通过混合纳米流体的神经网络进行不可逆性分析,以优化太阳能收集器的优化
Sayer Obaid Alharbi1, Taza Gul2, Ilyas Khan3
1Mathematics Department, College of Science Al-Zulfi, Majmaah University, 11952, Majmaah, Saudi Arabia. so.alharbi@mu.edu.sa.
Scientific reports
|August 16, 2023
概括
这项研究优化了使用混合纳米流体 (HNF) 与铜和氧化纳米颗粒的太阳能热系统. 结果显示,增强了传热率,提高了太阳能电池板的效率和稳定性.
科学领域:
- 太阳能工程 太阳能工程
- 纳米流体的热传递
- 热辐射优化热辐射优化
背景情况:
- 提高能源资产效率对于资源优化至关重要.
- 太阳能集热器需要先进的技术来改善热辐射管理和面板性能.
研究的目的:
- 使用混合纳米流体 (HNFs) 来优化太阳能收集器性能.
- 研究铜和氧化物纳米颗粒对热辐射和热传递的影响.
- 分析生成和贝扬数用于太阳能热系统中的能源优化.
主要方法:
- 利用混合纳米流体 (HNFs),其中包括含铜 (Cu) 和氧化 (Al2O3) 纳米颗粒的糖醇.
- 在太阳能热系统内的里加板上模拟停滞点流量.
- 使用控制体积有限元方法 (CVFEM) 和Runge-Kutta 4 (RK-4) 方法进行计算分析.
- 使用机器学习神经网络验证的结果.
主要成果:
- 证明了不同纳米粒子体积分数的热传递速率的显著提高.
- 分析了受物理参数影响的生成和贝扬数趋势.
- 确认了太阳能发电系统的热性能和稳定性的改进.
结论:
- 混合纳米流体显著提高了太阳能集热器的效率.
- 优化纳米粒子度是最大化传热的关键.
- 该研究为更稳定,更高效的太阳能热能系统提供了途径.
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