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  2023, Vol. 44 Issue (2): 209-221    
  研究论文 本期目录 | 过刊浏览 | 高级检索 |
基于GABP的压力容器表面裂纹断裂研究
张邢1,胡义锋1,李群2,师俊平1,梁浩3,徐勇3,张柏华4,曹小杉1
1. 西安理工大学
2. 西安交通大学
3. 中国工程物理研究院
4. 西北核技术研究所
Study on Fracture of Pressure Vessel with Surface Crack Based on GABP
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摘要 压力容器在长期运行过程中表面裂纹问题难以避免,进行基于断裂分析的安全评估对压力容器的稳定运行具有较强的现实意义。针对二维J-积分理论难以应用于表面半椭圆裂纹,数值模拟耗时冗长的问题,本文提出一种采用三维J-积分量化压力容器表面裂纹尖端应力强度,再结合神经网络进行预测的安全评估方法。通过有限元方法计算了1200例不同几何尺寸、裂纹尺寸和内压载荷的含表面裂纹的压力容器问题,分析了半椭圆裂纹尖端三维J-积分结果,构建修正系数F表征材料性能、裂纹尖端奇异性以及容器几何特征对三维J-积分的影响。基于生成的机器学习数据集,搭建反向传播神经网络(BPNN)模型,采用遗传算法优化,形成GABPNN预测模型。结果表明:BPNN和GABPNN模型预测精度高达96%以上,在未知数据上亦可以取得较为准确的结果,可以高效地预测裂纹尖端三维J-积分,对于实现计算机辅助压力容器安全性现场快速评定提供新的思路和方法。
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张邢
胡义锋
李群
师俊平
梁浩
徐勇
张柏华
曹小杉
关键词 三维J-积分 遗传算法 人工神经网络 压力容器 表面裂纹three-dimensional J-integral genetic algorithm artificial neural network pressure vessel surface crack    
Abstract:Surface cracks often exist in pressure vessels with long service. The safety assessment based on fracture analysis has a strong practical significance for the stable running of pressure vessels. The conventional method is to evaluate the safety of pressure vessels with surface cracks by using 2D J-integral, but there are two obvious problems in practice. One is the inapplicability of 2D J-integral for surface semi-elliptic cracks, the other is the numerical simulation is time-consuming for elastoplastic vessels. Aiming to solve these two problems, an artificial neural network safety evaluation method based on the three-dimensional J-integral is proposed in this paper. The 3D J-integral is applied to quantify the stress intensity at the crack tip of surface crack in pressure vessel and the trained neural network is to predict the 3D J-integral. By finite element method, 1200 cases of elastoplastic pressure vessels with surface cracks with different geometric sizes, crack sizes and internal pressures are calculated. The 3D J-integral results of semi-elliptical crack tip are analyzed. A correction factor F is constructed to characterize the material properties, the singularity of crack tip and the influence of vessel’s geometry on the 3D J-integral. Based on the generated machine learning data set from FEM calculation, the back propagation neural network (BPNN) model is built, and the GABPNN prediction model is formed by genetic algorithm optimization. The data set is randomly divided into training set(90%) and validation set(10%). The training process shows that genetic algorithm optimization can accelerate the convergence speed of neural network and improve the stability of training. The results show that the prediction accuracy of BPNN and GABPNN model is more than 96%, and relatively accurate results can be obtained on the unknown data. The 3D J-integral of crack tip can be predicted efficiently, which provides a new idea and method for the realization of the computer aided on-site safety assessment of pressure vessels with surface cracks.
收稿日期: 2022-09-28      出版日期: 2023-04-18
基金资助:基于分子动力学的开孔纳米多孔金属宏微观断裂研究;多因素耦合下含缺陷结构概率断裂分析与安全评定研究;表面效应作用下锂离子电池三维重构模型的力学及电化学行为;先进装备关键动力学与控制创新团队
通讯作者: 胡义锋     E-mail: yfhu@xaut.edu.cn
引用本文:   
张邢 胡义锋 李群 师俊平 梁浩 徐勇 张柏华 曹小杉. 基于GABP的压力容器表面裂纹断裂研究[J]. , 2023, 44(2): 209-221.
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