Thermal conductivity of hydraulic oil-GO/Fe3O4/TiO2 ternary hybrid nanofluid: Experimental study, RSM analysis, and development of optimized GPR model

Shahsavar, Amin; Sepehrnia, Mojtaba; Maleki, Hamid; Darabi, Reyhaneh

doi:10.1016/j.molliq.2023.122338

Cited by 16 publications

(1 citation statement)

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“…Examples of applications include property predictions such as fluid flow rate, pressure distribution, and heat-transfer coefficients. GPR has also the advantage of dealing with limited and noisy data effectively by further quantifying the uncertainty inside the predictions [31][32][33].…”

Section: Gaussian Process Regressionmentioning

confidence: 99%

Twofold Machine-Learning and Molecular Dynamics: A Computational Framework

Stavrogiannis,

Sofos,

Sagri

et al. 2023

Computers

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Data science and machine learning (ML) techniques are employed to shed light into the molecular mechanisms that affect fluid-transport properties at the nanoscale. Viscosity and thermal conductivity values of four basic monoatomic elements, namely, argon, krypton, nitrogen, and oxygen, are gathered from experimental and simulation data in the literature and constitute a primary database for further investigation. The data refers to a wide pressure–temperature (P-T) phase space, covering fluid states from gas to liquid and supercritical. The database is enriched with new simulation data extracted from our equilibrium molecular dynamics (MD) simulations. A machine learning (ML) framework with ensemble, classical, kernel-based, and stacked algorithmic techniques is also constructed to function in parallel with the MD model, trained by existing data and predicting the values of new phase space points. In terms of algorithmic performance, it is shown that the stacked and tree-based ML models have given the most accurate results for all elements and can be excellent choices for small to medium-sized datasets. In such a way, a twofold computational scheme is constructed, functioning as a computationally inexpensive route that achieves high accuracy, aiming to replace costly experiments and simulations, when feasible.

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Section: Gaussian Process Regressionmentioning

confidence: 99%