Abstract
The measurement of thermophysical properties is most often an indirect method, which requires the estimation of some parameters by comparing the experimental data with the mathematical modeling of the experimental bench. Moreover, the development of new materials and systems brings about the need of new methods designed for specific applications. This chapter aims at presenting some inverse methods that are of great interest for nowadays challenging problems related to the measurement of thermophysical properties. The basic concepts of inverse methods are presented, but emphasis is given to the solution of inverse problems within the Bayesian framework, in special, with the Markov Chain Monte Carlo (MCMC) methods. Three illustrative examples of applications are presented, namely: (i) thermal characterization of highly orthotropic carbon fiber composite materials; (ii) thermal characterization of metallic nanofilms for nonvolatile phase change memory; and (iii) simultaneous estimation of spatially varying thermal diffusivity and heat flux.
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Acknowledgments
The support provided by CNRS (France), CNPq (Brazil), CAPES (Brazil), and FAPERJ (Brazil – State of Rio de Janeiro) is greatly appreciated.
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Orlande, H.R.B., Fudym, O. (2018). Thermophysical Properties Measurement and Identification. In: Handbook of Thermal Science and Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-26695-4_5
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DOI: https://doi.org/10.1007/978-3-319-26695-4_5
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