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B-FADE:Python中的贝叶斯疲劳模型估计器及其应用于对El Haddad曲线的概率估计
Alessandro Tognan1, Enrico Salvati2
1Polytechnic Department of Engineering and Architecture, University of Udine, Via delle Scienze 206, 33100, Udine, Italy. tognan.alessandro@spes.uniud.it.
Scientific reports
|February 27, 2025
概括
本研究介绍了B-FADE,这是一个开源的Python包,用于概率性疲劳分析. 它可以对El Haddad进行可靠的校准.
科学领域:
- 材料科学 材料科学 材料科学
- 机械工程 机械工程
- 计算科学 计算科学
背景情况:
- 准确校准半经验性疲劳模型对于材料科学中可靠的预测至关重要.
- 埃尔·哈达德 (EH) 曲线被广泛用于金属合金中疲劳耐力极限的表征.
- 现有的决定性模型缺乏概率的稳定性和可访问的代码.
研究的目的:
- 发布B-FADE,一个开源的Python软件包,实现了对El Haddad曲线估计的概率方法.
- 提供加工前和后的公用事业,以方便部署后期最大限度 (MAP) 方法.
- 为未来的疲劳模型实现提供灵活和可扩展的平台.
主要方法:
- 利用后期最大限度 (MAP) 贝叶斯推理方法对EH曲线进行概率估计.
- 开发了B-FADE,这是一个Python包,将MAP方法与增强功能集成在一起.
- 集成用于数据预处理和结果可视化的实用程序.
主要成果:
- 成功实现并发布了B-FADE Python软件包.
- 该软件包允许对EH曲线进行可靠和概率的估计,即使数据有限.
- B-FADE为疲劳分析提供了更好的抽象性和可扩展性.
结论:
- 在疲劳分析方面,B-FADE为研究人员和工程师提供了一个有价值的开源工具.
- 该套件促进了对材料疲劳特征的概率方法的应用.
- 它的灵活设计支持整合未来的疲劳模型和方法.
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