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Uncertainty-quantified parametrically upscaled continuum damage mechanics (UQ-PUCDM) model from microstructural characteristics induced uncertainties in unidirectional composites
Computer Methods in Applied Mechanics and Engineering ( IF 6.9 ) Pub Date : 2024-11-26 , DOI: 10.1016/j.cma.2024.117571
Yanrong Xiao, Deniz Ozturk, Somnath Ghosh
Computer Methods in Applied Mechanics and Engineering ( IF 6.9 ) Pub Date : 2024-11-26 , DOI: 10.1016/j.cma.2024.117571
Yanrong Xiao, Deniz Ozturk, Somnath Ghosh
This paper develops an uncertainty-quantified parametrically upscaled continuum damage mechanics (UQ-PUCDM) model for efficient multiscale analysis of unidirectional composite structures. Its constitutive parameters explicitly incorporate representative aggregated microstructural parameters (RAMPs), connecting structural response to the local microstructure. Uncertainty quantification accounts for microstructure characteristic model reduction error, neural network-based model reduction error, and aleatoric uncertainty due to inherent microstructural variability through uncertainty propagation. Development of the UQ-PUCDM framework involves machine learning tools operating on datasets generated by micromechanical simulations. The model quantifies uncertainty in RAMPs due to dimensionality reduction and quantifies the uncertainty in the upscaled elastic stiffness and damage coefficients propagated through ANN. A Bayesian principal component analysis (BPCA) derives probabilistic microstructure-dependent constitutive parameters in the PUCDM model. A Taylor expansion-based uncertainty propagation method enables computationally efficient, time-integration of the stochastic material response with consideration of uncertainty in the RAMPs. Validation studies are conducted with homogenized micromechanical solutions of SERVEs, with Monte Carlo analysis, and limited experimental results with satisfactory agreement. Finally, a single-edge notched beam (SENB) simulation is conducted to explore multiscale damage evolution problems in a structure with uncertainty propagation.
中文翻译:
不确定性量化参数放大连续损伤力学 (UQ-PUCDM) 模型来自微观结构特征在单向复合材料中引起的不确定性
本文开发了一种不确定性量化的参数放大连续损伤力学 (UQ-PUCDM) 模型,用于单向复合材料结构的高效多尺度分析。其本构参数明确包含代表性的聚合微观结构参数 (RAMP),将结构响应与局部微观结构联系起来。不确定性量化考虑了微结构特征模型缩减误差、基于神经网络的模型缩减误差以及由于不确定性传播的固有微观结构可变性而导致的随机不确定性。UQ-PUCDM 框架的开发涉及在微机械仿真生成的数据集上运行的机器学习工具。该模型量化了由于降维而导致的 RAMP 中的不确定性,并量化了通过 ANN 传播的放大的弹性刚度和损伤系数的不确定性。贝叶斯主成分分析 (BPCA) 在 PUCDM 模型中推导出概率微观结构依赖性本构参数。基于泰勒展开的不确定性传播方法能够对随机材料响应进行高效的时间积分,同时考虑 RAMP 中的不确定性。验证研究使用 SERVE 的均质微机械溶液进行,进行蒙特卡洛分析,并得到令人满意的一致性的有限实验结果。最后,进行单边缺口梁 (SENB) 仿真,以探索具有不确定性传播的结构中的多尺度损伤演变问题。
更新日期:2024-11-26
中文翻译:
不确定性量化参数放大连续损伤力学 (UQ-PUCDM) 模型来自微观结构特征在单向复合材料中引起的不确定性
本文开发了一种不确定性量化的参数放大连续损伤力学 (UQ-PUCDM) 模型,用于单向复合材料结构的高效多尺度分析。其本构参数明确包含代表性的聚合微观结构参数 (RAMP),将结构响应与局部微观结构联系起来。不确定性量化考虑了微结构特征模型缩减误差、基于神经网络的模型缩减误差以及由于不确定性传播的固有微观结构可变性而导致的随机不确定性。UQ-PUCDM 框架的开发涉及在微机械仿真生成的数据集上运行的机器学习工具。该模型量化了由于降维而导致的 RAMP 中的不确定性,并量化了通过 ANN 传播的放大的弹性刚度和损伤系数的不确定性。贝叶斯主成分分析 (BPCA) 在 PUCDM 模型中推导出概率微观结构依赖性本构参数。基于泰勒展开的不确定性传播方法能够对随机材料响应进行高效的时间积分,同时考虑 RAMP 中的不确定性。验证研究使用 SERVE 的均质微机械溶液进行,进行蒙特卡洛分析,并得到令人满意的一致性的有限实验结果。最后,进行单边缺口梁 (SENB) 仿真,以探索具有不确定性传播的结构中的多尺度损伤演变问题。