flow channels during gas drilling are the most complex, involving parameters such as eccentricity, rotation, surface roughness, and multiphase flow interactions. This study focuses on the flow characteristics of horizontal annular channels under real-world engineering conditions, examining variations in operational parameters. The pressure drop in annular pipelines is influenced by factors such as flow velocity, eccentricity, and rotational speed, exhibiting complex variation patterns. However, previous studies have not fully considered the impact of rough wellbore walls and the interactions among various factors. Employing experimental methods, this research analyzes the pressure drop characteristics within annular geometries. The results reveal that surface roughness significantly affects pressure drop, with the inner pipe’s roughness having a greater impact when the outer pipe surface is rough compared to when it is smooth. An increase in eccentricity substantially reduces pressure drop, with both positive and negative eccentricities demonstrating symmetric pressure drop patterns. Moreover, a significant positive correlation exists between the total rough area of the annular channel and pressure drop. Furthermore, this study establishes a predictive model through dimensional analysis. Unlike existing models, this new model incorporates the influences of both roughness and eccentricity, achieving a prediction accuracy of over 99%. This research confirms the critical role of roughness in annular flow systems and provides practical implications for selecting more reliable pump power equipment in engineering fields. Full article

Currently, fastener installation within the narrow, confined space of a wing box must be performed manually, as existing robotic systems are unable to adequately meet the internal assembly requirements. To address this problem, a new robot with one prismatic and five revolute joints (1P5R) has been developed for entering and operating inside the wing box. Firstly, the mechanical structure and control system of the robot were designed and implemented. Then, an improved Probabilistic Roadmap (PRM) method was developed to enable rapid and smooth path planning, mainly depending on optimization of sampling strategy based on Halton sequence, an elliptical-region-based redundant point optimization strategy using control points, improving roadmap construction, and path smoothing based on B-spline curves. Finally, obstacle–avoidance path planning based on the improved PRM was simulated using the MoveIt platform, corresponding robotic motion experiments were conducted, and the improved PRM was validated. Full article

This paper presents a power smoothing strategy for wave energy converters (WECs) by means of energy storage systems (ESS) with integrated forecasting filtering algorithms applied to their control. The oscillatory nature of wave energy leads to high variability in power output, posing significant challenges for grid integration. A case study in Tenerife, Spain, was modeled in MATLAB-Simulink (release r2020b) to evaluate the impact of prediction-enhanced smoothing filters on ESS sizing. Various forecasting algorithms were assessed, including Bayesian Neural Networks, ARMA models, and persistence models. The simulation results demonstrate that the use of forecasting algorithms substantially reduces energy storage requirements while maintaining grid stability. Specifically, the application of Bayesian Neural Networks reduced the required ESS energy by up to 36.52% compared to traditional filters. In a perfect prediction scenario, reductions of up to 53.91% were achieved. These results highlight the importance of combining appropriate filtering strategies with advanced forecasting techniques to improve the technical and economic viability of wave energy projects. The paper concludes with a parametric analysis of moving average filter windows and prediction horizons, identifying the optimal combinations for different sea conditions. In summary, this study provides practical information into reducing the storage capacity required for power smoothing in wave energy systems, thereby contributing to the mitigation of grid integration challenges that may arise with the large-scale deployment of marine renewable energy Full article