Jove
Visualize
お問い合わせ
JoVE
x logofacebook logolinkedin logoyoutube logo
JoVEについて
概要リーダーシップブログJoVEヘルプセンター
著者向け
出版プロセス編集委員会範囲と方針査読よくある質問投稿
図書館員向け
推薦の声購読アクセスリソース図書館諮問委員会よくある質問
研究
JoVE JournalMethods CollectionsJoVE Encyclopedia of Experimentsアーカイブ
教育
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab Manual教員リソースセンター教員サイト
利用規約
プライバシーポリシー
ポリシー

関連する概念動画

Laminar and Turbulent Flow01:07

Laminar and Turbulent Flow

10.4K
Fluid dynamics is the study of fluids in motion. Velocity vectors are often used to illustrate fluid motion in applications like meteorology. For example, wind—the fluid motion of air in the atmosphere—can be represented by vectors indicating the speed and direction of the wind at any given point on a map. Another method for representing fluid motion is a streamline. A streamline represents the path of a small volume of fluid as it flows. When the flow pattern changes with time, the...
10.4K
Velocity Potential01:20

Velocity Potential

622
In steady, incompressible flow through a long, straight pipe with a uniform cross-section, the flow in the central region (far from the pipe walls) is irrotational. This irrotational nature means that fluid particles do not rotate around their axes, and a scalar function called the velocity potential, represented by ϕ, can be used to describe their movement. In irrotational flows, the velocity field V is defined as the gradient of the velocity potential:
622
Velocity and Acceleration in Steady and Unsteady Flow01:11

Velocity and Acceleration in Steady and Unsteady Flow

334
In fluid mechanics, velocity and acceleration are key concepts for analyzing particle motion in both steady and unsteady flow. Consider a fluid particle moving along a pathline, where its velocity depends on its position and time. The particle's acceleration is obtained by differentiating the velocity with respect to time.
The acceleration can be generalized to any point in the flow, and expressed as components along three perpendicular directions, representing changes in velocity over...
334
Pressure Variation in a Fluid at Rest01:11

Pressure Variation in a Fluid at Rest

680
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...
680
Steady Flow of a Fluid Stream01:27

Steady Flow of a Fluid Stream

616
Consider a control volume, such as a pipe with solid boundaries, through which fluid flows and changes direction due to the impulse exerted by the resulting force from the pipe walls. In steady flow, the mass of fluid entering the control volume at a given time, t, with velocity v1, is equal to the mass leaving after infinitesimal time dt, with velocity v2.
During this process, the momentum of the fluid within the control volume remains constant over the time interval dt. By applying the...
616
Streamlines, Streaklines, and Pathlines01:18

Streamlines, Streaklines, and Pathlines

1.8K
A streamline represents the trajectory that is always tangent to the fluid's velocity vector at any given point. The velocity of a fluid particle is always directed along the streamline, ensuring the particle continuously follows the streamline's path. Streamlines are particularly useful for visualizing the overall direction of flow in a fluid system, and they provide an instantaneous representation of the flow's velocity field. In steady flow, where conditions do not change over...
1.8K

こちらも読む

関連記事

共著者、ジャーナル、引用グラフによってこの研究に関連する記事。

並び替え
Same author

Automatic selection of the best neural architecture for time series forecasting.

Nature communications·2026
Same author

MR-AIV reveals in vivo brain-wide fluid flow with physics-informed AI.

Science advances·2026
Same author

A Multiscale Signaling-Biophysical Framework Reveals Mechanisms of Macrophage-Mediated RBC Clearance in Sickle Cell and Gaucher Disease.

bioRxiv : the preprint server for biology·2026
Same author

Robust MR-AIV: A Systematic Study of Robustness Improvement and Sensitivity Analysis of MR-AIV.

bioRxiv : the preprint server for biology·2026
Same author

Learning turbulent flows with generative models for super resolution and sparse flow reconstruction.

Nature communications·2026
Same author

In silico biophysics and rheology of blood and red blood cells in Gaucher Disease.

PLoS computational biology·2025

関連する実験動画

Updated: Dec 29, 2025

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole
09:37

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole

Published on: August 26, 2019

6.0K

隠された流体力学:フロービジュアライゼーションから速度と圧力フィールドを学習する

Maziar Raissi1,2, Alireza Yazdani3, George Em Karniadakis1

  • 1Division of Applied Mathematics, Brown University, Providence, RI 02906, USA. maziar.raissi@colorado.edu george_karniadakis@brown.edu.

Science (New York, N.Y.)
|February 1, 2020
PubMed
まとめ

隠された流体力学 (HFM) は 物理情報に基づいた ディープラーニングを使用して 流体の速度と圧力を 画像から抽出します この新しい枠組みは,直接的な測定が困難である複雑な流体力学の問題を解決します.

さらに関連する動画

High-speed Particle Image Velocimetry Near Surfaces
11:59

High-speed Particle Image Velocimetry Near Surfaces

Published on: June 24, 2013

33.7K
Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods
09:17

Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods

Published on: April 23, 2018

11.1K

関連する実験動画

Last Updated: Dec 29, 2025

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole
09:37

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole

Published on: August 26, 2019

6.0K
High-speed Particle Image Velocimetry Near Surfaces
11:59

High-speed Particle Image Velocimetry Near Surfaces

Published on: June 24, 2013

33.7K
Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods
09:17

Experimental Investigation of the Flow Structure over a Delta Wing Via Flow Visualization Methods

Published on: April 23, 2018

11.1K

科学分野:

  • 流体力学
  • 計算物理
  • バイオメディカルエンジニアリング

背景:

  • フロービジュアライゼーションは,物理的および生物学的システムにおける流体運動の研究を歴史的に支援してきました.
  • ナビエ=ストックスの方程式は理論的に流体の流れを記述しますが,視覚的観測から速度や圧力のような定量的データを抽出することは依然として困難です.
  • 流体力学の直接的な測定は,多くのシナリオでは困難または不可能である.

研究 の 目的:

  • 観測から定量的な流体力学データを抽出する際の制限を克服するために,新しい物理情報に基づくディープラーニングフレームワーク,Hidden Fluid Mechanics (HFM) を開発する.
  • ナビエ=ストークスの方程式をコードする多機能なフレームワークを作成し,多様な幾何学と条件の分析を可能にします.
  • 物理的および生物医学的なシステムからアクセスできない量的な情報を抽出するためのHFMの実用的なアプリケーションを実証する.

主な方法:

  • 物理情報に基づいたディープラーニングフレームワークであるHFMを開発した.
  • ニューラルネットワークのアーキテクチャに 直接ナビエ=ストックスを統合した
  • 設計されたHFMは,幅広い適用性のために特定の幾何学,初期および境界条件に無関係です.

主要な成果:

  • 様々な物理的および生物学的流れの問題に対してHFMを成功裏に実証しました.
  • フロービジュアライゼーションデータから定量的な速度と圧力フィールドの抽出を可能にしました.
  • 低解像度画像と観測データにおける大きなノイズに対するHFMの強さを示した.

結論:

  • HFMは 流体運動を定量的に分析するための 強力でデータに基づいたアプローチを提供します
  • 低解像度で騒々しいデータを処理できるフレームワークは,流体力学の研究と応用に新しい可能性を秘めています.
  • HFMは流体力学において,特に直接的な測定が不可能なシナリオにおいて,重要な進歩をもたらします.