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相关概念视频

Accelerating Fluids01:17

Accelerating Fluids

2.1K
When a fluid is in constant acceleration, the pressure and buoyant force equations are modified. Suppose a beaker is placed in an elevator accelerating upward with a constant acceleration, a. In the beaker, assume there is a thin cylinder of height h with an infinitesimal cross-sectional area, ΔS.
The motion of the liquid within this infinitesimal cylinder is considered to obtain the pressure difference. Three vertical forces act on this liquid:
2.1K

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相关实验视频

Updated: Jan 9, 2026

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications
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基于深度学习的气体-固体分离流体化床中的泡检测.

Weijie Wei1, Kesheng Li1, Lei Gao1

  • 1National Energy Group Xinjiang Chemical Energy Co., Ltd., Urmqi 830000, Xinjiang, China.

ACS omega
|December 1, 2025
PubMed
概括
此摘要是机器生成的。

一个改进的YOLOv8模型准确地监测干煤优化中的气泡,提高分离效率. 气泡动态,特别是气泡面积和数量,直接预测清洁煤产量和灰含量.

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科学领域:

  • * 流体动力学和粒子技术
  • *人工智能在工艺工程中的应用
  • * 材料科学和矿物加工

背景情况:

  • * 气体固体流化层中的气泡动力学对于稳定的流化和高效的干煤提炼至关重要.
  • *精确的实时泡监测对于优化分离过程至关重要.
  • *现有的检测方法往往缺乏工业应用所需的精度或速度.

研究的目的:

  • * 开发基于YOLOv8的先进模型,用于精确和实时检测干煤炼中的泡.
  • * 调查泡特性与煤炭分离效率之间的相关性.
  • *为智能干煤分离系统提供坚实的技术基础.

主要方法:

  • * 实施了改进的YOLOv8模型,包括多头自我注意 (MHSA),BiFPN-CONCAT和基于卷积的脱头.
  • *使用120个数据集进行实验验证,以评估与既有模型 (YOLO系列,Faster R-CNN,Mask R-CNN) 相比的检测性能.
  • *泡特征 (面积,数量) 和受益结果 (灰含量,清洁煤产量) 之间的相关性分析,其次是回归建模.

主要成果:

  • * 拟议的YOLOv8模型实现了高检测精度 (精度99.1%,回忆率96.0%,mAP@0.5 95.5%),超过了现有的检测器.
  • *发现了显著的相关性:平均气泡面积与灰含量负相关 (r = -0.72),气泡数量与清洁煤产量正相关 (r = +0.90).
  • *基于泡特征的回归模型预测了R2 = 0.89.89的分离性能.

结论:

  • * 增强的YOLOv8模型提供了精确的,实时的泡监测,用于干煤优化.
  • *泡特征是分离效率的可靠指标,可以进行预测控制.
  • * 该研究为智能煤炭加工系统提供了重要的理论和技术进步.