What does this research mean for the field?

Increasing nanoparticle concentration in a micropolar hybrid nanofluid flow reduces fluid velocity and enhances temperature distribution, while artificial neural networks can predict the associated heat transfer rates with high accuracy. Novelty: ClaimNovelty.NOVEL_FINDING. Consensus alignment: ConsensusAlignment.NEUTRAL.

What question did this study set out to answer?

Investigate thermal transfer dynamics in a micropolar hybrid nanofluid containing zinc oxide and copper oxide nanoparticles over a permeable stretching/shrinking surface.

September 14, 2026International Journal of Numerical Methods for Heat &amp Fluid Flow

Neuro-computing prediction of thermal transfer rate in micropolar hybrid nanofluid flow past a stretching/shrinking surface: a Keller box numerical scheme

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Authors

ASAbhishek Sharma VPVallampati Ramachandra Prasad RSRam Prakash Sharma

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Overview

Numerical modeling study demonstrates accurate neural network prediction of heat transfer in micropolar hybrid nanofluids, highlighting potential for advanced cooling systems.

Key Points

Investigate thermal transfer dynamics in a micropolar hybrid nanofluid containing zinc oxide and copper oxide nanoparticles over a permeable stretching/shrinking surface.
Transformed governing boundary-layer equations into non-dimensional ordinary differential equations using similarity transformations.
Solved the non-linear equations numerically using the Keller box method under convective heating, heat source/sink, and thermal radiation conditions.
Trained an artificial neural network using radiation parameter, heat source parameter, and Biot number as inputs to predict the Nusselt number.
Higher nanoparticle concentration reduced fluid velocity profiles while significantly enhancing temperature distribution.
The artificial neural network achieved high predictive performance for the Nusselt number, yielding an R2 value of 0.99849.

Cite This Study

Sharma et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b26d0926e14a848b0d1a https://doi.org/10.1108/hff-03-2026-0303

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September 14, 2026International Journal of Numerical Methods for Heat &amp Fluid Flow

Neuro-computing prediction of thermal transfer rate in micropolar hybrid nanofluid flow past a stretching/shrinking surface: a Keller box numerical scheme

View Full Paper

Ask AI

Bookmark

Authors

ASAbhishek Sharma VPVallampati Ramachandra Prasad RSRam Prakash Sharma

Discussion

Member takes

Overview

Numerical modeling study demonstrates accurate neural network prediction of heat transfer in micropolar hybrid nanofluids, highlighting potential for advanced cooling systems.

Key Points

Investigate thermal transfer dynamics in a micropolar hybrid nanofluid containing zinc oxide and copper oxide nanoparticles over a permeable stretching/shrinking surface.
Transformed governing boundary-layer equations into non-dimensional ordinary differential equations using similarity transformations.
Solved the non-linear equations numerically using the Keller box method under convective heating, heat source/sink, and thermal radiation conditions.
Trained an artificial neural network using radiation parameter, heat source parameter, and Biot number as inputs to predict the Nusselt number.
Higher nanoparticle concentration reduced fluid velocity profiles while significantly enhancing temperature distribution.
The artificial neural network achieved high predictive performance for the Nusselt number, yielding an R2 value of 0.99849.

Cite This Study

Sharma et al. (2026) studied this question.

synapsesocial.com/papers/6aa7b26d0926e14a848b0d1a https://doi.org/10.1108/hff-03-2026-0303

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