混沌山优化器通过多个基于对立的学习变体进行了改进,用于使用纳米流体的热交换器的理论热设计优化
Oguz Emrah Turgut1, Mustafa Asker2, Hayrullah Bilgeran Yesiloz3
1Department of Industrial Engineering, Faculty of Engineering and Architecture, Izmir Bakircay University, Menemen, İzmir 35665, Türkiye.
Biomimetics (Basel, Switzerland)
|July 25, 2025
概括
本研究介绍了一种增强的元启发算法,用于优化外和管热交换器. 使用纳米流体,特别是水+SiO2,可显著降低总成本16.3%.
科学领域:
- 工程优化工程优化
- 计算流体动力学的流体动力学.
- 材料科学 材料科学 材料科学
背景情况:
- 像山优化器 (MGO) 这样的元启发算法,用于复杂的工程设计,但可能会遭受过早的融合.
- 在许多工业应用中,优化外和管式换热器的热性能和经济性至关重要.
- 与传统流体相比,纳米流体有可能提高传热效率.
研究的目的:
- 为外和管热交换机的热经济设计提出一种新的混合元启发算法.
- 通过整合混乱序列和改进的准动态对立学习突变方案来增强山地优化方法.
- 评估拟议的算法在优化使用纳米流体的热交换机设计方面的性能.
主要方法:
- 一个混合算法是通过增加山地的优化方法与混乱的序列和一个新的准动态对立学习突变方案增强混合算法.
- 改进的算法包括一个自适应开关机制,以平衡勘探和开发.
- 在应用到热交换机设计之前,使用基准函数验证了算法的效率.
主要成果:
- 与原来的MGO相比,拟议的混合算法证明了搜索效率和解决方案质量的提高.
- 使用水+SiO2纳米流体的管热交换器的热经济设计,与普通水相比,总成本降低了16.3%.
- 为各种基于纳米粒子的纳米流体实现了最佳的设计配置.
结论:
- 增强的元启发算法有效地解决了MGO.中过早的融合问题.
- 纳米流体,特别是水+SiO2的集成,在和管式换热器设计中可以大大节省成本.
- 生物灵感优化算法是解决复杂工程设计问题的可行工具.
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