Due to the extremely high porosity and extremely low density of nano-porous thermal insulation materials, the characteristic size of the pores inside the materials and the characteristic size of the solid skeleton structure are on the nanometer scale, which leads to the obvious nanoscale effect of the heat transfer law inside the aerogel materials. Therefore, the nanoscale heat transfer characteristics inside the aerogel materials and the existing mathematical models for calculating the thermal conductivity of various heat transfer modes at the nanoscale need to be summarized in detail. Moreover, in order to verify the accuracy of the thermal conductivity calculation model of aerogel nano-porous materials, correct experimental data are required to modify the model. Because the medium is involved in radiation heat transfer, the existing test methods have a large error, which brings great difficulties to the design of nano-porous materials. In this paper, the heat transfer mechanism, characterization methods, and test methods of thermal conductivity of nano-porous materials are summarized and discussed. The main contents of this review are as follows. The first part introduces the structural characteristics and specific application environment of aerogel. In the second part, the characteristics of nanoscale heat transfer of aerogel insulation materials are analyzed. In the third part, the characterization methods of thermal conductivity of aerogel insulation materials are summarized. In the fourth part, the test methods of thermal conductivity of aerogel insulation materials are summarized. The fifth part gives a brief conclusion and prospect.

Fuel flowrate control system and fuel thermal management are very important for aeroengine and the overall aircraft, and it has been researched for several decades. This survey paper makes a comprehensive and systematic overview on the exiting fuel flowrate regulation methods, thermal load of fuel metering units, fuel-based thermal management, and the fuel tank’s thermal management topology network with drain and recirculation. This paper firstly reviews the mechanism, technical advantages, and technical challenges of the fuel metering unit with flowrate control valve and constant pressure difference valve compensator, flowrate control valve and variable displacement pump-based pressure difference compensator, and motor-based flowrate regulation. Then, the technical characteristics of above fuel flowrate control methods related to thermal management are discussed and compared. Meanwhile, the behaviors of recirculated fuel flow within single tank system and dual tank system are explored. Thirdly, the paper discusses the future directions of fuel flowrate control and thermal management. The survey is significant to the fuel flowrate control and fuel thermal management of the aircraft.

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.

This paper deals with the modeling of the thermal load and the simulation of thermal dynamic characteristics for the hydraulic system of a large airliner in a full mission profile. Firstly, the formation mechanism of the thermal load in the hydraulic system is analyzed, and thermal dynamic modeling is conducted of the hydraulic components of an hydraulic system with an immersed heat exchanger employing the lumped parameter thermal node method and oil temperature and power loss of each key node within the hydraulic system within a full mission profile. Then, a thermal dynamic simulation model based on MATLAB/Simulink is established, and the temperatures at the nodes of different components and the absorptive capacity of the fuel heat sink in the thermal management module are calculated. The simulation results show that the thermal management scheme of the heat exchanger, located in the return oil pipeline of the hydraulic piston pump housing and immersed in the central fuel tank, can dissipate the thermal load of the system. This work is of important significance for temperature analysis and thermal load dissipation of the airliner hydraulic system.

The present study proposes a novel optimization strategy (NOS) for quasi-steady algorithms to optimize the initial error in the fast calculation of conjugate heat transfer (CHT) simulations. In this approach, the change in Nusselt number at the fluid–solid coupling interface is dynamically monitored, and the update of the flow field is turned off according to a given Nusselt variation standard to speed up the solution of the transient temperature field. The NOS has been applied to problems of convective heat transfer in solid parts with internal heat sources. The feasibility of NOS is first verified by using an undisturbed boundary example, and the results show that the optimization strategy reduces the initial error by 92.3% compared with the quasi-steady algorithm, and the calculation time is reduced by 50% compared with the traditional coupling algorithm. The NOS is then combined with the quasi-steady algorithm, and boundary transient disturbances are added to the case. The results indicate that the computational time for NOS and the quasi-steady algorithm is 2.6 and 2.9 times greater than that of traditional algorithms. Nevertheless, NOS significantly optimizes the relative error of the quasi-steady algorithm by 97.3% during the initial computation phase.