Reliable models for calculating the condensation heat transfer inside smooth helical tubes of different flow directions utilizing smart computational techniques.

ABSTRACT: Condensers with helical tubes have received much attention in diverse industries. The optimal design of the mentioned equipment necessitates predictive tools for calculating the condensation heat transfer coefficient (HTC). However, literature models are applicable only to specific operational and geometrical conditions. The current study aims at developing reliable models for the condensation HTC within smooth helical tubes at all flow directions. Two machine learning (ML) techniques, namely Support Vector Machine (SVM) and Gaussian Process Method (GPM) were implemented to accomplish this target. To design and validate the models, 563 HTC data, encompassing a wide spectrum of conditions, were gathered from 10 experimental studies. While both SVM and GPM tools provided excellent prediction

SUBMITTER: Hsu CY 

PROVIDER: S-EPMC12365103 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature