The Investigation on Heat Transfer Characteristics of Steam Condensation in Presence of Noncondensable Gas under Natural Convection

Ma, Xizhen; Ma, Jiang; Heng, Tong; Jia, Haikun

doi:10.1155/2021/6689597

Cited by 8 publications

(1 citation statement)

References 14 publications

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“…As the mixture condenses, it becomes denser (air and nitrogen are heavier than steam) and moves downward. In a study conducted by Ma et al [94], steam condensation was examined within a pressure range spanning from 0.516 to 5.10 MPa while considering a broad spectrum of nitrogen mass fractions, ranging from 5% to 85%. The results indicated that the condensation HTC decreased from 1.393 to 0.165 kW/ (m 2 •K) at a pressure of 0.517 MPa, and nitrogen mass fractions ranged from 9.03% to 75.76%.…”

Section: Ncg Sources and Their Transport Perspective In Nppsmentioning

confidence: 99%

Steam Condensation Heat Transfer in the Presence of Noncondensable Gases (NCGs) in Nuclear Power Plants (NPPs): A Comprehensive Review of Fundamentals, Current Status, and Prospects for Future Research

Albdour,

Addad,

Alyammahi

et al. 2024

International Journal of Energy Research

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Efficient steam condensation is crucial for safe nuclear power plant (NPP) operations, preventing pressure buildup, overheating, and the release of radioactive materials. However, the presence of noncondensable gases (NCGs), such as air, nitrogen, hydrogen, or helium, can hinder the condensation process by creating a thermal resistance layer that impedes steam diffusion and condensation on the system’s surface. Maximizing the efficiency of steam condensation requires a thorough grasp of the fundamental processes, theories, advancements, and technical hurdles. Therefore, this work thoroughly addresses these needs, with a particular emphasis on addressing the challenges posed by NCGs by dividing the work into four thematic areas. The first theme relates to a comprehensive examination of pure steam condensation phenomena, which includes an exploration of familiar condensation scenarios and various film condensation types. The second theme examines condensation in the presence of NCGs, their mixture properties, and related theories and modelling of heat and mass transfer. The third theme investigates condensation in NPP by exploring passive cooling systems and condensation phenomena under both natural and forced convection conditions during nuclear accidents, the origin of NCGs in NPP and their transportation aspects. This is followed by experimental work related to condensation scenarios and scale. Finally, the last theme looks upon the recent advancements in computational fluid dynamics (CFD) modelling of wall condensation, system analysis codes coupling with CFD, and the implementation of machine learning (ML) for predicting the condensation HTC. By bridging the gap between fundamental knowledge and practical applications, the four thematic areas presented in this work are aimed at providing a comprehensive foundation for researchers and experts in the field of steam condensation when NCGs are involved. The ultimate objective is to bolster the safety and efficacy of NPP operations by understanding the heat and mass transfer mechanisms while mitigating the risk of catastrophic events.

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Section: Ncg Sources and Their Transport Perspective In Nppsmentioning

confidence: 99%

Steam Condensation Heat Transfer in the Presence of Noncondensable Gases (NCGs) in Nuclear Power Plants (NPPs): A Comprehensive Review of Fundamentals, Current Status, and Prospects for Future Research

Albdour,

Addad,

Alyammahi

et al. 2024

International Journal of Energy Research

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Derivation of a Condensation Heat Transfer Model for Light Water Reactor Applications Using Machine Learning Techniques

Albdour,

Addad,

Afgan

2024

Lecture Notes in Mechanical Engineering

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A Predictive Machine Learning-Based Analysis for In-Tube Co-current Steam Condensation Heat Transfer in the Presence of Non-condensable Gases

Albdour,

Addad,

Afgan

2024

Arab J Sci Eng

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The primary objective of this study is to develop a predictive model that accurately estimates the condensation heat transfer coefficient (HTC) in the context of in-tube co-current flow condensation, with specific consideration given to the influence of non-condensable gases (NCGs). The need to find useful models and/or correlations that can accurately predict how well a passive condenser in the Economic Simplified Boiling Water Reactor will transfer heat during condensation is greatly warranted. This innovative cooling system utilizes gravity to condense water vapor, effectively dissipating heat from the containment vessel in the event of an accident and transferring the heat into the secondary cooling system. For the development of the forecasting model, we employed Multi-Gens Genetic Programming implemented in MATLAB. This approach possesses the capability of enabling visualization and articulating a transparent mathematical expression that directly establishes the connection between inputs and outputs. This model was specifically developed for forecasting the HTC during the condensation process, considering two distinct types of NCG: air, helium, and a mixture of air and helium, in addition to data for pure steam. The model was trained using a dataset comprising 2913 data points gathered from five experimental references. The model's input comprises a varied range of parameters, including local location ($$z$$ z ), temperature of the steam/NCG mixture ($${T}_{mix}$$ T mix ), condenser wall temperature ($${T}_{wall}$$ T wall ), coolant flow rate ($$\beta_{c}$$ β c ), total pressure ($${P}_{tot}$$ P tot ), condenser inner diameter ($${D}_{1}$$ D 1 ), condenser outer diameter ($${D}_{2}$$ D 2 ), molecular weight (MM), NCG mass fraction ($${w}_{NCG}$$ w NCG ), and mixture Reynolds number ($${Re}_{mix}$$ Re mix ). The output of our model corresponds to the condensation HTC ($$h$$ h ). Four specific correlations were examined with the third correlation attaining the highest anticipated values within a deviation range of ± 30, reaching 90.9%, exhibiting good performance in terms of both performance index and coefficient of determination. This correlation was validated, and its accuracy assessed by employing an unused training dataset that included nitrogen as a NCG. The outcomes showed that 92.94% of the predicted data fell within a deviation range of ± 30%. The findings of this study signify a notable advancement in the analysis of datasets obtained from experiments. Particularly, it delves into the impact of NCGs by considering their molecular weight and mass fraction.

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The Investigation on Heat Transfer Characteristics of Steam Condensation in Presence of Noncondensable Gas under Natural Convection

Cited by 8 publications

References 14 publications

Steam Condensation Heat Transfer in the Presence of Noncondensable Gases (NCGs) in Nuclear Power Plants (NPPs): A Comprehensive Review of Fundamentals, Current Status, and Prospects for Future Research

Steam Condensation Heat Transfer in the Presence of Noncondensable Gases (NCGs) in Nuclear Power Plants (NPPs): A Comprehensive Review of Fundamentals, Current Status, and Prospects for Future Research

Derivation of a Condensation Heat Transfer Model for Light Water Reactor Applications Using Machine Learning Techniques

A Predictive Machine Learning-Based Analysis for In-Tube Co-current Steam Condensation Heat Transfer in the Presence of Non-condensable Gases

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