电梯车结构优化设计基于一个近似的模型
Haijian Wang1, Chengwen Yu1, Xishan Zhu2
1School of Mechanical and Electrical Engineering, Guilin University of Electronic Technology, Guilin Guangxi, Guilin, China.
PloS one
|March 28, 2024
概括
这项研究通过使用Aquila优化器回传神经网络和多目标遗传算法优化了电梯块制动设计. 优化的设计显著降低了车轮的温度,应力和质量,同时提高了热疲劳寿命.
科学领域:
- 机械工程 机械工程
- 材料科学 材料科学 材料科学
- 计算智能是一种计算智能.
背景情况:
- 电梯车系统需要提高性能和轻量设计,以提高安全性和效率.
- 传统的设计方法可能无法完全捕捉到设计变量和性能目标之间的复杂关系.
研究的目的:
- 优化电梯阻塞车的结构设计,以提高性能和减轻重量.
- 通过使用先进的计算技术,建立一个准确的制动性能预测模型.
主要方法:
- 利用Aquila优化器回传播 (AO-BP) 神经网络来建模设计变量和优化目标之间的关系.
- 定义了制约条件和制动系统的目标功能.
- 采用多目标遗传算法来优化结构块车设计.
主要成果:
- 优化的设计在紧急制动过程中将车轮的最大温度降低了14.2% (36.71°C).
- 最大等效应力下降了10.5% (28.87 MPa). 这是一个很大的变化.
- 车轮质量从58.85公斤减少到52.40公斤,热疲劳寿命从64周期增加到94周期.
结论:
- AO-BP神经网络和多目标遗传算法有效优化了电梯车设计.
- 优化的设计显示了在热性能,结构完整性,减重和耐用性方面的显著改进.
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