Abstract 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. Graphic abstract

中文翻译：

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神经网络在热致变色液晶温度场测量中的应用

摘要 本研究对使用热致变色液晶 (TLC) 进行温度测量的神经网络的适用性进行了调查，并讨论了常见校准方法的优缺点。为了表征测量技术，详细分析了 TLC 的颜色对温度和观察角度的依赖性，因此，对彩色相机视场内的位置进行了详细分析。为了考虑视野内的位置对颜色的影响，神经网络被应用于温度测量的校准。特别是，本研究的重点是分析不同网络配置的温度测量误差以及训练方法，对于瞬时测量，产生 0.1 K 范围内的平均绝对偏差和平均标准偏差。在将此标准偏差与另外两种校准方法的标准偏差进行比较的基础上，表明神经网络适用于通过 TLC 的颜色进行温度测量。最后，该测量技术的适用性在具有大纵横比的瑞利-贝纳德电池的水平面中的示例性温度测量中得到说明，这清楚地显示了通过温度场出现的对流流动模式。图形摘要 结果表明，神经网络适用于通过 TLC 的颜色进行温度测量。最后，该测量技术的适用性在具有大纵横比的瑞利-贝纳德电池的水平面中的示例性温度测量中得到说明，这清楚地显示了通过温度场出现的对流流动模式。图形摘要 结果表明，神经网络适用于通过 TLC 的颜色进行温度测量。最后，该测量技术的适用性在具有大纵横比的瑞利-贝纳德电池的水平面中的示例性温度测量中得到说明，这清楚地显示了通过温度场出现的对流流动模式。图形摘要