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相关概念视频

Typical Model Studies01:30

Typical Model Studies

155
Fluid mechanics model studies often utilize scaled-down systems to predict fluid behavior in full-scale environments, such as river flows, dam spillways, and structures interacting with open surfaces. Maintaining Froude number similarity in river models is crucial, as it replicates surface flow features like wave patterns and velocities.
155
Major Losses in Pipes01:28

Major Losses in Pipes

349
When a fluid flows through a pipe, it experiences energy losses due to frictional resistance along the pipe walls, known as major losses. These energy losses result in a pressure drop, which varies based on the flow conditions — whether laminar or turbulent — and the specific physical properties of the fluid and pipe.
Fluid flow can be classified as laminar or turbulent, primarily based on the Reynolds number. This dimensionless number reflects the relative influence of inertial to...
349
Pressure Variation in a Fluid at Rest01:11

Pressure Variation in a Fluid at Rest

176
In a fluid at rest, the pressure at any point beneath the fluid surface depends solely on the depth, not on the container's shape or size. This principle, known as hydrostatic pressure, arises because, in stationary fluids, there is no acceleration, meaning the forces within the fluid balance out. Only vertical forces, caused by the weight of the fluid above, contribute to pressure changes with depth.
When measuring pressure at two different levels within the fluid, the difference in...
176
Bernoulli's Equation for Flow Along a Streamline01:30

Bernoulli's Equation for Flow Along a Streamline

526
Bernoulli's equation relates the energy conservation in a fluid moving along a streamline. The equation applies to incompressible and inviscid fluids under steady flow. For such a flow, Newton's second law is applied to a small fluid element, which experiences forces due to pressure differences, gravity, and velocity variations. The force balance leads to the following form of Bernoulli's equation:
526
Bernoulli's Equation for Flow Normal to a Streamline01:16

Bernoulli's Equation for Flow Normal to a Streamline

458
Bernoulli's equation for flow normal to a streamline explains how pressure varies across curved streamlines due to the outward centrifugal forces induced by the fluid's curvature. The pressure is higher on the inner side of the curve, near the center of curvature, and decreases outward to balance these centrifugal forces.
The pressure difference depends on the fluid's velocity and radius of curvature. The pressure variation is minimal in flows with nearly straight streamlines.
458
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

43
Uniform depth channel flow keeps fluid depth consistent along channels such as irrigation canals. In natural channels, such as rivers, approximate uniform flow is often assumed. This condition occurs when the channel’s bottom slope matches the energy slope, balancing potential energy lost from gravity with head loss due to shear stress. This balance prevents depth changes along the channel length, resulting in a steady, uniform flow.Uniform flow in open channels with a constant cross-section...
43

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相关实验视频

Updated: May 11, 2025

A Cost-effective and Reliable Method to Predict Mechanical Stress in Single-use and Standard Pumps
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在垂直多相流中强大的压力下降预测模型:一种机器学习方法.

Fahd Saeed Alakbari1, Mohammed Abdalla Ayoub2, M A Awad3

  • 1Centre of Advanced Process Safety (CAPS), Universiti Teknologi PETRONAS, Seri Iskandar, Perak, Malaysia.

Scientific reports
|April 18, 2025
PubMed
概括

这项研究引入了一种新的自适应神经模糊推理系统 (ANFIS) 模型,用于准确的多相流量预测垂直井的压力下降. 安菲斯模型显著优于现有方法,提高了运营效率和设计准确性.

关键词:
适应性神经模糊推理系统 (ANFIS)多相流量流的多相流量在垂直井的压力下降.强大的压力下降模型.

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科学领域:

  • 石油工程是石油工程中的一个.
  • 人工智能在能源中的作用

背景情况:

  • 准确的压力下降预测对于石油和天然气生产优化至关重要.
  • 现有的模型往往缺乏准确性与多样化或异常数据集.

研究的目的:

  • 开发和验证一种新的自适应神经模糊推理系统 (ANFIS) 模型,用于在垂直多相流中精确预测压力下降.
  • 为了证明ANFIS模型在传统方法上的优越性.

主要方法:

  • 编制了335个实验记录的数据集,涵盖了广泛的参数范围.
  • 开发了一个ANFIS模型,使用关键输入:井头压力,流量,直径,温度和长度.
  • 使用统计指标 (AAPE,RMSE,R2) 评估模型性能,交叉图,错误分布,克鲁斯卡尔-瓦利斯测试和置信区间.

主要成果:

  • ANFIS模型实现了高精度,平均绝对百分比误差 (AAPE) 为2.92%,根平均平方误差 (RMSE) 为1.9638%,确定系数 (R2) 为0.9645.
  • 统计测试和错误分析证实了ANFIS模型的优越预测能力.
  • 在压力下降预测方面,ANFIS模型的表现优于常用的公布模型.

结论:

  • 拟议的ANFIS模型是一种可靠和先进的工具,用于预测多相流量垂直井的压力下降.
  • 这种改进的准确性导致了生产设计和运营效率的重大进步.