Numerical analysis on performance evaluation of photovoltaic thermal systems using ternary hybrid nanofluids and forced convection around copper cylinders

This study evaluates the performance of a photovoltaic thermal (PV/T) system using forced convection and ternary hybrid nanofluids, comprising water with cobalt, zinc, and silver. The PV/T system includes a glass absorber, polycrystalline silicon, and a flow channel with eight copper cylinders, positioned at 20 %, 40 %, 60 %, and 80 % along the channel, both above and below the flow path for optimal thermal conductivity. The working fluid is modeled under turbulent, steady-state conditions using the κ−ε turbulence model.Numerical simulations via COMSOL Multiphysics 6.0 evaluate the system's thermal and fluid dynamics, focusing on local Nusselt numbers and photovoltaic cell thermal efficiency compared to a baseline. The investigation spans Reynolds numbers from 10,000 to 100,000, nanoparticle volume fractions of 1 %–20 %, and aspect ratios of 0.1–0.3. The aspect ratio in this study is defined as the ratio of the height of a square obstacle within the flow channel to the total height of the flow channel. A supervised machine learning framework develops predictive regression models for system analysis. Results show fluid force at the outlet increases by about 1200 % and 1300 % for aspect ratios of 0.1 and 0.3 as the Reynolds number rises. The Nusselt number enhances by 420 % and 466 % at a 20 % nanoparticle volume fraction for aspect ratios of 0.1 and 0.3, respectively, with a 9 % improvement in photovoltaic cell efficiency. This work uniquely applies machine learning to derive regression equations for fluid force, Nusselt number, and electrical efficiency, an approach not previously explored in laminar flow studies. •Evaluation of a photovoltaic thermal system (PV/T) using forced convection and ternary hybrid nanofluids.•Outlet fluid force jumps 1200-1300% with Reynolds number increase (10,000-100,000) at 1% nanoparticle volume fraction.•The Nusselt number increases by 420 % and 466 % for a 20 % nanoparticle volume fraction at aspect ratios of 0.1 and 0.3.•Machine learning is used to derive predictive regression models for fluid force, Nusselt number, and electrical efficiency.•Aspect ratio, Reynolds number, and nanoparticle concentration impact convection, Nusselt number, and efficiency.