In order to improve the status that the spherical harmonics method in the ANSYS-Fluent software is limited to its lowest order (P1) form for radiation heat transfer calculations, based on the basic principles of solving the radiative transfer equation using high-order spherical harmonics, by coupling the numerical solution of the governing equations and boundary conditions using the user-defined function (UDF) and user-defined scalar (UDS) interfaces provided in the ANSYS-Fluent software, a high-order spherical harmonics radiation transfer equation solution model suitable for the software was developed. The model accuracy was verified by calculating the radiative heat transfer in one-dimensional, two-dimensional flat plates, and two-dimensional axisymmetric flame, and comparing with the corresponding analytical or photon Monte Carlo (PMC) solutions. Results show that the P3 model developed in this paper is more accurate than the built-in P1 model of ANSYS-Fluent software in calculating both the angular distribution of radiation intensity and the overall radiative heat source.

Ammonia combustion with circulating fluidized bed (CFB) technology is expected to address the challenges of its low flame propagation speed and unstable combustion characteristics with high efficiency and low cost, thereby facilitating the utilization of carbon-neutral fuels derived from renewable sources. A comprehensive mathematical model for CFB-based ammonia combustion was developed, incorporating both homogeneous and heterogeneous catalytic reactions of ammonia. The emission characteristics of an ammonia-fired CFB boiler, including ammonia slip and nitrogen oxide emissions, were analyzed alongside the impact of operating parameters such as bed temperature, excess air ratio, air staging, and fuel staging. Results show that directly employing the design and operational strategies of traditional coal-fired CFB boilers for ammonia combustion of CFB boilers results in relatively high levels of ammonia slip and nitrogen oxide emissions. However, suitable adjustment of operating parameters can markedly enhance emission characteristics.

To investigate the attenuation mechanism of audible acoustic waves by particle-laden dissipative media, the acoustic attenuation equations with different boundary layer thicknesses were derived by comparing the boundary layer thickness with the particle radius when the furnace of utility boiler was considered as the research subject. Numerical analysis was performed according to the boundary layer theory. The variation of acoustic attenuation coefficient with particle volume fraction, acoustic frequency, particle size, flue gas temperature, and flue gas density was elucidated under different boundary layer thicknesses. Results show that the acoustic attenuation within the thin boundary layers is primarily attributed to the absorption and scattering attenuation of the flue gas medium, whereas the acoustic attenuation within the medium and thick boundary layers is predominantly due to energy dissipation in the boundary layer of particulate medium surface. The acoustic attenuation coefficient is positively correlated with particle volume fraction, acoustic frequency, and flue gas temperature, but inversely correlated with flue gas density, which provides a theoretical foundation for the real-time monitoring of dust concentration.

Temperature measurement is essential for ensuring the safe and stable operation of power plant boilers and other large-scale industrial equipment. Traditional temperature measurement techniques are limited to acquiring point-specific parameters instead of continuous parameter fields. Acoustic tomography not only effectively captures the relative magnitude of temperature but also notably decreases cost and complexity, making it suitable for furnace temperature measurement. A straightforward two-dimensional bench-scale acoustic temperature measurement platform was constructed in the laboratory to validate temperature field reconstruction algorithms using acoustic methods. According to the relationship between sound velocity and gas medium temperature, the least square QR decomposition (LSQR) algorithm was capable of accurately representing the temperature distribution within the region of interest (ROI) when the temperature field reconstruction was performed. The incorporation of radial basis functions significantly enhanced the reconstruction accuracy of the LSQR algorithm. Results show that employing acoustic pyrometry for reconstructing a two-dimensional temperature field is viable in practical measurements.

To investigate the effect of suction surface synthetic jet on the aerodynamic performance of a subsonic axial flow compressor, a numerical simulation was conducted on a high-speed and subsonic axial flow compressor rotor. Results show that after being excited by the synthetic jet, the flow margin of the compressor decreases by 1.3%, but the peak efficiency increases by 0.47%. After analyzing the internal flow field, it is found that tip leakage separation loss and passage loss are the main sources of the compressor loss. The arrangement of synthetic jet excitation on the suction surface of the rotor blade can effectively reduce the tip leakage, separation loss, passage loss and especially the outlet loss. Moreover, the suction effect of the synthetic jet is more beneficial to reduce the compressor loss than the blowing effect. At the same time, it should also be noted that after being excited, the radial velocity of the flow in the compressor channel increases and gathers towards the tip of the blade, resulting in more serious tip blockage than the prototype compressor, and leading to a slight decrease in the stability margin.

The current arrangement method of manual iterative adjustment of nuclear safety-related piping supports occupies a large proportion of the design cycle of nuclear power plants, and cannot achieve the optimal selection of support quantity and position, making it difficult to balance economic cost and mechanical performance. Therefore, by utilizing finite element analysis and the non-dominated sorting genetic algorithm II (NSGA-II), a support intelligent optimization layout method for multi mechanical objective performance of three-dimensional nuclear power pipelines was proposed. Two typical examples of nuclear safety-related piping supports design demonstrate: this intelligent layout method has good effects, which can effectively improve design efficiency and reduce labor intensity; it not only makes the maximum stress of the pipeline less than the limit value specified in the RCC-M "Design and Construction Rules for Mechanical Equipment of Pressurized Water Reactor Nuclear Islands", but also has a certain margin; it can quickly analyze the influence of the number of supports and obtain the optimal solution that balances economy and safety. The proposed method may provide a new approach for the layout of supports in nuclear power piping systems.

In order to ensure the safe and efficient operation of the receiver, a coupled thermal-mechanical model was established for the solar high-temperature cavity receiver, and numerical simulations were conducted on the heat transfer performance and mechanical properties of the receiver under typical operating conditions. Results show that the temperature distribution on the surface of the receiver spiral coil of is uneven, with maximum temperature difference of 247.37 K, resulting in uneven distribution of thermal stress. The cloud cover causes frequent changes in the outlet temperature, with maximum difference of 185.70 K, affecting the stability of the system. The temperature and stress of the coil wall change drastically before and after cloud cover, which shortens the service life of the receiver. The effect of mass flow rate on coil stress is opposite between start-up and steady state. When the start-up mass flow rate is 0.02 kg/s, the outlet temperature of the working fluid is relatively high, while the maximum stress is close to the allowable stress, and the safety margin is low. In the actual operation, it is necessary to consider the balance of benefit and safety based on specific conditions, and then determine the optimal start-up mass flow rate.

For the forecasting of ultra-short-term photovoltaic power, a hybrid prediction model based on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), variational mode decomposition (VMD) and bidirectional gated recurrent unit (BiGRU) was developed. The photovoltaic power generation signal was decomposed using CEEMDAN, and the decomposed signals were clustered and reconstructed using sample entropy and the K-means methods. Then, the VMD was applied for the secondary decomposition of complex signals to mitigate signal non-stationarity. The decomposed signal components were employed as inputs for training, validation and prediction in the BiGRU model. Subsequently, the predicted results from each signal component were linearly combined to obtain the final forecasting results. Results show that the hybrid model outperforms single models, confirming the effectiveness of the model. By comparing the forecasting performances under typical weather conditions and various evaluation metrics, the generality of the proposed method was validated.

In order to investigate the feasibility and noise reduction degree of non-harmonic design on centrifugal impeller, aerodynamic noise characteristics of four kinds of diffused vane non-harmonic impels and original uniform impels were compared and analyzed by using unsteady numerical simulation and FW-H aerodynamic noise model. The influence mechanism of non-harmonic design on aerodynamic noise was revealed through the spectrum characteristics of pressure pulsation and turbulent kinetic energy distribution of blade surface. Results show that the circumferential angular non-harmonic design of splitter blade can reduce the discrete monophonic noise while ensuring the aerodynamic performance. In the comparison schemes adopted, the noise reduction effect of impeller A1 is the most obvious. At the 1st, 2nd and 3rd order, the sound spectral density of the blade passing frequency decreases by 43 percentage points, 44 percentage points and 50 percentage points respectively, and the corresponding pressure pulsation amplitude decreases by 25 percentage points, 25 percentage points and 30 percentage points respectively. At the same time, the broadband noise level increases significantly compared with the original uniform impeller.

For the 1.5 kW distributed new type of variable pitch wind turbine, the structural dynamic responses under uniform inflow and shear inflow were calculated with the numerical simulation method, and the influences of pitch angle, wind speed and shear index on the structural dynamic response characteristics of the new type of variable pitch wind turbine blade were analyzed. Results show that the stress response and displacement response of wind turbine blades decrease significantly with the increase of pitch angle, and the larger the incoming wind speed or shear index is, the greater the decrease is. Under the condition of shear flow, the stress response and displacement response of wind turbine blades increase with the increase of shear index. The variable pitch adjustment method is feasible for the blade load reduction under complex wind conditions, witch can provide a reference for the structure and performance optimization of distributed wind turbines under complex wind conditions.

In order to solve the problem of inadequate reactive power compensation under the background of high proportion of new energy resources access, a multi-objective optimal dispatch strategy considering reactive power support performance of wind farm was proposed. The reactive power support performance of wind farm was analyzed from the aspects of voltage stability, reactive power margin safety and power loss economy of the power system operation. A quadratic function group considering multi-objective satisfaction interval was constructed, and a multi-interval dynamic optimization model of the system was established. The adaptive chaotic differential krill herd algorithm (A-CDKH) was proposed to address the nonlinear and multi-constraint characteristics of the reactive power optimal dispatch problem. Finally, the advantages and effectiveness of the proposed strategy were proved through the simulation results of the modified IEEE30-node model and an actual wind farm model. Results show that, compared with the multi-objective fuzzy optimization model, the maximum voltage deviation index obtained by the multi-objective dynamic optimization model can reach 32.99% optimization degree. The A-CDKH can optimize 75.94% compared with other algorithms in the voltage deviation index.

Aiming at the problem of vibration suppression of high-speed flywheel energy storage rotor system supported by electromagnetic bearings, a reduced order linear active disturbance rejection control (rLADRC) method was proposed. Firstly, a four-degree-of-freedom radial second-order mathematical model of rigid flywheel rotor system was established, which considered gyroscopic effect and unbalanced force. Then, the intrinsic degree of freedom term in the mathematical model of each degree of freedom was treated as the internal disturbance, and the coupling and unbalance force terms introduced by other degrees of freedom terms were considered the external disturbances, a reduced order linear extended state observer (LESO) was designed to estimate the total disturbance in real time. Finally, the linear state error feedback (LSEF) control law was designed, and the total disturbance was compensated. Results show that the rLADRC can effectively decouples the radial four freedom degrees of the rotor. Compared with the traditional decentralized proportional-integral-derivative (PID) control and LADRC, rLADRC has superior dynamic performance and stronger disturbance suppression capabilities, witch can realize stable levitation and effective vibration suppression of the flywheel rotor system in the full speed range.

Aiming at strong nonlinear characteristics of wind turbine gearbox vibration signals, an improved variational mode decomposition method was proposed to decompose signals for extracting characteristic components, and the nonlinear changes of the signal were quantified by chaotic phase portraits and Lyapunov exponent. To ensure the reliability of fault feature extraction and improve the accuracy of fault diagnosis, the random nearest neighbor embedding algorithm was used to reduce redundant features of multi-modal nonlinear fault feature sets. The proposed method was applied to NREL GRC wind turbine gearbox faults due to the unsupervised fault diagnosis framework being more suitable for engineering applications without manual marking of fault samples. Results show that the improved variational mode decomposition method can accurately extract multi-modal features. Combined with the random nearest neighbor embedding algorithm, redundant features can be effectively eliminated to ensure the reliability of fault information. Moreover, the clustering of similar samples and the difference of heterogeneous samples increase, and the clustering performance is clearer, which improves the accuracy of fault classification.

Aiming at the problem of yaw bearing wind turbine damage identification, a damage identification method based on enhanced dual tree complex wavelet packet transform was proposed. Firstly, to determine the number of optimal decomposition layers, components average L-kurtosis values of different decomposition layers were calculated by combining dual tree complex wavelet packet transform and L-kurtosis. Secondly, wavelet coefficient and scale coefficient obtained by the optimal decomposition were modulated to enhance the energy of different signal components. Thirdly, the optimum modulation coefficients of each component were determined by dispersion entropy index, and the modified signal was obtained by inversion dual tree complex wavelet packet. Finally, the normalized square envelope spectrum of modified signal was used to extract the damage characteristic frequency. Results show that the proposed method can accurately identify the damage type of yaw bearing under complex working conditions, and has certain engineering reference value.

To address the issue of online monitoring and fault feature extraction in multi-modal operation process of condenser equipment, a fault feature extraction method integrating k-means clustering and reconstructed principal component analysis was proposed. First, k-means clustering was used to identify modes. The clustering results show significant differences in squared prediction error (SPE) statistics among different modes, and it is necessary to establish monitoring models for sub-modes. In order to obtain fault features effectively and suppress residual pollution in fault separation process, fault feature vectors were obtained by reconstructing principal component analysis method, and the fault feature extraction for multi-modal process was realized. Results show that fault variables and their feature vectors can be separated and detected by using the reconstructed contribution graph method, and the residual pollution problem can be avoided effectively, and the fault location accuracy is good.

Coal gasification fine ash (CGFA) is the product of incomplete coal gasification, showing the characteristics of high emissions and difficulties of treatment,which leads to the issues of the environmental pollution and resource waste. In order to further promote the sustainable and high-value utilization of CGFA, CGFA was used as a carbon source to prepare activated carbon for electric double layer supercapacitors through low-temperature alkali fusion coupled KOH activation treatment. The physical and chemical properties and electrochemical properties of activated carbon samples at different KOH dosages were compared. Results show that, in the range of mass ratio of deashing carbon (RC) and KOH from 1∶1 to 1∶4, the disorder, specific surface area (S_BET), total pore volume (V_total), and mesopore occupancy (V_mes/V_total) of the activated carbon samples show a gradual enhancement trend with the increase of KOH dosage. The specific capacitance of the activated carbon sample increase with aforementioned parameters. When m(RC)∶m(KOH)=1∶4, the activated carbon sample exhibited an S_BET of 1 938.03 m²/g, V_total of 1.62 cm³/g, V_mes/V_total of 82.82 %, mass specific capacitance of 215.56 F/g (current density is 1 A/g), and a rate performance of 88.66% (current density is 1-20 A/g). The energy density of the assembled symmetric supercapacitor is 7.23 (W·h)/kg (corresponding to a power density of 300 W/kg), and its capacitance retention rate is 100.45% after 100 000 cycles.

To enhance the treatment capacity of solid waste and achieve diversified utilization of waste, a novel integrated system for producing methanol and oil was proposed based on the coupling of refuse-derived fuel (RDF) plasma gasification, hydrogen supplementation from water electrolysis, and tire pyrolysis. The system centered on a plasma gasifier which drives a tire pyrolysis subsystem, achieving staged utilization of energy. Results show that the energy efficiency of the system is 58.24% and the exergy efficiency is 64.41%, with main energy loss in methanol synthesis and main exergy loss in plasma gasification. According to the results of the economic analysis,the total investment of the system is 35.665 6 million yuan. With a system life of 25 years and a dynamic payback period (DPP) of 3.15 years, the net present value (NPV) of the final system can reach 171.771 0 million yuan.

An integrated cold-electricity-heat energy system (ICEHS) operation optimization model with advanced adiabatic compressed air energy storage (AA-CAES) was proposed, considering the demand response (DR) of multiple loads including cooling, heating and power, introducing the carbon emission cost, and aiming at minimized total daily cost. Firstly, the cold, heat and power generated by AA-CAES was modeled considering the demand response of the three types of loads. Source-load uncertainty was then generated with combined Latin hypercubic sampling and K-means clustering. Finally, the minimization of the total cost summing the energy purchase cost, operation and maintenance cost, demand response cost, and carbon emission cost of the ICEHS was analyzed. Using a typical community ICEHS as an example, four typical scenarios were set up to verify the effectiveness of the proposed model. Results show that AA-CAES and DR can effectively reduce ICEHS cost and carbon emission.