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相关概念视频

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

Design Example: Creating a Hydraulic Model of a Dam Spillway

306
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.
306
Typical Model Studies01:30

Typical Model Studies

441
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.
441
Modeling and Similitude01:12

Modeling and Similitude

333
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...
333
Uniform Depth Channel Flow01:27

Uniform Depth Channel Flow

167
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...
167
Hydraulic Jump: Problem Solving01:16

Hydraulic Jump: Problem Solving

146
To analyze a hydraulic jump in a rectangular channel with a flow speed of 6 meters per second, follow these steps:Calculate Effective Upstream Velocity:When the downstream gate closes, a hydraulic jump forms, traveling upstream at 2 meters per second. This wave speed combines with the initial channel flow velocity, creating an effective upstream velocity.Identify Flow Velocities Before and After the Hydraulic Jump:Upstream of the hydraulic jump, the effective flow velocity includes both the...
146
Unsymmetric Loading of Thin-Walled Members: Problem Solving01:07

Unsymmetric Loading of Thin-Walled Members: Problem Solving

169
The shear center of a channel section with uniform thickness, height, and width, is determined by computing the shear force in the member and calculating the moments of inertia of the sections.
To compute the shear forces, find the shear flow at a specific distance from the endpoint using the vertical shear and the moment of inertia values. The total shear force on the flange is calculated by integrating the shear flow from one end of the flange to the other.
Next, calculate the moments of...
169

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在液压结构下倾斜的双截面墙壁的液压性能建模,使用优化合并机器学习.

Mohamed Kamel Elshaarawy1, Martina Zeleňáková2, Asaad M Armanuos3

  • 1Civil Engineering Department, Faculty of Engineering, Horus University-Egypt, New Damietta, 34517, Egypt. melshaarawy@horus.edu.eg.

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机器学习模型,包括CatBoost,使用倾斜的切断墙准确预测液压结构性能. CatBoost模型在预测提升力,液压梯度和泄漏排放方面表现出色,有助于实际工程应用.

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科学领域:

  • 地质技术工程 地质技术工程
  • 计算流体动力学的流体动力学.
  • 机器学习应用 机器学习应用

背景情况:

  • 液压结构需要有效的漏控制,以防止提升力并确保稳定性.
  • 倾斜的双切断墙是管理漏的常见方法,但它们的性能分析可能是复杂的.
  • 传统的分析方法可能无法完全捕捉到设计参数和漏控制有效性之间的复杂关系.

研究的目的:

  • 评估五种机器学习模型在预测液压结构下倾斜的双切断墙的性能方面的有效性.
  • 确定影响起重力,液压梯度和泄漏排放的最有影响力的设计参数.
  • 为工程师开发一个实用的工具,以优化隔离墙设计和预测漏相关的结果.

主要方法:

  • 利用了来自先前研究的630个样本的数据集,结合了相对切断壁距离 (L/B),倾斜角比 (θ2/θ1),深度比 (d2/d1) 和相对切断深度 (d2/D) 等参数.
  • 采用了五种机器学习模型:随机森林 (RF),自适应提升 (AdaBoost),极端梯度提升 (XGBoost),光梯度提升机 (LightGBM) 和分类提升 (CatBoost).
  • 使用贝叶斯优化 (BO) 进行了超参数优化,并使用五倍交叉验证,并使用夏普利增量扩展 (SHAP) 和部分依赖图 (PDP) 分析了特征重要性.

主要成果:

  • CatBoost 模型表现出卓越的预测准确性,在提升力 (U/Uo) 上实现了超过 0.95 的 R2 值,在液压梯度 (iR/iRo) 上达到 0.93 的 R2 值,在漏排放 (q/qo) 上达到 0.97 的 RMSE.
  • 特性重要性分析表明,L/B显著影响U/Uo和iR/iRo,而d2/D对q/qo.很重要.
  • 部分依赖图显示了特定的关系:L/B和U/Uo之间的正线性趋势,d2/d1对iR/iRo和q/qo的V形影响,以及θ2/θ1.1.的复杂非线性影响.

结论:

  • 机器学习,特别是CatBoost,为分析倾斜的双切断墙的性能提供了强大而准确的方法.
  • 该研究为关键设计参数的影响提供了有价值的见解,使得工程师可以做出更明智的决策.
  • 开发了一个交互式图形用户界面 (GUI),促进这些预测模型在现实世界水力工程项目中的实际应用.