NDLI: Coating thermal diffusivity and effusivity measurement optimization using regression-based sensitivity.

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Coating thermal diffusivity and effusivity measurement optimization using regression-based sensitivity.

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Coating thermal diffusivity and effusivity measurement optimization using regression-based sensitivity.

Content Provider	World Health Organization (WHO)-Global Index Medicus
Author	Bennett, T. D. Valdes, R.
Description	Country affiliation: United States Author Affiliation: Valdes R ( Department of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, California 93117-5070, USA.); Bennett TD ( Department of Mechanical Engineering, University of California, Santa Barbara, Santa Barbara, California 93117-5070, USA.)
Abstract	Measurements of thermal diffusivity and thermal effusivity are critical to developing a complete description of thermal transport within thermal barrier coating systems. Thermal diffusivity and thermal effusivity of coatings can be measured nondestructively using the phase of photothermal emission analysis experimental measurement. However, the complexity of the regression analysis required in this measurement makes determining the uncertainties associated with the best-fit values nontrivial. The aim of this paper is to develop a framework to carry out this uncertainty analysis and to minimize the uncertainties in fitted parameters. It is shown that the physical model can be used as an effective tool for identifying and removing data points afflicted by excessive bias error, which can occur in the limits of the observational data. It is revealed that this reduction in the dataset offers a tradeoff between increasing agreement between the data and the model while reducing the uniqueness of fitted parameter values. The current analysis demonstrates that this situation can be treated as an optimization problem, whereby uncertainties in fitted parameters can be minimized.
ISSN	00346748
Issue Number	1
Journal	Review of Scientific Instruments
Volume Number	86
e-ISSN	10897623
Language	English
Publisher	American Institute of Physics
Publisher Date	2015-01-01
Publisher Place	United States
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Discipline Physics Discipline Chemistry
Content Type	Text
Resource Type	Article
Subject	Medicine Instrumentation

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