Random forrest regression (RFr) data upscaling and artificial neural network (ANN) modeling for sinusoidal microchannel heatsinks with graphene nanofluids

Results in Engineering 2026 Open access

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Authors: Abdullah Aziz, Anas Alazzam
DOI: 10.1016/j.rineng.2026.110640
Cited by: 1
References: 27

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

Fields of Study & Subjects

Heat Transfer and Optimization Nanofluid Flow and Heat Transfer Fluid Dynamics and Vibration Analysis

Publication details

Journal / source
Results in Engineering
Publisher
Elsevier BV
Volume
30
Pages
110640–110640
Publication date
21 April 2026
Language
EN