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Welcome to this FRIC research result webinar!
2020‐12‐10
The webinar will start at 11 am
P l e a s e n o t i c e t h at t h e we b i n a r w i l l b e re co rd e d
The next FRIC webinars will be in 2021, more info on Teams – News and fric.no
@FRICfirecentre
SMOLDERING
A SHORT HISTORY
Bjarne Christian Hagen HVL - Haugesund
10. Desember 2020
SMOLDERING
• A low temperature combustion without flames but with smoke
• Produces a lot of toxic gases
• Difficult to detect and to extinguish
• Industrial hazard in production,
transportation and storage of different materials
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Fire in silo. Foto: Härnösand brannvesen.
RESEARCH ON SMOLDERING IN NORWAY
• 2008 – 2013 Smoldering and transition to flaming
• Cotton
• Ph.D‐candidate
• 3 articles
• 2015 – 2020 EMRIS
• Wood pellets
• 3 Ph.D‐candidates
• 19 articles and conference papers
• 2019 ‐ FRIC
• Modeling –Todays talk
• Larger scale experiments
• 2020 ‐ HVL
• Experiments that runs over weeks
• Suppression of smoldering fires
19
CHALLENGE WITHIN SMOLDERING
• Practical issues:
• Detection
• Suppression
• Tactics for fire service
• Research:
• We need large scale testing
• Modeling vs. experiments
• Smoldering experiments takes to long time.
• More testing … we need data
20
M O D E L L I N G
S M O U L D E R I N G F I R E
Christoph Meranera, Tian Lia,b, Ragni Fjellgaard
Mikalsena, Nieves Fernandez‐Anezc, Bjarne C. Hagenc
aRISE Fire Research AS
bNTNU – Norwegian University of Science and Technology
cWestern Norway University of Applied Science
Evidence‐based decision‐
making within fire safety
PREVENTION, IMPACT OF MEASURES, HIGH‐RISK GROUPS
DISSEMINATION, COMMUNICATION AND DATA HANDLING
Fire dynamics and modeling
WP2 WP1
Building technology and design
WP3
Fire safety measures, new technology in
buildings
WP4
EXPERIMENT NUMERICAL MODEL
• Designing experiments to observe and measure relevant physical
properties and phenomena.
• All physics is contained in nature.
• Develop numerical model that can reflect real world observations.
• All relevant physics needs to be implemented as sub‐models.
SMOULDERING FIRE
Slow, mostly low temperature, flameless burning of fuels.
Flameless: Oxidation of the solid phase.
Difficult to detect and suppress.
Opening slide from online presentation given by Kira Piechnik 10.11.2020
https://prezi.com/view/yVFHODruMbxK3e9yMLkF/
THREE STEP PROCESS
1. Drying (endotherm)
2. Preheating (endotherm)
3. Pyrolysis and oxidation (exotherm)
The balance between heat generation and heat losses is very important.
Ragni Fjellgaard Mikalsen, Doctoral Thesis,
Otto‐von‐Guericke‐University of Magdeburg 2018
HEAT TRANSFER
• Radiative heat transfer
(Importance is very temperature depending)
• Conductive heat transfer
(Important due to large smoldering timescale)
• Convective heat transfer
(forced vs natural convection)
Radiative heat transfer
Convective heat transfer
Conductive heat transfer
Heating
Air flow Front
Unburned fuel
• Forced air flow
• Relative homogeneous fuels
• One‐dimensional smouldering
• Tracking of the smouldering front
T Y P I C A L E X P E R I M E N TA L C O N F I G U R AT I O N S
Temperature
• Granular fuel bed (pellets).
• No forced air flow.
• Observations of local hotspots and pulsation
Ragni Fjellgaard Mikalsen, Doctoral Thesis, Otto‐von‐Guericke‐University of Magdeburg 2018
MODELLING CHALLENGES
• Random three‐dimensional nature, including localized hotspots.
• Changing fuel bed configuration (pellet shrinkage).
• Resolving natural convection without resolving the flow around individual pellets.
COMPUTATIONAL FLUID DYNAMICS
• Divide the interested volume into discrete cells
• Solve conservation equations of mass, momentum, and energy
Christoph Meraner, Doctoral thesis, NTNU, 2019
Li, et al. Energy and Fuels 2015, 29, 4328‐4338
COMPUTATIONAL FLUID DYNAMICS
Christoph Meraner, Doctoral thesis, NTNU, 2019
Fluidized bed H2 flame
DISCRETE ELEMENT MODELING
• Describe interactions between individual particles and boundaries
• Soft‐sphere model
Fixed bed combustion
HEAT TRANSFER
• Radiative heat transfer
• Conductive heat transfer
• Convective heat transfer
Radiative heat transfer
Convective heat transfer
Conductive heat transfer
DRYING
• Remove of the bound water and free water
• Endothermic process, shrinkage
• Kinetic rate
Wet biomass
Dry biomass H2O
Emositure RT
mositure
dm Ae m
dt
PYROLYSIS/DEVOLATILIZATION
• H
2, CO, CO
2, H
2O
• Light hydrocarbons (CH
4, C
2H
4, C
2H
6…)
• Tar such as toluene and phenol
• Shrinkage, slightly endothermic
Dry biomass Volatiles
Char
→ Gas (k1) Dry biomass → Tar (k2)
→ Char (k3)
FUEL/CHAR OXIDATION
• Dry biomass + O2 ‐> Char + Gas
• C + 0.5 O2 → CO
• C + O2 → CO2
• Exothermic
• Both mass transfer and chemcial kinitics
Char
Ash
O2
Sh D h d
RTEk Ae
eff
k h k
h k
Rate of the mass transfer of the reactant from the bulk into the reactive surface
Chemcal reaction, kinetic rate
Effective rate
Char O2
Gas Gas
Dry biomass
SIMULATION CONFIGURATION
PRELIMINARY RESULTS
SENSITIVE STUDIES
• Convective heat transfer correlation (Zanoni1, Ranz‐Marshall, Singhal2)
• Overlap between different stages (drying, devolatilization , fuel oxidation and char oxidation)
• Heat of reaction (devolatilization, fuel oxidation, and char oxidation)
• Kinetics (devolatilization, fuel oxidation, and char oxidation)
• Numerical parameters
• …
1Zanoni et al., International Journal of Heat and Mass Transfer 2017, 114, 90‐104
2Singhal et al. Chemical Engineering Science 2017, 172, 1‐12
Ragni Fjellgaard Mikalsen ragni.mikalsen@risefr.no (+47) 996 93 121
Christoph Meraner
christoph.meraner@risefr.no (+47) 415 48 419
Tian Li
tian.li@risefr.no (+47) 41 00 13 43
Bjarne Christian Hagen bjarne.hagen@hvl.no (+47) 975 03 578
