Centrifugal compressor is widely used in various engineering domains, and predicting the performance of a centrifugal compressor is an essential task for its conceptual design, optimization, and system simulation. For years, researchers seek to implement this mission through various kinds of methods, including interpolation, curve fitting, neural network, and other statistics‐based algorithms. However, these methods usually need a large amount of data, and obtaining data may cost considerable computing or experimental resources. This paper focuses on constructing the performance maps of pressure ratio and isentropic efficiency using a limited number of sample data while maintaining accuracy. Firstly, sample data are generated from simulation using Vista CCD. Then, corrected flow rate and corrected rotational speed are used as independent variables, and the regression expressions with physical meaning of pressure ratio and isentropic efficiency are derived and simplified through thermodynamic analysis and loss analysis of centrifugal compressor, resulting in two loss‐analysis‐based models. Meanwhile, kriging models based on a second‐order polynomial and neural network models are built. Results show that, when predicting inside data boundary, the loss‐analysis‐based model and the kriging model produce higher accuracy prediction even in a small data set, and the predicting result is stable, while the neural network model provides better results only in a more extensive data set with more speed lines. For the prediction outside the data boundary, the loss‐analysis‐based model can provide relatively accurate results. Besides, it takes less time to train and utilize a loss‐analysis‐based model than other models.

The integrated thermal management system of aircraft is essential to maintain a suitable environment for the cabin crew and devices. The system is composed of the air-cycle refrigeration subsystem, the vapor-compression refrigeration subsystem, the liquid-cooling subsystem and the fuel-cycle subsystem, which are coupled with each other through heat exchangers. Due to the complex structure and large number of components in the system, it is necessary to design a corresponding parameter-matching algorithm for its special structure and to select the appropriate optimization design method. In this paper, the structure of an integrated thermal management system is analyzed in depth. A hierarchical matching algorithm of system parameters was designed and realized. Meanwhile, a sensitivity analysis of the system was performed, where key parameters were selected. Besides, a variety of optimization algorithms was used to optimize the design calculations. The results show that the particle swarm optimization and genetic algorithm could effectively find the global optimal solution when taking the fuel penalty as the objective function. Furthermore, the particle swarm optimization method took less time.

There has recently been a considerable and dramatic change in the system design of some future aircraft. The use of electrical energy has led to a demand for rapid technology development in the environment control system (ECS). Extracting energy from the aero-engine in the form of compressed air and electric power to drive the ECS directly affects aero-engine fuel economy. There is an urgent demand for a task-level energy efficiency evaluation method to guide energy and heat sink scheduling. This paper takes the F22 Raptor fighter jet as the research object and analyzes the influence of bleed air and electric power on the thrust and thrust-specific fuel consumption (TSFC) based on the exergy analysis method. First, a two-step TSFC surrogate model is constructed, and a task-level energy efficiency evaluation method is proposed. The error of the TSFC surrogate model is less than 5%, which means the accuracy of the model is sufficient to meet the needs of engineering. Then, the task-level energy efficiency evaluation results show that the vapor cycle has significant fuel economy when the cooling capacity is large, while the air cycle has certain advantages with a small cooling capacity. The method of TSFC surrogate model reduces computational complexity of evaluation with enough accuracy, which can provide a reference for selecting ECS type and guide the optimization of the cooling capacity distribution of the air cycle and vapor cycle.

The emerging demands of electric vertical takeoff and landing and flying cars require power systems to have high takeoff and landing power and high cruising efficiency. Existing power solutions, including pure electric, turbo-electric, piston engines, and fuel cell, face significant challenges in meeting these requirements. We proposed a fuel cell-turbo compound power system, providing a very promising configuration for solving this problem. This study established the simulation models of this structure. The accuracy of the typical component model was verified through relevant experimental data. On this basis, the system performance under typical working conditions was further analyzed. The results show that both the energy recovery efficiency and the output power of the system have been substantially improved. Under the working conditions of high rotational speed and high flow rate, the maximum output power of the system can reach 367.8 kW, meeting the high-power demand during takeoff and landing. Under the working conditions of low rotational speed and low flow rate, the system efficiency can reach up to 43.38 % at most, meeting the high-efficiency requirements during cruising. In addition, the variation of rotational speed and the proportion of hydrogen participating in combustion play a crucial role in the system performance. Although increasing combustion chamber hydrogen can help enhance power output, excessive utilization of hydrogen can lead to performance degradation. These findings provide important insights for the optimization of the compound power system of the next generation of aircraft.

The supercritical carbon dioxide (S-CO₂) Brayton cycle has become one of the most promising power generation systems in recent years. Owing to the high density of S-CO₂, the turbine operates with a lower flow coefficient and a reduced blade height compared to conventional gas turbines, leading to relatively higher tip leakage and secondary flow losses. A properly designed partial admission scheme can increase blade height and improve turbine efficiency. In this study, the effects of partial admission ratio on the performance and flow characteristics of a partial admission S-CO₂ turbine were investigated using numerical methods. The results indicate that the decline in turbine efficiency accelerates when the partial admission rate falls below 0.3. Furthermore, the maximum blade torque begins to decrease once the partial admission ratio drops below 0.1. Stronger tip passage vortices and a large-scale leakage vortex were identified in the passage located at the sector interface. Blade loading analysis revealed a reduction in pressure on the pressure surface of blades just entering the active sector, and a significant increase in suction surface pressure for blades about to exit the active sector. These pressure variations result in reduced blade torque near the boundaries of the active sector.