Temperature-dependent viscosity analysis of SAE 10W-60 engine oil with RheolabQC rotational rheometer
The purpose of this work was to determine a viscositytemperature relationship for SAE 10W-60 engine oil. The rheological properties of this engine oil, for a temperature range of 20÷60 °C, were obtained with RheolabQC rotational rheometer. For the first reference temperature of 40 °C, the experiment...
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2017-01-01
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Online Access: | https://doi.org/10.1051/matecconf/201711210014 |
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doaj-9733879e6d4f488e93b2830fdc4a304c2021-02-02T09:05:21ZengEDP SciencesMATEC Web of Conferences2261-236X2017-01-011121001410.1051/matecconf/201711210014matecconf_imane2017_10014Temperature-dependent viscosity analysis of SAE 10W-60 engine oil with RheolabQC rotational rheometerZahariea Dănuț0Husaru Dorin Emil1“Gheorghe Asachi” Technical University of Iași, Department of Fluid Mechanics, Fluid Machinery and Fluid Power Systems“Gheorghe Asachi” Technical University of Iași, Department of Mechanical Engineering, Mecatronics and RoboticsThe purpose of this work was to determine a viscositytemperature relationship for SAE 10W-60 engine oil. The rheological properties of this engine oil, for a temperature range of 20÷60 °C, were obtained with RheolabQC rotational rheometer. For the first reference temperature of 40 °C, the experimental result was obtained with a relative error of 1.29%. The temperature-dependent viscosity was modelled, comparatively, with the Arrhenius and the 3rd degree polynomial models. Comparing the graphs of the fits with prediction bounds for 95% confidence level, as well as the goodness-of-fit statistics, the preliminary conclusion was that the 3rd degree polynomial could be the best fit model. However, the fit model should be used also for extrapolation, for the second reference temperature of 100 °C. This new approach changes the fit models order, the Arrhenius equation becoming the best fit model, because of the completely failed to predict the extrapolated value with the polynomial model.https://doi.org/10.1051/matecconf/201711210014 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Zahariea Dănuț Husaru Dorin Emil |
spellingShingle |
Zahariea Dănuț Husaru Dorin Emil Temperature-dependent viscosity analysis of SAE 10W-60 engine oil with RheolabQC rotational rheometer MATEC Web of Conferences |
author_facet |
Zahariea Dănuț Husaru Dorin Emil |
author_sort |
Zahariea Dănuț |
title |
Temperature-dependent viscosity analysis of SAE 10W-60 engine oil with RheolabQC rotational rheometer |
title_short |
Temperature-dependent viscosity analysis of SAE 10W-60 engine oil with RheolabQC rotational rheometer |
title_full |
Temperature-dependent viscosity analysis of SAE 10W-60 engine oil with RheolabQC rotational rheometer |
title_fullStr |
Temperature-dependent viscosity analysis of SAE 10W-60 engine oil with RheolabQC rotational rheometer |
title_full_unstemmed |
Temperature-dependent viscosity analysis of SAE 10W-60 engine oil with RheolabQC rotational rheometer |
title_sort |
temperature-dependent viscosity analysis of sae 10w-60 engine oil with rheolabqc rotational rheometer |
publisher |
EDP Sciences |
series |
MATEC Web of Conferences |
issn |
2261-236X |
publishDate |
2017-01-01 |
description |
The purpose of this work was to determine a viscositytemperature relationship for SAE 10W-60 engine oil. The rheological properties of this engine oil, for a temperature range of 20÷60 °C, were obtained with RheolabQC rotational rheometer. For the first reference temperature of 40 °C, the experimental result was obtained with a relative error of 1.29%. The temperature-dependent viscosity was modelled, comparatively, with the Arrhenius and the 3rd degree polynomial models. Comparing the graphs of the fits with prediction bounds for 95% confidence level, as well as the goodness-of-fit statistics, the preliminary conclusion was that the 3rd degree polynomial could be the best fit model. However, the fit model should be used also for extrapolation, for the second reference temperature of 100 °C. This new approach changes the fit models order, the Arrhenius equation becoming the best fit model, because of the completely failed to predict the extrapolated value with the polynomial model. |
url |
https://doi.org/10.1051/matecconf/201711210014 |
work_keys_str_mv |
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