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Laminar Flow: Problem Solving01:24

Laminar Flow: Problem Solving

250
Laminar flow occurs when a fluid moves smoothly in parallel layers with minimal mixing and turbulence. In fluid mechanics, ensuring laminar flow within a pipe is essential for precise control of flow characteristics, especially in engineering applications. The key factor in determining whether flow remains laminar is the Reynolds number, a dimensionless quantity that depends on the fluid's velocity, density, viscosity, and the pipe's diameter. A Reynolds number of 2100 or lower...
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Buoyancy and Stability for Submerged and Floating Bodies01:11

Buoyancy and Stability for Submerged and Floating Bodies

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In fluid mechanics, buoyancy and stability are key concepts for understanding the behavior of submerged and floating bodies. When a stationary body is fully or partially submerged in a fluid, the fluid exerts a force on the body known as the buoyant force. This force acts vertically upward through a point called the center of buoyancy, which is the center of the displaced fluid volume. According to Archimedes' principle, the magnitude of the buoyant force is equal to the weight of the fluid...
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Turbulent Flow: Problem Solving01:09

Turbulent Flow: Problem Solving

183
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.
Temperature is a key factor in CO2 solubility. In this case, the CO2 gas and the liquid are cooled to 20°C. Lower temperatures...
183
Newtonian Fluid: Problem Solving01:18

Newtonian Fluid: Problem Solving

390
Newtonian fluids exhibit a constant viscosity, meaning their shear stress and shear strain rate are directly proportional. This property ensures a predictable and stable response to applied forces, maintaining a linear relationship between force and flow. Examples include water, air, and light oils, consistently demonstrating this proportional behavior regardless of external conditions.
A velocity gradient forms within the fluid when a Newtonian fluid is placed between two parallel plates, with...
390
Unsymmetric Loading of Thin-Walled Members: Problem Solving01:07

Unsymmetric Loading of Thin-Walled Members: Problem Solving

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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...
163
Angle of Twist: Problem Solving01:13

Angle of Twist: Problem Solving

390
An electric motor applies a torque of 700 N·m to an aluminum shaft, triggering a stable rotation. Two pulleys, B and C, are subjected to torques of 300 N·m and 400 N·m, respectively. The modulus of rigidity is provided as 25 GPa. With the knowledge of the length and diameter of each segment, the twist angle between the two pulleys can be computed. First, a section cut is made between pulleys B and C, and the cut cross-section is analyzed using a free-body diagram. Given that the...
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Spatial Multiobjective Optimization of Agricultural Conservation Practices using a SWAT Model and an Evolutionary Algorithm
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LSWOA: 数値およびエンジニアリング設計の最適化問題のためのレヴィフライトとスパイラルフライトによる強化されたクジラ最適化アルゴリズム

Junhao Wei1, Yanzhao Gu1, Zhanxi Xie2

  • 1Faculty of Applied Sciences, Macao Polytechnic University, Macao, China.

PloS one
|September 3, 2025
PubMed
まとめ

改良されたクジラ最適化アルゴリズム (LSWOA) は,未熟な収束に対処し,探査を強化することで,元のものを改善します. この新しいアプローチは,ベンチマークとエンジニアリング最適化タスクの優れたパフォーマンスを示しています.

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科学分野:

  • コンピューター・インテリジェンス
  • メタヒューリスティック最適化
  • アルゴリズム強化

背景:

  • クジラ最適化アルゴリズム (WOA) は,早めの収束,人口多様性の減少,遅い収束率,低精度などの制限に直面しています.
  • 探査と採掘の段階の間の大きな不均衡は,複雑な最適化問題におけるWOAの有効性を妨げています.

研究 の 目的:

  • 新しく改良されたクジラ最適化アルゴリズム (LSWOA) を導入し,正規のWOAの固有の制限を克服するように設計した.
  • 全体の最適化性能,収束速度,およびWOAの精度を向上させる.

主な方法:

  • より高品質な検索ベースラインのためのGood Nodes Set初期化を実装しました.
  • 最適な脱出能力を強化するために 距離ベースの誘導捜索戦略を導入しました
  • 探査と採掘のバランスをとるため,ダイナミックなパラメータ調整による強化されたスパイラルアップデート戦略を開発しました.

主要な成果:

  • LSWOAは,他のメタヒューリスティックアルゴリズムとWOAの変数と比較して,さまざまな次元ベンチマーク機能の優れた最適化性能を示した.
  • このアルゴリズムは 7 つのエンジニアリング設計最適化問題で優れた結果を達成し,実際のアプリケーションでの有効性を示しました.
  • LSWOAはコンバージェンス速度と精度が向上し,複雑で高次元の最適化課題を効果的に解決しました.

結論:

  • 提案されたLSWOAは,標準的なWOAの弱点を効果的に軽減し,より堅牢で効率的な最適化ツールを提供します.
  • LSWOAは,その優れた性能と適応性により,現実世界のエンジニアリングの課題における多様なアプリケーションに重要な可能性を秘めています.