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Gasoline surrogates in nucleate boiling heat transfer

  • Arthur V. S. OliveiraEmail author
  • Guilherme H. M. Alegre
  • Rogério G. Santos
Technical Paper
  • 26 Downloads

Abstract

Many correlations to estimate the heat transfer coefficient (HTC) in nucleate boiling with binary, ternary or multicomponent mixtures are available in the literature. These correlations are usually based on phase-equilibrium parameters. Direct calculation of the HTC in mixtures with hundreds of components, such as gasoline, is thus practically impossible, as the phase-equilibrium data for such mixtures cannot be easily obtained or calculated. In research fields such as droplet evaporation and mixture thermodynamics, surrogates are constantly used to replace these multicomponent mixtures in calculations; however, this method has not yet been used in boiling research. Here, gasoline surrogates are proposed to estimate the HTC during nucleate boiling. A Monte Carlo search for the optimal composition is used to find the surrogates, and their applicability to gasoline–ethanol blends is evaluated. Two of the surrogates tested matched the experimental data well: surrogate B (16.4% n-butane + 83.6% isooctane) and surrogate C (26.2% n-butane + 42.1% n-hexane + 31.7% isooctane). Both surrogates allowed the HTC for gasoline (3.6% and 3.5% overall average deviation, respectively) and gasoline–ethanol blends (5.6% and 6.9% deviation at \(400\,\hbox {kW/m}^2\) heat flux, respectively) to be estimated accurately.

Keywords

Nucleate boiling Multicomponent mixture Gasoline Heat transfer coefficient Monte Carlo Surrogates 

Greek Letters

\(\Delta\)

Difference

\(\rho\)

Density (\(\text {kg}/\text {m}^3\))

\(\sigma\)

Surface tension (N/m)

Roman Letters

\(c_p\)

Specific heat (J/(kg K))

h

Heat transfer coefficient (\(\text {W}/(\text {m}^2 \text {K})\))

\(H_{lg}\)

Heat of vaporization (J/kg)

It

Iteration number in Monte Carlo

K

Degradation parameter

k

Thermal conductivity (W/(m K))

M

Molar mass (kg/kmol)

\(N_{\mathrm{comp}}\)

Number of components

\(N_{\mathrm{data}}\)

Amount of data

newOAD

OAD at current iteration in the Monte Carlo simulation

OAD

Overall average deviation

p

Pressure (Pa)

\(q^{\prime \prime }\)

Heat flux (\(\text {W}/\text {m}^2\))

Ra

Mean surface roughness (\(\upmu \text {m}\))

T

Temperature (K)

y

Mole fraction in vapor phase

x

Mole fraction in liquid phase

{x}

Mixture composition vector

Subscripts

bp

Boiling range

calc

Calculated

crit

Critical point

end

Monte Carlo output

exp

Experimental

f

Fluid

g

Vapor phase

Gor

Gorenflo

id

Ideal

In

Inoue et al.

l

Liquid phase

max

Maximum

r

Reduced property

ref

Reference condition

sat

Saturated condition

w

Wall

Notes

Acknowledgements

The authors would like to thank the R&D Department at Magneti Marelli Powertrain in Hortolândia, São Paulo, Brazil, for funding and making this project possible. Specific thanks are due to the Physical Components Department for helping with the design of the experimental rig; the Prototyping Laboratory for building the rig; and the Vehicle Preparation Laboratory for supplying the test fluids. The authors are especially grateful to Mr. Fernando Windlin, manager of the Physical Components Department, for sponsoring and supporting the project throughout.

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Copyright information

© The Brazilian Society of Mechanical Sciences and Engineering 2019

Authors and Affiliations

  • Arthur V. S. Oliveira
    • 1
    Email author
  • Guilherme H. M. Alegre
    • 2
  • Rogério G. Santos
    • 1
  1. 1.School of Mechanical EngineeringUniversity of CampinasCampinasBrazil
  2. 2.Powertrain DivisionMagneti Marelli Automotive SystemsHortolândiaBrazil

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