基于人工神经网络的固体推进剂细观损伤与宏观刚度映射关系  

Artificial neural network-based mapping of microscopic damage to macroscopic stiffness in solid propellants

作　　者：张滔韬 杨玉新 张二晗 校金友[1] 吕海宝[3] 文立华[1] 雷鸣 侯晓[4] ZHANG Taotao;YANG Yuxin;ZHANG Erhan;XIAO Jinyou;LYU Haibao;WEN Lihua;LEI Ming;HOU Xiao(School of Astronautics,Northwestern Polytechnical University,Xi'an 710072,China;Xi'an Aerospace Power Technology Research Institute,Xi'an 710025,China;Center for Composite Materials and Structure,Harbin Institute of Technology,Harbin 150001,China;China Aerospace Science and Technology Corporation,Beijing 100048,China)

机构地区：[1]西北工业大学航天学院,西安710072 [2]西安航天动力技术研究所,西安710025 [3]哈尔滨工业大学复合材料与结构研究所,哈尔滨150001 [4]中国航天科技集团公司,北京100048

出　　处：《复合材料学报》2024年第9期4765-4777,共13页Acta Materiae Compositae Sinica

基　　金：国家自然科学基金-联合基金资助项目:高装填比高能推进剂装药结构损伤的跨尺度分析(U22B20131)。

摘　　要：作为一种高夹杂比颗粒增强聚合物基复合材料,固体推进剂的宏观力学性能主要由其细观结构决定。外加载荷下,初始缺陷或细观颗粒团聚均可诱发局部应力集中,导致颗粒-基体细观界面脱粘,材料宏观力学性能劣化。如何构建细观损伤与宏观性能间的映射关系,已成为推进剂细观实验结果合理运用、固体火箭发动机结构灾变准确预报的关键。为此,本文发展了基于连续介质力学框架的人工神经网络,以变形梯度的不变量为输入、自由能为输出,遴选现有自由能函数和损伤增长函数形式为神经网络设计激活函数,使神经网络先验地满足变形连续性、坐标不变性、热力学一致性等要求。基于上述物理相容性,神经网络能在稀疏训练数据条件下快速收敛,还能够自下而上地实现损伤状态的遗传映射。最后,采用有限元分析获取的数据集,验证了该网络模型对不同预损伤下的推进剂在单轴拉伸、等双轴拉伸、纯剪切3种加载条件下的宏观刚度预报能力。As a particle-reinforced polymer composite with high inclusion ratio,the macro-mechanical properties of solid propellants depend on their meso-structures.Especially,under external loads,the stress concentration usually happens besides the regions of the initial imperfections and the particle agglomerations,leading to the interfacial debonding between the particles and the polymeric binders,consequently deteriorating macroscopic mechanical properties.How to build a relationship between the microscopic damage states and the macroscopic mechanical performances is the key issue for both the rational usage of the microscopic experimental results of solid propellants and the accurate prediction of structural disasters in solid rocket motors.For this purpose,this article develops an artificial neural network(ANN)based on the framework of continuum mechanics,with the scalar invariant of the deformation gradient tensor as the input and the scalar free energy as the output.Existing free energy functions and damage growth functions are selected as the activation functions of the ANN,and therefore the ANN can naturally satisfy the requirements of the continuum mechanics,including the deformation continuity,the coordinate invariance,and the thermodynamic consistency.These merits can guarantee the rapid convergence of the ANN with sparse training data,and additionally can obtain a bottom-up mapping of the microscopic damage states towards the macroscopic mechanical performances.Finally,using the dataset obtained from finite element analysis,the predictive ability of the ANN on the mechanical properties of solid propellants with different predamage states under uniaxial tension,biaxial tension,and pure shear are validated.

关 键 词：固体推进剂 力学本构关系 人工神经网络 细观损伤 细-宏观映射 

分 类 号：TB330.1[一般工业技术—材料科学与工程]