发表机构
University of Bergen（卑尔根大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
研究将数据驱动求解策略GO - ADM扩展到几何精确梁结构分析，通过传统有限元分析初始化数据，研究热机械一致性并采用惩罚方法，数值例子表明该策略能获热机械一致的离散场，且比标准ADM求解器更能逼近全局最优解。
AI 中文摘要
在过去十年中，数据驱动计算力学（DDCM）成为计算力学中的新范式，能直接使用本构数据，避免信息损失。本文将结合贪婪优化算法与交替方向法（ADM）的数据驱动求解策略GO - ADM扩展到基于方向运动学的几何精确梁的结构分析。讨论了基于规定本构模型对相同结构进行传统有限元分析的非线性系统数据初始化策略。研究了数据集和离散解的热机械一致性，并通过惩罚方法在离散解中弱强制实现这种一致性。数值例子表明所提惩罚项能得到热机械一致的离散应力和应变场，且GO - ADM求解策略比基于标准ADM的直接求解器能更好地逼近全局最优解。
英文摘要
In the last decade, data-driven computational mechanics (DDCM) has emerged as a novel paradigm in computational mechanics, enabling the direct use of constitutive data - such as stress-strain pairs obtained from experiments, without relying on ad-hoc material models and thereby avoiding information loss. In this work, we extend our data-driven solving strategy GO-ADM, which combines a greedy optimization algorithm with the alternating direction method (ADM), to the structural analysis of geometrically exact beams formulated using director-based kinematics. We discuss a data initialization strategy for nonlinear systems based on a conventional finite element analysis of the same structure using a prescribed constitutive model. The resulting discrete stress and strain fields, possibly obtained under multiple loading scenarios, may also be employed as artificial datasets for the subsequent data-driven computations. Furthermore, we investigate the thermomechanical consistency of both the dataset and the discrete solution, and propose a weak enforcement of this consistency in the latter via a penalty approach. Numerical examples involving single- and multi-member structures demonstrate that the proposed penalty term leads to thermomechanically consistent discrete stress and strain fields. Moreover, for the studied examples, the solving strategy GO-ADM yields a generally improved approximation of the globally optimal solution compared to the standard ADM-based direct solver.
