The thickness of the hot component in a turbine engine is usually small. Therefore, the traditional prediction model of anisotropic thermal conductivity (ATC) based on the periodic hypothesis may be improper for use in the thermal analysis of ceramic matrix composite (CMC) components with a thin-wall structure. Thus, the prediction model for the ATC of a 2.5-D braided CMC was investigated, taking into account the actual thickness of the CMC thin-wall structure. An RVE (Representative Volume Element) model with a periodic boundary and a full-size model with the actual thickness were built to study the temperature field, the heat flux field, and the effective thermal conductivity of the CMC. A validation experiment was carried out to verify the accuracy of the two prediction models. The effect of the composite’s thickness on the ATC and the critical thickness suitable for the RVE model were also studied. The results showed that in the thermal analysis of the thin-wall structure, the RVE model had a large deviation in the estimation of the effective thermal conductivity in the thickness direction. The relative error between the numerical data based on the RVE model and the experimental data reached 10.93%, while the relative error was only 3.53% for the full-size model. Additionally, with increasing thickness, the effective thermal conductivities, based on the RVE model and the full-size model, were close to each other. For the critical thickness for the RVE model, which would be suitable for the prediction of the ATC, if the material’s thermal properties such as the absolute value ratio and the level of anisotropy changed, the corresponding critical thickness was also different. For the ATC of the SiC/SiC composites used in this study, the critical thickness was found to be 18.4 mm, nearly 31 times larger than the RVE model’s thickness.

The effects of different configurations of a metal foam flow field on current density and temperature gradient of a single channel solid oxide fuel cell (SOFC) are investigated, and the overall performance of the optimum configuration is optimized. First, model A (conventional channel-rib flow field) is compared with three different configurations (i.e., model B with metal foam flow field only at anode, model C with that only at cathode, and model D at both). Although model C achieves the highest current density, its temperature is also fairly high. Although model B achieves the lowest temperature gradient, its current density is also fairly low. Model D performs well in both current density and temperature gradient. Then, the effect of electrode thickness and metal foam thickness on the performance of model D is investigated. The Ohmic polarization of model D remains almost constant with different electrode thicknesses, and its concentration polarization decreases as the electrode thickness decreases, which is totally different from the channel-rib flow field. Moreover, a thinner cathode causes lower activation overpotential, whereas a thinner anode causes higher activation overpotential. In general, a thick anode, a thin cathode, and thin metal foam can maximize the current density of model D, while a thick electrode and metal foam can always reduce the temperature gradient. Finally, the Taguchi method and gray relational analysis are used to optimize the current density and temperature gradient of model D. The current density of an optimized model is 3.27% higher than that of the original model with a temperature gradient of 9.05 K/cm.

Channel structure design usually plays an important role in improving the performance of solid oxide fuel cell (SOFC). A new channel structure of SOFC with spherical obstacles was proposed in this work. First, compared with the rectangular obstacle channel, the power density of SOFC with spherical obstacles increased by 0.86%, the pressure drop decreased by 31.1%, and the temperature gradient decreased by 4.29%, which means the SOFC with spherical obstacles has better performance. Then, the effect of the configuration parameters, operational parameters, and porosity on the performance of SOFC was studied, and the satisfactory surrogate model with a maximum error smaller than 5% was obtained by back propagation neural network and Latin hypercube sampling to predict the performance of SOFC with spherical obstacles. Finally, the multi-objective optimization technique was employed by non-dominated sorting genetic algorithm and the linear programming method for multidimensional analysis of preference method to improve the performance of SOFC with spherical obstacles. The results showed that the SOFC with optimized spherical obstacle channel obtained a maximum power density of 2809.9 A/m2 and a minimum temperature gradient of 5.89 K/cm, improving the power density by about 6.7% and decreasing the temperature gradient by 12.09% in comparison with the original spherical obstacle channel. Overall, the present work provides an optimized channel design approach to improve the performance of the SOFC.

Addressing the power matching problem of the solid oxide fuel cell (SOFC) and gas turbine (GT) hybrid power system for aviation, a multi-objective optimization approach based on neural networks was proposed. Numerical simulations were used to analyze the impact of different power distributions between the SOFC and GT on the system performance (including total system weight, thermal efficiency, electrical efficiency, fuel consumption rate, and CO₂ emission rate). The BP neural network surrogate model was employed to predict the performance of various power matching schemes, and a multi-objective genetic algorithm combined with the TOPSIS decision-making method was used to optimize the power matching solution. The optimal solution was found with the SOFC output power of 133.092kW and the GT output power of 66.908kW. The optimization results show that the total system weight is 117.124 kg, the fuel consumption rate is 0.196 kg/s, and the thermal efficiency is 73.21%. Compared to a solution with equal power outputs from the SOFC and GT, the optimized solution reduces total system weight by 27.7%. This study provides a new method for optimizing power matching in aviation hybrid power systems.

The comparison of the angular light-scattering method (ALSM) and the spectral extinction method (SEM) in solving the inverse problem of aerosol size distribution (ASD) are studied. The inverse problem is solved by a SPSO-DE hybrid algorithm, which is based on the stochastic particle swarm optimization (SPSO) algorithm and differential evolution (DE) algorithm. To improve the retrieval accuracy, the sensitivity analysis of measurement signals to characteristic parameters in ASDs is studied; and the corresponding optimal measurement angle selection region for ALSM and optimal measurement wavelength selection region for SEM are proposed, respectively. Results show that more satisfactory convergence properties can be obtained by using the SPSO-DE hybrid algorithm. Moreover, short measurement wavelengths and forward measurement angles are beneficial to obtaining more accurate results. Then, common monomodal and bimodal ASDs are estimated under different random measurement errors by using ALSM and SEM, respectively. Numerical tests show that retrieval results by using ALSM show better convergence accuracy and robustness than those by using SEM, which is attributed to the distribution of the objective function value. As a whole, considering the convergence properties and the independence on prior optical information, the ALSM combined with SPSO-DE hybrid algorithm provides a more effective and reliable technique to obtain the ASDs.