Remaining useful life prognostics during boiling-induced surface degradation

After sustaining long-term nucleate boiling, any surface is prone to changes over time, which signifies an important operational characteristic for reliable thermal management; however, recent research of this topic is extremely scarce. In this work, we first present long-term pool boiling experiments on samples of different surface topography and morphology in de-ionized water and in an aqueous salt solution. We show the changes induced by vigorous bubble nucleation over the course of several hundred hours of operation, discussing the underlying degradation phenomena. Following this, we present an adaptive data-driven prognostic approach, capable of estimating the remaining useful life (RUL) of a boiling surface in real time, based on change of surface temperature. The method consists of (i) a Kalman filter to identify the degradation drift model and (ii) a Monte Carlo simulation to propagate the drift to the terminal threshold, obtaining a distribution of predicted RUL values. Its practical applicability, validated on experimental results, is reinforced by its advantageous features of (i) prognostic performance not depending on the boiling surface or its degradation mechanisms, (ii) low computational demand and (iii) numerous options for individual tuning and extensions.