To address the issues of reduced combustion efficiency and increased pollution caused by the easy deposition of pulverized coal particles from lower burners of coal-fired boilers in cold ash hoppers, a CFD numerical simulation method is used to comparatively analyze the boiler combustion characteristics under the working conditions before the supplementary lifting air is applied, when the swirl burners near the side walls are deflected by 5° toward the center of the furnace, and after the supplementary lifting air is applied. The results show that after the supplementary lifting air is applied, the lifting effect on the lower pulverized coal airflow is enhanced, the deposition amount of unburned carbon particles is reduced by 30.5%, and the burnout rate is increased to 99.44%. The deflection of the burners makes the flame narrow and elongated, reduces the temperature of the side walls, but increases the CO concentration in the cold ash hopper. The supplementary lifting air reduces the CO concentration by enhancing the O₂ supply at the bottom. After the lifting air is supplemented, the air staging is significantly intensified, the reducing atmosphere in the main combustion zone is enhanced, and the NO mass concentration (standard condition) at the furnace outlet is reduced from 315.3 mg/m³ to 282.1 mg/m³. The retrofit of theburner deflection and lifting air at the bottom of the boiler can effectively regulate the pulverized coal transport path, inhibit particle sedimentation, and reduce pollutant emissions. The research results can provide a theoretical basis and engineering practice guidance for related boiler transformations.

In order to solve the technical problem that the cooling margin of the cylindrical hole is insufficient at low blowing ratios and the cold flow is separated from the wall at high blowing ratios, based on the tip-covered vortex generator (TCVG), a new type of vortex generator (VG) is proposed, which is called tile-shaped vortex generator (TVG). The conventional cylindrical hole and the cylindrical hole with TVG are numerically simulated. The results show that the film cooling efficiency of the cylindrical hole with TVG is 200% higher than that of the conventional cylindrical hole. Moreover, it solves the problem that the cold flow of the conventional cylindrical film hole will separate from the wall at high blowing ratios. With the increase of TVG width, the film cooling efficiency increases, and tends to be stable when the width reaches twice the film hole diameter. With the increase of TVG height, the suppression effect of TVG on cold flow is weakened, and there is an uncooled gap in the near-field area at high blowing ratios, and the film cooling efficiency shows a downward trend. The expansion angle of TVG has little effect on the film cooling effect, and the optimal expansion angle is 7.5°.

The effectiveness of a data-driven model relies on the completeness of its training samples. For operating conditions beyond the scope of the training samples, the model’s generalization ability is compromised. Therefore, to develop a condenser model that can adapt to the wide load variation of the unit, it is essential for the training samples to involve a diverse range of power generation loads and ambient temperatures. However, achieving this complete dataset is difficult for newly-commissioned units because of their short operation time. To address these challenges, a method for characterizing condensing units using multi-fidelity data and transfer learning is proposed, even with incomplete data. In this method, a pre-trained model is firstly built based on the comprehensive operational dataset collected from a similar unit. On the basis of the pre-trained model, additional linear and nonlinear calibration networks are introduced. The calibration networks are updated through the incomplete data of newly constructed units, enabling the transfer of the pre-training model to the feature space that is adapted to the incomplete dataset. The effectiveness of this method is validated through the condenser of a 1 000 MW supercritical unit. The results indicate that, even with limited training samples, the method accurately predicts parameters such as condenser pressure and circulating water outlet temperature, with an average R² of 0.95, significantly outperforming the conventional data-driven model based on a single data set, of which the average R² is only 0.81.

Scramjet engines are mainstream power systems for hypersonic vehicles, and it has significant thermal protection demand and power supply demand during the long-time and high-Mach-number flight of hypersonic vehicle. An integrated cooling and power generation system based on supercritical CO₂ Brayton cycle is designed for a certain type of scramjet engine. The characterization model of wall heat source for this scramjet engine is constructed. The influences of key design parameters, such as heat absorption pressure, turbine inlet temperature, heat release pressure, and regeneration degree, on the system thermodynamic performance are investigated, as well as the coupling relationships among various design parameters. The optimal design schemes and performance of the integrated cooling and power generation system are obtained by the multi-parameter collaborative optimization. The thermodynamic advantages of the proposed system are also evaluated by comparing with the conventional system with a simple cycle as the baseline. The results show that the proposed integrated cooling and power generation system can achieve a maximum power generation efficiency of 15.9% and a continuous power supply of 206.2 kW, which exhibits a good potential for actual application. The smaller the pinch temperature difference during the heat absorption process of the engine wall, the more significant the thermal performance advantage of the proposed system compared to the benchmark scheme, and the maximum relative increase in power generation efficiency can reach 9.3%.

To explore the ignition and stable combustion performance of ammonia fuel in simulated combustion chambers of gas turbines, ignition and combustion experiments were conducted on ammonia gas with different preheating temperatures and cracking degrees, and the ignition and combustion laws of ammonia fuel under certain experimental conditions were obtained. The results indicate that, stable combustion of ammonia requires a cracking degree of not less than 30% and an air preheater temperature of not less than 643 K. Within the temperature range of 743~943 K and combustion duration of 5~40 seconds in the air preheater, the internal temperature, tail temperature, and pressure of the combustion chamber generally increase with the preheater temperature and combustion duration. The NO emission volume fraction is significantly affected by the temperature of the preheater, it reaches the minimum (376 μL/L) at 673 K when the combustion efficiency is 96%. The zero dimensional simulation results show that, increasing pressure, ammonia cracking degree and temperature can help shorten the ignition delay time, and higher hydrogen content and slightly enriched combustion state can promote the increase of laminar flame velocity and optimize the combustion of ammonia.

A hybrid prediction model combining enhanced grey wolf optimization algorithm (EGWO) and long short-term memory (LSTM) neural network is proposed to address the problem of low accuracy in predicting the mass concentration of NO_x at the outlet of selective catalytic reduction (SCR) denitrification reactors using conventional mechanism modeling methods. Firstly, based on principal component analysis (PCA), the raw data is processed and filtered to achieve dimensionality reduction of input variables. Then, the EGWO is used to optimize the hyperparameters of LSTM. Finally, the input variables are used as inputs for the EGWO-LSTM model to predict the mass concentration of NO_x at the outlet. Taking a 1 000 MW ultra supercritical thermal power unit in China as an example, simulation results show that the proposed model performs the best in error control, with root mean square error reduces by 50.36% compared to the conventional LSTM model, and by 76.14% compared to the BP model, and the mean absolute percentage error of the model is only 1.01%. The EGWO has fewer iterations and higher convergence accuracy compared to the GWO when converging to the optimal solution.

Based on the design and operational conditions of Guangdong Huaying LNG Terminal and its surrounding industrial environment, a cascade utilization scheme integrating thermodynamic power generation with shallow cold storage was developed. Moreover, key process parameters were modeled and solved using HYSYS software to enhance energy efficiency and maximize cold energy utilization. The results show that, under the condition of minimum daily send-out (228 t/h), the original single-stage thermodynamic cycle coupled with cold storage achieved an annual power generation exceeding 32.83 GW·h while meeting the cooling demand of a 7 500 m³ cold storage facility. The optimized scheme adopts a two-stage thermodynamic cycle with shallow cold storage, via employing a 40% (weight percentage) ethane and 60% (weight percentage) propane mixed working fluid, and elevating heat source temperature, this improved design increased the annual power generation to 62.04 GW·h, and raised the net power output per unit mass of LNG from 17.54(kW·h)/t to 33.02 (kW·h)/t, with estimated annual electricity cost savings of approximately 53.641 million yuan. Although multi-stage heat engine cycles can reduce irreversible losses caused by temperature differences, considering factors such as cost-benefit ratio and operational reliability, the second scheme demonstrates strong engineering feasibility and economic viability by closely aligning with the actual conditions of the Huaying LNG Receiving Terminal. Both cascade utilization designs demonstrate distinct advantages for different development stages of the receiving terminal and different evaluation indicators for LNG cold energy utilization, providing valuable references for post-commissioning cold energy applications.

A novel healthy state monitoring method for offshore booster station platforms is proposed to enhance the damage detection capabilities under complex operating conditions. Using a deep learning framework based on memory unit autoencoders, the method magnifies fault-relevant features via denoising and angular domain resampling high-frequency vibration data from offshore boosting stations. The model employs a deep convolutional neural network to learn historical data patterns, constructs a hidden state memory bank, and achieves sparse matching between sample encoded features and the memory bank. Finally, a Gaussian mixture probability model is employed to model the generated membership scores to assess the health status of the booster station. A case study of the offshore booster station in Rudong, Jiangsu, validates the approach, achieving an anomaly recall rate and accuracy of over 98%, outperforming other comparison algorithms.

The Brayton cycle is widely recognized as a key power cycle in the third-generation solar thermal power generation technology. Leveraging the strengths of artificial neural network methods for importance evaluation and quantitative analysis, this research employs a control variable approach to identify critical parameters, including turbine inlet temperature and compression ratio, from a range of operating parameters. In this method, the significance of parameters increases as the R² value decreases. Notably, when excluding these key parameters, the R² values fall to 0.57 and 0.64, respectively, both are lower than other operating parameters. Furthermore, the quantitative analysis of output power in the Brayton cycle yields exceptional results, achieving an R² value exceeding 0.999. The R² values for thermal efficiency and input heat are 0.992 and 0.988, respectively. Finally, the multi-objective optimization results suggest optimal settings of 500 ℃ for turbine inlet temperature and 2.19 for the compression ratio, corresponding to a thermal efficiency of 46.58%, output power of 100.97 kJ/kg, and input heat of –176.5 kJ/kg. This study offers valuable insights for the operational efficiency and performance assessment of the Brayton cycle in solar thermal power plants.

In modern power systems, unit coordinated control faces complex dynamic characteristics and is influenced by faults and external disturbances. To address this challenge, a fault-tolerant control scheme designed for unit coordinated control systems under fault conditions is proposed. Firstly, the transfer function of the unit system is derived using mechanism analysis, and a mathematical model incorporating actuator faults is developed. This model enables the analysis of transient response, stability, and dynamic performance of the system. Secondly, by integrating adaptive techniques with H_∞ control theory, an adaptive fault-tolerant guaranteed-cost tracking control method is designed. This method can automatically compensate for degraded signals when faults occur in the system while further enhancing the robustness and fault tolerance of the system through performance indicator optimization. It satisfies the combined requirements for tracking accuracy and dynamic response. Finally, a simulation is conducted using a 300 MW unit coordinated control system as an example. The results demonstrate that, compared with the conventional fault-tolerant control methods, the proposed approach exhibits superior dynamic tracking performance, disturbance rejection capability, and fault recovery ability. This study provides a reliable control solution for ensuring the safe and stable operation of unit systems in power systems.