Optimal Design of Evaporation Performance for Evaporator Tubes Based on a Small-Sample Agent Model

To enhance the atomization and evaporation performance of evaporative tubes, this study employs an optimization design methodology based on a few-sample surrogate model. By modifying the geometric parameters of the evaporative tube, we investigated how structural parameters affect the Sauter Mean Diameter and evaporation rate. The optimization process consisted of 100 initial CFD samples and five rounds of iterative parameter additions, with six additional CFD re-computation samples added per round, resulting in a total of 130 CFD evaluations for surrogate model updates and optimization validation. A baseline configuration was used for relative improvement rate calculations and excluded from the optimization sample count. After five rounds of optimization, the Sauter Mean Diameter surrogate model achieved a maximum error of 4.80%, a mean error of 2.74%, and an R2 modeling score of 0.962 in the fifth iteration. The evaporation rate surrogate model demonstrated a maximum error of 14.27%, a mean error of 6.59%, and an R2 score of 0.988. Based on statistics from 30 CFD re-computation samples generated across five iterations, the SMD showed an average improvement of 24.26%, and the evaporation rate improved by 234.35%, demonstrating that this few-sample optimization approach effectively identifies optimal design solutions with limited CFD evaluation resources.