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Surrogate Modeling of High-Fidelity Fracture Simulations for Real-Time Residual Strength Predictions

机译:用于实时残余强度预测的高保真断裂模拟的替代模型

摘要

A surrogate model methodology is described for predicting, during flight, the residual strength of aircraft structures that sustain discrete-source damage. Starting with design of experiment, an artificial neural network is developed that takes as input discrete-source damage parameters and outputs a prediction of the structural residual strength. Target residual strength values used to train the artificial neural network are derived from 3D finite element-based fracture simulations. Two ductile fracture simulations are presented to show that crack growth and residual strength are determined more accurately in discrete-source damage cases by using an elastic-plastic fracture framework rather than a linear-elastic fracture mechanics-based method. Improving accuracy of the residual strength training data does, in turn, improve accuracy of the surrogate model. When combined, the surrogate model methodology and high fidelity fracture simulation framework provide useful tools for adaptive flight technology.
机译:描述了一种替代模型方法,用于在飞行期间预测遭受离散源损坏的飞机结构的剩余强度。从实验设计开始,开发了一个人工神经网络,该神经网络将离散源损伤参数作为输入并输出结构残余强度的预测。用于训练人工神经网络的目标残余强度值是从基于3D有限元的断裂模拟中得出的。提出了两个延性断裂模拟,以表明在离散源损伤情况下,通过使用弹塑性断裂框架而不是基于线性弹性断裂力学的方法,可以更准确地确定裂纹扩展和残余强度。改善剩余强度训练数据的准确性确实会提高替代模型的准确性。组合使用时,替代模型方法和高保真度裂缝模拟框架可为自适应飞行技术提供有用的工具。

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