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相关概念视频

Typical Model Studies01:30

Typical Model Studies

649
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.
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Plane Potential Flows01:23

Plane Potential Flows

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Plane potential flows simplify fluid motion by assuming the fluid to be irrotational and incompressible. These characteristics allow these flows to be described by a velocity potential function, ϕ, representing the flow speed in a given direction, and a stream function, ψ, that visualizes the flow path, both governed by Laplace's equation. These parameters help in estimating flow patterns, velocity distributions, and pressure fields around various hydraulic structures.
Uniform...
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Rapidly Varying Flow01:24

Rapidly Varying Flow

545
Rapidly varying flow (RVF) in open channels is characterized by abrupt changes in flow depth over a short distance, with the rate of depth change relative to distance often approaching unity. These flows are inherently complex due to their transient and multi-dimensional nature, making exact analysis difficult. However, approximate solutions using simplified models provide valuable insights into their behavior.Key Features of Rapidly Varying FlowRVF is commonly observed in scenarios involving...
545
Pressure Variation in a Fluid at Rest01:11

Pressure Variation in a Fluid at Rest

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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...
892
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

675
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...
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Bernoulli's Equation for Flow Normal to a Streamline01:16

Bernoulli's Equation for Flow Normal to a Streamline

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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. However, the...
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A Modeling and Simulation Method for Preliminary Design of an Electro-Variable Displacement Pump
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在储压力管理中,用于基于物理的机器学习的可差异化的多相流模型.

Harun Ur Rashid1, Aleksandra Pachalieva2, Daniel O'Malley2

  • 1Earth and Environmental Sciences Division, Los Alamos National Laboratory, Los Alamos, NM, 87545, USA. hrashid@lanl.gov.

Scientific reports
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概括

本研究介绍了一种基于物理的机器学习模型,用于地下水库压力控制. 它通过使用转移学习显著减少了昂贵的模拟的需求,从而使实际和准确的预测成为可能.

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科学领域:

  • 地质科学 地质科学
  • 计算科学 计算科学
  • 机器学习 机器学习

背景情况:

  • 由于地质异质性和多相流动力学,地下水库压力控制是复杂的.
  • 高准确度的基于物理的模拟在计算上是昂贵的,并且通常无法预测水库的行为.
  • 不确定和异质的水库特性需要大量的模拟,这构成了重大挑战.

研究的目的:

  • 开发一个计算效率高,准确的方法来控制地下水库的压力.
  • 解决传统基于物理的模拟在处理复杂的水库动态方面的局限性.
  • 为了实现现实的注射-提取场景的实际预测.

主要方法:

  • 一个基于物理的机器学习工作流程,将可微分的多相流模拟器 (DPFEHM框架) 与卷积神经网络 (CNN) 结合起来.
  • CNN学会预测来自异质透性场的液体提取速率,以强制执行压力限制.
  • 采用转移学习,在微调多相场景之前,先在更便宜的单相稳定状态模拟上预训练模型.

主要成果:

  • 开发的方法实现了高精度的训练,与以前的估计 (高达一千万) 相比,模拟的次数明显减少 (不到三千次).
  • 结合短暂的多相流体物理学可以提高注射-提取场景的预测准确性.
  • 工作流程展示了复杂的地下流动的实际和准确的预测.

结论:

  • 基于物理的机器学习为储压力控制的传统模拟提供了一个计算效率高的替代方案.
  • 从简单的模拟转移学习大大降低了训练复杂的多相流模型的计算成本.
  • 这种方法使得地下水库管理更容易获得,更准确.