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<p>This study explores the development and rheological performance of innovative polyalphaolefin (PAO)-based hybrid nanolubricants, infused with cerium oxide (CeO<sub>2</sub>) and copper oxide (CuO) nanoparticles in various ratios (90:10–10:90). Evaluated under diverse shear rates (200–1200&#xa0;s⁻<sup>1</sup>) and temperatures (40&#xa0;°C, 60&#xa0;°C, 80&#xa0;°C, and 100&#xa0;°C), these nanolubricants exhibited significant enhancements in viscosity and thermal stability. Temperature emerged as the dominant factor, reducing viscosity by approximately 50% as the temperature increased from 40 to 100&#xa0;°C. At lower temperatures, hybrid ratios such as R2 and R1 delivered up to 40% viscosity improvement relative to the base oil, leveraging stable nanoparticle dispersion, while their effects diminished at higher temperatures due to nanoparticle aggregation. Shear rate had a negligible impact, with all samples demonstrating Newtonian behavior across the tested range. To model and predict dynamic viscosity, a novel empirical correlation achieved high reliability (<i>R</i><sup>2</sup> = 0.998, MAE = 0.21, and MSE = 0.07), offering a quick and efficient computational approach. Furthermore, an Artificial Neural Network (ANN) model, designed with three hidden layers (128, 128, and 64 neurons), outperformed traditional methods by capturing complex nonlinear interactions with exceptional accuracy (<i>R</i><sup>2</sup> = 0.99991, MSE = 0.006986, and MAE = 0.066104). This superior predictive performance underscores the ANN model’s potential for real-time optimization in dynamic lubrication systems. The integration of experimental findings with advanced computational models presents a robust framework for optimizing hybrid nanolubricants. This research not only enhances lubrication performance but also advances the understanding of nanomaterial behavior and fluid mechanics in engineering applications, paving the way for tailored solutions in high-performance lubrication systems.</p>

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Experimental and Predictive Modeling of Dynamic Viscosity in Novel Hybrid Nanolubricants Using Correlation and ANN Approaches

  • 期刊论文

  • 发表日期：

    2025年3月7日

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  • Siraj Azam,
  • Sang-Shin Park

摘要

This study explores the development and rheological performance of innovative polyalphaolefin (PAO)-based hybrid nanolubricants, infused with cerium oxide (CeO2) and copper oxide (CuO) nanoparticles in various ratios (90:10–10:90). Evaluated under diverse shear rates (200–1200 s⁻1) and temperatures (40 °C, 60 °C, 80 °C, and 100 °C), these nanolubricants exhibited significant enhancements in viscosity and thermal stability. Temperature emerged as the dominant factor, reducing viscosity by approximately 50% as the temperature increased from 40 to 100 °C. At lower temperatures, hybrid ratios such as R2 and R1 delivered up to 40% viscosity improvement relative to the base oil, leveraging stable nanoparticle dispersion, while their effects diminished at higher temperatures due to nanoparticle aggregation. Shear rate had a negligible impact, with all samples demonstrating Newtonian behavior across the tested range. To model and predict dynamic viscosity, a novel empirical correlation achieved high reliability (R2 = 0.998, MAE = 0.21, and MSE = 0.07), offering a quick and efficient computational approach. Furthermore, an Artificial Neural Network (ANN) model, designed with three hidden layers (128, 128, and 64 neurons), outperformed traditional methods by capturing complex nonlinear interactions with exceptional accuracy (R2 = 0.99991, MSE = 0.006986, and MAE = 0.066104). This superior predictive performance underscores the ANN model’s potential for real-time optimization in dynamic lubrication systems. The integration of experimental findings with advanced computational models presents a robust framework for optimizing hybrid nanolubricants. This research not only enhances lubrication performance but also advances the understanding of nanomaterial behavior and fluid mechanics in engineering applications, paving the way for tailored solutions in high-performance lubrication systems.