On the application of neural networks for temperature field measurements using thermochromic liquid crystals

This study presents an investigation regarding the applicability of neural networks for temperature measurements using thermochromic liquid crystals (TLCs) and discusses advantages as well as disadvantages of common calibration approaches. For the characterization of the measurement technique, the dependency of the color of the TLCs on the temperature as well as on the observation angle and, therefore, on the position within the field of view of a color camera is analyzed in detail. In order to consider the influence of the position within the field of view on the color, neural networks are applied for the calibration of the temperature measurements. In particular, the focus of this study is on analysis of the error of temperature measurement for different network configurations as well as training methods, yielding a mean absolute deviation and a mean standard deviation in the range of 0.1 K for instantaneous measurements. On the basis of a comparison of this standard deviation to that of two further calibration approaches, it is shown that neural networks are suited for temperature measurements via the color of TLCs. Finally, the applicability of this measurement technique is illustrated at an exemplary temperature measurement in a horizontal plane of a Rayleigh-Bénard cell with large aspect ratio, which clearly shows the emergence of convective flow patterns by means of the temperature field.