Experimental and Predictive Modeling of Dynamic Viscosity in Novel Hybrid Nanolubricants Using Correlation and ANN Approaches.
Saved in:
| Title: | Experimental and Predictive Modeling of Dynamic Viscosity in Novel Hybrid Nanolubricants Using Correlation and ANN Approaches. |
|---|---|
| Authors: | Azam, Siraj1 (AUTHOR), Park, Sang-Shin1 (AUTHOR) pss@ynu.ac.kr |
| Source: | Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ). Sep2025, Vol. 50 Issue 17, p14277-14299. 23p. |
| Subjects: | Dynamic viscosity, Artificial neural networks, Rheology, Fluid mechanics, Copper oxide, Cerium oxides, Synthetic lubricants, Lubricant additives |
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
| Copyright of Arabian Journal for Science & Engineering (Springer Science & Business Media B.V. ) is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| Abstract: | 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. [ABSTRACT FROM AUTHOR] |
|---|---|
| ISSN: | 2193567X |
| DOI: | 10.1007/s13369-025-10049-5 |
