A predictive correlation for the constant-pressure specific heat capacity of pure and ill-defined liquid hydrocarbons

Dadgostar, Nafiseh; Shaw, John M.

doi:10.1016/j.fluid.2011.09.015

Cited by 36 publications

(27 citation statements)

References 40 publications

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“…As it was mentioned previously, c p of the three n-alkanes was determined from the DSC curves. The results together with the reported data obtained from the literature [52][53][54][55][56] are presented in Fig. 8.…”

Section: N-alkanementioning

confidence: 63%

Temperature-dependent thermal properties of solid/liquid phase change even-numbered n-alkanes: n-Hexadecane, n-octadecane and n-eicosane

2015

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Section: N-alkanementioning

confidence: 63%

Temperature-dependent thermal properties of solid/liquid phase change even-numbered n-alkanes: n-Hexadecane, n-octadecane and n-eicosane

2015

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“…The temperature dependence of heat capacity, applicable to all groups, is approximated as [59][60][61]: 236 The liquid viscosity of iso-alkanes was estimated based on Expression (9) with coefficients given in the liquid thermal conductivity of iso-alkanes was estimated using the Latini formula (Equations (11) and (12)). 281 B.5.…”

Section: A4 Liquid Heat Capacity 235mentioning

confidence: 99%

“…The liquid 295 viscosity was estimated using Equation (9) with the coefficients given in Table 11. Following [59][60][61], the liquid 296 heat capacity was calculated using Equation (10). Following [1, 58,63], the liquid thermal conductivity was esti-297 mated using the Latini formula with the coefficients given in Table 6.…”

Section: Bar) (20) 292mentioning

confidence: 99%

“…The liquid viscosities of the charac-314 teristic components for indanes/naphthalenes, cycloalkanes and olefins were estimated using Expression (9) with 315 the coefficients given in Table 14. Following [59][60][61], the liquid heat capacity of the characteristic components for 316 indanes/naphthalenes, cycloalkanes and olefins were calculated using Equation (10) with the coefficients given in 317 Table 5. Following [1, 58,63], the liquid thermal conductivities of the characteristic components for indanes/naph-318 thalenes, cycloalkanes and olefins were estimated using the Latini formula (Equations (11) and (12)) with the 319 coefficients given in Table 6.…”

Section: Bar) (20) 292mentioning

confidence: 99%

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Modelling of gasoline fuel droplets heating and evaporation

Qubeissi

Sazhin

Turner

et al. 2015

Fuel

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The paper presents a new approach to modelling of the heating and evaporation of gasoline fuel droplets with a specific application to conditions representative of internal combustion engines. A number of the components of gasoline with identical chemical formulae and close thermodynamic and transport properties are replaced with characteristic components leading to reducing the original composition of gasoline fuel (83 components) to 20 components only. Furthermore, the approximation to the composition of gasoline with these components is replaced with a smaller number of hypothetical quasi-components/components as previously suggested in the multi-dimensional quasi-discrete (MDQD) model. The transient diffusion of quasi-components and single components in the liquid phase as well as the temperature gradient and recirculation inside the droplets, due to the relative velocities between the droplets and the ambient air, are accounted for in the model. In the original MDQD model, n-alkanes and iso-alkanes are considered as one group of alkanes. In this new approach, the contributions of these two groups are taken into account separately. The values for the initial model parameters were selected from experimental data measured in a research engine prior to combustion. The results are compared with the predictions of the single-component model in which the transport and thermodynamic properties of components are averaged, diffusion of species is ignored and liquid thermal conductivity is assumed to be infinitely large, or approximated by those of iso-octane. It is shown that the application of the latter models leads to an under-prediction of the droplet evaporation time by approximately 67% (averaged) and 47% (iso-octane), respectively, compared to those obtained using the discrete component model, taking into account the contributions of 20 components. It is shown that the approximation of the actual composition of gasoline fuel by 6 quasi-components/components, using the MDQD model, leads to an under-prediction of the estimated droplet surface temperatures and evaporation times by approximately 0.9% and 6.6% respectively, for the same engine conditions. The application of the latter model has resulted in an approximately 70% reduction in CPU processor time compared to the model taking into account all 20 components of gasoline fuel.

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“…Las especificaciones del biodiésel B4 fueron suministradas por la Coordinación de Inspección de Calidad de Ecopetrol S.A. En adición, se usaron correlaciones experimentales para estimar otras propiedades, tales como Capacidad Calorífica (Cp) [10], Poder Calorífico Alto (HHV) [11] y Calor de Vaporización (ΔHvap) [12] (Tabla 1). Tabla 1.…”

Section: Metodologíaunclassified

Predicción y análisis de emisiones de mezclas combustibles diésel/biodiésel modificadas con nanopartículas de alúmina y ácido oleico

Peñaloza

Rincón

Herrera

et al. 2017

rev.ion

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ResumenEl software Diésel-RK fue utilizado para evaluar el efecto del uso de un aditivo basado en nanopartículas de alúmina y ácido oleico para la reducción de emisiones de gases contaminantes en mezclas diésel/biodiésel. Para esto, se estimaron las propiedades físico-químicas de las mezclas combustibles con ayuda de correlaciones para dispersiones coloidales y reglas de mezclado, fijando concentraciones de 10 y 20ppm del aditivo. Los resultados obtenidos mostraron reducciones del 11% y 25% en emisiones de SO 2 y material particulado, respectivamente; mientras que las emisiones de CO 2 no mostraron mejoras significativas, así como tampoco se presentaron mejoras en el consumo específico de combustible, los cuales permanecieron por debajo del 1,0%. En cuanto a las emisiones de NO 2 , se observó un incremento desde 2,1g/kWh a 3,1g/kWh con la adición de las diversas concentraciones de nanoaditivo; de esta forma se evidenciaron mejoras en la disminución en el retraso de los tiempos de ignición, lo cual es apropiado para el adecuado funcionamiento de los motores. Palabras clave: nanoaditivos, biocombustibles, diésel, gases contaminantes.Abstract Diesel-RK software was used to evaluate the effect of alumina nanoparticles and oleic acid as additives for reducing greenhouse gas emissions in diesel/biodiesel fuel blends. Thus, it was estimated the physicochemical properties of the fuel blends using correlations for colloidal dispersions and mixing rules. This was achieved by fixing the concentrations of the additives at 10 and 20ppm. The results showed reductions of 11% and 25% for SO 2 emissions and particulate material, respectively; while CO 2 emissions showed no significant improvements, as well as no significant enhancement was estimated for specific fuel consumption, which remained below 1.0%. Regarding NO 2 emissions, an increase from 2.1g/kWh to 3.1g/kWh was observed for the addition of several concentration of nanoadditives; thus, it was evidenced a decrease in the lag time of ignition, which is appropriate for the proper operation of engines.Keywords: nanoadditives, biofuel, diesel, greenhouse gas. 45Cita: Peñaloza AD, Rincón AJ, Herrera AP, Ojeda KA. Predicción y análisis de emisiones de mezclas combustibles diesel/biodiesel modificadas con nanopartículas de alúmina y ácido oleico. rev.ion. 2017;30(1):45-55.

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A predictive correlation for the constant-pressure specific heat capacity of pure and ill-defined liquid hydrocarbons

Cited by 36 publications

References 40 publications

Temperature-dependent thermal properties of solid/liquid phase change even-numbered n-alkanes: n-Hexadecane, n-octadecane and n-eicosane

Temperature-dependent thermal properties of solid/liquid phase change even-numbered n-alkanes: n-Hexadecane, n-octadecane and n-eicosane

Modelling of gasoline fuel droplets heating and evaporation

Predicción y análisis de emisiones de mezclas combustibles diésel/biodiésel modificadas con nanopartículas de alúmina y ácido oleico

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