Intelligent performance prediction of nanoparticle-enhanced automotive radiator cooling using CFD and machine learning
Abstract
Active thermal control of automotive engines is vital for improving performance, robustness and conventional coolants like water and air frequently bring insufficient heat dissipation. To counterpart this ill effect, the propose study examines and investigate the performance enrichment of an automotive radiator using Al₂O₃–ethylene glycol (EG) nanofluids concluded by collective numerical, experimental, and data-driven approach. Initially, three-dimensional radiator model was developed using AUTOCAD software and computational study was performed in ANSYS-FLUENT, using a pressure-based model and a realizable k–ε turbulence model. The Grid-independence test was directed to ensure numerical accuracy, and CFD predictions were experimentally validated through experimental radiator test rig. The CFD result for pure EG predicted an outlet temperature of 70.11 °C that was closely agreed with the experimental value of 69.11 °C confirming the consistency of the numerical model. The blend of Al₂O₃ nanoparticles significantly enhanced heat transfer characteristics, whereas growing nanoparticle blend leads to progressive reductions in outlet temperature. At 2% concentration, the outlet temperature reduced to 65.51 °C, while the maximum cooling performance was achieved at 5% concentration, yielding an outlet temperature of 60.87 °C. Although the pressure drop amplified moderately with nanoparticle loading—from 36.79 Pa at 1% to 38.72 Pa at 5%, the rise remained relatively small,…
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Evidence Hub reads this as a simulation / modelling, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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