PSI - Issue 57

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ScienceDirect

Procedia Structural Integrity 57 (2024) 104–111 Fatigue Design 2023 00 (2023) 000–000 Fatigue Design 2023 00 (2023) 000–000

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Fatigue Design 2023 (FatDes 2023) Impact of daily and seasonal temperature variation on rolling contact Fatigue Design 2023 (FatDes 2023) Impact of daily and seasonal temperature variation on rolling contact

fatigue damage in the rail Olivier Vo Van a, ∗ , Vincent Laurent b,c fatigue damage in the rail Olivier Vo Van a, ∗ , Vincent Laurent b,c a SNCF, 1-3 Avenue Franc¸ois Mitterrand, Saint-Denis 93210, France b ENS Paris Saclay, 4 avenue des Sciences, 91190 Gif-sur-Yvette, France c Eurobios, 61 Avenue de Pre´sident Wilson, 94230 Cachan, France a SNCF, 1-3 Avenue Franc¸ois Mitterrand, Saint-Denis 93210, France b ENS Paris Saclay, 4 avenue des Sciences, 91190 Gif-sur-Yvette, France c Eurobios, 61 Avenue de Pre´sident Wilson, 94230 Cachan, France

© 2024 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0 ) Peer-review under responsibility of the scientific committee of the Fatigue Design 2023 organizers Abstract Rolling contact fatigue is the most frequent degradation mode observed on the rail. Many mechanical studies have revealed the main physical parameters that generate the crack initiation, which are all linked with the track geometry, the dynamic of the wheel and contact conditions. Except for extreme climate, exogenous phenomena such as ambient temperature have not been analysed. The main reason is that temperature variations induce stress cycles in the rail that are under the endurance limit of the material. Therefore, most fatigue life estimation methods do not consider these cycles. In this paper, 6 years of daily temperature measurement over all the French railway network are used and integrated in a machine learning model developed for computing residual lifespan of each rail segment before crack initiation by rolling contact fatigue. Temperature data are aggregated using the continuum damage mechanics model developed by Lemaitre and Chaboche that gives the possibility to let low amplitude cycles to contribute to fatigue damage. Classification models are then implemented, and importance variables are finally quantified. Results show that daily temperature variations have more impact than seasonal variations and that its contribution to fatigue is significant. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http: // creativecommons.org / licenses / by-nc-nd / 4.0 / ) Peer-review under responsibility of the scientific committee of the Fatigue Design 2023 organizers. Keywords: Fatigue; Rail; Weather; Machine-Learning Abstract Rolling contact fatigue is the most frequent degradation mode observed on the rail. Many mechanical studies have revealed the main physical parameters that generate the crack initiation, which are all linked with the track geometry, the dynamic of the wheel and contact conditions. Except for extreme climate, exogenous phenomena such as ambient temperature have not been analysed. The main reason is that temperature variations induce stress cycles in the rail that are under the endurance limit of the material. Therefore, most fatigue life estimation methods do not consider these cycles. In this paper, 6 years of daily temperature measurement over all the French railway network are used and integrated in a machine learning model developed for computing residual lifespan of each rail segment before crack initiation by rolling contact fatigue. Temperature data are aggregated using the continuum damage mechanics model developed by Lemaitre and Chaboche that gives the possibility to let low amplitude cycles to contribute to fatigue damage. Classification models are then implemented, and importance variables are finally quantified. Results show that daily temperature variations have more impact than seasonal variations and that its contribution to fatigue is significant. © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http: // creativecommons.org / licenses / by-nc-nd / 4.0 / ) Peer-review under responsibility of the scientific committee of the Fatigue Design 2023 organizers. Keywords: Fatigue; Rail; Weather; Machine-Learning

1. Introduction 1. Introduction

Repeated passage of train wheels over the rail, with a small contact patch and high hydrostatic pressure, can lead to the development of cracks on the rail’s surface or subsurface. This phenomenon, known as rolling contact fatigue ( RCF ), is the primary cause of rail degradation and presents a risk of rail failure and potential derailment. To prevent such situations, costly maintenance operations are carried out. In modern infrastructure, the jointed track that absorbs rail dilatation in these joints have been replaced by continuous welded rails ( CWR ), in which dilatation is constrained by friction with rail pads, thus inducing thermal stresses in the rail. The notorious e ff ects of rail temperature are on its Repeated passage of train wheels over the rail, with a small contact patch and high hydrostatic pressure, can lead to the development of cracks on the rail’s surface or subsurface. This phenomenon, known as rolling contact fatigue ( RCF ), is the primary cause of rail degradation and presents a risk of rail failure and potential derailment. To prevent such situations, costly maintenance operations are carried out. In modern infrastructure, the jointed track that absorbs rail dilatation in these joints have been replaced by continuous welded rails ( CWR ), in which dilatation is constrained by friction with rail pads, thus inducing thermal stresses in the rail. The notorious e ff ects of rail temperature are on its

∗ Corresponding author. Tel.: + 33-601-857-573. E-mail address: olivier.vovan@sncf.fr ∗ Corresponding author. Tel.: + 33-601-857-573. E-mail address: olivier.vovan@sncf.fr

2452-3216 © 2024 The Authors. Published by ELSEVIER B.V. This is an open access article under the CC BY-NC-ND license (https://creativecommons.org/licenses/by-nc-nd/4.0) Peer-review under responsibility of the scientific committee of the Fatigue Design 2023 organizers 10.1016/j.prostr.2024.03.013 2210-7843 © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http: // creativecommons.org / licenses / by-nc-nd / 4.0 / ) Peer-review under responsibility of the scientific committee of the Fatigue Design 2023 organizers. 2210-7843 © 2023 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http: // creativecommons.org / licenses / by-nc-nd / 4.0 / ) Peer-review under responsibility of the scientific committee of the Fatigue Design 2023 organizers.