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在therminol 55中混合氧化铜纳米颗粒的表征和机器学习分析,用于中温传热流体
G Kadirgama1, D Ramasamy1, K Kadirgama2,3,4
1Faculty of Mechanical and Automotive Engineering Technology, Universiti Malaysia Pahang, Pekan, 26600, Pahang, Malaysia.
Scientific reports
|March 12, 2025
概括
混合纳米流体与氧化-铜氧化物纳米粒子显著提高传热流体的导热性. 机器学习准确地预测性能,帮助太阳能热系统和工业冷却中的应用.
科学领域:
- 材料科学 材料科学 材料科学
- 化学工程是化学工程的重要组成部分.
- 纳米技术纳米技术
背景情况:
- 高效的散热对于工业系统至关重要,需要先进的热管理材料.
- 高热导率和稳定性是有效的传热流体的关键特性.
研究的目的:
- 在Therminol 55中研究混合Al2O3-CuO纳米流体的导热性,粘度和稳定性.
- 评估不同纳米粒子组成和度对流体特性的影响.
- 使用机器学习开发热导电性的预测模型.
主要方法:
- 混合Al2O3-CuO纳米流体在不同的质量比 (10:90, 20:80, 30:70) 和度 (0.1-1.0 wt%) 的制备.
- 实验测量导热率,粘度和稳定性的实验测量.
- 用2型模糊神经网络 (T2FNN) 来预测导热率的应用.
主要成果:
- 混合纳米流体表现出增强的导热性,在1.0重%度下最大增加32.82%.
- 粘度随着纳米粒子加载而增加,需要优化以实现实际使用.
- 在T2FNN模型中,预测热导率的高相关系数为96.892%.
结论:
- 混合Al2O3-CuO纳米流体为传热应用提供了更好的热性能.
- 机器学习集成促进了纳米流体行为的高效建模和优化.
- 这些发现支持在太阳能热系统,热交换器和工业冷却中使用混合纳米流体.
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