优化用于PEMFC冷却的基础流体组成:一种机器学习方法来平衡热性能和风湿性能
Praveen Kumar Kanti1,2, Prashantha Kumar H G3,4, Nejla Mahjoub Said5
1Institute of Power Engineering, Universiti Tenaga Nasional, IKRAM-UNITEN, Jalan, 43000, Selangor, Malaysia.
Scientific reports
|July 27, 2025
概括
这项研究研究了用于冷却质子交换膜燃料电池 (PEMFC) 的减少氧化石墨烯 (rGO) 混合纳米流体. XGBoost模型准确预测导热率和粘度,优化PEMFC的热管理.
科学领域:
- 材料科学与工程 材料科学与工程
- 纳米技术纳米技术
- 可持续能源系统 可持续能源系统
背景情况:
- 质子交换膜燃料电池 (PEMFC) 对可持续能源至关重要,需要有效的热管理.
- 与传统流体相比,纳米流体提供了优越的冷却效果,而减少的氧化石墨烯 (rGO) 混合纳米流体显示出有前途.
- 对于用于PEMFC应用的基于rGO的混合纳米流体存在有限的研究,这凸显了这项研究的必要性.
研究的目的:
- 实验性地研究Al2O3和rGO混合纳米流体的热和质性质.
- 探索基础流体组成 (乙烯基醇和水) 和纳米颗粒度对流体特性的影响.
- 使用机器学习技术开发和验证导热率和粘度的预测模型.
主要方法:
- 在不同的乙烯基醇 (EG) 和水 (W) 混合物中制备具有不同度的Al2O3和rGO的混合纳米流体.
- 在各种温度和度下对分散稳定性,粘度和导热性的实验性评估.
- 机器学习模型的应用 (线性回归,决策树,极端梯度提升) 用于预测热和质性质.
主要成果:
- 增加EG比例降低了导热性,但增加了粘度.
- 在80:20 W:EG (1vol%,60°C) 时最大的导热增强 (1.23比);在20:80 W:EG (30°C) 时最大的粘度增强 (1.48比).
- XGBoost模型在导热率 (试验R2 = 0.9941) 和粘度 (试验R2 = 0.9944) 方面都表现出卓越的预测准确度.
结论:
- 基于rGO的混合纳米流体显示出增强PEMFC热管理的巨大潜力.
- 机器学习模型,特别是XGBoost,准确地预测纳米流体的特性,有助于高效的冷却系统设计.
- 了解基础流体比率,温度和度的影响对于优化PEMFC中纳米流体性能至关重要.
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