Maximising the fuel efficiency of engine oils: The role of tribology

Korček, Š.; Jensen, R. K.; Johnson, M. D.; Sorab, Jagadish

doi:10.1002/tt.3020070302

Cited by 6 publications

(4 citation statements)

References 7 publications

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“…Currently the sulfur contribution due to oil consumption is estimated to be roughly equivalent to 2 ppm sulfur in fuel (assuming 10,000 miles per quart oil consumption) [22]. Hence, the sulfur content of oil will only become a significant issue in the event that fuel sulfur levels are regulated to 5-10 ppm or less.…”

Section: Sulfur Poisoningmentioning

confidence: 99%

Ceria and Other Oxygen Storage Components in Automotive Catalysts

Shelef

Graham

McCabe

2002

Catalysis by Ceria and Related Materials

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Section: Sulfur Poisoningmentioning

confidence: 99%

Ceria and Other Oxygen Storage Components in Automotive Catalysts

Shelef

Graham

McCabe

2002

Catalysis by Ceria and Related Materials

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“…Oils also have inferior thermal transfer properties than water, being most suitable for heat transfer duties in which the fluid has increased boiling point requirements. The success rate of engine oils depends on the complex additives that are blended into these, being categorized as chemically active, with the capacity of interaction with metals at low oxidation and degradation costs, and chemically inert, which improve physical properties and effective performance [4,5].…”

Section: Heat Transfer Fluidsmentioning

confidence: 99%

Carbon Nanotubes in a Fluidic Medium: Critical Analysis

Fonseca¹,

Lamas²,

Abreu³

et al. 2013

Physical and Chemical Properties of Carbon Nanotubes

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“…The success of engine oils relates highly to the complex additives that are blended into the base oils [1]. Additives are mixtures of inorganic and/or organic compounds formulated to enhance specific properties of the oil.…”

Section: Introductionmentioning

confidence: 99%

Predicting the thermal behaviour of engine oils using artificial neural networks

Abou-Ziyan

Mahmoud

Zaid

2009

Proceedings of the Institution of Mechanical Engineers, Part J:

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A multi-layer neural network (NN) was developed to analyse experimental boiling data obtained for five engine oils at bulk temperatures ranging from 40 to 150 °C and heat fluxes ranging from 30 to 400 kW/m2. The inputs to the NN were the oil chemical composition (nine elements) along with the wall superheats for different oil bulk temperatures and the NN output was the corresponding heat fluxes. The developed NN model predicted boiling curves that are in close agreement with experimental data ( R2 ≈ 1). The NN was then trained on the experimental data of four of these oils and allowed to predict the boiling behaviour of the fifth. The NN, therefore, demonstrated its ability to predict the boiling characteristics of untested oils, provided their chemical compositions fall within the range of the training data. In addition, for oils with constituents outside the considered range, the NN was able to predict their boiling characteristics at all bulk temperatures when trained on a sample of experimental measurement. The success of such a technique provides researchers and developers of oils with the thermal performance of oils (based on chemical constituents) without testing or with very limited measurements. This, as a result, has the advantages of saving experimental time and cost of oil testing.

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Maximising the fuel efficiency of engine oils: The role of tribology

Cited by 6 publications

References 7 publications

Ceria and Other Oxygen Storage Components in Automotive Catalysts

Ceria and Other Oxygen Storage Components in Automotive Catalysts

Carbon Nanotubes in a Fluidic Medium: Critical Analysis

Predicting the thermal behaviour of engine oils using artificial neural networks

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