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相关概念视频

Typical Model Studies01:30

Typical Model Studies

362
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.
362
Modeling and Similitude01:12

Modeling and Similitude

269
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...
269
Design Example: Creating a Hydraulic Model of a Dam Spillway01:21

Design Example: Creating a Hydraulic Model of a Dam Spillway

187
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.
187
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

77
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...
77
Rapidly Varying Flow01:24

Rapidly Varying Flow

64
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...
64
Design Example: Measuring Distance Between Two Points with Obstructions01:10

Design Example: Measuring Distance Between Two Points with Obstructions

41
When measuring distances in areas with physical obstructions, such as a lake in a field, surveyors must employ techniques to calculate accurate lengths without direct line measurements. One effective method is the offset technique, which allows for precise distance estimation over inaccessible stretches.In this scenario, a surveyor must measure a side of an area that crosses a lake. Since the measuring tape cannot span the lake, the surveyor begins by establishing a baseline that aligns with...
41

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相关实验视频

Updated: Jul 10, 2025

Visualization of Flow Field Around a Vibrating Pipeline Within an Equilibrium Scour Hole
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基于物理和机器学习的模型用于准确的扫描深度预测.

Ajay Jatoliya1, Debayan Bhattacharya1, Bappaditya Manna1

  • 1Department of Civil Engineering, Indian Institute of Technology Delhi, Hauz Khas, New Delhi 110016, India.

Philosophical transactions. Series A, Mathematical, physical, and engineering sciences
|November 19, 2023
PubMed
概括
此摘要是机器生成的。

这项研究比较了基于物理的数值建模和机器学习 (ML) 来估计海上结构的扫描深度. 机器学习模型,特别是人工神经网络,显示出高效率,补充了用于准确和及时评估扫描的数值分析.

关键词:
机器学习是机器学习.数值分析是指数值分析.离岸基金会离岸基金会扫描深度 扫描深度风能是风能中的一种.

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相关实验视频

Last Updated: Jul 10, 2025

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00:09

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Experimental Multiscale Methodology for Predicting Material Fouling Resistance
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科学领域:

  • 地质技术工程 地质技术工程
  • 海洋工程 海洋工程
  • 计算流体动力学的流体动力学.

背景情况:

  • 冲浪现象对离岸结构的稳定性构成重大风险.
  • 准确的扫地深度估计对于结构完整性和安全性至关重要.
  • 在复杂的海洋环境中,现有的方法可能缺乏效率或准确性.

研究的目的:

  • 使用基于物理的数值建模和机器学习 (ML) 算法估计扫描深度.
  • 为了比较不同ML模型的有效性,并与实验数据对数值结果进行验证.
  • 突出ML和数值建模的联合潜力,以实现高效和准确的扫描评估.

主要方法:

  • 机器学习 (ML) 算法,包括人工神经网络和自适应神经模糊接口系统,在现有数据集上进行训练.
  • 使用REEF3D计算流体动力学 (CFD) 平台进行基于物理的数值建模.
  • 对于单电流和合波电流条件进行了数值模拟.
  • 模型结果与统计措施,报告结果和实验研究相对验证.

主要成果:

  • 机器学习模型,特别是人工神经网络和自适应的神经模糊接口系统,在预测扫描深度方面表现出高效.
  • 数字分析结果与报告的实验值有很好的一致性.
  • 仅在当前条件下,正常化的扫地深度 (S/D) 为0.65 (前) 和0.81 (后).
  • 在波流条件下,正常化的扫地深度 (S/D) 为0.26.

结论:

  • 基于机器学习和基于物理的数值建模都是评估海上结构的扫地深度的有价值工具.
  • 机器学习算法提供了一种有效和高效的方法,补充了传统的数值方法.
  • 该研究证实了数值模拟的可靠性和ML的潜力,用于及时和准确的扫描分析.