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This work presents an approach for the automatic modeling and high-efficiency simulation of the mechanical properties of plain-woven composites. For the beam element finite element model, a technical scheme for the automatic modeling of plain-woven fabrics is presented. Considering the constraint relationship between the matrix and the fibers, a method for creating matrix beam elements is proposed. Then, a beam element solver was compiled to simulate the tensile properties of the plain-woven composites. In addition, a method of rendering a three-dimensional model of yarn and the simulation results were presented, and a comparison between real fabric images and modeled fabric structure confirmed that the method proposed in this work could effectively model plain-weave fabric structures. Experimental results showed that the deviations of the tensile modulus and strength were 0.41% and −7.59% respectively, which verified the validity of the beam element model used in this study. Moreover, testing verification of the method used in this study could realize the automatic modeling and simulation of representative cells of plain-woven composites in a few seconds. The above results show that this work offers the potential to handle the efficient calculations of large-scale woven structure models. [ABSTRACT FROM AUTHOR]

This work presents a beam element approach for the modeling and high-efficiency simulation of mechanical properties of 2.5D woven composite. For the beam element model, a technical scheme for the parametric modeling of 2.5D woven fabrics was produced. Considering the constraint relationship between the matrix and the fibers, we proposed a method for creating matrix beam elements. Then, a beam element solver was compiled to simulate the mechanical properties of the 2.5D woven composite. Meso-scale analysis of curved shallow-crossing linking 2.5D woven composite using the proposed model obtained the accurate tensile modulus. Full-scale analysis was implemented to simulate bending behavior of straight shallow-crossing linking 2.5D woven composite, and the simulation results agreed well with the experimental results. The above analysis could automatically complete modeling, analysis and post-processing in a relatively short time, reflecting the efficiency advantages of the method in this paper. [ABSTRACT FROM AUTHOR]

The loss prediction model plays a crucial role in turbine design for fast performance prediction and a shorter design cycle. Owing to the design requirements of high-efficiency turbines under a wide range of operating conditions, the loss prediction of the off-design incidence is increasingly important. However, limited by the modeling database and traditional modeling methods, the accuracy and adaptability of the existing off-design incidence loss predictions are insufficient. This paper proposes an adaptive prediction method based on machine learning and develops a multi-objective optimization process based on adaptive prediction. Machine learning (neural network) is applied for more flexible and accurate loss predictions over a wide incidence range. Compared with two classic loss models (Ainley and Mathieson model and Benner model), the adaptive prediction model significantly improves the ability to predict turbine profile loss with off-design incidence, particularly under large incidence conditions. The prediction root mean square error can be reduced by up to 73.8% (absolute value: 0.063). Furthermore, the multi-objective optimization method based on adaptive prediction is applied to the aerodynamic optimization of the original cascades with a wide incidence range. The weighted objective of the optimized cascade (Cri = 0.211) is reduced by 8.7% compared with that of the original cascade (Cri = 0.231). Within the range of full incidence angle (−40° to +20°), the variation of profile loss is reduced by 24.0%. This study is a preliminary exploration aimed at establishing an accurate turbine loss prediction system based on machine learning, the feasibility, and superiority of this approach are confirmed. [ABSTRACT FROM AUTHOR]