Solid oxide fuel cells (SOFCs) directly convert chemical energy to electricity with high electrical efficiency. It involves gas transport through the porous electrode to the three-phase boundaries (TPB). The tortuosity of gas transport relates the bulk diffusion of gas in free space to the effective diffusion coefficient of gas migrating through a porous material. Therefore, determining the tortuosity is of great importance. This paper tests button SOFCs with NiO-YSZ as anode material followed by dual beam-focused ion beam scanning electron microscopy (FIB-SEM) to obtain 2D serial slice images. Based on processed 2D images and reconstructed 3D microstructure, the tortuosity is calculated using three approaches i.e., porosity-tortuosity correlations, voxel-based, and path-length-based approaches. The test results show that a decrease in Ni content in the anode greatly decreases the cell performance due to a decreased percolated electronic phase. The sample with low performance has high tortuosity. Different approaches vary regarding the tortuosity value and computational time. The path-length-based approach can achieve reasonable accuracy in a relatively short time but is only valid for using the longest path length.

Maintenance is inevitable for repairable components or systems in modern industries. Since the maintenance effectiveness has a great influence on the subsequent operations and in addition, different maintenance options are possible for the components of the system during the break between any two successive missions, the optimal selective maintenance strategy needs to be determined for a system performing successive missions. A number of selective maintenance models were set up on the basis that the durations of each mission are predetermined, the maintenance time is negligible, and the states of the components at the end of the previous mission can be accurately obtained. However, in the actual industrial and military missions, these premises may not always hold. In this paper, a novel selective maintenance model under uncertainties and limited maintenance time is proposed to improve these deficiencies. The genetic algorithm is selected to solve the optimization problem, and an illustrative example is presented to demonstrate the proposed method. The optimal selective maintenance decision without the constraint of maintenance time is used for comparison.

To improve the predictive ability in trajectory of large unmanned aerial vehicle (UAV) and the calculation performance in complicated circumstances with mixed airspace, multiple aircraft types, and joint operations, the concept of phased trajectory deviation (PTD) is introduced and a corresponding minimal interval algorithm (PTD-MI) is set up. This algorithm is capable of deriving the minimal interval between various aircraft types according to the crosswind impacts and the UAV characteristics at different flight phases and thus achieves the effective safety evaluation in airspace operation. To demonstrate the rationality and generality of the proposed algorithm, several simulation experiments are conducted. Based on the experimental results, flight procedure protection area is plotted by PTD-MI algorithm and compared with that generated by Ground-Based Augmentation System (GBAS). Results indicate that the proposed algorithm is capable of deriving a more scientific basis for airspace assignment and outperform GBAS in dealing with wide-area space problems. And, compared with GBAS, PTD-MI algorithm shows a more stable calculation performance and is easier to output the results. PTD-MI algorithm is proposed under the flight safety regulation designed by the International Civil Aviation Organization (ICAO) and designed to provide effective technical supports for the safe and normal operations of aircrafts.