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- Abstract：ObjectivesIn response to global climate change, China has committed to achieving carbon neutrality by 2060, which will inevitably bring strategic transformations across various industries and present an opportunity to develop new quality productive forces. This paper analyzes the potential transformations driven by China’s carbon neutrality goal from the perspective of technological innovation.MethodsThis paper investigates the main challenges China faces in achieving carbon neutrality, discusses the relationship between carbon neutrality and the development of new quality productive forces, and highlights research directions in zero-carbon energy supply, fossil resource utilization, and CO2 capture and utilization.ConclusionsAchieving carbon neutrality in China requires large-scale development of new energy and advanced transformation and upgrading of traditional industries, especially the fossil resource utilization. It also requires continuous scientific and technological innovation and application, as well as breakthroughs in disruptive technologies. The successful realization of carbon neutrality also relies on CO2 capture and utilization.Keywords：dual carbon;carbon neutrality;new energy;renewable energy;zero-carbon energy;fossil resource utilization;carbon capture;carbon emissions;new quality productive forces67|379|10<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Carbon Neutrality
Research on Short-Term Forecasting of Multiple Loads in Integrated Energy Systems Based on Composite Correlation Coefficients AI Introduction
Abstract：ObjectivesTo ensure the stable operation of the power grid, it is crucial to accurately predict the multivariate loads within an integrated energy system. To address the issues of low prediction accuracy and the coupling between multivariate loads in current algorithms, a predictive algorithm based on the composite correlation coefficient (CCC) is proposed.MethodsThe Savitzky-Golay (SG) filter and sparrow search algorithm (SSA) are used to optimize variational mode decomposition (VMD) for processing the characteristics of raw data. Subsequently, the CCC is utilized to analyze coupling, and highly correlated features are manually extracted and input into a gated recurrent unit (GRU) neural network combined with an encoder-decoder (ED) structure to obtain multivariate load prediction results. Finally, based on the aforementioned algorithm, experimental verification is carried out using numerical examples.ResultsCompared with traditional GRU, long short-term memory network, and bidirectional GRU prediction models, the proposed algorithm reduces the prediction errors of electric load by 0.55%, 0.49%, and 0.17%, respectively. The prediction errors of cooling load are reduced by 0.71%, 0.54%, and 0.19%, respectively. The prediction errors of heat load are reduced by 0.62%, 0.55%, and 0.19%, respectively.ConclusionsThe proposed algorithm model captures key information more effectively, demonstrates certain advantages over other algorithms, and improves prediction accuracy.Keywords：integrated energy systems;multivariate load prediction;energy coupling;composite correlation coefficient;encoder-decoder;gated recurrent unit;filtering46|337|4<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Research on the Development Trends and Countermeasures of Integrated Energy Services AI Introduction
Abstract：ObjectivesWith the continuous advancement of the national “dual carbon” strategy, integrated energy services, as a new model and new form of the energy industry, are gradually becoming an important means for China to build a new energy system and create new quality productive forces in the energy field. In order to facilitate the high-quality development of China’s integrated energy service industry, the development trends and strategies of integrated energy services are studied.MethodsThrough in-depth analysis of the development status of integrated energy services at home and abroad, the structure-conduct-performance (SCP) analysis model is used to analyze the development and trend of the integrated energy service industry under the new situation.ConclusionsFrom the national level, the countermeasures and suggestions of “three synergies” for the high-quality development of the integrated energy service industry are put forward, focusing on “policy-reform-planning”. From the enterprise level, the implementation path of “four advantages” for high-quality development is put forward, focusing on “layout-model-science and innovation-brand”. These strategies aim to support the healthy and sustainable development of the integrated energy service industry, providing innovative demonstrations and value contributions to the construction of China’s new power system and the implementation of the “dual carbon” strategy.Keywords：integrated energy services;new power system;renewable energy;new energy;double carbon;energy efficiency;energy structure;high-quality development176|662|3<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Integrated Energy
Optimized Operation Strategy of Wind-Solar-Storage Integrated Charging Station Considering Power-to-Hydrogen and Demand Response AI Introduction
Abstract：ObjectivesTo meet the charging demands of new energy vehicles and promote the utilization of renewable energy, an optimized operation strategy of a wind-solar-storage integrated charging station, considering power-to-hydrogen conversion and demand response is proposed.MethodsFirstly, an optimized model for electricity price at energy charging stations is developed based on the Logistic function response mechanism. This model is then solved using the non-dominated sorting genetic algorithm Ⅱ (NSGA-Ⅱ), thereby guiding users to reasonably shift the charging load. Secondly, based on the optimized electricity price and load distribution, and by comprehensively considering energy procurement costs, operational and maintenance expenses, and constraints such as power balance at energy charging stations, an optimized operational model is established for a wind-solar-storage integrated charging station equipped with a power-to-hydrogen conversion device. This model aimed to minimize daily operational costs. Finally, simulation research is conducted on the MATLAB platform.ResultsConsidering the power-to-hydrogen conversion and demand response, the daily operational costs of the charging stations are reduce by 34.74%, and the utilization rates of the wind and solar power increased by 25.84% and 61.60% respectively.ConclusionsThrough the implementation of power-to-hydrogen conversion and demand response measures, the operational costs of the charging stations can be significantly reduced, and the utilization of wind and solar power can be improved.Keywords：new energy vehicles;wind-solar-storage integrated charging station;power-to-hydrogen;demand response;electricity price;optimized operation;renewable energy consumption;non-dominated sorting genetic algorithm Ⅱ(NSGA-Ⅱ)92|740|7<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Research Progress on Hydrated Salt-Based Inorganic Composite Phase Change Materials and Their Applications in Battery Thermal Management AI Introduction
Abstract：ObjectivesPhase change heat storage technology is one of the most concerned energy storage and management techniques in recent years, which is of great significance to realize the comprehensive gradient utilization of energy and improve the efficiency of energy utilization. Hydrated salt-based inorganic composite phase change material (PCM) exhibits significant potential for energy storage and thermal management. This review analyzes the physical properties of materials and the application in battery thermal management, to clarify the technical challenges faced in improving properties and applications. The corresponding improvement strategies are proposed to promote the application of hydrated salt-based inorganic composite PCM.MethodsBy analyzing the research results of hydrated salt PCM at home and abroad in recent years, the improvement effects of additives modification and reasonable encapsulation on the inherent defects such as supercooling, phase separation and liquid phase leakage of hydrated salt PCM, as well as the effects on phase change temperature, latent heat, thermal conductivity and thermal cycle stability are discussed.ResultsThe modification technology significantly improves the inherent defects of the hydrated salt PCM, and significantly improves the thermal conductivity and thermal cycle stability of the material. Furthermore, hydrated salt-based inorganic composite PCM demonstrates excellent performance in lithium-ion battery thermal management, significantly lowering the maximum temperature and temperature differential, thereby improving overall battery performance and safety.ConclusionsThe large-scale application of hydrated salt-based inorganic composite PCM still faces challenges related to thermal stability and cost. Future research should further optimize the composition and structure of these materials, develop new encapsulation materials and additives, and comprehensively improve the overall performance of the materials. Particularly in the area of thermal management for lithium-ion batteries, it is crucial to strengthen the coupling research between the thermal physical characteristics of the materials and the electrochemical performance of the batteries, and provide sufficient theoretical and experimental basis for designing efficient and safe battery thermal management systems.Keywords：phase change heat storage;hydrated salt;composite phase change materials;lithium-ion battery;battery thermal management;thermal runaway protection;additives;encapsulation materials470|1416|2<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Energy Storage
- Abstract：ObjectivesThe onboard hydrogen supply system serves as a core component of hydrogen fuel cell vehicles. How to achieve complete hydrogen gas recycling and improve hydrogen utilization efficiency have become one of the key bottlenecks limiting the development of hydrogen fuel cell vehicles. Therefore, an analysis of the current development status of onboard hydrogen supply systems is conducted.MethodsThis paper introduces the basic working principles of onboard hydrogen supply systems, and summarizes the structure, working principles, and development status of six types of hydrogen-recycling supply systems and two types of non-recycling supply systems. The advantages and disadvantages of different hydrogen supply system modes are compared. The technical status of key components in onboard hydrogen supply systems, such as hydrogen storage cylinders, hydrogen refueling modules, and combination valves is reviewed. Additionally, the development status of core valves such as pressure relief valves, overcurrent valves, and manual valves is analyzed. Finally, the development trends and research directions for onboard hydrogen supply systems are discussed.ConclusionsThe recycling mode of hydrogen supply systems is the main development direction for the future. Advanced information technologies, automatic control technologies, and intelligent decision-making algorithms will be gradually applied to onboard hydrogen supply systems. The components of onboard hydrogen supply systems will gradually evolve towards lightweight, high reliability, low cost, standardization, and modularization.Keywords：hydrogen fuel cell;hydrogen energy;onboard hydrogen supply system;hydrogen recovery cycle;hydrogen supply modes;hydrogen storage;combination valves191|649|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Research on Fault Diagnosis Method of Photovoltaic Arrays Based on Improved Grey Wolf Algorithm Optimized Extreme Learning Machine AI Introduction
Abstract：ObjectivesPhotovoltaic arrays operating under complex outdoor conditions encounter various fault types with varying degrees of severity. To accurately assess the working status of photovoltaic arrays, a fault diagnosis method based on an improved grey wolf optimized extreme learning machine (IGWO-ELM) is proposed.MethodsFirstly, nine fault simulation output characteristics are analyzed, and a five-dimensional fault feature vector is established, consisting of short-circuit current, open-circuit voltage, maximum power point current, maximum power point voltage, and fill factor. Secondly, to address the limitations of the grey wolf algorithm, such as uneven distribution of initial position and imbalance between global search and local exploitation, Circle mapping and nonlinear convergence factors are incorporated. An improved grey wolf optimization algorithm is then proposed, which optimizes the input layer weights and hidden layer node biases of the extreme learning machine to improve performance. Finally, simulation models and experimental platforms are developed to collect fault data, which are divided using K-fold cross validation. The data are input into the IGWO-ELM model for accuracy verification and compared with other algorithms.ResultsThe IGWO-ELM model demonstrates high recognition rates for various fault types in photovoltaic arrays, achieving classification accuracy of 98.32% and 95.48% for simulation and experimental data, respectively.ConclusionsThe fault diagnosis method based on IGWO-ELM offers high accuracy, requires fewer iterations, and achieves fast convergence speed, effectively judging the working state of photovoltaic arrays.Keywords：solar power generation;photovoltaic arrays;fault diagnosis;improved grey wolf optimization (IGWO) algorithm;extreme learning machine (ELM);K-fold cross validation;feature extraction;simulation85|402|3<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Modeling of Efficiency Distribution Characteristics of Photovoltaic Arrays and Its Application in Operating State Evaluation AI Introduction
Abstract：ObjectivesAs the installed capacity of photovoltaic (PV) power generation continues to expand, accurately evaluating the operating state of PV arrays has become increasingly important in the intelligent operation of PV power stations. Due to the influence of various uncontrollable factors such as clouds, weather, and environmental conditions, the output of PV arrays shows significant uncertainty, posing challenges to the analysis of their operating state. To address this issue, a modeling method for efficiency distribution characteristics of PV arrays based on nonparametric estimation is proposed, and the modeling results are used to evaluate the operating state of PV arrays.MethodsFirst, the distribution characteristics and influencing factors of PV array efficiency are analyzed. Next, a distribution model of PV array efficiency is established using the kernel density estimation method. Finally, by setting the confidence levels, threshold intervals for different operating states of PV arrays are determined, enabling the identification of the operating state of PV arrays.ResultsUsing actual operating data, the proposed method is validated across multiple dimensions, including output current, voltage, and efficiency of PV arrays, demonstrating its effectiveness in classifying the operating state of PV arrays.ConclusionsThe efficiency distribution characteristics of PV arrays can effectively represent the operating state, addressing the uncertainty in state classification caused by random fluctuations in PV output and enabling precise identification of PV array operating states.Keywords：photovoltaic arrays;photovoltaic power stations;state evaluation;power generation efficiency;nonparametric estimation;kernel density estimation;intelligent operation91|272|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Short-Term Wind Power Prediction Method Based on Multimodal Feature Extraction-Convolutional Neural Network-Long-Short Term Memory Network AI Introduction
Abstract：ObjectivesWeather and random factors can alter the statistical characteristics of errors. Therefore, this study considers feature extraction of various climate factors that affect wind power. To optimize the extraction of power time series features, a wind power prediction method based on multi-feature extraction (MFE), convolutional neural network (CNN), and long short-term memory (LSTM) network is proposed.MethodsFirstly, 11 statistical features are extracted from numerical weather prediction (NWP) data. By extracting basic and statistical features, the original data is clustered, and prediction models are established according to categories to improve the adaptability of prediction models. Next, the network architecture of LSTM is improved. By leveraging the feature extraction ability of CNN and the nonlinear sequence prediction ability of LSTM, the historical information of wind power and NWP data is thoroughly explored. Finally, using the data from a wind farm in Xinjiang, China, the effectiveness and advantages of the proposed short-term wind power prediction method are verified by MFE and CNN ablation experiments.ResultsThe MFE-CNN-LSTM prediction method shows a decrease in both root mean square error and mean absolute error, compared with the autoregressive integrated moving average (ARIMA), fully recurrent neural network (FRNN), MFE-LSTM and CNN-LSTM models.ConclusionsThe MFE-CNN-LSTM prediction method can effectively extract features, and MFE and CNN effectively improve prediction accuracy.Keywords：multi-feature extraction;convolutional neural network;long short-term memory networks;k-means clustering algorithm;wind power prediction;short-term prediction;ablation experiment66|200|5<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Wind Speed Distribution Fitting and Annual Electricity Generation Estimation of Wind Turbine Based on Improved Mixture Gaussian Model AI Introduction
Abstract：ObjectivesTo address the significant errors in low wind speed section, high wind speed section, and peak and trough sections of the mixture Gaussian model, this paper proposes an improved mixture Gaussian model.MethodsIn the improved model, all subcomponents have the same shape parameter, and the wind speed sample values are used to replace the position parameters. Meanwhile, the nonlinear least squares method is employed to optimize and adjust the shape parameters and the weights of the subcomponents, allowing the model to accurately approximate the probability density distribution, including local points of wind speed samples. Based on four sets of wind speed distribution data from both domestic and international sources, the fitting performance of this model is compared with that of the mixed kernel density model and the Gaussian mixture model. The goodness of fit of the three models is evaluated using two error metrics and the Chi-square test coefficient.ResultsThe improved Gaussian mixture model significantly enhances the fitting performance for complex wind speed distributions, and it can accurately fit the wind speed distribution probabilities in low wind speed section, high wind speed section, and peak and trough sections. Additionally, by comparing the annual electricity generation estimation of wind turbines based on the three models, the effectiveness and advantages of the improved model are further verified.ConclusionsThe proposed wind speed distribution probability model of higher precision helps accurately evaluate the power generation potential and economic benefits of wind farms, providing crucial guidance for wind farm planning and design.Keywords：wind power generation;wind speed probability distribution;mixture Gaussian model;nonlinear least squares method;fitting performance;wind turbine;annual electricity generation83|359|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Research on Tidal Optimization of Voltage Overrun Problem in Photovoltaic Distribution Network With Electrical Energy Router AI Introduction
Abstract：ObjectivesWith the access of high-penetration distributed photovoltaic to the distribution network, the problems of power back-feeding and voltage overrun become more and more serious. Therefore, an optimization scheme of electrical energy router based on improved black hole algorithm is proposed.MethodsA unified model of the high-penetration photovoltaic distribution network with multi-port electrical energy router is established by port steady state modeling of electrical energy router. The optimization objectives of voltage deviation and network loss minimization are proposed for the model. Under the simplified model of the distribution network in a certain area of Gansu province, the improved black hole optimization algorithm is used to optimize the solution, and the data of the distribution network system before and after the addition of the electrical energy router are compared and analyzed.ResultsWhen the photovoltaic output power are 0%, 50% and 100%, the voltage min/max values are improved/reduced by 10.86%, 3.16% and 9.64%, respectively. The voltage amplitude at each node is controlled within the range of 0.93-1.07 pu. The average line loss is decreased by 57.94%-81.56%.ConclusionsThe tidal optimization of the distributed photovoltaic distribution network is achieved by incorporating electrical energy router and performing the corresponding optimization strategy. The research results are of great significance to improve the penetration of distributed photovoltaic in the distribution grid.Keywords：new energy;photovoltaic power generation;photovoltaic distribution network;electrical energy router;steady state modelling;tidal optimization;improved black hole optimization algorithm;voltage overrun99|407|6<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
New Energy
- Abstract：ObjectivesIn the design and manufacturing process of gas turbines, there are many uncertainties in the geometric structure and operating conditions of the components, leading to significant fluctuations in the thermal performance of the gas turbine. In order to accurately identify the operating state of the gas turbine, it is necessary to quantitatively evaluate the impact of these uncertainties on the overall performance of related components and the entire system.MethodsBased on the Monte Carlo method and the thermal process mechanism equations of the gas turbine, an uncertainty analysis model for the overall turbine system is developed to study the quantitative propagation characteristics of component efficiency and flow uncertainty. For the structure of aero-derivative gas turbine, the uncertainty distribution of four independent parameters — compressor efficiency, compressor inlet flow, turbine efficiency, and cooling air flow are introduced. The probability distribution of the overall performance and component operation parameters of the gas turbine are obtained under the conditions of both coupled and independent uncertainties of each parameter.ResultsCompared with the other three parameters, the uncertainty of turbine efficiency has a more significant impact on the output power distribution, efficiency, and exhaust temperature of gas turbine. While compressor efficiency has a greater impact on the operating state of other components, its impact is reduced when combined with other uncertainties. The temperatures and output power at each point of the entire system are more susceptible to the uncertainty of the components.ConclusionsThe turbine should be a key focus in both design and operation. During gas turbine operation, monitoring temperature and output power is more effective in capturing fluctuations in component performance.Keywords：gas turbine;generator set;Monte Carlo method;uncertainty;overall performance;thermal performance;operating status76|187|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Research on Coal Calorific Value Prediction Based on K-Means Clustering and Ridge Regression Algorithm AI Introduction
Abstract：ObjectivesThe calorific value of coal is one of the important evaluation criteria for measuring coal quality, and is also the main basis for valuation of power coal. In order to realize high-precision and rapid prediction of coal calorific value while reducing prediction costs, a new prediction method is proposed.MethodsThe K-Means clustering algorithm is used to cluster similar coal types. The sample data comes from 4 269 entry testing information from a self-contained coal yard of a power plant in Shandong in the past 6 years. On the basis of clustering, Ridge regression models are established from industrial analysis data to received base calorific value, which is used as a prediction model for coal calorific value.ResultsThe established K-Means clustering and Ridge regression hybrid model exhibits excellent prediction performance. Compared with the traditional multiple linear regression model, this hybrid model can reduce the mean absolute error by up to 30.525%, reduce the root mean square error by up to 60.054%, and increase the correlation coefficient by up to 2.320%.ConclusionsThe mixed model of K-Means clustering and Ridge regression reduces the prediction cost of coal calorific value, and also improves the accuracy and speed of prediction, providing a new idea for predicting coal calorific value.Keywords：coal-fired power plants;calorific value of coal;proximate analysis;prediction;K-Means clustering;Ridge regression50|491|4<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Numerical Simulation of Fly Ash Deposition Characteristics and Flow Field Optimization for SCR External Flue AI Introduction
Abstract：ObjectivesSelective catalytic reduction (SCR) denitrification is widely used in denitrification of boiler flue gas due to its high denitrification efficiency and reliable operation. However, under high-ash arrangements, the denitrification flue gas has not yet been de-treated, and fly ash in the flue gas will block the catalyst and cause wear on it. In order to reduce the wear and clogging of fly ash particles on SCR catalysts, an SCR external flue is designed and studied to trap and separate fly ash particles in flue gas.MethodsBased on simulation software and its user defined function, numerical simulation of fly ash deposition characteristics and flow field optimization for SCR external flue is carried out.ResultsWhen the flue gas flow rate increases from 1 to 2 m/s, the fly ash deposition rate decreases from 24.0% to 1.88%. Therefore, as the increase of flow rate, the fly ash deposition rate decreases rapidly. It is difficult to trap fly ash particles below 50 μm. However, with the increase of the fly ash particle size, the fly ash deposition rate increases rapidly, and the fly ash is trapped by the SCR external flue. The flow velocity distribution at the inlet of the denitrification reactor is extremely uneven. Based on the flow field analysis, an optimization plan for the flow field at the inlet of the denitrification reactor is proposed. The flow field distribution is improved through the optimized design of the guide plate, and the velocity unevenness coefficient at the inlet of the denitrification reactor is reduced from 93.7% to 14.7%.ConclusionsThe research results may provide reliable theoretical guidance for the field operation of SCR external flue.Keywords：coal-fired power plants;boiler flue gas;selective catalytic reduction (SCR);fly ash deposition;flow field optimization;numerical simulation82|300|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Improvement and Engineering Application on Pulse Power Supply of Electrostatic Precipitator in Coal-Fired Power Plant AI Introduction
Abstract：ObjectivesHigh voltage power supply is the core equipment of electrostatic precipitator (ESP). Pulse power supply can achieve efficient removal of high specific resistance dust, but there are problems such as high pulse voltage causing heating and electromagnetic interference, which affect the stability of operation. Therefore, based on the conventional pulse power supply, an improved pulse power supply is designed.MethodsThe upgraded supporting technologies of pulse power supply, including dual-path pulse transformer, full-film oil-immersed capacitors, heat dissipation system combined with air-oil-natural forced cooling, anti-interference system, and pulse width adaptive algorithm, are proposed. The pulse power supply is tested on two ESPs on side A and side B of one furnace of 660 MW coal-fired unit. The first four electric fields on A-side use high-frequency power supply, while the fifth electric field uses an improved pulse power supply. As a comparison, all five electric fields on B-side use high-frequency power supply.ResultsThe performance indicators reach a pulse peak voltage of 100 kV, a pulse peak current of 350 A, and a pulse repetition rate of 1-400 Hz, and the improved improved performance comparison test results show that the dust concentration at the outlet of the ESP is below 5 mg/m3, and the high voltage power consumption of the ESP decreases by 24.5% after the addition of improved pulse power supply and control system.ConclusionsThe improved pulse power supply increases parameters such as secondary voltage, solves problems such as heating and electromagnetic interference, and improves the efficiency of electrostatic precipitator, which is of great significance for improving the efficiency of high specific resistance dust capture.Keywords：coal-fired boiler;electrostatic precipitator (ESP);pulse power supply;pulse width adaptive algorithm;ultra-low emission;power consumption;electromagnetic interference;dust control105|376|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Cavitation State Identification Method of Hydraulic Turbine Based on Knowledge Distillation and Convolutional Neural Network AI Introduction
Abstract：ObjectivesWhen cavitation faults occur in hydraulic turbines, the efficiency of the unit decreases, component erosion accelerates, and in severe cases, safety accidents may even be triggered. Therefore, accurately and rapidly identifying the cavitation state of hydraulic turbines is crucial for the efficient and safe operation of hydropower stations. Aiming at the problems of slow recognition speed in complex convolutional neural network (CNN) models and low recognition accuracy in simple CNN models, a turbine cavitation state recognition method based on knowledge distillation and convolutional neural network (KD-CNN) is proposed.MethodsFirstly, the interaction mechanism of teacher model and student model in knowledge distillation theory are introduced, and the teacher model is defined as three-layer CNN network, while the student model is defined as single-layer CNN network. Then, the cavitation acoustic emission signal data obtained from the experiment are used to train the teacher model. Finally, the data labels representing the types of cavitation state are replaced with the output of the teacher model, and new dataset are learned by the student model to minimize the cross entropy. The trained model is the KD-CNN model, which is used for cavitation state recognition experiments on various conditions.ResultsThe KD-CNN model can complete the identification of the cavitation state of the turbine within 2 s, and the recognition accuracy of each working condition is higher than 97%.ConclusionsThe KD-CNN model has a simple structure, the recognition speed of the student model, and the recognition accuracy of the teacher model. It can provide a theoretical basis for real-time monitoring of cavitation in hydraulic turbines.Keywords：hydraulic turbine cavitation;convolutional neural network (CNN);knowledge distillation (KD);acoustic emission signal;deep learning;state recognition85|283|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Kinetic Characterization and NOx Reduction Mechanism of Mixed Ammonia-Hydrogen Combustion in Cyclone Combustor AI Introduction
Abstract：ObjectiveAmmonia plays a key role in achieving the “dual carbon” goal, but ammonia has a low combustion rate and high NOx emission. In order to improve the combustion rate, a method of ammonia-hydrogen blending combustion was proposed.MethodsThe combined computational fluid dynamics and ammonia-hydrogen mixed combustion mechanism are used to numerically simulate the premixed combustion of ammonia, hydrogen and air. The effects of equivalent ratio and inlet air flow on combustion characteristic and NOx emission are analyzed. In addition, in order to better control the emission of NOx during combustion, the kinetic analysis of ammonia-hydrogen blending combustion reaction is carried out using Chemkin software.ResultsIncreasing the inlet air flow rate and equivalent ratio can effectively reduce NO emission in cyclone burners, and the main way of NO formation is the oxidation of imino groups.ConclusionThe results provide a reference for the characterization analysis of ammonia-hydrogen miscible combustion and the reduction of NOx emissions in cyclone combustors.Keywords：double carbon;ammonia doped with hydrogen;premixed combustion;numerical simulation;NOx emission;characteristics of combustion kinetic;cyclone combustor119|29|5<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29- Abstract：ObjectivesUrban rivers and lakes often serve as carriers of sewage, with a large amount of sediment enriched with nitrogen and heavy metal elements. The combustion disposal method is clean, efficient, economical, and environmentally friendly. Studying the combustion kinetics of the sediment is crucial for its disposal and utilization.MethodsThe thermogravimetric analysis method is employed to compare and analyze the single combustion characteristics of the sediment in Dianchi Lake. In order to provide a reference for practical engineering applications, the sediment is co-combusted with coal at mixing ratios of 10%, 20%, and 30%. Using KAS, FWO, and Coats methods, 11 commonly used kinetic mechanism functions and solid-state reaction mechanism functions are selected to fit the activation energy and mechanism functions of each reaction stage.ResultsThe combustion of sediment is divided into four stages. Compared with sludge and coal slurry, it has a lower ignition temperature, making it easier to burn, but a higher burnout temperature, indicating differences in the overall combustion characteristics. When sediment is burned at different heating rates, lower heating rates are more conducive to combustion. Co-combusting sediment with coal shows a synergistic effect, and their interaction has a positive impact on the combustion characteristics. As the mixing ratio increases, the ignition performance improves. However, when the mixing ratio reaches 30%, the combustibility index significantly decreases, indicating that excessive mixing ratio is detrimental to improving the combustion performance of the fuel. The final fitting shows that the reaction mechanisms in the second and third stages of the combustion process are consistent, while the fourth stage is different.ConclusionsSediment can be used as a fuel, burning of sediment and coal can improve the ignition performance of coal and conducive to the stable combustion of coal, but the mixing ratio should not exceed 30%. The research results provide a reference for practical engineering applications.Keywords：sediment;thermogravimetric experiment;combustion of sediment;combustion properties;kinetic analysis;apparent activation energy;reaction mechanism function;mixing ratio63|196|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Thermal Stress Analysis of Transversally Corrugated Tube in Solar Receiver Under Semi-Circumference Heating AI Introduction
Abstract：ObjectiveIn daily operation, the outer wall of solar receiver tube is subjected to uneven heat flux between the inner and outer sides at cylindrical receiver, which leads to the uneven distribution of wall temperature and large temperature gradient. It is easy to generate high thermal stress, which causes the deformation and structural damage of receiver tube. Therefore, the thermal stress of the transversally corrugated tube in the cylindrical receiver is studied.MethodsThe three-dimensional model of the transversally corrugated tube is established by Design Modeler in Ansys, and the mesh is divided by Mesh. The thermal stress of the transversally corrugated tube under semi-circumference heating is numerically simulated by Fluent.ResultsThe outer wall thermal stress at high heat flux side of the tube is the largest, and the wall thermal stress at the junction of the high and low heat flux surfaces is the lowest. The thermal stress value at the groove section of transversally corrugated tube is larger than that of the smooth section. For the same groove width, the groove depth is deeper, and the maximum equivalent thermal stress on the groove section is bigger. For the same groove depth, the groove width is wider, and the maximum equivalent thermal stress is smaller. The maximum equivalent thermal stress of the transversally corrugated tube increases with the rise of molten salt inlet temperature, and the maximum equivalent thermal stress reduces with the increase of molten salt inlet flow rate.ConclusionThrough the thermal stress analysis, it can be seen that the influence law of the thermal stress on transversally corrugated tube changes with heat flux, groove depth and width, inlet temperature and inlet flow rate, which is of great significance to prolong the service life of the cylindrical receiver.Keywords：solar thermal power;solar receiver;cylindrical receiver;transversally corrugated tube;thermal stress;molten salt;numerical simulation63|261|0<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29Study on the Full Electromagnetic Model of Collective Thomson Scattering in Magnetic Confinement Fusion AI Introduction
Abstract：ObjectivesCollective Thomson scattering (CTS) is one of the few fusion diagnostic technologies that can provide fast ion kinetic property measurement in the center of fusion devices. However, the physical models that CTS diagnosis relies on at home and abroad currently have problems such as lack of electromagnetic effects and inaccurate plasma dielectric properties, which hinders the development of CTS diagnostic theory. Therefore, it is urgent to develop a more comprehensive CTS physical model code to support the development of CTS diagnosis.MethodsThe establishment and derivation of the full electromagnetic model is introduced, and the similarities and differences between the full electromagnetic model and the electrostatic model are analysed. The spectrum characteristics of the full electromagnetic model CTS under different parameters are studied based on the HL-2A device.ResultsThe full electromagnetic model is consistent with the electrostatic model results in conventional diagnosis. In addition, the full electromagnetic model has obvious advantages in diagnosing structures such as ion-Bernstein waves. The full electromagnetic model is used to optimize the system parameters for the CTS system of the HL-2A device.ConclusionsIn CTS spectrum research, the full electromagnetic model has more complete functions and huge potential, and can provide a powerful tool for CTS diagnosis of physical quantities such as ion ratios. In the future, based on more devices at home and abroad, the full electromagnetic model will have broader application prospects.Keywords：nuclear fusion;magnetic confinement fusion;fast ions;collective Thomson scattering;fully electromagnetic model;ion-Bernstein wave;ion ratio152|450|1<HTML><L-PDF> <WORD> <Meta-XML>Updated：2026-06-29
Power Generation and Environmental Protection
