14-11-14 Fire Protection of Underground Transportation Systems: A Decision Support Tool for Designers and Rescue Services Karl Fridolf Researcher, SP Fire Research Post graduate student, Lund university TuFT: Tunnel Fire Tools Fire Text based input TuFT Comma separated output Evacuation Tunnel Simulation in TuFT Rescue Operation 1 14-11-14 Fire dynamics • Underlying equations based on research by Ingason (2012) • Empirically derived hand calculation models • One-dimensional bulk flow of gases • Assumptions • Wind direction • Completely mixted Fire dynamics • Gas temperatures, concentrations and visibility assessed as cross-sectional averaged values x m from the fire at t s into the fire development • Consequences? • • Far from as exact as, for example, any CFD model Computational time TuFT << CFD model Ingason, H. (2012). Fire dynamics in tunnels. In A. Beard & R. Carvel (Eds.), Handbook of Tunnel Fire Safety (Second ed., pp. 273-307). London, UK: ICE Publishing. Fire dynamics Fire dynamics 1. Tunnel Start Tunnel type, length, width, height, wind speed and direction, ambient temperature and distances between emergency exits 2. Fire • Growth/Decay type and rates, maximum HRR and burning time, heat of combustion, burning efficiency, mass optical density, yields (CO, CO2 and HCN), position in the tunnel 3. Prediction position(s) • YES t=t+1s • Is t(sim) < t(tot) NO Evaluate gas temperature, gas concentrations and visibility at x m for time t s Print information in all results vectors to comma separated textfile Save evaluations in corresponding results vector Stop Position(s) in the tunnel HRR Backlayering 2 14-11-14 Gas temperature Evacuation • • • • • Assessment based on FD calculations One dimensional movement Incapacitation/Death predicted with FED Walking speed dependent on visibility Evaluations done every s of the specified scenario Start Evacuation NO 1. Individuals (road tunnel) • Start position, perception and preparation times, time before leaving vehicle, evacuation to emergency exit or portal, type of walking speed estimation Same as for individuals, but parameters defined in intervals YES Is t < t(preevacuation)? NO Is agent incapacitated? 2. Groups (road tunnel) • YES Start position, length, number of available doors, door width, number of passengers, perception and preparation times, safe place, type of walking speed estimation NO Update and save position (same as in previous time step). NO Is t < t(leave vehicle)? YES Is agent incapacitated? Update and save position (same as in previous time step). NO Determine walking speed. Is t < t(leave vehicle)? YES Evaluate heat transfer at position x m for time t s and save in corresponding vector. Update position based on distance moved for time step t s. YES Update position based on distance moved for time step t s. Calculate FED of heat and save in corresponding results vector. NO Evaluate gas temperature, heat transfer, gas concentrations and visibility at position x m for time t s and save in corresponding results vector. Calculate FED of toxicants and heat and save in corresponding results vector. YES Is agent safe/ dead? t=t+1s Position in the tunnel YES Is t < t(preevacuation)? NO Determine walking speed. 3. Train (rail tunnel) • Evacuation YES Is agent down stream fire? NO Stop and print evaluations and results to comma separated textfiles. NO YES Is agent safe/ dead? t=t+1s Visibility 3 14-11-14 • • • • • FID (asphyxiants) FID (heat) Rescue operation Rescue operation Assessment based on FD calculations One dimensional movement 2 fire fighters at a time approaching the fire Evaluation of increased body temperature Start Evaluations done every s of the specified scenario Is t < t(preparation)? 1. Rescue operation • Preparation time, if operation be done up or downstream fire, if operation be done from portal or emergency exit, number of fire fighters, availability to thermal imaging camera, size of air bottles, length of long and short hoses, times to connect branches and hoses YES t=t+1s NO Any fire fighters available? NO YES NO Is t(active) < t(available) for current fire fighters? YES NO Is increased body temperature < 2.5°C? Rescue operation Request instruction. Rescue operation Connect hose/ branch Move toward fire. Determine walking speed. Evaluate increased body temperature at position x m for time t s and save in corresponding vector. Update position based on distance moved for time step t s. NO t=t+1s Is fire source reached? Update position (same as in previous time step). YES Stop and print results to comma separated textfiles. 4 14-11-14 Concluding TuFT Simple tool for quick estimations Fire dynamics Evacuation Rescue operation Coarse sub models • Predictive capability good, see Fridolf & Wahlqvist (2014) • Focus on calculation technique • Model requires “perfect” user • • • • • brand.lth.se/TuFT • • • • Latest version of TuFT Technical documentation (Swedish) User manual (Swedish and English) TuFT Macro Available for download November 24! Fridolf, K., & Wahlqvist, J. (2014). Predictive Capabilities of Computer Models for Simulation of Tunnel Fires. Lund: Lund University. Thank you! Karl Fridolf Researcher, SP Fire Research Post graduate student, Lund university [email protected] 5
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