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Journal of Enhanced Heat Transfer
インパクトファクター: 0.562 5年インパクトファクター: 0.605 SJR: 0.211 SNIP: 0.361 CiteScore™: 0.33

ISSN 印刷: 1065-5131
ISSN オンライン: 1026-5511

Journal of Enhanced Heat Transfer

DOI: 10.1615/JEnhHeatTransf.v11.i1.60
pages 75-86

Mechanistic Modeling of Steam Condensation onto Finned Tube Heat Exchangers in Presence of Noncondensable Gases and Aerosols, under Cross-Flow Conditions: Aerosol Fouling and Noncondensable Gases Effects on Heat Transfer

J. L. Munoz-Cobo
Polytechnic University of Valencia, Department of Chemical and Nuclear Engineering, Camino de Vera 14, 46022 Valencia, Spain
A. Escriva
Polytechnic University of Valencia, Department of Chemical and Nuclear Engineering, Camino de Vera 14, 46022 Valencia, Spain
Luis E. Herranz
CIEMAT, Department of Nuclear Fission, Avenida Complutense 22, 28040 Madrid, Spain

要約

In this article, a mechanistic model to predict the steam condensation on containment finned tube heat exchangers in the presence of noncondensable gases (NC) and aerosols is presented. The total thermal resistance from the bulk gas to the coolant is formulated as a parallel combination of the convective and condensation gas resistances coupled in series to those of the condensate layer, the aerosol fouling layer, the wall, and the coolant. The condensate layer thermal resistance is calculated by means of an Adamek-based model. The fouling layer resistance is estimated from the thickness of the particle deposit; removal mechanisms such as diffusiophoresis, settling, and impaction are taken into account. Finally, the gas mixture thermal resistance is formulated based on diffusion layer modeling. The model results are compared with available experimental data for condensation in the presence of NC gases and aerosols. The results show a good performance for the thermal-hydraulic part of the model and a sound consistency when estimating the role of aerosols in the scenario. A satisfactory explanation of the experimental differences concerning aerosol depletion onto finned surfaces has been provided through the model predictions.


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