用传统和基于人工智能的技术优化用于太阳能系统的基于MXene的水性离子液体
Mohamed Bechir Ben Hamida1, Ali B M Ali2, Narinderjit Singh Sawaran Singh3
1Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
Scientific reports
|July 2, 2025
概括
这项研究优化了基于MXene的水性离子液体用于太阳能系统,增强了传热. 数据驱动的方法确定了改善热导率,粘度和特定热容量的最佳条件.
科学领域:
- 材料科学 材料科学 材料科学
- 可再生能源工程可再生能源工程
- 化学工程是化学工程的重要组成部分.
背景情况:
- 基于MXene的水性离子液体显示了太阳能系统的潜力,但需要性能优化.
- 同时优化热导率 (TC),动态粘度 (DV) 和特定热容量 (SHC) 对于高效的热传输至关重要.
- 目前对基于MXene的纳米流体热物理性质的理解需要进一步的数据驱动探索.
研究的目的:
- 优化基于MXene的水性离子液体,以提高太阳能应用中的热物理性能.
- 为了研究系统温度和MXene质量分数 (MF) 对传热性能的影响.
- 开发一种数据驱动的方法来优化纳米流体的特性.
主要方法:
- 利用响应表面方法 (RSM) 进行热物理性质的预测建模.
- 应用多目标优化算法:增强登 (EHC),非主导排序遗传算法II (NSGA-II) 和多目标通用正常分布优化器 (MOGNDO).
- 采用权重决策工具 (可取性函数,MARCOS方法) 来改进最佳解决方案.
主要成果:
- 立方体RSM模型准确地预测了输入变量和热物理反应之间的关系.
- 与NSGA-II相比,MOGNDO提供了优越的帕雷托前线覆盖面和解决方案多样性.
- 在50°C下达到最佳性能,MXene的质量分数在0.00188%至0.2%之间.
结论:
- 实现了高达0.797 W/m·K的最佳导热率,DV在2.0282.157 mPa·s之间,SHC在2.1922.503 J/g·K之间.
- 数据驱动的方法为太阳能系统优化基于MXene的纳米流体提供了一个可扩展的策略.
- 这些发现有助于推进可再生能源解决方案,并为工程优化问题提供框架.
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