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一个数值研究基于机器学习的超声波断层扫描泡式两相流的数值研究
Yuki Wada1, Yoshiyasu Hirose1, Yasuteru Sibamoto1
1Japan Atomic Energy Agency, 319-1195, 2-4 Shirakata, Tokai, Ibaraki, Japan.
Ultrasonics
|May 31, 2024
概括
超声波断层扫描与机器学习相结合,可以准确预测两个相流中的密集泡分布. 这种先进的方法克服了先前研究对复杂泡流量测量的局限性.
科学领域:
- 流体动力学 流体动力学
- 非侵入性成像技术的使用.
- 计算建模计算建模
背景情况:
- 传统的超声波断层扫描 (UT) 由于超声波传输特征而与复杂,密集的泡流作斗争.
- 以前的研究仅限于分析几个泡,而不是实际流量中发现的众多复杂分布.
研究的目的:
- 评估将超声断层扫描 (UT) 与机器学习 (ML) 结合起来,以预测密集泡分布的可行性.
- 优化UT系统参数,包括波形时长和传感器计数,以提高泡分布预测.
- 评估同步传感器驱动的适用性,以减少复杂泡流中的测量时间.
主要方法:
- 使用有限元素方法模拟器进行二维超声波数值模拟.
- 应用机器学习算法来预测基于模拟超声数据的泡分布.
- 研究了时间波形长度和传感器数量对预测准确性的影响.
- 模拟同时激活传感器以评估缩短的测量时间.
主要成果:
- 成功预测了多达20个气泡的密集气泡分布 (空隙分数~0.29).
- 证明UT与ML相结合,为复杂的多泡场景提供了足够的预测性能.
- 在预测横截面平均空隙分数方面得到确认的高准确性.
结论:
- 与机器学习集成的超声波断层扫描是一种可行且准确的方法,用于分析复杂的泡式两相流.
- 开发的方法克服了以前的局限性,使密集泡分布的详细分析成为可能.
- 这种方法为非侵入性流量测量技术带来了重大进步.
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