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The high-order methods is difficultly applied in various elements. The development of a 3D solver by using the spectral difference method of unstructured grids via mixed elements is presented. A mixed tri-prism and tetrahedral grid is firstly refined using one-level h-refinement to generate a hexahedral grid while keeping the curvature of wall boundaries. The SD method designed for hexahedral elements can subsequently be applied for refining the unstructured grid. Through a series of numerical tests, the present method is high-order accurate for both inviscid and viscous flows is demonstrated; the results obtained for inviscid and viscous compressible flows compare well with other published results.

Based on the theory of linear elastic fracture mechanics, the relationships between material constants c3, c4 required in Abaqus and constants C, m in Paris formula are derived. Then extended finite element method (XFEM) of Abaqus software is used to is used to predict the crack propagation path and life of the plate with center inclined through crack and typical stiffened wing spar with initial crack under the constant amplitude fatigue load. The results show that the predicted crack propagation path is in good agreement with the experimental results, and the crack propagation life error is less than 6.3%. It also shows that this method can accurately predict the fatigue crack growth path and life of two-dimensional and three-dimensional complex structures under constant amplitude loads. This study can provide an effective way for damage tolerance analysis of structures and has certain engineering application value.

Firstly, the problem of group-air grouping is analyzed to introduce the aircraft attribute grouping model and aircraft fuel consumption grouping model. Then, SATC-ALO optimized by Chaos optimization algorithm and Tournament Selection strategy and SOM neural network are used to solve the formation grouping model. Finally, comparative experiments of similarity calculation method and formation grouping method were performed with 50 groups of data. The experimental results show that hybrid method is superior to Euclidean distance method. SATC-ALO algorithm has the highest grouping accuracyand meets the real-time requirements. However, the number of groups needs to be specified in advance. The accuracy of SOM neural network grouping is slightly lower than SATC-ALO algorithm, but the grouping time is lower than SATC-ALO algorithm, and there is no need to specify the number of groups. Both SOM neural network and SATC-ALO algorithm can perfectly solve the problem of group-air grouping and have practical application value.