A new assessment method for the evaluation of supercritical heat transfer correlations, particularly with regard to the "multiple/no solutions" problem. (September 2018)

• Record Type: Journal Article
• Authors: Gschnaidtner, T.; Schatte, G.A.; Kohlhepp, A.; Wang, Y.; Wieland, C.; Spliethoff, H.
• Is Part Of: Thermal science and engineering progress. Volume 7(2018)
• Page Start: 267
• Page End: 278
• Publication Date: 2018-09
• Subjects: Assessment method -- Heat transfer coefficient -- Implicit and explicit correlations -- Numerical stability -- Supercritical water; Heat engineering -- Periodicals; Heat engineering; Thermodynamics; Periodicals; 621.402
• DOI: 10.1016/j.tsep.2018.07.006
• Languages: English
• ISSNs: 2451-9049

Abstract

Highlights: Two assessment methods available: the recalculation method and the prediction method. The two methods yield different results. Development of a new assessment method with respect to ambiguous results. All implicit correlations yield multiple or no solution. The new assessment method indicates the need for more accurate correlations. Abstract: The heat transfer coefficient is an essential measure in the predesign of supercritical water-cooled reactors. At supercritical pressures, the distinct heat transfer modes, normal, improved, and deteriorated, differ from those at subcritical pressures, resulting in unusual behavior of the heat transfer coefficient. Over 30 correlations have been published in the past decades in order to predict this abnormal behavior at supercritical pressures. Due to unsatisfactory results, in recent years the trend has been to propose more complex and implicit correlations. In addition, various efforts have been made to find the most accurate correlation for all three heat transfer modes. However, using different assessment methods, namely the recalculation method and the prediction method, contradictory results have been obtained. As a result, this paper presents a new assessment approach that evaluates the heat transfer correlations with regard to prediction accuracy as well as numerical stability. Based on the proposed assessment method, 11 correlations from the literature are evaluated against a database that contains more than 12, 000 experimental data points. The results show, that all implicit correlations have multiple and/or unfeasible solutions. Furthermore, a detailed assessment of the prediction accuracy of the correlations indicates the need for more accurate correlations, especially in the vicinity of the pseudo-critical point.