2011
DOI: 10.1016/j.ijheatmasstransfer.2010.10.027
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Correcting lateral heat conduction effect in image-based heat flux measurements as an inverse problem

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Cited by 23 publications
(2 citation statements)
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“…The Image Processing Toolbox includes built-in functions that deconvolve an image with a pointspread function (PSF) that describes the blur in order to produce a deblurred image; the deconvwnr function was employed here [24]. Reference [25] describes the mathematics of image deconvolution for a similar application, heat diffusion in temperature-sensitive paint modeled by a Gaussian PSF. The PSF for the blurred rotor was constructed as follows.…”
Section: Image Deblurringmentioning
confidence: 99%
“…The Image Processing Toolbox includes built-in functions that deconvolve an image with a pointspread function (PSF) that describes the blur in order to produce a deblurred image; the deconvwnr function was employed here [24]. Reference [25] describes the mathematics of image deconvolution for a similar application, heat diffusion in temperature-sensitive paint modeled by a Gaussian PSF. The PSF for the blurred rotor was constructed as follows.…”
Section: Image Deblurringmentioning
confidence: 99%
“…The method based on the discrete Fourier law for a data reduction model [20] is relatively accurate for a base with high thermal conductivity, assuming that the base temperature equals the initial temperature after a short amount of time. In contrast with the above data reduction model, Liu et al [21][22][23][24] offered a systematic analysis for determining surface heat flux in TSP measurements by developing an exact one-dimensional analytical inverse solution as well as a series of correction methods for the effects of lateral heat conduction, temperature-dependent thermal diffusivity and other factors. Cai et al [25] proposed a numerical method based on an iterative algorithm to solve inverse problems with temperature-dependent thermal properties.…”
Section: Introductionmentioning
confidence: 99%