Experimental and Data-Driven Modeling of Thermo-Hydraulic Performance in Carbon Nanofluids at Ultra-Low Concentrations

Key Points

• The aim is to assess the thermo-hydraulic performance of carbon-based nanofluids at ultra-low concentrations and develop predictive modeling frameworks.
• Experimental evaluation of graphene and other carbon nanofluids at 0.005 and 0.01 wt.% concentrations in a copper tube.
• Use of machine learning models including OLS, Theil–Sen, and Elastic Net to create predictive correlations based on experimental data.
• Analysis conducted under constant heat flux with varying Reynolds numbers from 250 to 5400.
• Graphene nanofluid at 0.005 wt.% showed >20% heat transfer enhancement with <15% increase in pumping power.
• Maximum heat transfer enhancements were ∼19.97% for FMWCNT and ∼18.15% for graphene.
• Pressure drop penalties were ∼31% for MWCNT in laminar flow conditions.