Jove
Visualize
联系我们
JoVE
x logofacebook logolinkedin logoyoutube logo
关于 JoVE
概览领导团队博客JoVE 帮助中心
作者
出版流程编辑委员会范围与政策同行评审常见问题投稿
图书馆员
用户评价订阅访问资源图书馆顾问委员会常见问题
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experiments存档
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教师资源中心教师网站
使用条款与条件
隐私政策
政策

相关概念视频

Typical Model Studies01:30

Typical Model Studies

440
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.
440
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

295
Scaled hydraulic models of dam spillways provide a practical way to replicate and study the intricate flow dynamics of these structures. Often built to a 1:15 ratio, these models allow for observing critical water behavior, such as velocity distribution, flow patterns, and energy dissipation.
295
The Fluid Mosaic Model01:34

The Fluid Mosaic Model

152.4K
The fluid mosaic model was first proposed as a visual representation of research observations. The model comprises the composition and dynamics of membranes and serves as a foundation for future membrane-related studies. The model depicts the structure of the plasma membrane with a variety of components, which include phospholipids, proteins, and carbohydrates. These integral molecules are loosely bound, defining the cell’s border and providing fluidity for optimal function.
152.4K
Accelerating Fluids01:17

Accelerating Fluids

1.4K
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:
1.4K
Modeling and Similitude01:12

Modeling and Similitude

329
Scaled modeling is a fundamental technique in engineering, enabling the study of large and complex systems by creating smaller, manageable replicas that recreate critical characteristics of the original. In hydrology and civil infrastructure, for example, scaled models of dams help analyze water flow, turbulence, and pressure. This method allows for accurate predictions of real-world behavior within a controlled environment, significantly reducing the cost and time involved in full-scale...
329
Fluid Pressure01:14

Fluid Pressure

774
In mechanical engineering, fluid pressure plays a critical role in designing systems that utilize liquid flow, such as hydraulic systems, pumps, and valves. When designing these systems, engineers must ensure they can withstand the forces created by fluid pressure to avoid damage or failure.
According to Pascal's law, a fluid at rest will generate equal pressure in all directions. This pressure is measured as a force per unit area, and its magnitude depends on the fluid's specific...
774

您也可能阅读

相关文章

通过共同作者、期刊和引用图与本文相关的文章。

排序
Same author

Integrated pixel-wise remote sensing and explainable machine learning for natural hydrogen exploration in southeastern part of Pricaspian Basin, Western Kazakhstan.

Scientific reports·2026
Same author

Atomistic Modeling of Rare Earth Ions in Photonic Materials.

Luminescence : the journal of biological and chemical luminescence·2025
Same author

Q-DFTNet: A Chemistry-Informed Neural Network Framework for Predicting Molecular Dipole Moments via DFT-Driven QM9 Data.

Journal of computational chemistry·2025
Same author

Data-driven total organic carbon prediction using feature selection methods incorporated in an automated machine learning framework.

Scientific reports·2025
Same author

[The value of long-term postoperative follow-up after curative resection of lung cancer and common problems associated with it].

Nihon Geka Gakkai zasshi·2007
Same author

Identification of a type III thioesterase reveals the function of an operon crucial for Mtb virulence.

Chemistry & biology·2007

相关实验视频

Updated: Sep 10, 2025

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications
08:38

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications

Published on: January 16, 2018

10.6K

机器学习增强的完全合的液体-固体相互作用模型用于液压断裂中的推进力动力学.

Dennis Delali Kwesi Wayo1, Sonny Irawan2, Lei Wang3

  • 1Faculty of Chemical and Process Engineering Technology, Universiti Malaysia Pahang Al-Sultan Abdullah, Kuantan, 26300, Malaysia.

Scientific reports
|August 20, 2025
PubMed
概括

这项研究引入了一种混合模型,将物理和机器学习结合起来,以准确预测液压压裂过程中的推进剂沉降率 (PSR). 新的框架为骨折设计提供了高效和可解释的预测.

关键词:
计算地质力学的计算地质力学流体固体的关系水力压裂是指水力压裂的方法.机器学习 机器学习一个对立的对立者.

更多相关视频

Microfluidic Fabrication Techniques for High-Pressure Testing of Microscale Supercritical CO2 Foam Transport in Fractured Unconventional Reservoirs
10:06

Microfluidic Fabrication Techniques for High-Pressure Testing of Microscale Supercritical CO2 Foam Transport in Fractured Unconventional Reservoirs

Published on: July 2, 2020

6.9K
Experimental Measurement of Settling Velocity of Spherical Particles in Unconfined and Confined Surfactant-based Shear Thinning Viscoelastic Fluids
10:28

Experimental Measurement of Settling Velocity of Spherical Particles in Unconfined and Confined Surfactant-based Shear Thinning Viscoelastic Fluids

Published on: January 3, 2014

13.8K

相关实验视频

Last Updated: Sep 10, 2025

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications
08:38

Microfluidic Devices for Characterizing Pore-scale Event Processes in Porous Media for Oil Recovery Applications

Published on: January 16, 2018

10.6K
Microfluidic Fabrication Techniques for High-Pressure Testing of Microscale Supercritical CO2 Foam Transport in Fractured Unconventional Reservoirs
10:06

Microfluidic Fabrication Techniques for High-Pressure Testing of Microscale Supercritical CO2 Foam Transport in Fractured Unconventional Reservoirs

Published on: July 2, 2020

6.9K
Experimental Measurement of Settling Velocity of Spherical Particles in Unconfined and Confined Surfactant-based Shear Thinning Viscoelastic Fluids
10:28

Experimental Measurement of Settling Velocity of Spherical Particles in Unconfined and Confined Surfactant-based Shear Thinning Viscoelastic Fluids

Published on: January 3, 2014

13.8K

科学领域:

  • 石油工程是石油工程中的一个.
  • 计算流体动力学的流体动力学.
  • 机器学习 机器学习

背景情况:

  • 精确预测推进剂沉降率 (PSR) 对于优化液压压裂和确保有效的推进剂运输至关重要.
  • 像计算流体动力学-离散元件方法 (CFD-DEM) 模拟等传统方法在计算上昂贵且耗时.
  • 为PSR预测开发高效和可解释的模型对于骨折设计中的实时决策支持至关重要.

研究的目的:

  • 开发一个混合建模框架,整合基于符号物理的衍生,参数模拟和整体机器学习,用于预测推进剂沉降率 (PSR).
  • 与传统方法相比,验证拟议框架的物理一致性和性能.
  • 提供一种可解释,准确和计算效率高的替代方案,以对全尺寸的CFD-DEM模拟进行支柱运输分析.

主要方法:

  • 使用斯托克斯定律,拖动方程和压力梯度动力学来制定PSR的符号表达式.
  • 生成合成符号和CFD信息的数据集,涵盖现实的物理参数范围 (驱动剂密度,流体粘度,粒子直径,应变,压力梯度).
  • 在组合数据集上使用RidgeCV元学习器训练堆叠集团回归器 (随机森林,额外树木,梯度提升,XGBoost,SVR).

主要成果:

  • 基于物理的符号模型实现了高精度 (R2 = 0.9934,RMSE = 0.0436).
  • 差价合约模拟提供了补充数据,产生R2 = 0.9941和RMSE = 0.2033.
  • 混合组合模型表现出优异的性能,R2 = 0.9970和RMSE = 0.1801,优于单个模型.
  • 参数研究显示,受应变和压力梯度影响,沉速度和深度显著降低.

结论:

  • 混合建模框架提供了一个准确的,可解释的,计算效率高的方法来预测Proppant结算率 (PSR).
  • 这种方法消除了对广泛的CFD-DEM模拟的需求,促进了在水力压裂中更快的决策.
  • 该框架非常适合用于多尺度断裂设计和实时支物运输分析.