Ben Hamida Mohamed Bechir, Ali Ali B M, Sawaran Singh Narinderjit Singh, Mostafa Loghman
Deanship of Scientific Research, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia.
Air Conditioning Engineering Department, College of Engineering, University of Warith Al-Anbiyaa, Karbala, Iraq.
Sci Rep. 2025 Jul 1;15(1):20565. doi: 10.1038/s41598-025-06702-6.
MXene-based aqueous ionic liquids hold significant promise for enhancing heat transfer in solar energy systems. However, their full potential remains underexplored, particularly concerning the simultaneous optimization of key thermophysical properties such as thermal conductivity (TC), dynamic viscosity (DV), and specific heat capacity (SHC). This study employs an integrated data-driven approach to optimize MXene-based aqueous ionic liquids by varying system temperature and MXene mass fraction (MF). Response surface methodology (RSM) is used for predictive modeling, while enhanced hill climbing (EHC), non-dominated sorting genetic algorithm II (NSGA-II), and the multi-objective generalized normal distribution optimizer (MOGNDO) are applied for multi-objective optimization. Weighted decision-making tools, including the desirability function and the MARCOS method, refine the selection of optimal solutions. The cubic RSM models effectively captured the relationships between input variables and responses, facilitating accurate optimization. MOGNDO demonstrated broader solution diversity and more comprehensive Pareto front coverage compared to NSGA-II. Optimal thermophysical performance was observed at 50 °C with MF ranging from 0.00188 to 0.2%. Predicted optimal values include TC up to 0.797 W/m K, DV between 2.028 and 2.157 mPa s, and SHC ranging from 2.192 to 2.503 J/g K. The proposed methodology offers a reliable and scalable strategy for optimizing MXene-based nanofluids, contributing to improved thermo-hydraulic performance in solar systems. These findings support the advancement of renewable energy solutions and provide a robust framework applicable to broader engineering optimization problems.
基于MXene的水性离子液体在增强太阳能系统中的热传递方面具有巨大潜力。然而，它们的全部潜力仍未得到充分探索，特别是在同时优化关键热物理性质方面，如热导率（TC）、动态粘度（DV）和比热容（SHC）。本研究采用综合数据驱动方法，通过改变系统温度和MXene质量分数（MF）来优化基于MXene的水性离子液体。响应面方法（RSM）用于预测建模，而增强爬山法（EHC）、非支配排序遗传算法II（NSGA-II）和多目标广义正态分布优化器（MOGNDO）用于多目标优化。包括合意性函数和MARCOS方法在内 的加权决策工具完善了最优解的选择。三次RSM模型有效地捕捉了输入变量与响应之间的关系，便于进行精确优化。与NSGA-II相比，MOGNDO表现出更广泛的解多样性和更全面的帕累托前沿覆盖。在50°C且MF范围为0.00188至0.2%时观察到了最佳热物理性能。预测 的最佳值包括TC高达0.797 W/m K、DV在2.028至2.157 mPa s之间以及SHC范围为2.192至2.503 J/g K。所提出的方法为优化基于MXene的纳米流体提供了一种可靠且可扩展 的策略有助于提高太阳能系统中的热工水力性能这些发现支持可再生能源解决方案的进步并提供了一个适用于更广泛工程优化问题 的强大框架。
