Heat transfer performance analysis of a helical coil tube with supercritical CO2 as a working fluid

Helical coil heat exchangers offer intensified performance through compact design and secondary flows. Optimising such exchangers requires understanding complex interactions between operating conditions, design parameters, and hydrodynamics. Thermodynamics advancement also leads to the development of new thermodynamic concepts, i.e., synergy between velocity and temperature fields and heat transfer ability of heat transfer systems. Thus, this study investigates steady-state turbulent flow and heat transfer in helical coils using computational fluid dynamics (CFD) and evaluates performance with conventional (energy, entropy, exergy) and novel (entransy, field synergy principle) thermodynamics performance metrics. Supercritical CO 2 (sCO 2 ) is used as the working fluid due to its superior thermal-hydraulic advantages at the critical point. Validated three-dimensional (3D) CFD model explores effects of inlet temperature (300-330 K), inlet pressure (8.0-11.0 MPa), heat flux (15.5-25.5 kW m -2 ), curvature ratio (0.010-0.100), non-dimensional pitch (0.04-0.20), and coil turns (4-10) on the performance. Based on the face-centred central composite design (FCCCD), statistically significant regression models (0.819 ≤ R 2 ≤ 0.994) are developed for each output response. Response surface methodology (RSM) identifies the optimum configuration with the highest desirability for maximum heat transfer. Results reveal that entransy is the only performance metric consistent with heat transfer. Finally, analysis of variance (ANOVA) identifies dominant parameters affecting heat transfer performance and energy utilisation efficiency. This study establishes the optimisation framework of integrating CFD with RSM for sCO 2 in helical coils, offering practical design insights for industrial applications. • CFD analysis of turbulent supercritical heat transfer in helical coil tubes • RSM evaluates design parameters’ effects on heat transfer and energy efficiency • Regression models are verified via ANOVA and normal probability plots • Single-objective optimization to maximise heat transfer and energy efficiency • Response surface plots reveal interactions between multiple parameters