Evidence map›Paper›PMID 31766764›Full record

ArticleSensors (Basel, Switzerland)2019

OPTYRE-Real Time Estimation of Rolling Resistance for Intelligent Tyres.

Nicola Roveri, Gianluca Pepe, Federica Mezzani, Antonio Carcaterra, Antonio Culla, Silvia Milana

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Nicola RoveriDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy.
Gianluca PepeDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy.
Federica MezzaniDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy.
Antonio CarcaterraDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy.
Antonio CullaDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy.
Silvia MilanaDepartment of Mechanical and Aerospace Engineering, Sapienza University of Rome, 00184 Rome, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The study of the rolling tyre is a problem framed in the general context of nonlinear elasticity. The dynamics of the related phenomena is still an open topic, even though few examples and models of tyres can be found in the technical literature. The interest in the dissipation effects associated with the rolling motion is justified by their importance in fuel-saving and in the context of an eco-friendly design. However, a general lack of knowledge characterizes the phenomenon, since not even direct experience on the rolling tyre can reveal the insights of the correlated different dissipation effects, as the friction between the rubber and the road, the contact kinematics and dynamics, the tyre hysteretic behaviour and the grip. A new technology, based on fibre Bragg grating strain sensors and conceived within the OPTYRE project, is illustrated for the specific investigation of the tyre dissipation related phenomena. The remarkable power of this wireless optical system stands in the chance of directly accessing the behaviour of the inner tyre in terms of stresses when a real-condition-rolling is experimentally observed. The ad hoc developed tyre model has allowed the identification of the instant grip conditions, of the area of the contact patch and allows the estimation of the instant dissipated power, which is the focus of this paper.

Indexed as

fibre Bragg gratingintelligent tyrerolling resistance

Identifiers

PMID31766764
PMCPMC6929126

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.