Critical Analysis of Dual-Probe Heat-Pulse Technique Applied to Measuring Thermal Diffusivity

Critical Analysis of Dual-Probe Heat-Pulse Technique Applied to Measuring Thermal Diffusivity The paper presents an analysis of the experimental parameters involved in application of the dual-probe heat pulse technique, followed by a critical review of methods for processing thermal response data (e.g., maximum detection and nonlinear least square regression) and the consequent obtainable uncertainty. Glycerol was selected as testing liquid, and its thermal diffusivity was evaluated over the temperature range from − 20 °C to 60 °C. In addition, Monte Carlo simulation was used to assess the uncertainty propagation for maximum detection. It was concluded that maximum detection approach to process thermal response data gives the closest results to the reference data inasmuch nonlinear regression results are affected by major uncertainties due to partial correlation between the evaluated parameters. Besides, the interpolation of temperature data with a polynomial to find the maximum leads to a systematic difference between measured and reference data, as put into evidence by the Monte Carlo simulations; through its correction, this systematic error can be reduced to a negligible value, about 0.8 %. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Thermophysics Springer Journals

Critical Analysis of Dual-Probe Heat-Pulse Technique Applied to Measuring Thermal Diffusivity

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Publisher
Springer US
Copyright
Copyright © 2018 by Springer Science+Business Media, LLC, part of Springer Nature
Subject
Physics; Condensed Matter Physics; Classical Mechanics; Industrial Chemistry/Chemical Engineering; Physical Chemistry
ISSN
0195-928X
eISSN
1572-9567
D.O.I.
10.1007/s10765-018-2402-3
Publisher site
See Article on Publisher Site

Abstract

The paper presents an analysis of the experimental parameters involved in application of the dual-probe heat pulse technique, followed by a critical review of methods for processing thermal response data (e.g., maximum detection and nonlinear least square regression) and the consequent obtainable uncertainty. Glycerol was selected as testing liquid, and its thermal diffusivity was evaluated over the temperature range from − 20 °C to 60 °C. In addition, Monte Carlo simulation was used to assess the uncertainty propagation for maximum detection. It was concluded that maximum detection approach to process thermal response data gives the closest results to the reference data inasmuch nonlinear regression results are affected by major uncertainties due to partial correlation between the evaluated parameters. Besides, the interpolation of temperature data with a polynomial to find the maximum leads to a systematic difference between measured and reference data, as put into evidence by the Monte Carlo simulations; through its correction, this systematic error can be reduced to a negligible value, about 0.8 %.

Journal

International Journal of ThermophysicsSpringer Journals

Published: May 28, 2018

References

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