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Title:Downscaling of sample entropy of nanofluids by carbon allotropes : a thermal lens study
Authors:Swapna, Mohanachandran Nair Sindhu (Author)
Raj, Vimal (Author)
Sreejyothi, S. (Author)
Satheesh Kumar, K. (Author)
Sankararaman, Sankaranarayana Iyer (Author)
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Work type:Unknown ()
Tipology:1.01 - Original Scientific Article
Organization:UNG - University of Nova Gorica
Abstract:The work reported in this paper is the first attempt to delineate the molecular or particle dynamics from the thermal lens signal of carbon allotropic nanofluids (CANs), employing time series and fractal analyses. The nanofluids of multi-walled carbon nanotubes and graphene are prepared in base fluid, coconut oil, at low volume fraction and are subjected to thermal lens study. We have studied the thermal diffusivity and refractive index variations of the medium by analyzing the thermal lens (TL) signal. By segmenting the TL signal, the complex dynamics involved during its evolution is investigated through the phase portrait, fractal dimension, Hurst exponent, and sample entropy using time series and fractal analyses. The study also explains how the increase of the photothermal energy turns a system into stochastic and anti-persistent. The sample entropy (S) and refractive index analyses of the TL signal by segmenting into five regions reveal the evolution of S with the increase of enthalpy. The lowering of S in CAN along with its thermal diffusivity (50%–57% below) as a result of heat-trapping suggests the technique of downscaling sample entropy of the base fluid using carbon allotropes and thereby opening a novel method of improving the efficiency of thermal systems.
Keywords:carbon allotropic nanofluids, time series, entropy, MWCNT, thermal lens signal
Year of publishing:2020
Number of pages:str. 073116-1-07311-9
Numbering:iss. 7, Vol. 30
COBISS_ID:113296387 Link is opened in a new window
ISSN on article:1054-1500
DOI:10.1063/5.0009756 Link is opened in a new window
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Shortened title:Chaos
Publisher:American Institute of Physics
COBISS.SI-ID:2800167 New window