Chamber Dynamic Response Modeling Zoran Dragojlovic.

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Chamber Dynamic Response Modeling Zoran Dragojlovic
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Transcript of Chamber Dynamic Response Modeling Zoran Dragojlovic.

Page 1: Chamber Dynamic Response Modeling Zoran Dragojlovic.

Chamber Dynamic Response Modeling

Zoran Dragojlovic

Page 2: Chamber Dynamic Response Modeling Zoran Dragojlovic.

Accomplishments• IFE Chamber dynamics code SPARTAN is developed: Simulation of Physics by Algorithms based on Robust

Turbulent Approximation of Navier-Stokes Equations.• SPARTAN current features:

– 2-D Navier Stokes equations, viscosity and thermal conductivity included

– arbitrary geometry– Adaptive Mesh Refinement

• SPARTAN tests:– Acoustic wave propagation.– Viscous channel flow.– Mach reflection.– Analysis of discretization errors to find code accuracy.

• Initial conditions from BUCKY code are used for simulations.• Two Journal articles on SPARTAN are in preparation.

• IFE Chamber dynamics code SPARTAN is developed: Simulation of Physics by Algorithms based on Robust

Turbulent Approximation of Navier-Stokes Equations.• SPARTAN current features:

– 2-D Navier Stokes equations, viscosity and thermal conductivity included

– arbitrary geometry– Adaptive Mesh Refinement

• SPARTAN tests:– Acoustic wave propagation.– Viscous channel flow.– Mach reflection.– Analysis of discretization errors to find code accuracy.

• Initial conditions from BUCKY code are used for simulations.• Two Journal articles on SPARTAN are in preparation.

Page 3: Chamber Dynamic Response Modeling Zoran Dragojlovic.

Adaptive Mesh Refinement• Motivation: efficient grid distribution results in reasonable CPU

time.• Grid organized into levels from coarse to fine.• Solution tagging based on density and energy gradients.• Grid is refined at every time step.• Solution interpolated in space and time between the grid levels.• Referenced in: Almgren et al., 1993.

• Motivation: efficient grid distribution results in reasonable CPU time.

• Grid organized into levels from coarse to fine.• Solution tagging based on density and energy gradients.• Grid is refined at every time step.• Solution interpolated in space and time between the grid levels.• Referenced in: Almgren et al., 1993.

chamber beam channel

wall

Page 4: Chamber Dynamic Response Modeling Zoran Dragojlovic.

Example of Adaptive Mesh Refinement

maxmin

geometry

density contour plot

Page 5: Chamber Dynamic Response Modeling Zoran Dragojlovic.

IFE Chamber Dynamics Simulations

Objectives• Determine the influence of the following factors on the chamber

state at 100 ms:- viscosity- blast position in the chamber- heat conduction from gas to the wall.

• Chamber density, pressure, temperature, and velocity distribution prior to insertion of next target are calculated.

Objectives• Determine the influence of the following factors on the chamber

state at 100 ms:- viscosity- blast position in the chamber- heat conduction from gas to the wall.

• Chamber density, pressure, temperature, and velocity distribution prior to insertion of next target are calculated.

Page 6: Chamber Dynamic Response Modeling Zoran Dragojlovic.

Numerical Simulations

IFE Chamber Simulation• 2-D cylindrical chamber with a laser beam channel on the side.• 160 MJ NRL target• Boundary conditions:

– Zero particle flux, Reflective velocity– Zero energy flux or determined by heat conduction.

• Physical time: 500 s (BUCKY initial conditions) to 100 ms.

IFE Chamber Simulation• 2-D cylindrical chamber with a laser beam channel on the side.• 160 MJ NRL target• Boundary conditions:

– Zero particle flux, Reflective velocity– Zero energy flux or determined by heat conduction.

• Physical time: 500 s (BUCKY initial conditions) to 100 ms.

Page 7: Chamber Dynamic Response Modeling Zoran Dragojlovic.

Numerical Simulations

Initial Conditions• 1-D BUCKY solution for density, velocity and temperature at

500 s imposed by rotation and interpolation.• Target blast has arbitrary location near the center of the

chamber.• Solution was advanced by SPARTAN code until 100 ms were

reached.

Initial Conditions• 1-D BUCKY solution for density, velocity and temperature at

500 s imposed by rotation and interpolation.• Target blast has arbitrary location near the center of the

chamber.• Solution was advanced by SPARTAN code until 100 ms were

reached.

0.00E+00

2.00E-03

0.0000 8.0000

Page 8: Chamber Dynamic Response Modeling Zoran Dragojlovic.

Estimating Viscosity:

- Neutral Xe gas at 800K: = 5.4 x 10-5 Pas

- Ionized Xe at (10,000 – 60,000)K: = (4.9 x 10-11 – 4.4 x 10-9) Pas

- Simulations are done with ionized gas values (to be conservative).

- Simulations indicate viscosity is important even at such small values.

- Analysis should include a combination of neutral and ionized gas.

Estimating Viscosity:

- Neutral Xe gas at 800K: = 5.4 x 10-5 Pas

- Ionized Xe at (10,000 – 60,000)K: = (4.9 x 10-11 – 4.4 x 10-9) Pas

- Simulations are done with ionized gas values (to be conservative).

- Simulations indicate viscosity is important even at such small values.

- Analysis should include a combination of neutral and ionized gas.

Effect of Viscosity on Chamber State at 100 ms

2.21 1016– T

2.5Ai

0.5

Z4 ln

---------------------kgms-------=

Page 9: Chamber Dynamic Response Modeling Zoran Dragojlovic.

Effect of Viscosity on Chamber State at 100 ms

inviscid flow at 100 ms

pressure, pmean = 569.69 Pa

temperature, Tmean = 5.08 104 K

(CvT)mean = 1.412 103 J/m3

pressure, pmean = 564.87 Pa

temperature, Tmean = 4.7 104 K

(CvT)mean = 1.424 103 J/m3

viscous flow at 100 ms

Temperature is more evenly distributed with viscous flow.Temperature is more evenly distributed with viscous flow.

maxmin

Page 10: Chamber Dynamic Response Modeling Zoran Dragojlovic.

Effect of Viscosity on Chamber State at 100 ms

Pressure across the chamber at 0.1s.

- viscous

- inviscid

pres

sure

[P

a]

6.50.0 13.0distance [m]

Pressure at the chamber wall versus time.

1001100210031004100510061007100

0 0.01 0.02 0.03time [s]

pres

sure

[Pa]

viscousinviscid

Page 11: Chamber Dynamic Response Modeling Zoran Dragojlovic.

Effect of Blast Position on Chamber State at 100ms

pressure, pmean = 564.87 Pa

temperature, Tmean = 4.7 104 K

centered blast at 100 mspressure, pmean = 564.43 Pa

temperature, Tmean = 4.74 104 K

eccentric blast at 100 ms

Both cases feature random fluctuations, little difference in the flow field.

Both cases feature random fluctuations, little difference in the flow field.

maxmin

Page 12: Chamber Dynamic Response Modeling Zoran Dragojlovic.

Effect of Blast Position on Chamber State at 100 ms

0

1000

2000

3000

4000

5000

6000

0 0.02 0.04 0.06 0.08 0.1

time [s]

pre

ss

ure

[P

a]

centered blasteccentric blast

pressure at the wall pressure at the mirror

•Mirror is normal to the beam tube.

•Pressure is conservative by an order of magnitude.

•Pressure on the mirror is so small that the mechanical response is negligible.

•Mirror is normal to the beam tube.

•Pressure is conservative by an order of magnitude.

•Pressure on the mirror is so small that the mechanical response is negligible.

200

400

600

800

1000

1200

0 0.02 0.04 0.06 0.08 0.1

time [s]

pre

ss

ure

[P

a]

centered blasteccentric blast

Page 13: Chamber Dynamic Response Modeling Zoran Dragojlovic.

Effect of Wall Heat Conduction on Chamber State at 100 ms

Estimated Thermal Conductivity:

– Neutral Xe gas at 800K: k = 0.013 W/m-K

– Ionized Xe at (10,000 – 60,000)K: k = (0.022 – 1.94) W/m-K

- Initial simulations are done with ionized gas values (to assess impact of ionized gas conduction).

- Simulations indicate substantial impact on gas temperature with higher conductivity value.

- Analysis should include a combination of neutral and ionized gas.

Estimated Thermal Conductivity:

– Neutral Xe gas at 800K: k = 0.013 W/m-K

– Ionized Xe at (10,000 – 60,000)K: k = (0.022 – 1.94) W/m-K

- Initial simulations are done with ionized gas values (to assess impact of ionized gas conduction).

- Simulations indicate substantial impact on gas temperature with higher conductivity value.

- Analysis should include a combination of neutral and ionized gas.

k 5.86 1010– T

2.5

Z ln------------

WmK---------=

Page 14: Chamber Dynamic Response Modeling Zoran Dragojlovic.

Effect of Wall Heat Conduction on Chamber State at 100 ms

pressure, pmean = 564.431 Pa

temperature, tmean = 4.736 104 K

insulated wall

pressure, pmean = 402.073 Pa

temperature, tmean = 2.537 104 K

wall conduction

Wall heat conduction helps to achieve a more quiescent state of the chamber gas.

Wall heat conduction helps to achieve a more quiescent state of the chamber gas.

maxmin

Page 15: Chamber Dynamic Response Modeling Zoran Dragojlovic.

pressure

temperature

density

Chamber Gas Dynamicsmaxmin

Page 16: Chamber Dynamic Response Modeling Zoran Dragojlovic.

Prediction of chamber condition at long time scale is the goal of chamber simulation research.

Chamber dynamics simulation program is on schedule. Program is based on: Staged development of Spartan simulation code. Periodic release of the code and extensive simulations while development

of next-stage code is in progress.

Chamber dynamics simulation program is on schedule. Program is based on: Staged development of Spartan simulation code. Periodic release of the code and extensive simulations while development

of next-stage code is in progress.

Documentation and Release of Spartan (v1.0) Two papers are under preparation

Exercise Spartan (v1.x) Code Use hybrid models for viscosity and thermal conduction. Parametric survey of chamber conditions for different initial conditions

(gas constituent, pressure, temperature, etc.) Need a series of Bucky runs as initial conditions for these cases. We should run Bucky using Spartan results to model the following

shot and see real “equilibrium” condition. Investigate scaling effects to define simulation experiments.

Documentation and Release of Spartan (v1.0) Two papers are under preparation

Exercise Spartan (v1.x) Code Use hybrid models for viscosity and thermal conduction. Parametric survey of chamber conditions for different initial conditions

(gas constituent, pressure, temperature, etc.) Need a series of Bucky runs as initial conditions for these cases. We should run Bucky using Spartan results to model the following

shot and see real “equilibrium” condition. Investigate scaling effects to define simulation experiments.

Page 17: Chamber Dynamic Response Modeling Zoran Dragojlovic.

Several upgrades are planned for Spartan (v2.0)

Numeric:

Implementation of multi-species capability: Neutral gases, ions, and electrons to account for different thermal

conductivity, viscosity, and radiative losses.

Physics:

Evaluation of long-term transport of various species in the chamber (e.g., material deposition on the wall, beam tubes, mirrors) Atomics and particulate release from the wall; Particulates and aerosol formation and transport in the chamber.

Improved modeling of temperature/pressure evolution in the chamber: Radiation heat transport; Equation of state; Turbulence models.

Numeric:

Implementation of multi-species capability: Neutral gases, ions, and electrons to account for different thermal

conductivity, viscosity, and radiative losses.

Physics:

Evaluation of long-term transport of various species in the chamber (e.g., material deposition on the wall, beam tubes, mirrors) Atomics and particulate release from the wall; Particulates and aerosol formation and transport in the chamber.

Improved modeling of temperature/pressure evolution in the chamber: Radiation heat transport; Equation of state; Turbulence models.