To solve the problem of ANSYS-Fluent software being unable to provide both high-precision and high-efficiency radiation spectrum models, secondary development was carried out for the full-spectrum correlated-k distribution (FSCK) model based on look-up table method and machine learning method, and the model was embeded into Fluent and coupled with built-in radiative transfer equation (RTE) solvers for radiation heat transfer calculation for mintures of common combustion gases and soots. Radiation heat transfer results of one-dimensional slabs and two group of flames were calculated by the model, and using the line by line (LBL) model as a benchmark, which were compared with the results calculated by gray gas weighted sum (WSGG) model in Fluent. Results show that the FSCK model yields more accurate solutions than the built-in WSGG model in Fluent, regardless of the presence of soot.

Through aging tests, the equilibrium phase composition and the evolution of γ' phase of nickel-based superalloy Inconel 617 for advanced ultra-supercritical units were studied. The average diameter, shape, numerical density, nucleation rate and particle size distribution of γ' phase were investigated at different temperatures. Results show that the precipitates of Inconel 617 include γ, γ', σ, μ and carbides. The equilibrium precipitation amount of γ' phase increases with the reduction of aging temperature. As the aging process prolongs, the average diameter of γ' phase increases and undergoes cubization, the nucleation rate decreases, and the number density of γ' increases firstly and then decreases. Compared with the aging time, temperature has more significant effect on the coarsening of the γ' phase. The particle size of the γ'phase exhibits a unimodal distribution, and the effects of temperature and aging time on the particle size distribution are similar.

Based on the 660 MW coal-fired generator unit, a new solar-coal complementary power generation system was proposed, and the key equipment model and subsystem model were established and verified. The dynamic characteristics and peak regulation performance of the system were studied by simulation method. Results show that after configuring the heat storage system, the system can restore balance in about 10 minutes with minimal changes in the main parameters. The system can achieve continuous and stable operation on typical daily after configuring a thermal storage subsystem with a storage duration of 10 hours. By the configuration of heat storage system and extraction steam storage process, the load range of the system is expanded from 198.00-660.00 MW to 187.62-723.13 MW, and the peak regulation performance is significantly improved.

To analyze the influence of unit load command and environmental parameters on gas turbine operating characteristics, the performance monitoring method based on the historical operating data of a power plant was studied. Multi-layer perceptron (MLP) neural network, extreme gradient boosting (XGBoost) algorithm and classification gradient boosting (CatBoost) algorithm were used to construct predict model of compressor efficiency, combustion efficiency and turbine efficiency. Results show that for each 1 MW increase in unit load command, the reference fuel mass flow rate increases by 0.03 kg/s, the reference pressure ratio increases by 0.028 9, and the reference temperature ratio increases by 0.013. While the atmospheric temperature increases by 1 K, the reference compressor exhaust temperature increases by 1.34 K. Compared to the MLP neural network and CatBoost algorithm, the XGBoost algorithm converges faster, and the root mean square error of the test set is reduced by 0.019 and 0.001, the mean absolute error is reduced by 0.028 and 0.004, and R² is increased by 0.041 and 0.002.

A three-dimensional numerical model of honeycomb seal was established, and the influence of a tilting rotor on its leakage characteristics and dynamic characteristics of honeycomb seal was analyzed by computational fluid dynamics method. The results show that tilting rotor can reduce the leakage of honeycomb seal. When the tilting rotor angle increases from 0° to 0.3°, the leakage decreases by 1.9%-6.5%. The larger the tilting rotor angle is, the larger the direct stiffness coefficient is and the smaller the direct damping coefficient is. At high frequency (f>130 Hz), tilting rotor leads to the reduction of effective damping coefficient, which is easy to cause the deterioration of the stability of the rotor-seal system. When rotor tilted, the pressure in the same cavities (No.2, No.7-8, No.13) along the circumference changes in a sinusoidal pattern. With the increase of tilting rotor angle, the absolute amplitude of circumferential pressure increases, the pressure difference at the same axial position gap increases, and the rotor-seal system is more unstable.

The epoxy coating was prepared with bisphenol A-type epoxy resin and polyamide curing agent as main materials. The effects of nuclear power plant design basis accident (DBA) and severe accident environments on the physicochemical properties of the coating were studied respectively. Fourier transform infrared spectroscopy (FTIR) was used to compare and analyze the structure of the samples after test, and characteristic absorption peaks of benzoquinone structure were detected after irradiation test. Thermogravimetric analyzer (TGA) was used to determine the thermal stability parameters of the samples after test. Adhesion tests were executed to evaluate the adhesion strength between carbon steel plate and epoxy coating under various test conditions. Results show that the formation of benzoquinone during irradiation test is the main cause of coating yellowing. Irradiation and high temperature and pressure treatment can lead to the rearrangement and fracture of the network structure of the epoxy coating, with inconsistent effects on different thermal stability parameters. The damp heat test and DBA test have minimal effect on the adhesion strength of the coating, while irradiation test and serious accident conditions significantly reduce the coating adhesion strength.

Since the prediction accuracy of wind power from a single wind turbine was not so satisfactory without the consideration of spatial-temporal features, a many-to-one combined model for short-term wind power prediction has been proposed based on a modified sequence to sequence (Seq2Seq) model. Firstly, the k-nearest neighbor (k-NN) algorithm was used to divide the wind turbines from wind farm into spatial regions and obtain the data of k neighboring wind turbines. The abnormal data were identified, filtered and filled by isolation forest (IF) algorithm. Then the bi-directional gated recurrent unit (BiGRU) and self-attention (SA) mechanism were adopted to improve the Seq2Seq model, and the adjacency matrix with spatial features was used for weight optimization in the model. The many-to-one short-term wind power prediction was performed along with the wind power output of target wind turbines. The proposed combined model was compared with five other models such as long short-term memory (LSTM) to verify its reliability with the actual operation data of wind farms in the United States. Results show that the combined model shows satisfactory stability and robustness in spatial-temporal wind power prediction and effectively improves its accuracy.

Taking IEA 15 MW floating offshore wind turbine and UMaine VolturnUS-S semi-submersible platform as research subjects, a structure design process of wind turbine floating platform was proposed in this study. The stability analyses on the dimensional parameters of each component in the floating platform were conducted using Ansys Aqwa hydrodynamic analysis software and OpenFAST simulation analysis software. The impact of column spacing, side column height, side column diameter, pontoon height, pontoon width, and heave plate diameter on the overall dynamic response of wind turbine system and mooring cable tension was investigated. The effect of dimensional changes on the stability of wind turbine system was explored. Results show that increasing the column spacing and side column diameter can significantly enhance the overall stability of floating wind turbine system during the operation. The changes of side column height above the waterline have almost no impact on the stability of wind turbine system. Pontoon width and pontoon height primarily affect the motion response in the heave direction, while increasing the heave plate diameter significantly improves the longitudinal and heave direction stability of floating platform.

In order to solve the problem of flow separation phenomenon caused by high-angle-of-attack airfoils under weak turbulent conditions, a soft actor-critic (SAC) deep reinforcement learning (DRL) algorithm was used to train a neural network for closed-loop active flow control strategy. In complex environments, an optimized strategy was developed by introducing a zero-mass jet constraint and utilizing three jets, obtaining average drag coefficient reductions for different angles of attack. Results show that using the DRL training strategy to control the jet velocity can inhibit the flow separation of airfoil effectively. When single-jet control is used and airfoil attack angles are 10°, 13° and 15°, the average drag coefficients are reduced by 25%, 15.3% and 11.7%, respectively. Under the condition of large angle of attack, the DRL-based active flow control method has a good effect, which verifies high efficiency of the method in restraining flow separation of airfoil. The introduction of the synthetic jet also enables the agent to find a better control strategy and further reduce the drag coefficient.

The thermodynamic model of an underground artificial cavern in a 10 MW compressed air energy storage (CAES) system was developed with a simulation software. The boundary conditions of charging and discharging physical processes for the CAES cycle of the underground artificial cavern were given according to a domestic CAES project. The mesh independence and time step independence tests were carried out. The dynamic analysis of the gas thermal properties in the underground artificial cavern during the single charging-discharging cycle process was carried out. Results show that the pressure and temperature in the artificial cavern change with time are all rising-falling-decreasing-rising during the process of charging, standing, discharging, and standing. The pressure inside the cavern is evenly distributed, the temperature inside the cavern is stable and step-like, and the gas temperature in the upper space is higher than that in the lower space. Affected by the gravity, the pressure at the wellbore inlet is lower than that at the wellbore bottom. From the discharging end to the second standing process after the discharging end, a certain negative temperature space exists in the cavern space, which is not conducive to the stable operation of the cavern.

The flexibility enhancement will significantly affect the thermal comfort and operation economy of combined heat and power (CHP) units. In order to optimize the comprehensive performances of the unit in the process of rapid load change in a large range, a multi-objective cooperative control strategy based on the predictive control was proposed. Firstly, a multi-objective evaluation index system of the unit was established, which considered flexibility, thermal comfort, economy, and stationarity. In order to achieve multi-objective coordination, a power-heat-coordination control strategy based on the predictive control was proposed. The coal consumption index was introduced into the rolling optimization to achieve economic optimization, and the solution method of multi-objective control rates was given. Then, with the multi-objective evaluation index as the objective function of the particle swarm optimization, the optimal parameter setting of the control strategy was obtained based on the offline optimization of the simulation model, and the comprehensive performance of the unit was optimized. Finally, the simulation test was conducted on a reference 300 MW unit. Results show that the proposed strategy can enhance the operational flexibility of the combined heat and power unit, and ensure the thermal comfort and operation economy of the unit to a considerable extent, which provides a basis for the multi-objective optimization control of the combined heat and power unit.

To address the response issues caused by time lag and load fluctuations in NO_<i>x emission control of SCR denitrification system, reinforcement learning was employed to adjust the proportional-integral-derivative (PID) parameters. The loss function of Critic network was redesigned according to the deep deterministic policy gradient (DDPG) algorithm, and a delay queue was introduced to simulate system latency. The proposed control strategy has been applied to a 660 MW ultra-supercritical coal-fired power unit in China. Results show that the reinforcement learning control method is superior to traditional PID control in terms of adjustment time, overshoot, and stability. The proposed strategy overcomes the time lag and load fluctuations that traditional PID control cannot resolve, demonstrating the significant theoretical and practical values.

Aiming at the problem of insufficient feature learning ability and poor diagnosis effect of traditional deep neural network under the variable operating conditions of wind turbine rolling bearings, a fault diagnosis model was proposed, which combined channel attention mechanism (CA) and capsule residual network (CPRN),to achieve high performance fault diagnosis of rolling bearings under varying working conditions. Firstly, the corresponding time-frequency map was generated by continuous wavelet transform of the vibration signal. It was used as a training sample after matrix reconstruction. And the weight of different features was assigned through the channel attention module to weaken the influence of redundant features on the recognition results. Then It was input into the CPRN model built by stacking convolutional layers, residual blocks and capsule layers, and an affine transformation matrix with weights sharing was adopted to replace the fully connected capsule layers to reduce the number of parameters, improve the training speed, and finally output the diagnostic results. CWRU bearing data set and test bench simulation data were used for experiments. Results show that the average diagnostic accuracy of CA-CPRN under variable working conditions reaches 97.63% and 98.23% respectively, indicating better generalization ability compared with other models. In addition, the diagnostic accuracy on both datasets is better than that of other models in the case of noise, and the average accuracy reaches 99.09% and 96.32% respectively, which proves the superiority of the model in noise resistance.

Using the desulfurization wastewater bypass evaporation system of a power plant as the study object, the effect of the system on water and chlorine balance of the whole plant was comprehensively analyzed. Results show that wastewater evaporation causes a temperature drop of 2-5 K in the flue gas at inlet of desulfurization tower, and reduces the usage of process water to a certain degree. After the commissioning of the bypass evaporation system, about 3% of the chloride ions are released as gaseous HCl, causing the increase of mass concentration of chlorine to 75.6 mg/m³ at outlet of the dry tower. The residual chlorine is introduced into the fly ash, resulting in the increase of mass fraction of chlorine in fly ash of main flue to 1 mg/g. Although the chlorine content in fly ash is increased, the effect on the comprehensive utilization of fly ash in power plant is very limited.

To investigate chlorine release characteristics in desulfurization wastewater bypass hot flue gas evaporation technology, the effects of temperature, pH and metal cations on chloride migration and transformation characteristics during desulfurization wastewater droplet evaporation were studied based on single droplet evaporation experiment. The difference of chlorine release characteristics among different wastewater was compared. Results show that, there are three release stages of chlorine ion, including free water evaporation stage, shell instability stage and chloride pyrolysis stage, and the release of gaseous HCl predominantly occurs in shell instability stage. The increase of temperature promotes the volatilization of chlorine. Especially in shell instability stage, the increase of temperature leads to significant chlorine loss. As the pH increases from 3 to 9, the proportion of chlorine evaporating from solution to gas phase decreases from 42.5% to 25.6%. The promoting effect of metal cations on the chlorine release is Mg²⁺>Ca²⁺>Na⁺, and the HCl produced by the pyrolysis of chlorine-containing crystalline salts, especially MgCl₂·6H₂O, is an essential source of gas phase chlorine.

Due to the complementary advantages of natural gas, such as stability, continuity, high calorific value and convenient transportation, and solar energy, such as clean, environmental protection and renewable, a micro-gas turbine coupled low-concentrating photovoltaic/thermal (LCPV/T) CCHP system was proposed. An optimization model was established with the objectives of primary energy saving rate, carbon dioxide emission reduction rate, annual total cost saving rate, and exergy efficiency, and the strength Pareto evolutionary algorithm II (SPEA-II) optimization algorithm was used to solve the model comprehensively with the decision-making method combining the technique for order preference by similarity to an ideal solution (TOPSIS) and weight entropy method to obtain Pareto optimal solution. Results show that the comprehensive performance of the CCHP system is best when the LCPV/T area is 48.09 m³, the LCPV/T flow rate is 50 L/h, the capacity of the low-temperature water source heat pump is 80.41 kW, the capacity of the high-temperature water source heat pump is 250.03 kW, the capacity of the gas-fired boiler is 400.11 kW, and the heat transfer efficiency of the waste heat recovery boiler is 96.07%. After optimization, the average annual primary energy saving rate, carbon dioxide emission reduction rate, annual total cost saving rate and the exergy efficiency of the system improved by 7.11%, 6.46%, 4.35% and 5.64% compared with the pre-optimization, respectively.

Based on the solid oxide fuel cell (SOFC) power generation system with traditional oxygen rich combustion and carbon capture, an improved novel SOFC-gas turbine (GT)-organic Rankine cycle (ORC) system with near zero carbon emission was proposed. The system model was established by Aspen Plus software and embedded FORTRAN language, and analyses were conducted on the effects of anode recirculation, rear combustion chamber exhaust gas recirculation, current density, fuel utilization rate, and steam carbon ratio on the system performance. Results show that, the improved system can effectively avoid problems such as high power consumption and low oxygen separation rate of traditional air separators. Under design conditions, the net power generation of SOFC-GT-ORC system considering carbon capture is 1 304.09 kW, the carbon capture mole flow rate is 7.9 kmol/h, and the capture purity is as high as 99.8%. The net power generation efficiency is 75.68%, which is 6.68% higher than that of traditional systems. The use of anodes and post combustion chamber exhaust gas recirculation significantly improves the power generation efficiency of SOFC battery stacks, and there are optimal cycle ratios of R_an=0.15 and R_cc=0.27, resulting in the highest efficiency of the combined cycle system. The power generation efficiency of the system is significantly improved with the increase of fuel utilization rate, and the increase of current density and steam carbon ratio is detrimental to the power generation performance of the system. The exergy losses of SOFC battery stacks and heat exchangers in the system are relatively high, but the optimization potential of heat exchangers is greater.

The impact of urban heating network energy storage characteristics on the peak-shaving capabilities of thermal power units was studied. The passive heat storage and release characteristics of the heating network were explored by utilizing the peak and valley characteristics of the power grid. Results show that storing heat during peak electricity consumption periods by using the heating network and releasing heat during low electricity consumption periods can reduce the demand for heating extraction steam, thereby reducing the main steam mass flow rate of the unit and lowering the electrical load. By shifting the peak and filling the valley, thermal electric decoupling can be further achieved. The proposed method of utilizing the large inertia characteristics of urban heating networks for large-scale heat storage can further enhance the flexibility of thermal power units, which is an innovative practice in China, providing a new idea for the thermal electric decoupling of cogeneration units.