扩展数字模型的规模
Deniz Karanfil1,2, Bahram Ravani3,4
1Department of Mechanical and Aerospace Engineering, University of California, Davis, Davis, CA, USA. dkaranfil@ucdavis.edu.
Scientific reports
|January 21, 2026
概括
本研究介绍了一种使用机器学习和维度分析来在不同系统大小之间扩展数字模型 (DM) 的新方法. 这种方法减少了对多个变体进行广泛校准的需求,节省了时间和资源.
科学领域:
- 工程 工程师 工程师 工程师
- 计算科学 计算科学
背景情况:
- 准确的数字模型 (DMs) 需要忠实的物理表示.
- 实验性校准可以提高模型的可靠性,但对于多个系统变体来说,它是昂贵的.
研究的目的:
- 引入一个新的方法和计算框架来扩展DMs.
- 允许在一个系统上进行校准,并将结果扩展到其他尺寸.
主要方法:
- 利用机器学习 (ML) 和维度分析 (DA).
- 制定一个针对DMs量身定制的一致的缩放框架.
- 解决传统DA中扭曲的缩放因子带来的挑战.
主要成果:
- 展示了在不同系统大小之间扩展DM的方法.
- 在工业和实验室系统之间成功缩放了一台轮式装载机的数字模型.
- 在单个代表性系统上启用校准,结果可扩展到其他变体.
结论:
- 拟议的ML和DA框架为扩展DM提供了一个有效的解决方案.
- 降低了与多个系统配置的实验校准相关的成本和时间.
- 提供了一个一致的缩放框架,以改善跨变体的DM忠实性.
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