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Characterization of Thermal Transport in One-dimensional Solid Materials
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Coating thermal diffusivity and effusivity measurement optimization using regression-based sensitivity.

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  • 1Department of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, California 93117-5070, USA.

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Summary
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This study presents a framework for analyzing uncertainties in thermal transport measurements of thermal barrier coatings. It optimizes data analysis to minimize errors and improve the accuracy of thermal diffusivity and effusivity values.

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Area of Science:

  • Materials Science
  • Thermal Engineering
  • Non-destructive Testing

Background:

  • Accurate measurement of thermal transport properties like thermal diffusivity and thermal effusivity is crucial for thermal barrier coating (TBC) systems.
  • Photothermal emission analysis offers a non-destructive method for these measurements, but regression analysis complexity hinders uncertainty determination.

Purpose of the Study:

  • To develop a framework for uncertainty analysis in photothermal measurements of TBCs.
  • To minimize uncertainties in fitted thermal transport parameters.
  • To improve the reliability of thermal property characterization in TBCs.

Main Methods:

  • Utilized photothermal emission analysis for non-destructive measurement of thermal properties.
  • Developed a regression analysis framework to quantify uncertainties in fitted parameters.
  • Employed a physical model to identify and remove biased data points.
  • Treated parameter uncertainty minimization as an optimization problem.

Main Results:

  • Established a robust framework for uncertainty analysis in photothermal measurements.
  • Demonstrated the effectiveness of a physical model in identifying and removing biased data.
  • Showcased a tradeoff between model-data agreement and parameter uniqueness after data reduction.
  • Successfully minimized uncertainties in fitted thermal diffusivity and effusivity parameters.

Conclusions:

  • The developed framework provides a reliable method for uncertainty quantification in TBC thermal property measurements.
  • Physical model-assisted data cleaning is effective in improving measurement accuracy.
  • Optimization strategies can effectively minimize parameter uncertainties, leading to more precise thermal transport characterization.