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April 23, 2026Results in Engineering1 citationsOpen Access

Random Forrest Regression (RFr) Data Upscaling and Artificial Neural Network (ANN) Modeling for Sinusoidal Microchannel Heatsinks with Graphene Nanofluids

AAAbdullah AzizKhalifa University of Science and Technology AAAnas AlazzamKhalifa University of Science and Technology

Key Points

The research aims to predict heat transfer in sinusoidal microchannel heat sinks using machine learning techniques.
Integrated computational fluid dynamics (CFD) with a two-step hybrid machine learning framework.
Utilized Random Forest regression to upscale numerical data before training an artificial neural network.
Analyzed key parameters including Reynolds number and nanoparticle volume fraction.
Amplitude increased heat transfer by up to 5.9%; however, high frequencies reduced the Nusselt number by 2.7%.
Nanoparticles enhanced heat transfer more significantly at low Reynolds numbers compared to high.
The machine learning framework reduced mean squared error by 58.4% and achieved average prediction errors below 5%.

Abstract

• Amplitude increased heat transfer up to +5.9% in sinusoidal microchannels. • High ω–A coupling reduced Nu by up to −2.7%. • Nanoparticles raised Nu more at low Re (+2.9%) than high Re (+1.4%). • RFr cut MSE by 58.4% and improved R by 8.8%. • ANN predictions validated with <5% average error (min 0.07%). Predicting heat transfer in sinusoidal microchannel heat sinks is challenging because numerical simulations are computationally expensive and the resulting datasets remain sparse across the design space. This study integrates high-fidelity CFD with a two-step hybrid machine learning framework to model graphene nanofluid flow in sinusoidal microchannels. Key parameters include Re, nanoparticle volume fraction, waveform amplitude, frequency, width, and number of segments. The results show that amplitude enhances heat transfer by up to 5.9%, although high frequency and amplitude combinations reduce the average Nusselt number by 2.7%. Nanoparticle effects are more pronounced at low Reynolds numbers, and geometric variations, such as increasing the width, reduce the Nusselt number. To overcome data sparsity, Random Forest regression is used to upscale the numerical dataset before training an artificial neural network surrogate model, reducing mean squared error by 58.4% and improving regression by 8.8%. Validation against additional CFD cases yields an average prediction error below 5%. The proposed framework offers a computationally efficient and scalable tool for microchannel heat-sink design.

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