使用物理增强神经网络的适应性灵活结构的反向设计
Moslem Mohammadi1, Abbas Z Kouzani1, Mahdi Bodaghi2
1School of Engineering, Deakin University, Geelong, VIC, Australia.
概括
本研究介绍了机械元材料的高效反向设计框架,显著减少了计算时间. 新方法准确预测非线性反应,并使定制材料设计成为可能.
科学领域:
- 机械工程 机械工程
- 材料科学 材料科学 材料科学
- 计算力学 计算力学 计算力学
背景情况:
- 机械超材料表现出复杂的非线性行为,使传统的设计和分析过程漫长且计算密集.
- 在拉力负荷下曲是这些材料非线性应变应力反应的关键因素.
- 需要有效的设计方法来克服当前方法的局限性.
研究的目的:
- 为机械超材料开发一个计算效率高的反向设计框架.
- 准确预测非线性应变应激反应,包括曲行为.
- 为了使金属材料结构的反向设计能够达到所需的刚性特征.
主要方法:
- 在MATLAB/Simscape中使用灵活结构的减少顺序模型 (ROM) 进行设计和模拟.
- 实施了在ROM结果上训练的物理增强神经网络 (PENN),以快速预测刚度曲线.
- 采用进化优化来代地改进结构参数,以实现目标应变应激反应.
主要成果:
- ROM模型在12核CPU上实现了平均4.5分钟的计算时间.
- 经过训练的PENN模型在单核CPU上在不到一秒的时间内预测了刚度曲线,证明了显著的加速度.
- 反向设计成功地产生了具有所需应变应激反应的超材料结构.
- 通过3D打印进行实验验证,证实了拟议方法的效率和有效性.
结论:
- 拟议的框架为设计具有非线性特征的机械元材料提供了一个计算效率高的替代方案.
- ROM和PENN的集成加速了预测和反向设计过程.
- 该方法经过实验验证,突出了其在创建定制元材料结构中的实际应用性.
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