ǂa ǂthermal lens study

Povzetek

The present work suggests a method of improving the thermal system efficiency, through entropy minimisation, and unveils the mechanism involved by analysing the molecular/particle dynamics in soot nanofluids (SNFs) using the time series, power spectrum, and wavelet analyses of the thermal lens signal (TLS). The photothermal energy deposition in the SNF lowers the refractive index due to the temperature rise. It triggers the particle dynamics that are investigated by segmenting the TLS and analysing the refractive index, phase portrait, fractal dimension (D), Hurst exponent (H), and sample entropy (SampEn). The wavelet analysis gives information about the relation between the entropy and the frequency components. When the phase portrait analysis reflects the complex dynamics from region 1 to 2 for all the samples, the SampEn analysis supports it. The decreasing value of D (from 1.59 of the base fluid to 1.55 and 1.52) and the SampEn (from 1.11 of the base fluid to 0.385 and 0.699) with the incorporation of diesel and camphor soot, indicate its ability to lower the complexity, randomness, and entropy. The increase of SampEn with photothermal energy deposition suggests its relation to the thermodynamic entropy (S). The lowering of thermal diffusivity value of the base fluid from 1.4 × 10−7 m2/s to 1.1 × 10−7 and 0.5 × 10−7 m2 /s upon diesel and camphor soot incorporation suggests the heat-trapping and reduced molecular dynamics in heat dissipation.

Ključne besede

soot;entropy;thermal system;photothermal;time series;nanofluid;fractal;

Podatki

Jezik: Angleški jezik
Leto izida:
Tipologija: 1.01 - Izvirni znanstveni članek
Organizacija: UNG - Univerza v Novi Gorici
UDK: 53
COBISS: 113289219 Povezava se bo odprla v novem oknu
ISSN: 0167-7322
Št. ogledov: 516
Št. prenosov: 0
Ocena: 0 (0 glasov)
Metapodatki: JSON JSON-RDF JSON-LD TURTLE N-TRIPLES XML RDFA MICRODATA DC-XML DC-RDF RDF

Ostali podatki

URN: URN:SI:UNG
Strani: str. 1-8
Zvezek: ǂVol. ǂ318
Čas izdaje: 2020
DOI: 10.1016/j.molliq.2020.114038
ID: 15786683