The Anonymous Widower

Optimal Prediction of Sand For Adhesion

This project was one of the winners in the First Of A Kind 2022 competition run by Innovate UK.

In this document, this is said about the project.

Project No: 10039258

Project title: Optimal Prediction of Sand For Adhesion
Project grant: £153,228

Public description: Train services are affected by seasonal variables particularly leaf fall between September and
December. They can also be compromised by wet weather, icy and snowy conditions at a regional
or very localised level on a particular route. Maintaining wheel-rail contact to ensure adequate and
safe braking requires the use of sand in low adhesion conditions. Sand is dispensed to trains in
response to a combination of train service plans and of weather forecast. However, not all trains
are currently able to be replenished during overnight stabling and servicing with attendant risks of
delays and damage to trains and infrastructure. Also, there is a high level of safety risk when sand
replenishment on trains is carried out on a third-rail yard.

“Optimal Prediction of Sand for Adhesion” (OPSA) lead by Govia Thameslink Railway, the major
Train Operating Company on third rail in the UK, will deliver a more efficient and cost-effective
means of predicting the dispensing of sand to trains to ensure services are not compromised by
adhesion losses and train sets are not required to be removed from planned operating diagrams
because of inadequate on board sand supplies. The algorithm developed as a results of this project
will base the estimates on an integrated framework that includes the forecast adhesion, track
maintenance and the expected speed profile in order to capture the change in weather and the seasonal factors.

The algorithm developed represents a cost effective solution to predict the use of sand and
schedule the maintenance of trains enhancing in turn safety and reducing the impact of delays on
the timetable. The algorithm will be developed including direct measure of sand dispersion, braking,
wheel slip and line speed diagram also accounting for human behaviour effects such as driving
Govia Thameslink Railway has engaged with Cranfield University to deliver the disruptive
innovation proposed in this project. The algorithm will enable a more efficient train scheduling
improving public performance measure (PPM) addressing train delay targeting in particular the
25% of delay up to 15 minutes cause by several concurrent issues including train rescheduling and
the National Rail Passenger Survey satisfaction.

My Thoughts And Conclusions

November 18, 2022 - Posted by | Computing, Transport/Travel | , , , , ,

1 Comment »

  1. […] 10039258 – Optimal Prediction of Sand For Adhesion […]

    Pingback by First Of A Kind 2022 Winners Announced « The Anonymous Widower | November 19, 2022 | Reply

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