Prediction of the Heat Transfer Coefficient in a Small Channel with the Superposition and Asymptotic Correlations

Abstract

Heat transfer coefficient as an important characteristic in heat exchanger design is determined by the correlation developed from previous experimental work or accumulation of published data. Although discrepancies still exist between the existing correlations and practical data, several researchers claimed theirs as a generalized heat transfer correlation. Through optimization method, this study predicts the heat transfer coefficient of two-phase flow of propane in a small channel at the saturation temperature of 10[Formula: see text]C using two categories of correlation — superposition and asymptotic. Both methods consist of the contribution of nucleate boiling and forced convective heat transfer, the mechanisms that contribute to the total two-phase heat transfer coefficient, which become as two objective functions to be maximized. The optimization of experimental parameters of heat flux, mass flux, channel diameter and vapor quality is done by using genetic algorithm within a range of 5–20[Formula: see text]kW/m2, 100–250[Formula: see text]kg/m2[Formula: see text]s, 1.5–3[Formula: see text]mm and 0.009–0.99, respectively. In the result, the selected correlations under optimized condition agreed on the dominant mechanism at low and high vapor qualities are caused by the nucleate boiling and forced convective heat transfer, respectively. The optimization work served as an alternative approach in identifying optimized parameters from different correlations to achieve high heat transfer coefficient by giving a fast prediction of parameter range, particularly for the investigation of any new refrigerant. In parallel with some experimental works, a quick prediction is possible to reduce time and cost. From the four selected generalized correlations, Bertsch et al. show the closer trend with the reference experimental work until vapor quality of 0.6.