Advances in the analysis of transient heat transfer experiments using multiple thermochromic liquid crystals

Transient heat transfer experiments that employ thermochromic liquid crystals (TLCs) provide high‐resolution surface‐temperature data but are hampered by noisy intensity signals, non‐ideal fluid‐temperature steps, and the challenge of resolving multiple TLC responses at pixel level. In this paper we introduce a fully automated analysis framework that (i) smooths and baseline‐corrects raw video data with a Whittaker‐algorithm whose regularisation parameter is explicitly linked to the signal density, (ii) identifies multiple intensity peaks per pixel via interval‐wise detection, and (iii) evaluates the local convective heat transfer coefficient using a stepwise‐partial‐integration (SPI) formulation of Duhamel’s principle. For each pixel, the heat transfer coefficient is determined for each individual TLC indication; the final value is formed by an inverse‐variance weighted average, guaranteeing that the most reliable indication dominates.The methodology is applied to a rotating two-pass cooling channel coated with a stack of two different narrow‐band TLC layers. Compared to conventional analysis using semi-infinite walls, the proposed workflow significantly improves the standard method by automatically detecting the peaks of multiple narrow-band TLCs. To illustrate the benefits of multiple TLCs, the additional information is used in this work to reduce the measurement uncertainty of the experiments. This is feasible because the selection of the optimal indication temperature for the TLCs depends on the heat transfer coefficient and the driving temperature difference at each individual point. Consequently, the results are evaluated post-experiment and weighted according to their respective standard deviations. This entire workflow enables multi-parameter analyses to be carried out in order to determine further variables from single experiments.