Condensation Modelling in Presence of Non-Condensable Gases for Nuclear Energy Applications

Abstract

Steam condensation ensures industrial heat exchangers operate safely, preventing pressure build-up, overheating, and reducing freshwater intake for enhanced efficiency and sustainability. However, the presence of non-condensable gases (NCGs) can hinder steam condensation by creating a thermal resistance layer. Therefore, this study seeks to enhance the understanding of condensation heat transfer efficiency in industrial heat exchangers affected by NCGs, with a focus on passive cooling systems in nuclear power plants (NPPs). Addressing this challenge necessitates a comprehensive strategy involving experimental research, computational modelling, and a profound understanding of the transport mechanisms governing condensation scenarios. Throughout this study, diverse modelling techniques were utilized to examine condensation phenomena on external surfaces and in-tube condensation under various flow conditions, including co-current and counter-current flow in the presence of NCGs. This involved deploying advanced soft computing methods like machine learning (ML) models alongside the TRACE system code, complemented by experimental investigations. In the ML section, extensive datasets were gathered for external wall (1,888 datasets) and in-tube (2,913 datasets) condensation, covering air, nitrogen, helium, and a mixture of air and helium. A multilayer perceptron (MLP) model was employed for analyzing external tube condensation, while a multi-gens genetic programming (MGGP) model was devised for examining in-tube co-current flow condensation. Comprehensive examinations pinpointed the factors that impact heat transfer, leading to the development of robust correlations for predicting condensation heat transfer coefficients (HTC) in the presence of NCGs. Assessments against established correlations provided valuable insights into their precision and dependability. Additionally, the TRACE system code was used to simulate the vertical tube in KAIST's passive containment cooling system (PCCS) facility, exploring NCG effects on condensation in both co-current and counter-current flow configurations while investigating counter-current flow limitation (CCFL) onset. Results show the impact of gas molecular weight and mass fraction on condensate formation rate and CCFL occurrence. Due to limited data on counter-current flow condensation, a separate experimental facility exclusively focused on pure steam condensation experiments was established. Results were compared to predictions from the TRACE system code, which closely matched observed temperature distributions, validating the experimental outcomes.

Date of Award	10 May 2024
Original language	American English
Supervisor	Imran Afgan (Supervisor)