Full-spectrum solar energy utilization through spectral splitting offers an effective pathway to improve overall solar energy conversion efficiency by allocating different wavelength bands to suitable energy conversion devices. Linear Fresnel lens-based systems are particularly attractive due to their structural simplicity and scalability. However, the optical efficiency and optical distribution uniformity of such systems are highly sensitive to structural parameters and tracking deviations. The objective of this study is to enhance the optical performance of a linear Fresnel lens-based full-spectrum solar splitting system by optimizing the installation configuration of the photovoltaic (PV) module and by systematically evaluating the influence of incident angle deviations on system performance.

An optical ray-tracing model of the proposed system was established using TracePro software. The model incorporated the geometric configuration of the linear Fresnel lens, spectral splitting characteristics, PV module positioning, and reflective components. To ensure model reliability, a prototype system was constructed, and experimental measurements were conducted under controlled conditions. The simulation results were validated against experimental data by comparing optical efficiency values. Subsequently, a parametric study was performed to investigate the influence of PV module installation height and tilt angle on the optical distribution uniformity and total optical efficiency. In addition, the effects of lateral and longitudinal incident angle deviations, which represent practical solar tracking errors, were quantitatively analyzed. Key performance indicators included total optical efficiency and optical distribution uniformity on the PV surface.

The comparison between simulation and experimental results showed a relative error within 1%, confirming the accuracy and validity of the established optical model. Parametric optimization revealed that when the PV module installation height was set to 540 mm and the inclination angle was 135°, the system achieved optimal optical performance. Under these conditions, the PV surface attained a maximum optical distribution uniformity of 0.86, and the total optical efficiency reached 80.1%. The sensitivity analysis demonstrated that optical performance is significantly affected by incident angle deviations. When the lateral deviation angle increased from 0° to 3.0°, the total optical efficiency decreased from 80.1% to 67.1%, while the optical distribution uniformity declined from 0.86 to 0.79. The influence of longitudinal deviation was even more pronounced. As the longitudinal deviation angle increased from 0° to 30.0°, the total optical efficiency sharply decreased from 80.1% to 26.4%, and the optical distribution uniformity dropped from 0.86 to 0.74. These results indicate that longitudinal tracking errors have a more severe impact on optical performance than lateral deviations, highlighting the importance of precise solar tracking in practical operation.

This study establishes and experimentally validates an accurate optical model for a linear Fresnel lens-based full-spectrum solar splitting system. The results demonstrate that appropriate configuration of PV installation parameters can significantly enhance optical distribution uniformity and overall optical efficiency. Furthermore, the system exhibits strong sensitivity to incident angle deviations, particularly in the longitudinal direction, which must be carefully controlled in engineering applications. The findings provide theoretical support and quantitative guidance for the structural design, parameter optimization, and operational control of full-spectrum solar splitting systems, contributing to the advancement of high-efficiency solar energy utilization technologies.

This study aims to satisfy the growing demand for peak-load regulation in power systems and low-carbon hydrogen production.

A detailed numerical model for the coupled ammonia-hydrogen combustion and decomposition process is established by employing ammonia as an energy storage and hydrogen carrier medium. The model systematically investigates the influences of different burner configurations, such as conventional burners, single-layer porous burners, double-layer porous burners, and staged burners, as well as the inlet ammonia velocity within the decomposition zone on NO_x emission characteristics and ammonia decomposition efficiency. By integrating heterogeneous catalytic kinetics of the Ni-Pt/Al₂O₃ catalyst with porous-medium resistance and heat-transfer models, the simulation framework captures the complex thermo-chemical interactions within the integrated reactor. The reliability of the numerical model is validated through comparison with experimental data reported in the literature, showing an average absolute error of less than 4.4%, which confirms its capability to accurately predict the coupled combustion-decomposition behavior.

The simulation results reveal that the endothermic ammonia decomposition process significantly alters the thermal field within the reactor. The strong heat absorption associated with catalytic decomposition reduces the peak temperature in the combustion zone, thereby effectively suppressing the formation of thermal NO. Although the concentration of N₂O exhibits a slight increase (approximately 7×10^–5%), the overall NO_x emissions are substantially reduced due to the dominant decrease in NO formation. All four burner configurations can achieve an ammonia decomposition rate up to 99.99%. However, notable differences exist in the spatial distribution of regions with high decomposition rates and in the associated emission characteristics. Specifically, the staged burner demonstrates strong capability in NO_x mitigation because of the distributed combustion strategy. Nevertheless, the secondary injection of relatively cold ammonia leads to a delayed initiation of the decomposition reaction, which may influence the system stability under certain operating conditions. The double-layer porous burner exhibits superior thermal storage capacity, enabling sustained catalytic activity. However, localized high-temperature zones within the porous matrix tend to promote the formation of NO. In contrast, the single-layer porous burner provides a more balanced thermal environment, achieving an optimal compromise between NO_x suppression and efficient heat supply for ammonia decomposition, thus demonstrating the most favorable integrated performance. Further parametric analysis indicates that increasing the inlet ammonia velocity in the decomposition zone enhances convective heat transfer and strengthens the heat-absorption effect of the decomposition reaction. As a result, the combustion temperature is further reduced, leading to a more pronounced decrease in NO formation compared with the slight increase in N₂O. Consequently, the overall NO_x emissions continue to decline with the increasing inlet ammonia velocity. Notably, even at a relatively high inlet ammonia velocity of 10 m/s, the ammonia decomposition rate remains above 90%, indicating robust catalytic performance under intensified flow conditions.

This work elucidates the thermal-chemical synergy mechanism underlying ammonia-hydrogen combustion-decomposition integration. It identifies the single-layer porous burner as the most suitable configuration for power-generation-side peak-load regulation scenarios. The findings provide a solid theoretical foundation and valuable engineering guidance for the integrated design of ammonia energy storage, hydrogen production, and ultra-low-NO_x combustion systems.

With the continuous development of the wind power industry toward high power and large capacity, the soaring unit capacity and expanding blade radius of large-scale wind turbines have resulted in increasingly complex spatial distributions of the inflow wind field in front of the turbine and significantly enhanced vertical wind shear effects. The conventional method of characterizing wind conditions using single-point wind speed at hub height can no longer fully reflect the wind speed distribution differences and dynamic patterns within the ultra-large rotor swept area, which is prone to causing issues such as wind power prediction deviations and inadequate adaptability of operation control strategies. To address these challenges, this study proposes a multi-point feature wind speed selection and estimation method for large-scale wind turbines, which can accurately capture key wind speed information in the rotor swept area, overcome the limitations of single-point feature wind speed, and provide data support for the optimal operation of wind turbines.

To achieve the aforementioned research objective, this study adopts a step-by-step technical approach for systematic investigation. Firstly, based on high-precision grid data of the inflow wind field in front of the turbine, a two-stage stepwise feature selection algorithm is proposed, which first carries out preliminary selection with random forest and then implements refined selection via Boruta (RF-Boruta). The Boruta algorithm is employed to conduct significance tests on the feature importance scores output by the random forest model, thereby eliminating redundant and irrelevant wind speed grid points and realizing stable and accurate selection of feature wind speed points within the ultra-large rotor swept area. Secondly, for the selected feature wind speed points, the extended long short-term memory neural network (xLSTM-Mixer) algorithm is introduced, combined with an embedded feature engineering strategy that accounts for input-output delay orders. This strategy fully exploits the temporal correlation and spatial correlation of wind speed sequences, and constructs a unit dynamics-driven ultra-short-term multi-step dynamic estimation model for multi-point feature wind speed points. Finally, to verify the effectiveness and superiority of the proposed method, large eddy simulation (LES) of a 10 MW wind turbine is performed on the SOWFA platform. Meanwhile, 7 typical wind conditions covering the full wind speed range specified in the IEC standards (including complex wind conditions such as shear wind and turbulent wind) are configured for numerical simulation and flow field data collection. The feature selection performance and speed estimation accuracy of the proposed method are comprehensively validated based on the collected high-fidelity data.

The numerical simulation and verification results demonstrate that four representative feature wind speed points, including the hub center, are identified via the RF-Boruta stepwise algorithm. These feature points are arranged at a radius of 50~60 m with an angular interval of 120°, which can effectively cover the key regions of the rotor swept area and fully characterize the spatial distribution features of the inflow wind field. The constructed xLSTM-Mixer model exhibits excellent performance in the multi-point feature wind speed estimation task: the relative error of 80-step-ahead (second-level) prediction for multi-point wind speeds is ≤2.8%, achieving second-level high-precision estimation. Statistical characteristic analysis shows that the Kolmogorov-Smirnov (KS) statistic between the model estimation results and the actual wind speed data is ≤0.2, and the structural similarity index (SSIM) is ≥0.96, indicating a high degree of consistency in both distribution characteristics and structural features between the two datasets. Comparative experiments with mainstream time-series prediction models such as the conventional LSTM and Transformer reveal that the estimation accuracy of the xLSTM-Mixer model is improved by approximately 10%, with distinct advantages in wind speed distribution matching and spatial structure capture capabilities.

The multi-point feature wind speed selection and estimation method proposed in this study effectively breaks through the limitations of conventional single-point feature wind speed, realizing accurate selection and efficient estimation of key wind speed information within the ultra-large rotor swept area. The high-precision multi-point wind speed data provided by this method can reliably support wind power prediction, operation control optimization, and power generation evaluation of wind turbines, helping to enhance the operational stability and energy utilization efficiency of wind turbines. It holds important theoretical significance and engineering application value for promoting the high-quality development of the wind power industry.

The stability of the steam inlet system in solid oxide electrolysis cell (SOEC) systems is crucial for enhancing the electrolysis efficiency of the electrolytic stack and prolonging its service life. However, the nonlinear coupling between steam pressure and flow rate imposes high demands on the control strategy.

An experimental platform for the steam inlet system tailored for the 50 kW-class SOEC system was established to investigate the control of steam flow rate error and pressure fluctuation. Based on the collected operation data of the experimental platform, a double-delay deep deterministic policy gradient agent was trained. A multi-objective intelligent control (IC) strategy based on deep reinforcement learning was proposed, aiming to achieve the control goals of the system output flow error not exceeding 3% and pressure fluctuation not exceeding 1 kPa.

The experimental results show that the maximum error of the output steam flow rate under the IC method is 1.4%, and the maximum pressure fluctuation is ±0.67 kPa. While under the PID control method, the maximum error of the steady-state output flow rate of the system is 3.8%, and the maximum fluctuation of the pressure is ±1.25 kPa. Compared with PID control, the IC method reduces the maximum flow rate error by 63.2% and the pressure fluctuation by 46.4%.

The proposed IC method demonstrates significantly superior control performance compared to the PID method.

High-efficiency film cooling technology is an important means of increasing the turbine inlet temperature of gas turbines, and how to obtain the optimal film cooling hole and flow channel geometries has become a critical engineering issue to enhance the film cooling effectiveness of hot-section components in gas turbines.

In this study, the hole geometry of film cooling holes was parameterized, wherein relative coordinates and angles were adopted as the input parameters. Sampling was performed within the selected ranges of input parameters, and parameter optimization was conducted by coupling the BP neural network with the genetic algorithm, with the objective of maximizing the film cooling effectiveness of the film cooling holes. The influences of the shape and inclination angle of the film cooling hole channel on the film cooling effectiveness were investigated.

Compared with cylindrical holes, the film cooling hole with the optimal configuration has a bigger spanwise width, smaller edge expansion angles on both spanwise sides, the same streamwise length as the cylindrical hole, and small-angle protrusion at the trailing edge. Under the condition of the same spanwise width and smooth flow channel, the area-averaged film cooling effectiveness of the optimal configuration is 18.28% higher than that of the dustpan-shaped hole. For the optimal configuration, the area-averaged film cooling effectiveness of the smooth flow channel is 5.3% higher than that of the unsmooth case. The optimized film cooling hole suppresses the trend of the mainstream entraining the cooling flow from both sides below, and alters the rotation direction of the kidney vortex pairs. Specifically, under the condition of the blowing ratio of 1 and hole inclination angles of 30°, 45° and 60° respectively, the area-averaged film cooling effectiveness of this configuration is 814.6%, 1 002.4% and 772.7% higher than that of the cylindrical hole.

The novel film cooling holes finally optimized in this study can reduce the intensity of kidney vortex pairs, enhance the wall adherence of coolant, and simultaneously delay the damping of film cooling effectiveness on the flat plate downstream of the film cooling hole. The optimized configuration maintains high film cooling effectiveness on the flat plate wall even in case of an increasing hole inclination angle, thus exhibiting broader adaptability to hole inclination angles. The optimal configuration with a smooth flow channel obtained in this study has certain engineering reference value.

At present, the greenhouse effect is becoming increasingly severe, making it crucial to control CO₂ emissions from fossil fuel combustion. Carbon dioxide capture technology represents both the primary step and the critical pathway, serving as a vital means for reducing carbon emissions in the future.

Based on a novel double-contact carbon capture gas-liquid two-phase absorption bed, and to investigate its enhanced mass transfer performance for ammonia-based carbon capture, this study employs an Eulerian-Lagrangian CFD framework. By integrating dual-film theory into a secondary development of Fluent, a mass transfer model for ammonia-based carbon capture is constructed.

The droplet load exhibits a unimodal distribution along the tower height, being low near the walls and high towards the center. As the liquid-to-gas ratio decreases, the net CO₂ flux increases, leading to a higher droplet load. Significant flow deviation exists at the flue gas inlet, with its severity diminishing as the liquid-to-gas ratio decreases. The uneven droplet distribution causes non-uniformity in CO₂ absorption and concentration field distribution. When the liquid-to-gas ratio decreases from 0.40 m³/m³ to 0.17 m³/m³, the CO₂ capture efficiency drops from 89.19% to 77.29%, a reduction of 13.34%, while the outlet CO₂ molar fraction rises from 1.45% to 3.00%. The overall gas-phase mass transfer coefficient (K_G) remains relatively high below three-quarters of the bed height. A banded region of low K_G forms beneath the nozzle manifold. In the region above the nozzles, K_G gradually decreases with increasing bed height due to insufficient mass transfer driving force and the influence of mass transfer resistance from the gas film side. As the liquid-to-gas ratio decreases, the high K_G zone contracts, and the banded low K_G zone exhibits a parabolic upward trajectory. Regarding the flow field, two high-velocity zones and vortices form within the bed due to the inlet and Venturi effect. The average gas phase velocity in the Z-direction exhibits a bimodal distribution along the bed height, a symmetrical three-segment oscillation along the X-cross section, and a non-monotonic distribution along the Y-cross section. As the liquid-to-gas ratio decreases, the disturbance between the gas and liquid phases intensifies, causing the average gas phase velocity along the Y=0 cross section and the X-cross section to gradually increase.

The double-contact carbon capture gas-liquid two-phase absorption bed exhibits favourable mass transfer characteristics, with the liquid-to-gas ratio exerting a significant regulatory effect on both flow and mass transfer. These findings provide a theoretical basis for optimizing carbon capture equipment.

Traditional time-series forecasting methods often struggle to simultaneously capture cross-scale nonlinear fluctuations and long-range temporal dependencies, which leads to limited accuracy in short-term electricity price prediction for spot markets, especially when prices exhibit spikes, volatility clustering, and pronounced non-stationarity.

To address these challenges, this study proposes a short-term electricity price forecasting framework based on a fused global-residual Mamba model that combines series decomposition, dual-branch selective state-space encoding, and global residual learning to strengthen representation power and improve training stability. First, a moving average filter is applied to the normalized electricity price sequence to decouple it into a trend component and a residual component, separating relatively stable low-frequency movements from high-frequency stochastic variations. The decomposed sequences are concatenated along the temporal dimension and mapped through a high-dimensional embedding layer to obtain a richer latent representation capable of characterizing complex market dynamics. To better reflect the multi-factor formation mechanism of spot prices, the model also incorporates exogenous variables, such as regional load and weather-related information (e.g., temperature and meteorological conditions). Building on these inputs, a parallel dual-branch Mamba encoder is designed to extract local-to-global dynamic features from complementary perspectives. The variable-correlation branch focuses on learning time-varying interdependencies between electricity prices and exogenous drivers, explicitly modeling cross-variable coupling and market co-movements. In parallel, the feature-interaction branch targets nonlinear transformations and interactions within the embedded feature space; by permuting tensor dimensions so that selective scanning operates along the embedding dimension rather than only along time, it uncovers abstract interaction patterns that conventional temporal scanning may overlook. To integrate heterogeneous information from both branches, their outputs are concatenated and passed to a global residual learning module, which performs additive fusion between the fused representations and the original embedded input. This global residual pathway provides a stable channel for information flow, alleviates gradient degradation in deeper state-space architectures, and enhances the model’s ability to capture multi-scale patterns by preserving original signals while enriching them with learned cross-variable and cross-feature dynamics. For robust performance and reduced manual effort, Bayesian optimization is applied under a time-series cross-validation (TS-CV) setting to tune key hyperparameters, while training further adopts learning-rate scheduling and early stopping to improve efficiency and stability.

Experiments on real operational data from the Australian Energy Market Operator (AEMO) for 24-hour-ahead forecasting demonstrate clear performance gains: the proposed Residual Mamba reduces RMSE by 29.22% relative to the baseline Mamba model and by 35.33% relative to LSTM, confirming superior accuracy and robustness.

Ablation results further highlight the essential role of the series decomposition module, the importance of the variable-correlation branch in the dual-branch design, and the effectiveness of global residual connections in stabilizing training and improving feature expression for highly volatile spot-market price series.