In this paper, the strength of braided textile composites is predicted using a multi-scale approach bridging the mesoscale and microscale regimes. Mesoscale finite element models of representative unit cells of biaxial and triaxial braided composites are developed for predicting strength. The constituent stresses of tows inside the braided unit cell are calculated using micromechanics. Correlations between mesoscale stresses and microscale constituent stresses are established by using stress amplification factors. After calculating microscale stresses, a micromechanics-based progressive damage model is employed to determine the damage statuses of braided composites. A volume-averaging homogenization method is utilized to eliminate damage localization in the matrix of tows, and a parametric study is performed to evaluate the effects of damage homogenization. Subsequently, the ultimate strength is predicted for braided composites in which the braiding angle ranges from 15° to 75°. The prediction results are compared with the experimental values, and good agreement is observed.
In order to improve the intelligent level of fault diagnosis and condition maintenance of hydropower units, an Imitation medical diagnosis method (IMDM) is proposed in this study. IMDM uses Bayesian networks (BN) as the technical framework, including three components: machine learning BN model, expert empirical BN model, and maintenance decision model. Its characteristics are as follows: (i) the machine learning model uses a new node selection method to solve the problem that the traditional fault diagnosis model is difficult to connect with the state monitoring system. (ii) The expert experience BN model improves the traditional method: using the fault tree model to transform the BN structure, Noisy-Or model to simplify conditional probability table, and fuzzy comprehensive evaluation method to obtain the conditional probability. (iii) By introducing the expected utility theory, a maintenance decision model is innovated, which makes sure the optimal maintenance decision scheme after the fault can be better selected. The performance of this proposed method is evaluated by using the experimental data. The results show that the accuracy of the fault reasoning model is higher than 80%, and the maintenance decision model successfully selects 236 optimal maintenance decision schemes from 3159 schemes generated by 13 faults.
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