Peripheral Heat Transfer Coefficient during Flow Boiling: Comparison between 2-D and 1-D Data Reduction and Discussion about Their Applicability

Abstract

This paper presents a critical analysis of possible data reduction procedures for the evaluation of local heat transfer coefficient during flow boiling experiments. The benchmark method using one-dimensional (1-D) heat transfer in a heated tube was compared to a new data reduction method in which both radial and circumferential contributions to the conductive heat transfer inside a metal tube are considered. Using published experimental flow boiling data, the circumferential profiles of the wall superheat, inner wall heat flux, and heat transfer coefficients were independently calculated with the two data reduction procedures. The differences between the two methods were then examined according to the different heat transfer behavior observed (symmetric or asymmetric), which in turn was related to the two-phase flow regimes occurring in a channel during evaporation. A statistical analysis using the mean absolute percentage error (MAPE) index was then performed for a database of 417 collected flow boiling data taken under different operating conditions in terms of working fluid, saturation temperature, mass velocity, vapor quality, and imposed heat flux. Results showed that the maximum deviations between the two methods could reach up to 130% in the case of asymmetric heat transfer. Finally, the possible uses of the two data reduction methods are discussed, pointing out that the two-dimensional (2-D) model is the most reliable method to be employed in the case of high-level modeling of two-phase flow or advanced design of heat exchangers and heat spreader systems.

Boiling Synchronization in Two Parallel Minichannels—Image Analysis

“…The challenge considering the identification and description of boiling synchronization in a flow includes choosing an appropriate method for the stated task. Already, numerous approaches have been taken in order to analyze two-phase flow, such as nonlinear analysis [5,8,9], Eulerian space-time correlations [10], modeling [11,12], and image analysis [10,13,14]. The method mentioned last is based on processing frames registered by, for example, a high-speed camera, which presents a real-time image of the observed flow.…”