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CFD / SPACS / COS George Mason University
Fluid-Structure Interaction Calculations With Breakage and Dust
Rainald Löhner, Joseph D. Baum, Orlando A. Soto and Fumiya Togashi
Center for Computational Fluid DynamicsSPACS, George Mason University, Fairfax, VA, USA
SAIC, McLean, VA, USA
cfd.gmu.edu/~rlohnerwww.scs.gmu
CFD / SPACS / COS George Mason University
Overview
• Motivation / Applications Targeted• Particle/Flow Interaction• Examples• Shock/Dust Interaction• Conclusions and Outlook
CFD / SPACS / COS George Mason University
Motivation/Applications Targeted
CFD / SPACS / COS George Mason University
Motivation/Applications Targeted
• Fine Particles• Suspensions• Dust• Mist/Droplets
• Enhancement of Combustion• Aluminum in Solid Rocket Motors
• Shock/Dust Interaction
CFD / SPACS / COS George Mason University
Cased Weapons
Adapted CFD mesh and Pressure Contours at 125 ms
Pressure Contours, t=244 ms
CSD Velocity, t=244 ms
CFD / SPACS / COS George Mason University
Basic Phenomena
a) Detonation/Fragmentation b) Fragment Transport
c) Frags Hit/Pulverize Walls d) Dust/Shock Interaction
CFD / SPACS / COS George Mason University
Physics
CFD / SPACS / COS George Mason University
Physics
• Eulerian• Treat As Dilute Phase• Concentration: Transport (Advection, Diffusion, Reaction) Eqns.
• Lagrangian• Treat As `Particle in Fluid’• Individual (or Group): Movement, Evaporation, Heat, …Eqs.
• Focus Here: Lagrangian Treatment
CFD / SPACS / COS George Mason University
Flow: Euler/Navier-Stokes Equations
jjij
ji
ijij
jijjij
jijjij
i
kTqvv
eρpp
qv
p)ρevpvρvρv
ρeρvρ
,
jv
ja
vat,
;)(
...] JWL, Table, Gas, Ideal[),(
;;0F
(;;F
;;u
SFFu
CFD / SPACS / COS George Mason University
Momentum Transfer
• Drag Force of Each Particle
• Drag Coefficient and Reynolds-Number
piip
2
vv2
1
4 vv
di cd
D
dcd
p687.0 Re;Re15.01Re
24,1.0max
vv
CFD / SPACS / COS George Mason University
Heat Transfer
• Heat Flux For Each Particle
• Film Coefficient, Nusselt- and Prandtl-Number
442pp TTTThdQ
kc
Nud
Nukh p
Pr;RePr459.02; 55.0333.0
CFD / SPACS / COS George Mason University
Numerics
CFD / SPACS / COS George Mason University
Conservation Laws…
• Conservation Law:
• Galerkin FEM:
• Consistent Numerical Flux:
• k-Step Runge-Kutta Scheme:
SFu t ,
1
1
iki
iij
ijijt
ij sfCruM ,
1innin ur)uM(u ti
)CUSP,...Osher,vanLeer,Roe,Godunov,(~~
ijijij fff
CFD / SPACS / COS George Mason University
Particle Motion and Temperature (1)
• Velocity and Position
• Temperature
• Integrated Explicitly; 4th Order Runge-Kutta
ppp
3
;6
vx
Dv
dt
d
dt
ddp
Qdt
dTdcppp p
3
6
CFD / SPACS / COS George Mason University
Particle Motion and Temperature (2)
• Position, Velocity and Temperature:
• Integrated Explicitly; Typically: 4th Order Runge-Kutta
),,,( turdt
dufux
1
1
iki
),,,( 111 ininfi
inin turtuu ux
CFD / SPACS / COS George Mason University
Particle Tracking
• Need: Flow Variables At Location of Particle Need Host Element for Each Particle
• Initialization: Bins + Near-Neighbour Search• Incremental: Near-Neighbour Search
• Vectorized and Parallelized for OMP• Also Running in MPI
CFD / SPACS / COS George Mason University
Walls: Boundary Conditions
• Walls: Bouncing, Sticking, Gliding, …
• Embedded Surfaces
Bouncing Sticking Gliding
CFD / SPACS / COS George Mason University
Numerical Issues (1)
• Suppose: Very Small Particle• Low Re-Nr High Relative Drag • Drag: ~ r2
• Mass: ~ r3
• Large CFD Timestep In One Timestep, Velocity Can Exceed Flow Veloc Physically Wrong (!)• Solutions:
• Substepping (Expensive, Load Balance Issues)• Limiting
CFD / SPACS / COS George Mason University
Numerical Issues (2)
• Suppose: Very Small Particle• Low Nu-Nr High Relative Heating• Heat Flux: ~ r2
• Heat Capacity: ~ r3
• Large CFD Timestep In One Timestep, Temperature Can Exceed Flow Temp Physically Wrong (!)• Solutions:
• Substepping (Expensive, Load Balance Issues)• Limiting
CFD / SPACS / COS George Mason University
Numerical Issues (3)
• Assume 1-D: Difference in Velocities at tn: Δvn = vf - vnp
• If Δvn > 0 : Δvn+1 ≥ 0• If Δvn < 0 : Δvn+1 ≤ 0
• Same Applies to Temperatures
• Imposed at Every Runge-Kutta Stage
CFD / SPACS / COS George Mason University
Particle-Flow Interaction
• Change in Momentum for 1 Particle
• Change in Energy for 1 Particle
• Multiply By Number of (True) Particles in Packet
t
d nn
pp
)(
6pp
13 vvF
t
TTdcq
nn
pppp
)(
6pp
13
CFD / SPACS / COS George Mason University
Particle-Flow Interaction
• Need:• Conservative Transfer of Mass, Momentum and Energy Use Shape-Functions to Project Mass, Momentum and
Energy Increments to Flow Grid
Ni
CFD / SPACS / COS George Mason University
Accurate Flow Particle ; Particle Flow
• Need Sufficient Particles Per Element
Split Up Particles If Too Few/Element Size Increases
Agglomerate Particles If Too Many in One Element
Refine Mesh If Too Many Particles in One Element
CFD / SPACS / COS George Mason University
Numerical Issues (1)
• Suppose: Very Heavy / Large / Many Particles• Outside Limits of Theory, But Sometimes Encountered In Runs
• Large CFD Timestep In One Timestep, Force Exerted By Particles May Lead
To Flow Velocity That Exceeds Particle Velocity Physically Wrong (!)• Solutions:
• Substepping Expensive, Reduction of Timestep• Limiting Non-Conservative
CFD / SPACS / COS George Mason University
Numerical Issues (2)
• Suppose: Very Heavy / Large / Many Particles• Outside Limits of Theory, But Sometimes Encountered In Runs
• Large CFD Timestep In One Timestep, Energy Flux From Particles May
Lead To Flow Temperatures That Exceeds Particle Temperature
Physically Wrong (!)• Solutions:
• Substepping Expensive, Reduction of Timestep• Limiting Non-Conservative
CFD / SPACS / COS George Mason University
Numerical Issues (3)
• Add Momentum/Energy From Particles• Compare Velocities/Temperatures• Limit to Physically Reasonable Values• Add to Source-Terms
CFD / SPACS / COS George Mason University
Particle Contact (1)
• Volume of np Particles:
Equivalent Radius:
• Overlap Distance:
• Average Overlap Distance of Particles:
6
3p
K
p dnV
pK
pa dn
Vr3/13/1
84
3
jiijijaj
aiij ddrrdo xx ;
ji ppijij
s
nndodo
11
2
1
CFD / SPACS / COS George Mason University
Particle Contact (2)
• Define Unit Normal:
• Define Tangential Direction from Velocity:
ji
jiij
xx
xxn
ijijn
ijijt
ijijijn
ijij vv nvvnvvvv ;;
tij
tij
ijv
vt
CFD / SPACS / COS George Mason University
Particle Contact (3)
• Normal and Tangential Forces
• Limit Tangential Force to Avoid Reversal of Velocities• Add Velocity-Based Damping Force in Normal Direction
• Limit:
nijji
tij
sijji
nij fhhfdokkf
2
1;
2
1
ji
jijinijij
nd
mm
kkhhvf
ijnd
ijn
ijnd fff ,max
CFD / SPACS / COS George Mason University
Particle Contact (4)
• Complete Force:
• Estimation of Contact Stiffness:• Assume Particle At Rest in Incoming Flow• Another Particle Behind• Penetration Factor ξ
ijt
ijijnd
ijn
ijij fff tnf
8
v1.0;
v3 2
1Re1Re
dkk
CFD / SPACS / COS George Mason University
Particle Contact (5)
• Spatial Neighbour Information Options:• Bins• Octrees• Element Neighbour Lists
• Used Here: Bins
CFD / SPACS / COS George Mason University
Particles and MPI
CFD / SPACS / COS George Mason University
Particles and MPI (1)
• Approach Based on Passing Particle Info Across Overlapping Elements
• If Mesh Is Changed/Read In:
• Obtain All Elements That Overlap Domains• Order Border Elements According to Communication
Schedule
1 Layer ofOverlap
Ω1 Ω2
CFD / SPACS / COS George Mason University
Particles and MPI (2)
• For Each Timestep:• Obtain Particles in Each Element• For Each Exchange Pass:
• From List of Border Elements: Get Particles That Need to be Sent to Neighbouring Domain
• Exchange Info Of How Many Parts Sent/Received• Exchange (Send/Receive) Particles
CFD / SPACS / COS George Mason University
Particles and MPI (3)
• Duplicate Particles:• Reason: CFL < 1• Best Solution: Universal Unique Number• Integer Filter of Duplicate Particles
• Particles With Same Location• Reason: Flow Physics, Geometric Singularities (e.g. Corners)• Best Solution: Traverse Elements, Remove/Separate Particles
With Same Location
CFD / SPACS / COS George Mason University
Particles and MPI (4)
• Further Improvements:• Extensive OMP Parallelization of Particle Subs/Modules• Extensive Timing/Optimization of All Particle Subs/Modules• Improvements in MPI Send/Receive Modules
• Before Improvements: 2-3 Min/Timestep• After Improvements: 2-3 Seconds/Timestep
CFD / SPACS / COS George Mason University
Link to CSD
CFD / SPACS / COS George Mason University
CSD to Particles (1)
CSD
CFD
Faces Passed to CFD
CFD
CSD
Before Failure After Failure
CFD / SPACS / COS George Mason University
CSD to Particles (2)
• Model Pulverization• Main Steps
• Take Failed CSD Element (Hexahedron, Tetrahedron)• Pass These to CFD via FEMAP• User Specifies Particle Size/Density/… Distribution• Initial Velocity of Particles
• From CSD• From Experimental Evidence [Can Be O(400-100 m/sec)]
• CFD Updates Flowfield and Particles
CFD / SPACS / COS George Mason University
Examples
CFD / SPACS / COS George Mason University
WEPACT-4
CFD / SPACS / COS George Mason University
WEPACT-4 Initial Conditions
60 cmz
high p low p
250 cm
dust+airNOT TO SCALE
air only
250 cm
Closed end for variants B&C
Z>250cm: filled with air and dust (0.1g/cc)Dust particle: 2.3g/cc, D=100m
The air in z >0: p = 1.01E6 dynes/cm2 and T = 15.15 °C = 288.3 °K. The air in z < 0: p = 4000 psi = 2.7579E8 dyne/cm2 and T = 1430.6 °K.
CFD / SPACS / COS George Mason University
Computed Gas Density & Velocity
CFD / SPACS / COS George Mason University
Computed Gas Pressure & Mach-Nr.
CFD / SPACS / COS George Mason University
Computed Gas Energy & Temperature
CFD / SPACS / COS George Mason University
Computed Dust Density & Velocity
CFD / SPACS / COS George Mason University
3D Plot of Dust Density Profile
Dust Density
x
Time
CFD / SPACS / COS George Mason University
Movies – Gas Velocity & Dust Velocity
CFD / SPACS / COS George Mason University
WEPACT-5
CFD / SPACS / COS George Mason University
WEPACT-5 Initial condition
60 cmz
high p low p
250 cm
dust+air
NOT TO SCALE
air only
250 cm 180 cm
air only
430cm > Z >250cm: filled with air and dust (0.1g/cc)Dust particle: 2.3g/cc, D=100m
CFD / SPACS / COS George Mason University
Computed Gas & Dust Density
CFD / SPACS / COS George Mason University
Computed Gas Velocity
CFD / SPACS / COS George Mason University
Computed Mach-Nr.
CFD / SPACS / COS George Mason University
Computed Gas Pressure
CFD / SPACS / COS George Mason University
Computed Gas Energy
CFD / SPACS / COS George Mason University
Computed Gas Temperature
CFD / SPACS / COS George Mason University
Computed Dust Velocity
CFD / SPACS / COS George Mason University
Computed Dust Temperature
CFD / SPACS / COS George Mason University
Movies
CFD / SPACS / COS George Mason University
Effect of Dust on Blasts in Tunnels
• Experimental Observation: Damping Due to Dust• Step 1: Simplify in Order to Understand No CSD CFD:
• Simple EOS• Particles
• Study the Effects of Dust on:• Shock Waves• Overall Flowfield
• Solver: FEM-FCT + Particles
CFD / SPACS / COS George Mason University
Blast in Tunnel
CFD / SPACS / COS George Mason University
Blast in Tunnel
CFD / SPACS / COS George Mason University
Station Placement
CFD / SPACS / COS George Mason University
Station 8
CFD / SPACS / COS George Mason University
Station 13
CFD / SPACS / COS George Mason University
Generic Production Runs
CFD / SPACS / COS George Mason University
Blast In Room With Particles
CFD / SPACS / COS George Mason University
Blast In Room With Particles
CFD / SPACS / COS George Mason University
Fluid and Particle Velocities at 2.5msρ=2.0gr/cc, d=0.1cm
ρ=2.0gr/cc, d=0.0464cm
ρ=0.4gr/cc, d=0.1cm
ρ=1.0gr/cc, d=0.02cm
CFD / SPACS / COS George Mason University
Fluid and Particle Velocities at 5.0msρ=2.0gr/cc, d=0.1cm
ρ=2.0gr/cc, d=0.0464cm
ρ=0.4gr/cc, d=0.1cm
ρ=1.0gr/cc, d=0.02cm
CFD / SPACS / COS George Mason University
Fluid and Particle Velocities at 10.0msρ=2.0gr/cc, d=0.1cm
ρ=2.0gr/cc, d=0.0464cm
ρ=0.4gr/cc, d=0.1cm
ρ=1.0gr/cc, d=0.02cm
CFD / SPACS / COS George Mason University
Conclusions and Outlook
CFD / SPACS / COS George Mason University
Particle / Flow Capability
• Important In Order to Model Complex Phenomena
• Significant Effects Due To:• Momentum Loss / Transfer [Particle Acceleration]• Energy Loss / Transfer [Particle Heating]
• Have Helped to Explain Puzzling Experimental Results
CFD / SPACS / COS George Mason University
Future Work
• Contact Between Particles• Contact Stiffness ?• Stability ?
• Burn With Oxygen Deficiency• Link to Chemical Reactions
• Volume Blockage Effect of Particles• For High Densities