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Development of a Thermal Stresses Based
Fragmentation Model for Pulverized Coal
Particles Gasification by Low Temperature
Air Thermal Plasma
25th May 2016
ANSYS Convergence Regional Conferences
City Hotel, Ljubljana, Slovenia
Belgrade University, Institute of Nuclear Sciences “Vinca” ,
Laboratory for Thermal Engineering and Energy, Belgrade,
Serbia
Rastko Jovanović, Dejan Cvetinović, Predrag Stefanović,
Predrag Škobalj, Zoran Marković
Introduction
Motivation
Model description
Results
 Conclusions
 Future work
Presentation outline
2/26
Introduction, Plasma fire support technology
3/26
Air-coal
mixture
channel
Plasma
generator Boiler furnace
Hot air plasma produced in plasma
generators is introduced into air – coal
mixture duct.
The plasma flame with high thermal
energy induces coal gasification and
partial char oxidation producing highly
reactive mixture
This highly combustible mixture is
easily ignited at the furnace entry
ensuring high flame stability and overall
increased combustion efficiency.
Introduction, Plasma coal gasification process
4/26
Specific phenomena
have to be taken into
account:
.Very high
temperatures
Complex reaction
mechanisms and
kinetics
Fragmentation
Motivation
5/26
It is necessary to demonstrate its advantages over conventional
systems
The main challenge is that the most CFD codes are suitable for p.
f. combustion simulation under conventional conditions.
Particle fragmentation is commonly neglected.
During plasma coal gasification, very high plasma temperature
induces strong particle thermal stresses.
These stresses lead to “thermal shock” and extensive particle
fragmentation.
Fragmentation intensifies devolatilisation (3-4 times) and
significantly accelerates char oxidation.
The main aims of this work are:
 development of fragmentation model based on calculations of the
thermal stresses inside pulverized coal particles
 Model implementation in ANSYS FLUENT combustion model using
User Defined Functions (UDFs).
Mathematical model, general features
6/26
The reactive flow field was described in Eulerian manner.
The turbulence was modeled using the standard k − ε
turbulence model together with standard wall functions.
Radiation was modeled using Discrete Ordinates (DO)
model.
The gas radiation absorption coefficient is calculated as
function of characteristic cell-size and gas concentrations.
Gaseous reactions were modeled using finite rate/eddy
dissipation model.
Char reactions were modeled using kinetic rate/diffusion
limited model.
The pulverized coal particles combustion is modeled in a
Lagrangian reference frame.
Mathematical model, fragmentation modeling
7/26
Physical
particles with
same properties
Computational
cell
Strength = 6
Strength = 4
Computational
particles − parcels
In the Lagrangian approach number of computational particles are chosen to
represent actual physical particles with a same characteristics.
In order to take into account number of physical particles in a single parcel
additional variable termed “strength” is used.
Mathematical model, fragmentation modeling
8/26
Mathematical model, fragmentation modeling
9/26
Standard model − zero dimensional model
TP
rP
vP
Tgas
radiation
convection
Mathematical model, fragmentation modeling
10/26
UDF based model − one dimensional model
rP,0 rP,max
i i+1i-1
rP,i-1
TP,i-1
rP,i+1
TP,i+1
rP,i
TP,i
Tgas
radiation
convection
Mathematical model, implementation
11/26
Governing transport equations
DPM equations
User FV code -> TP[i]
inert heating
devolatilization + fragmentation
(Dp,new, mP,new, ρP)
char combustion
σ1,3[i] ≥ σuu/N
σr[i], σt[i] = f(TP[i]) -> σ1[i], σ2[i], σ3[i]
inert heating
devolatilization
char combustion
yes no
Results, temperature distribution
12/26
No fragmentation
Results, temperature distribution
13/26
5 fragments
Results, temperature distribution
14/26
8 fragments
Results, temperature distribution
15/26
10 fragments
Results, volatile species mass fraction
16/26
10 fragments
No fragmentation
Results, CO species mass fraction
17/26
10 fragments
No fragmentation
Results, volatile and char conversion
18/26
Fragmentation model based on fracture mechanics was developed and
implemented into ANSYS FLUENT solver with extensive use of UDFs
Model is able to predict crack time and location of crack initialization
inside combusting coal particle
Model was successfully applied to plasma supported coal particles
gasification
The obtained results show high influence of “thermal shock”
phenomenon on plasma gasification performance
Model will be further developed using trial and error procedure,
testing different kinetic constants, particle thermal and transport
properties, different particle sizes and different fragmentation
criteria
In the final stage it is expected to compare numerically predicted
data with experimental data on pilot plasma burner which is under
modernization in Laboratory for Thermal Engineering and Energy, Vinca
Institute of Nuclear Sciences, Serbia
Conclusions and future work
19/26
THANK YOU FOR YOUR ATTENTION
Acknowledgments
The authors would like to acknowledge high appreciation
for the support and promotion of this work to the Public
Enterprise ”Electric power industry of Serbia”, Belgrade,
Serbia, and Ministry of Education and Science of Republic of
Serbia (Project No. III42010 and TR33050)

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Development of a Thermal Stresses Based Fragmentation Model for Pulverized Coal Particles Gasification by Low Temperature Air Thermal Plasma

  • 1. Development of a Thermal Stresses Based Fragmentation Model for Pulverized Coal Particles Gasification by Low Temperature Air Thermal Plasma 25th May 2016 ANSYS Convergence Regional Conferences City Hotel, Ljubljana, Slovenia Belgrade University, Institute of Nuclear Sciences “Vinca” , Laboratory for Thermal Engineering and Energy, Belgrade, Serbia Rastko Jovanović, Dejan Cvetinović, Predrag Stefanović, Predrag Škobalj, Zoran Marković
  • 3. Introduction, Plasma fire support technology 3/26 Air-coal mixture channel Plasma generator Boiler furnace Hot air plasma produced in plasma generators is introduced into air – coal mixture duct. The plasma flame with high thermal energy induces coal gasification and partial char oxidation producing highly reactive mixture This highly combustible mixture is easily ignited at the furnace entry ensuring high flame stability and overall increased combustion efficiency.
  • 4. Introduction, Plasma coal gasification process 4/26 Specific phenomena have to be taken into account: .Very high temperatures Complex reaction mechanisms and kinetics Fragmentation
  • 5. Motivation 5/26 It is necessary to demonstrate its advantages over conventional systems The main challenge is that the most CFD codes are suitable for p. f. combustion simulation under conventional conditions. Particle fragmentation is commonly neglected. During plasma coal gasification, very high plasma temperature induces strong particle thermal stresses. These stresses lead to “thermal shock” and extensive particle fragmentation. Fragmentation intensifies devolatilisation (3-4 times) and significantly accelerates char oxidation. The main aims of this work are:  development of fragmentation model based on calculations of the thermal stresses inside pulverized coal particles  Model implementation in ANSYS FLUENT combustion model using User Defined Functions (UDFs).
  • 6. Mathematical model, general features 6/26 The reactive flow field was described in Eulerian manner. The turbulence was modeled using the standard k − ε turbulence model together with standard wall functions. Radiation was modeled using Discrete Ordinates (DO) model. The gas radiation absorption coefficient is calculated as function of characteristic cell-size and gas concentrations. Gaseous reactions were modeled using finite rate/eddy dissipation model. Char reactions were modeled using kinetic rate/diffusion limited model. The pulverized coal particles combustion is modeled in a Lagrangian reference frame.
  • 7. Mathematical model, fragmentation modeling 7/26 Physical particles with same properties Computational cell Strength = 6 Strength = 4 Computational particles − parcels In the Lagrangian approach number of computational particles are chosen to represent actual physical particles with a same characteristics. In order to take into account number of physical particles in a single parcel additional variable termed “strength” is used.
  • 9. Mathematical model, fragmentation modeling 9/26 Standard model − zero dimensional model TP rP vP Tgas radiation convection
  • 10. Mathematical model, fragmentation modeling 10/26 UDF based model − one dimensional model rP,0 rP,max i i+1i-1 rP,i-1 TP,i-1 rP,i+1 TP,i+1 rP,i TP,i Tgas radiation convection
  • 11. Mathematical model, implementation 11/26 Governing transport equations DPM equations User FV code -> TP[i] inert heating devolatilization + fragmentation (Dp,new, mP,new, ρP) char combustion σ1,3[i] ≥ σuu/N σr[i], σt[i] = f(TP[i]) -> σ1[i], σ2[i], σ3[i] inert heating devolatilization char combustion yes no
  • 16. Results, volatile species mass fraction 16/26 10 fragments No fragmentation
  • 17. Results, CO species mass fraction 17/26 10 fragments No fragmentation
  • 18. Results, volatile and char conversion 18/26
  • 19. Fragmentation model based on fracture mechanics was developed and implemented into ANSYS FLUENT solver with extensive use of UDFs Model is able to predict crack time and location of crack initialization inside combusting coal particle Model was successfully applied to plasma supported coal particles gasification The obtained results show high influence of “thermal shock” phenomenon on plasma gasification performance Model will be further developed using trial and error procedure, testing different kinetic constants, particle thermal and transport properties, different particle sizes and different fragmentation criteria In the final stage it is expected to compare numerically predicted data with experimental data on pilot plasma burner which is under modernization in Laboratory for Thermal Engineering and Energy, Vinca Institute of Nuclear Sciences, Serbia Conclusions and future work 19/26
  • 20. THANK YOU FOR YOUR ATTENTION Acknowledgments The authors would like to acknowledge high appreciation for the support and promotion of this work to the Public Enterprise ”Electric power industry of Serbia”, Belgrade, Serbia, and Ministry of Education and Science of Republic of Serbia (Project No. III42010 and TR33050)