Development of RP-3 Surrogate Fuels via Multi-Objective Genetic Algorithm for Regenerative Cooling CFD with Supercritical Property Fidelity

Supercritical heat transfer in regenerative cooling channels is strongly influenced by thermophysical property variations near the pseudo-critical temperature, yet their direct implications for cooling performance have not been fully addressed. This study investigates how incorporating supercritical property considerations into surrogate fuel formulation affects heat transfer behavior in a regenerative cooling channel. RP-3 surrogate fuels were constructed using a genetic algorithm by matching both temperature-independent properties and temperature-dependent properties under supercritical conditions. Unlike previous approaches employing distillation curves as a secondary objective, the present formulation adopted supercritical density distribution and pseudo-critical temperature (T_pc) as optimization targets. The formulated surrogate fuels were evaluated in a regenerative cooling channel model surrounding a combustor, and their flow and heat transfer characteristics were compared with those of literature-based surrogate fuels. The results show that differences in T_pc and density variation trends significantly influence buoyancy-induced asymmetric flow structures and the onset of heat transfer deterioration. Surrogate fuels with lower T_pc exhibit earlier density reduction and earlier development of asymmetric flow, whereas fuels with higher T_pc demonstrate relatively mitigated wall temperature rise. The results of the present study suggest that surrogate fuel formulation based on supercritical thermophysical properties can have a significant influence on the predicted heat transfer behavior in regenerative cooling channels under the operating conditions considered.