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With the wide application of fibre-reinforced composites in aerospace, the fatigue problem of composites is becoming more prominent. In order to achieve efficient and accurate fatigue damage analysis, a data-mechanism driven method for the progressive analysis of fatigue damage in composites is proposed, in which a single-hiddenlayer neural network as its fatigue constitutive law for simulations of fatigue delamination under cyclic loading. The Paris-law-informed regulation is used to achieve data-mechanism fusion for neural network model training. The ability to analyze fatigue delamination is validated in the full range of mode-Ⅰ and mode-Ⅱ as well as mixed modes of different mode ratios using double cantilever beam (DCB) and 4-point end flexure (4ENF). The applicability of the cohesive model in the case of complex fatigue delamination front is verified by using the reinforced double cantilever beam (R-DCB) model. The results show that the data-mechanism driven fatigue damage progressive analysis method for composites could rapidly and effectively simulate the composite delamination propagation with high fidelity, which can provide a new idea and method for composite structure design and safety assurance.

The variant aircraft can change its shape to adapt to different flight states and improve aerodynamic properties of aircraft. Aiming at a new hypersonic vehicle concept machine, the applicability of the wing variable sweep design on this hypersonic aircraft is discussed, and the influence of the deformation design of directly extending the trailing edge of the wing on the aerodynamics of the aircraft is studied. Firstly, the 3D model of the hypersonic vehicle with different deformable wings is obtained. Then, based on the k-ε turbulence model, the numerical simulation of the external flow field around the hypersonic vehicle is carried out respectively. Finally, the aerodynamic characteristics of the hypersonic vehicle under different angles of attack and different wing deformation schemes are analyzed. The research results show that the variable-sweep wing scheme cannot achieve the goal of improving the liftto-drag ratio under the design conditions of the hypersonic vehicle, and the deformation design of directly extending the wing can improve the aerodynamic characteristics of the hypersonic vehicle under the design conditions. The research results can provide reference for the design of hypersonic variant aircraft, and have certain practical value.

As composite materials are playing more important role in advanced aircraft structures,the change of mechanical properties of composites during service is of significant importance for the overall safety of the aircraft.In order to achieve the goal of fatigue evaluation and life prediction of composite components of aircraft based on guided wave in-situ detection,firstly,the fatigue evolution law of composite materials is studied from the perspectives of macroscopic phenomenology and microscopic physics.Then,the potential of guided wave phase velocity and mode conversion phenomenon for fatigue characterization is discussed through analyzing the guided wave field.At the same time,a deep learning framework is constructed to extract fatigue evolution features from the guided wave field in a data-driven manner.Finally,a fatigue evolution model based on the Bayesian model averaging method is proposed to predict the residual fatigue life of the composite specimen.The results show that,by extracting and analyzing the guided wave propagating features,the fatigue state of composite materials can be accurately characterized.Combining the Bayesian model averaging method and the confidence interval criterion,the goal of residual life prediction before specimen fatigue failure is achieved.