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Title: Numerical Simulation and Full-scale Tests of a New Approach for Fire Management in Passenger Trains // Proceedings of the Ninth International Seminar on Fire and Explosion Hazards. Vol. 1: 21-26 April 2019, Saint Petersburg, Russia
Creators: Lázaro M.; Lázaro D.; Cortabarria E.; Alvear D.
Organization: University of Cantabria; Construcciones y Auxiliar de Ferrocarriles
Imprint: Saint Petersburg, 2019
Collection: Общая коллекция
Document type: Article, report
File type: PDF
Language: English
DOI: 10.18720/SPBPU/2/k19-71
Rights: Свободный доступ из сети Интернет (чтение, печать, копирование)

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The aim of this paper is to analyse an innovative alternative developed by Spanish railway manufacturer CAF, based on a smoke extraction system to guarantee safe conditions during evacuation processes in a passenger train. Several full-scale experiments in a new CAF’s commuter unit were designed to assess the extraction performance, supported by the application of fire computer modelling. The new smoke exhaust system must extract the smoke generated during a fire in the passenger area by exhaust fans, allowing the ingress of fresh exterior air in the lower part of the rear ends, all of them located in the body ends of the car, avoiding the smoke propagation in the other coaches. Full-scale fire tests were developed in the train unit following the Australian standard AS 4391. A fire of 140 kW was used and the smoke was generated by a clean smoke machine. Measurement points included 6 thermocouple trees, 10 gas flow velocity probes and 2 GoPro HD video cameras (for the estimation of the visibility). The expected objective was that visibility and temperature at the first vestibule of the non-fire car does not fail the tenability criteria. The system performance was successful with the tenability criteria, since the value of visibility at the nonfire car was greater than 30 m and the temperature was lower than 30 ºC during all the tests at a height of 1.7 m above the car floor level. Experimental results were used to validate the computational model. The computational model results show a good accuracy compared with the tests.

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