Modeling of thermometric characteristics of thermodiode sensors by using the dimensionless sensitivity

Author:

Smertenko P.S.,

Abstract

Dimensionless sensitivity and slope of its characteristic in the forms α=d(log V)/d(log T) and γ=d(log α)/d(log T) have been proposed as a base for modeling of thermometric characteristics V(T). The differential analysis of V(T) curves within the range from 4.2 up to 400 K by numerical differentiation has allowed obtaining the analytical approximation in the form V(T)=ATαexp[-BTγ1(1+CTγ1)], where A, B and C are the constants depending on physical parameters of thermodiode silicon sensor. This approach is useful both for the analysis of these characteristics as well as for modeling and determining an approximating function by finding out the regions where power-like or exponential dependences are the adequate expressions to describe the thermometric characteristic sections. By contrast to the known methods, one should not know beforehand the function that describes the process or the characteristic. It permits to elucidate fine peculiarities of thermometric characteristics and to achieve high accuracy of modeling by using the analytical expressions. In view of the practical purposes, the thermometric characteristics are approximated within the three temperature ranges. The errors of approximation do not exceed ±0.02%, ±0.2% and ±0.4% within the temperature ranges 4.2…40 K, 40…170 K and 170…400 K, respectively.

Publisher

National Academy of Sciences of Ukraine (Co. LTD Ukrinformnauka)

Subject

Electrical and Electronic Engineering,Atomic and Molecular Physics, and Optics,Electronic, Optical and Magnetic Materials

Reference15 articles.

1. 1. Temperature Measurement and Control Catalog. Cryogenic Sensors, Instruments and Accessories. Lake Shore. 2020.

2. 2. Handbook of Measurement in Science and Engineering. Vol. 3. Ed. M. Kutz. John Willey & Sons. 2016.

3. 3. Jackowska-Strumillo L. Hybrid analytical and ANN-based modeling of temperature sensors non-linear dynamic properties. Hybrid Artificial Intelli-gent Systems. 2011. P. 356-363.

4. 4. Goshtasbi A., Chen J., Waldecker J.R., Hirano S. and Ersal T. Effective parameterization of PEM fuel cell models. Part I: Sensitivity analysis and parameter identifiability. J. Electrochem. Soc. 2020. 167, No 4. P. 044504..

5. 5. Eck C., Garcke H., Knaber P. Mathematische Modellierung. Springer, 2008.

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