In-Vessel Retention (IVR) as an emergency strategy in the event of the core melting severe accident. The ERVC can remove the residual heat of the core, which can end the accident process inside the RPV.
    The paper utilizes the method of enhanced heat transfer to improve CHF threshold by fin structure and genetic algorithm to optimize design of fin structure parameters. In this paper, according to investigating the three types fin structures (Longitudinal fins, Rectangular fins, Cylindrical fins) are selected and placed on the thermal insulation layer, which play the role of turbulence disturbance to improve CHF of the external channel. For the numerical model, the wall boiling model is used to compare the CHF threshold for three fin structures controlling the same fin parameters, as well as the calculations show that the CHF effect of cylindrical fin is better than rectangular fin and longitudinal fin, since that the turbulence intensity of around cylindrical fin is stronger than rectangular fin and longitudinal fin. This article adopts the genetic algorithm to optimize the design of cylindrical fin parameters. According to the database for training based on 50 sets of numerical calculations around fins spacing range 0~20mm, fins height range 0~60mm, fins diameter range 0~20mm. The optimization study is carried out fins spacing, fins height and fins diameter for CHF value as an objective function. The results show that the ability of fin height and fin pitch in the ranges of 0-10 mm to CHF value of ERVC system is positively correlated besides negatively correlated above 10 mm. The fins diameter in the range of 4-12mm reveals negative correlation with CHF value of ERVC system, thus the fin height of 9mm, fin spacing of 9mm, and fin diameter of 5mm are optimal. The genetic algorithm overcomes the reliance on empirical correlations in traditional methods. The calculation results can provide certain reference for ERVC system design and cylindrical fin parameters optimization design.

Severe accident,IVR-ERVC,Two phase flow,Fin structure,Thermal Optimization,Genetic Algorithm