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相关概念视频

Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

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Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
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机器学习辅助的气泡动力学在气体-固体流体化床的实验性表征.

Shuxian Jiang1, Kaiqiao Wu1,2, Victor Francia3

  • 1Centre for Nature-Inspired Engineering and Department of Chemical Engineering, University College London, London WC1E 6BT, United Kingdom.

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概括

一种机器学习方法准确地识别了气体固体流体化床中的气泡. 该工具通过在各种条件下提供一致和可重复的泡分析来增强水力学研究.

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科学领域:

  • 流体动力学 流体动力学
  • 化学工程是化学工程的组成部分.
  • 机器学习应用程序 机器学习应用程序

背景情况:

  • 准确的气泡识别对于理解气体固体流体化床至关重要.
  • 传统方法面临着不同的照明和焦点的挑战,影响水力动力学研究的可重复性.
  • 振荡的流体化床呈现出复杂的泡动态,难以分析.

研究的目的:

  • 开发和验证一种机器学习辅助的图像细分方法,用于在气体固体流体化床中自动识别气泡.
  • 为了提高泡跟踪和分析的准确性和一致性.
  • 将该方法应用于具有挑战性的振荡流体化床,并确定新的流体特性.

主要方法:

  • 利用机器学习 (ML) 模型进行二进制图像分割以识别泡.
  • 开发了一种内部拉格朗追踪技术来监测泡的演变.
  • 验证了ML辅助的细分和跟踪在不同的操作条件和颗粒大小,包括振荡床.

主要成果:

  • 在泡识别中达到98.75%的准确性,过出常见的不确定性来源.
  • 成功捕获了复杂的泡动态和速度和尺寸分布的微妙变化.
  • 确定了振荡床的新特征,将泡形态和流动稳定性与操作参数联系起来.

结论:

  • 机器学习辅助方法提供了一个高效,标准化和可重复的工具,用于在流体化床中的水力动力学研究.
  • 该技术在各种颗粒大小和操作条件下显示出多功能性和有效性.
  • 这种方法有可能在其他多相流系统中得到更广泛的应用.
  • 这项研究为振荡的流体化床的水力动力学提供了新的见解.