Artificial neural network architecture for rheological property prediction of a novel hybrid nanolubricant for automotive spark-ignition engine

Unveiling the rheological properties of fiber suspensions is of paramount interest to many industrial applications. There are multiple factors, such as fiber aspect ratio and volume fraction, that play a significant role in altering the rheological behavior of suspensions. Three-dimensional (3D) numerical simulations of coupled differential equations of the suspension of fibers are computationally expensive and time-consuming. Machine learning algorithms can be trained on the available data and make predictions for the cases where no numerical data are available. However, some widely used machine learning surrogates, such as neural networks, require a relatively large training dataset to produce accurate predictions. Multi-fidelity models, which combine high-fidelity data from numerical simulations and less expensive lower fidelity data from resources such as simplified constitutive equations, can pave the way for more accurate predictions. Here, we focus on neural networks and the Gaussian processes with two levels of fidelity, i.e., high and low fidelity networks, to predict the steady-state rheological properties, and compare them to the single-fidelity network. High-fidelity data are obtained from direct numerical simulations based on an immersed boundary method to couple the fluid and solid motion. The low-fidelity data are produced by using constitutive equations. Multiple neural networks and the Gaussian process structures are used for the hyperparameter tuning purpose. Results indicate that with the best choice of hyperparameters, both the multi-fidelity Gaussian processes and neural networks are capable of making predictions with a high level of accuracy with neural networks demonstrating marginally better performance.

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Optimizing Spark-Ignition Engine Performance with Ternary Blend Fuels and Hybrid Nanolubricants: A Response Surface Methodology Study

Bharath,

Selvan

2024

SAE Int. J. Engines.

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<div>The current research elucidates the application of response surface methodology to optimize the collective impact of methanol–isobutanol–gasoline blends and nanolubricants on the operational parameters of a spark-ignition engine. Diverse alcohol blends in conjunction with gasoline are employed in engine trials at 2500 rpm across varying engine loads. The alcohol blends exhibit notable enhancements in brake thermal efficiency, peak in-cylinder pressure, and heat release rate. At 2500 rpm and 75% load, the break thermal efficiency of iBM15 surpasses that of gasoline by 33.5%. Alcohol blends significantly reduce hydrocarbon and carbon monoxide emissions compared to gasoline. The iBM15 demonstrates a reduction of 25.2% and 51.12% in vibration along the Z and Y axes, respectively, relative to gasoline. As per the response surface methodology analysis, the optimal parameters are identified: an alcohol content of 29.99%, an engine load of 99.06%, and a nanolubricant concentration of 0.1%. It is noteworthy that ternary blends can be viably employed in spark-ignition engines, offering a partial replacement for conventional fossil fuels. This research highlights that employing isobutanol–methanol–gasoline ternary blends and the ZnO-TiO<sub>2</sub>/5W30 hybrid nanolubricant improves spark-ignition engine performance, cuts emissions, and minimizes engine vibration compared to conventional gasoline.</div>

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Artificial neural network architecture for rheological property prediction of a novel hybrid nanolubricant for automotive spark-ignition engine

Cited by 4 publications

References 57 publications

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Anti-wear behavior of 1-octanethiol and tween-80 capped ZnO nanoparticles as lubricating oil additives

Multi-fidelity modeling to predict the rheological properties of a suspension of fibers using neural networks and Gaussian processes

Optimizing Spark-Ignition Engine Performance with Ternary Blend Fuels and Hybrid Nanolubricants: A Response Surface Methodology Study

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