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Conference object . 2018
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Numerical and Experimental Predictions of Pavement Texture Effects on Vehicle Fuel Consumption

Authors: Mansura, Dmytro; Thom, Nicholas; Beckedahl, Hartmut;

Numerical and Experimental Predictions of Pavement Texture Effects on Vehicle Fuel Consumption

Abstract

119 litres is the average annual amount of fuel used by a typical car to overcome rolling resistance (RR) depending not only on vehicle-related factors but also pavement-related ones. By rearranging surface texture, fuel consumption may decrease bringing substantial long-term socio-economic benefits, which would help in meeting EU limits on CO2 (95 g/km until 2021) fostering sustainable construction of pavements. This paper outlines a newly developed multi-scale 3-D numerical model and two in-laboratory and one in-situ experimental tests to calculate micro-distortional RR due to indentation of aggregates into visco-elastic tread compound. Two computational and two experimental approaches to quantify micro-distortional RR were developed. The contact forces appeared of a reasonable distribution and magnitude. It was found that micro-distortional RR is higher on a rougher/sparsely packed surface compared to a smoother/tightly packed case; longitudinally grooved concrete was the most energy efficient texture studied. The predictions were confirmed qualitatively using the experiments.

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Keywords

bituminous and concrete pavement textures; multi-scale model; experiments; micro-distortional rolling resistance; fuel consumption

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selected citations
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This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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popularity
This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.
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influence
This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).
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