| 冯凡丁,姜清辉.平衡因子C随机取值算法及其应用研究[J].计算力学学报,2025,42(2):182~188 |
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| 平衡因子C随机取值算法及其应用研究 |
| Randomized balancing factor C algorithm and its application research |
| 投稿时间：2023-09-13 修订日期：2023-11-08 |
| DOI：10.7511/jslx20230912002 |
| 中文关键词: 数据驱动 计算力学 平衡因子 随机取值算法 收敛精度 |
| 英文关键词:data-driven computational mechanics balance factor randomized value selection algorithm convergence accuracy |
| 基金项目:国家自然科学基金面上项目(51879127)资助. |
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| 摘要点击次数: 12 |
| 全文下载次数: 8 |
| 中文摘要: |
| 为了避免平衡因子C值选取不当导致数据驱动计算力学陷入局部最优或收敛精度过低,提出了平衡因子C随机取值算法,即平衡因子不再是事先给定的一个定值,而是根据数据库的应变或应力大小进行聚类分组,并求出各组的切线刚度,将数据库各组中的最小、最大切线刚度作为C的取值范围,在每一次迭代过程中都使用随机的平衡因子进行计算。在非线弹性桁架问题、线弹性和非线弹性平面问题中对平衡因子随机取值算法进行了对比验证,通过比较不同定C取值情况下和随机取值算法下代表性点的迭代路径及应变-应力相对误差,发现在相同的数据库容量下,平衡因子随机取值算法在各种弹性问题中均可以取得更好的收敛精度。 |
| 英文摘要: |
| The Randomized Balancing Factor C Algorithm was proposed to address the issue of inappropriate selection of the balancing factor C,which can lead to local optima or low convergence accuracy in data-driven computational mechanics.In this algorithm,the balancing factor C was no longer a predefined constant value,but was determined based on clustering the strains or stresses in the database and calculating the tangent stiffness of each cluster.The minimum and maximum tangent stiffness values within each cluster were used as the range of C values,and a random balancing factor was employed in each iteration.The effectiveness of the randomized balancing factor C algorithm was evaluated through a comparative analysis in the context of nonlinear elastic truss problems,as well as linear and nonlinear plane problems.Iteration paths of representative points and strain-stress relative errors were compared under different predefined C values and the randomized algorithm.The results indicate that,under the same database capacity,the randomized balancing factor C algorithm achieves better convergence accuracy in the various elastic problems. |
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