Multi-spectral imaging for Thermochromic Liquid Crystal based Particle Image Thermometry: A proof of concept

Abstract

In this contribution, a novel imaging approach for Thermochromic Liquid Crystal (TLC) based Particle Image Thermometry (PIT) is demonstrated. In contrast to state of the art approaches, a multi-spectral camera was used to record the color response of the Thermochromic Liquid Crystals seeding particles. An experiment with a transparent, water-filled, cylindrical cell as the central element was set up to investigate the novel approach. The temperature in the cell can be controlled by adjusting the temperature of the bottom and top plate. Calibration images at eleven different temperatures ranging from 18 ^◦C to 21.6 ^◦C, as well as images of a stable thermal stratification, were recorded. 90 percent of the calibration data was used to train a neural network (NN) to predict the temperature. The remaining 10 percent of the calibration data and the data of the stable thermal stratification were used to test the NN. The tests show that the deviation between predicted and ground truth temperature is mostly below 0.1 K and that the linear profile of the stable thermal stratification can be predicted with a maximum deviation of ≈ 0.15 K. This shows that multi-spectral imaging with neural networks for data processing is feasible and a promising concept.