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To calculate the flow rate for a trapezoidal channel, first, identify the bottom width, side slope, and flow depth of the channel. The cross-sectional area (A) corresponding to the depth of flow (y), channel bottom width (B), and side slope (θ) is determined by:Next, calculate the wetted perimeter, which includes the bottom width and the sloped side lengths in contact with the water. Using the values of the cross-sectional area and the wetted perimeter, determine the hydraulic radius by...
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Trapezoidal channels are widely used in irrigation systems due to their cost-effectiveness and efficiency in conveying water. Trapezoidal channels feature a flat bottom and sloping sides, making them stable and easier to construct compared to other shapes. The bottom width and side slope ratio are determined based on the required flow capacity and site conditions. The side slope is kept gentle for unlined channels to prevent soil erosion.Hydraulic parameters in channel design include the flow...
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Carbonation is a process used to dissolve carbon dioxide gas in a liquid, commonly used in the production of carbonated beverages. Achieving efficient carbonation requires careful control of temperature, pressure, and flow conditions. By adjusting these parameters, carbonation efficiency can be maximized, producing a higher concentration of CO2 in the liquid.
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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...
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Related Experiment Video

Updated: Dec 17, 2025

Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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Offline Learning with a Selection Hyper-Heuristic: An Application to Water Distribution Network Optimisation.

William B Yates1, Edward C Keedwell2

  • 1Computer Science, College of Engineering, Mathematics and Physical Sciences, University of Exeter, Exeter, EX4 4QF, UK wy254@exeter.ac.uk.

Evolutionary Computation
|June 23, 2020
PubMed
Summary
This summary is machine-generated.

A novel hyper-heuristic optimizes water distribution networks efficiently. Offline learning significantly improves performance, even when transferred from smaller to larger problems.

Keywords:
Machine learningselection hyper-heuristicswater distribution networks.

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Area of Science:

  • Engineering
  • Computer Science
  • Optimization

Background:

  • Water distribution networks require efficient optimization.
  • Multiobjective evolutionary algorithms are commonly used but can be computationally expensive.

Purpose of the Study:

  • To evaluate a sequence-based selection hyper-heuristic with online learning for water distribution network optimization.
  • To compare its performance against multiobjective evolutionary algorithms.
  • To enhance the hyper-heuristic's performance using offline learning.

Main Methods:

  • A sequence-based selection hyper-heuristic with online learning was applied to 12 water distribution networks.
  • Performance was compared against five multiobjective evolutionary algorithms.
  • An offline learning algorithm was developed and evaluated, including a new methodology for transferring learning.

Main Results:

  • The hyper-heuristic demonstrated computational efficiency compared to multiobjective evolutionary algorithms.
  • Offline learning significantly enhanced the hyper-heuristic's optimization performance across all 12 networks.
  • Learning was successfully transferred from smaller to larger, computationally expensive problems with statistically significant improvements.

Conclusions:

  • The sequence-based selection hyper-heuristic is a computationally efficient alternative for water distribution network optimization.
  • Offline learning, particularly the proposed transfer methodology, substantially boosts optimization performance.
  • The findings suggest a promising approach for tackling complex optimization problems in water systems engineering.