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

Design Example: Analyzing Capacity Contours for Flood Risk Assessment01:17

Design Example: Analyzing Capacity Contours for Flood Risk Assessment

46
Flood risk assessment involves careful planning and analysis to ensure the safety of communities near water retention structures. Capacity contours are a vital tool in this process, as they illustrate the potential spread of water at specific levels in a given area. In the context of building a bund across a small valley, these contours play a critical role in evaluating the safety of nearby residential areas.In this example, the bund is intended to store stormwater in the valley. The engineers...
46
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

164
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.
164
Modeling and Similitude01:12

Modeling and Similitude

266
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...
266
Rapidly Varying Flow01:24

Rapidly Varying Flow

60
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...
60
Gradually Varying Flow01:29

Gradually Varying Flow

46
Gradually varying flow (GVF) in open channels describes situations where water depth changes slowly along the channel due to factors like non-uniform bed slope, channel shape variations, or obstructions. This flow type occurs when the depth adjusts gradually to balance gravitational forces, shear forces, and energy requirements, resulting in a low rate of depth change.Characteristics of Gradually Varying FlowGVF is commonly observed in natural streams, rivers, and canals, where flow depth...
46
Weir01:24

Weir

40
A weir is a hydraulic structure designed to partially obstruct an open channel, enabling precise control and measurement of water flow. By forcing water to flow over or through it, a weir allows for accurate determination of discharge rates, making it an essential tool in water resource management. These structures are extensively used in regulating river flows, irrigation systems, and flood control channels.Types of Weirs and Their FeaturesWeirs are categorized primarily into sharp-crested and...
40

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

Non-point source pollution prediction and dynamics simulation in urban runoff: a physics-informed neural network approach.

Water research·2026
Same author

Physics-guided networks for probabilistic hydrodynamic forecasting in canal systems.

Environmental science and ecotechnology·2026
Same author

A topology-derived flow inference approach to optimize sensor placement for effective inflow and infiltration detection in sewer networks.

Water research·2026
Same author

An interpretable multi-task learning model for effluent quality and greenhouse gas emissions prediction in wastewater treatment plants.

Water research·2026
Same author

Global data-water symbiosis reduces AI infrastructure's carbon and water footprint.

Environmental science and ecotechnology·2026
Same author

Fourteen-analyser high-resolution hard X-ray emission spectrometer at I20 beamline at Diamond Light Source.

Journal of synchrotron radiation·2026
Same journal

The overlooked risk of horizontal transfer of plasmid-borne antibiotic resistance genes induced by organophosphate esters in aquaculture environments.

Water research·2026
Same journal

Coastal saltmarshes as nature-based solutions for pesticide mitigation through groundwater-surface water interactions.

Water research·2026
Same journal

Coupled geochemical profiling and metagenomics reveal controls on phosphine preservation and emission in a eutrophic Estuary.

Water research·2026
Same journal

Enabling smart decentralized constructed wetlands for greywater reuse with an attention-enhanced ensemble model: from nutrient treatment optimization to process-informed modeling.

Water research·2026
Same journal

Patterns and mechanisms of cross-media antimicrobial resistance development in a typical reclaimed water-receiving urban river.

Water research·2026
Same journal

Development of an electronic nose to characterize geosmin and 2-methylisoborneol of water collected from different phases in water treatment plants.

Water research·2026
查看所有相关文章

相关实验视频

Updated: Jun 28, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.0K

制造浪潮:朝着以数据为中心的水利工程

Guangtao Fu1, Dragan Savic2, David Butler1

  • 1Centre for Water Systems, University of Exeter, Exeter EX4 4QF, United Kingdom.

Water research
|April 10, 2024
PubMed
概括
此摘要是机器生成的。

人工智能 (AI) 正在推动水利工程的新时代. 由人工智能驱动的以数据为中心的方法将改变水资源管理和基础设施规划,以适应不断变化的世界.

关键词:
人工智能的人工智能是人工智能.数据中心的数据中心.以模型为中心的模型.科学范式的科学范式水利工程是水利工程.

更多相关视频

A Microfluidic Platform to Study Bioclogging in Porous Media
05:10

A Microfluidic Platform to Study Bioclogging in Porous Media

Published on: October 13, 2022

1.9K
Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
09:49

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Published on: November 18, 2015

12.2K

相关实验视频

Last Updated: Jun 28, 2025

Watershed Planning within a Quantitative Scenario Analysis Framework
12:44

Watershed Planning within a Quantitative Scenario Analysis Framework

Published on: July 24, 2016

8.0K
A Microfluidic Platform to Study Bioclogging in Porous Media
05:10

A Microfluidic Platform to Study Bioclogging in Porous Media

Published on: October 13, 2022

1.9K
Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation
09:49

Visualizing Hyporheic Flow Through Bedforms Using Dye Experiments and Simulation

Published on: November 18, 2015

12.2K

科学领域:

  • 环境工程 环境工程
  • 管理水资源 管理水资源
  • 计算机科学 计算机科学

背景情况:

  • 水利工程已经通过经验,理论和计算范式发展.
  • 人工智能 (AI) 最近的进步正在实现一种新的方法.
  • 应对全球水资源挑战需要创新的解决方案.

研究的目的:

  • 提出一个以数据为中心的水利工程框架.
  • 概述这个新兴范式的原则和要求.
  • 加速人工智能在水资源领域的应用.

主要方法:

  • 定义一个数据管道,用AI将数据转化为知识.
  • 建立核心原则:数据优先,整合和决策.
  • 确定跨学科合作和道德框架的需求.

主要成果:

  • 介绍了以数据为中心的水利工程的新框架.
  • 人工智能技术是拟议数据管道的核心.
  • 三个关键原则指导了这种范式转变.

结论:

  • 以数据为中心的水利工程代表了一个重要的范式转变.
  • 成功实施需要跨学科的努力,文化变革和道德准则.
  • 这种方法将从根本上改变水基础设施管理.