Ahmed Anas, Ilyas Suhaib Umer, Hira Noor E, Lock Serene Sow Mun, Alsaady Mustafa, Aljehani Ahmed, Abdulrahman Aymn
Industrial and Systems Engineering Department, University of Jeddah, Jeddah 23890, Kingdom of Saudi Arabia.
Chemical Engineering Department, University of Jeddah, Jeddah 23890, Kingdom of Saudi Arabia.
ACS Omega. 2025 Jul 21;10(29):31331-31347. doi: 10.1021/acsomega.4c10749. eCollection 2025 Jul 29.
The tremendous potential of nanofluid technology has been demonstrated in several applications, including lubrication and thermal control. The rheological profile of the hybrid nanofluid (NF) must be thoroughly examined to elucidate the fluid flow behavior. The rheological characteristics of carbon nanofiber and multiwalled carbon nanotube (CNF-MWCNT) dispersions in thermal oil are evidenced by this investigation. Both nanomaterials are known to have high thermal conductivity. However, limited attention has been given to the flow behavior characteristics in non-Newtonian fluids. Several characterizations of nanomaterials were carried out to assess the morphology and stability in thermal oil. To achieve high stability, a combination of stabilizer addition and ultrasonication was used. Different concentrations (0.5, 0.8, 1.0, 1.2, 1.5%) of hybrid nanofluids were used to inspect the impact of shear rate and viscosity in the experimental study. A broad shear range of 30-2000 s and five distinct temperatures, ranging from 25 to 65 °C, were used to measure the viscosity trend of all concentrations of MWCNT+CNF in thermal oil. It exhibited non-Newtonian fluid behavior, following shear-thinning characteristics. The effects of the temperature, concentration, and shear rate are all considered in a parametric study of rheological behavior. The modeling of flow behavior indicates that the nanofluids followed Herschel-Bulkley's model; however, the pure thermal oil follows Power law behavior. The machine learning approach, i.e., ANN modeling, was used to build a model and optimize it using a feed-forward multilayer perceptron method to predict the viscosity of the hybrid nanofluids using experimental data. The model's performance was assessed, and the expected outcomes showed excellent agreement with the experiment, with an of 99.99%. This result indicates the importance of the rheological behavior of hybrid NFs with non-Newtonian behavior for their application in heat transfer and lubrication.
纳米流体技术的巨大潜力已在包括润滑和热控制在内的多种应用中得到证明。必须全面研究混合纳米流体（NF）的流变特性，以阐明流体的流动行为。本研究证实了碳纳米纤维和多壁碳纳米管（CNF-MWCNT）在导热油中的分散体的流变特性。已知这两种纳米材料都具有高导热性。然而，对于非牛顿流体中的流动行为特性关注有限。对纳米材料进行了多种表征，以评估其在导热油中的形态和稳定性。为了实现高稳定性，采用了添加稳定剂和超声处理相结合的方法。在实验研究中，使用不同浓度（0.5%、0.8%、1.0%、1.2%、1.5%）的混合纳米流体来考察剪切速率和粘度的影响。在30 - 2000 s的宽剪切范围内以及25至65°C的五个不同温度下，测量了导热油中所有浓度的MWCNT + CNF的粘度趋势。它表现出非牛顿流体行为，具有剪切变稀特性。在流变行为的参数研究中考虑了温度、浓度和剪切速率的影响。流动行为建模表明，纳米流体遵循赫谢尔 - 巴克利模型；然而，纯导热油遵循幂律行为。采用机器学习方法，即人工神经网络（ANN）建模，使用前馈多层感知器方法构建模型并对其进行优化，以利用实验数据预测混合纳米流体的粘度。对模型性能进行了评估，预期结果与实验结果显示出极好的一致性，相关系数为99.99%。这一结果表明具有非牛顿行为的混合纳米流体的流变行为在传热和润滑应用中的重要性。
