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Record W7065186191

Effekter av top-of-rail friktionsmodifierare på friktion, slitage och sprickor på järnvägsräls

2019· article· en· W7065186191 on OpenAlexaboutno aff

Bibliographic record

VenueKTH Publication Database DiVA (KTH Royal Institute of Technology) · 2019
Typearticle
Languageen
FieldEngineering
TopicRailway Engineering and Dynamics
Canadian institutionsnot available
Fundersnot available
KeywordsTreadTraction (geology)SlippageAxleTrainFriction modifierAxle loadTractive force
DOInot available

Abstract

fetched live from OpenAlex

The railway is an economical and environmentally friendly mode of transport for long distances and heavy loads. The demands on the operators are increasing with increased competition in the market, and therefore they are currently demanding more track capacity. In the short term, the existing network is expected to deliver the increased capacity. In order to achieve increased capacity without introducing double track, either the axle load or the number of trains (i.e. the annual gross tonnage) needs to be increased, which will decrease the life of the rail and thus increase the maintenance cost. To increase the lifetime of the rails without compromising with regard to the axle load and speed, one must increase the strength of the rails, decrease the traction forces between the rails and wheels, or introduce a third body with anti-wear and anti-crack properties that can reduce the wear and rolling contact fatigue (RCF) without reducing the traction forces below the safety limit. The traction forces depend on several variables, for example third bodies in the wheel-rail interface, the train dynamics, the wheel and rail profiles, etc. Third bodies in the wheel-rail interface are one of the important influencing factors. The additive third bodies with anti-wear properties and friction reduction capabilities reduces both the wear and the RCF. However, a friction coefficient in the wheel tread and the top of the rail below 0.3 can cause slippage and a long braking distance. To reduce the degree of utilised friction to a value close to 0.35 from dry conditions with a value of 0.55, and thereby reduce the wear, a product known as top-of-rail friction modifier (TOR-FM) was developed in North America and presented in 2003 at the heavy haul conference. The TOR-FM manufacturers claim that their products provide a fixed range of friction coefficients (μ) and Kalker’s coefficients in the wheel-rail interface. Kalker’s coefficient considers the tendency of creepage between the rail and wheel as a function of the traction forces at lower creepage levels. Field and laboratory tests in the USA, Canada and China have determined the benefits of using friction control products, which include the reduction of RCF, wear, corrugation, bogie hunting, noise, and fuel consumption without any side effects. In contrast, researchers at Luleå University of Technology (LTU) have found that such products in certain conditions give unacceptably low friction that can cause long braking distances and slippage. Initial measurements performed using a wayside TOR-FM system on the Iron Ore Line (IOL – “Malmbanan” in Swedish) could not find any benefits of implementing such systems. Trafikverket is considering the implementation of the TOR-FM technology on the IOL. Directly implementing such technology can be inappropriate and expensive, because the reliability of a TOR-FM system has never been assessed for the conditions of the IOL. The IOL is the northernmost railway line in Sweden and is experiencing the problem of RCF, especially on its curves. This railway line is a single track and is mainly utilised by the ore freight trains operated by the Swedish mining company LKAB. The freight trains run by LKAB have an axle load of 30 tonnes, which is the heaviest in Europe. At present LKAB is planning to increase the axle load of their heavy haul trains to 32.5 tonnes, which will increase the RCF and wear issues. The present research investigated the effects of TOR-FMs using computer-based simulations, laboratory tests and field tests. The results from all the tests and simulations were used to calculate the life cycle cost of wayside and on-board systems. The simulation results have shown that by reducing the friction, the RCF can be reduced. This reduction in the RCF is greater on narrow curves than on larger curves as the traction forces decrease with an increase in the curve radius. Curves with a radius larger than m are not prone to RCF. The damage index method used in the simulation has also shown that on circular curves with a radius smaller than 300 m, the so-called “magic wear” rate can be achieved. Magic wear means that the wear rate due to normal operation is equal to the crack generation rate. The field results obtained using a handheld tribometer have shown that by using a TOR-FM, both the wear and the friction coefficients can be reduced. The content of the TOR-FM can have a significant effect on the carry distance and, generally, non-drying FMs have a longer carry distance. Excessive use of TOR-FM may cause unacceptably low friction and a high operational cost, and only result in an insignificant increase in the carry distance. In addition, it was also concluded that in the case of the wayside system during extreme winters, the equipment could have maintenance issues and thus a high operational cost. The on-board system is an economical alternative to the wayside system, as it has lower operation and maintenance costs. The results have also shown that snow and ice formation in the winter act as a lubricant. However, further investigations are needed to provide knowledge of the efficiency of such natural lubricants and their retention on the rail. The present research has taken the IOL as a case study, but the results will be applicable all over the world.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.583
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.008
GPT teacher head0.214
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2019
Admission routes1
Has abstractyes

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