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Progress on Target Survival

Progress on Target Survival. Presented by A.R. Raffray Other Contributors: B. Christensen, M. S. Tillack UCSD D. Goodin General Atomics HAPL Meeting UCLA Los Angeles, CA June 2-3, 2004. Outline. Benchmark analysis with U. Roch. LLE (D. Harding)

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Progress on Target Survival

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  1. Progress on Target Survival Presented by A.R. Raffray Other Contributors: B. Christensen, M. S. Tillack UCSD D. Goodin General Atomics HAPL Meeting UCLA Los Angeles, CA June 2-3, 2004 HAPL meeting, UCLA

  2. Outline • Benchmark analysis with U. Roch. LLE (D. Harding) - Purchase more advanced version of “DSMC” - Number flux and heat flux analysis - Effect of accommodation and sticking coefficients • Modeling experimental results from LANL (J. Hoffer/D. Geller) HAPL meeting, UCLA

  3. Capabilities: • Axisymmetric flow. • Adjustable sticking (condensation) coefficient. • Adjustable accommodation coefficient. • Output: • Heat flux, number flux, drag force,etc… • Injected Target Modeling: • Simulated by flow over stationary target (hydrodynamic similarity). • Could not find a correct way of modeling moving target in stationary gas with this version. DS2V was Purchased for Modeling the Thermal Loading from the Background Gas Figure Above Shows the Temperature Field Around a Direct Drive Target. • Xe flowing at 400 m/s in the positive x-dir. • 4000 K Xe stream temperature. • 3.22x1021 m-3 Xe stream density. • Sticking coefficient = 0. • Target surface temperature = 18 K.

  4. The Number Flux and Heat Flux at the Target ReachQuasi-Steady State in a Short Time Figure Above Shows the Number Flux and Heat Flux Around a Direct Drive Target. • Xe stream flowing at 400 m/s. • 4000 K stream temperature. • 3.22x1021 m-3 stream density. • Sticking coefficient = 0. • Target surface temperature = 18 K.

  5. As the Stream Density Is Increased the Sticking Coefficient (sigma) Has a Greater Effect • The number flux is not a function of the sticking coefficient (sigma) when the stream density is low. • The number flux decreases with increasing sigma when the stream density is high. • Kinetic theory and DS2V show good agreement (sigma=1, no shielding effect). Low Density Stream, n = 3.22x1019 m-3 High Density Stream, n = 3.22x1021 m-3 HAPL meeting, UCLA

  6. The Heat Flux is Significantly Affected by the Stream Density, Temperature, and Sticking Coefficient Low Density Stream, n = 3.22x1019 m-3 • The effect of latent heat is not included in DS2V; needs to be included in post processing. • By neglecting the latent heat the “shielding” effect of a non-condensing gas (sigma = 0) is seen. • Virtually no “shielding” for the low density stream. • Significant “shielding” for the high density stream. • The rapid change in heat flux with position suggests that the average max. heat flux could be reduced by tumbling the target. High Density Stream, n = 3.22x1021 m-3 HAPL meeting, UCLA

  7. Conclusions from DS2V Study • Simulate injected target situation by flow over stationary target (hydrodynamic similarity) • The number flux and heat flux at the target reach quasi-steady state in a relatively short time (no need to run longer except if outside conditions (gas) change) • The effect of latent heat is not included in DS2V; needs to be included in post processing. • “Shielding” effect dependent on sticking coefficient for high density gas - Virtually no “shielding” for the low density stream (~1 mTorr). - Significant “shielding” for the high density stream ( q’’ reduced by a factor of 2 or more when sigma changes from 1 to 0 for example case at ~100 mTorr) • Experimental determination of the sticking coefficient is needed (U. Roch.) • The accommodation coefficient should also be determined if the sticking coefficient is found to be significantly less than one. HAPL meeting, UCLA

  8. epoxy layer coil Initial Modeling of Direct Heating Experiments at LANL (J. Hoffer/D. Geller) • 1-D spherical numerical model. • Constant heat flux. • Initial temperature = 18 K. • DT thickness = 400 mm. HAPL meeting, UCLA

  9. The Time to Triple Point, as Predicted by the Two Numerical Models, is Generally Consistent with Experimental Results HAPL meeting, UCLA

  10. There are Large Differences in the Melt Layer Thickness Results HAPL meeting, UCLA

  11. Summary • Encouraging that melting time seems to be predicted quite accurately, • Some question marks on melt layer thickness experimental and modeling results • Modeling these experimental results can be improved: - Create 1-D cylindrical model. - Allow for variable heat flux (for melt layer computations) - Code optimization: meshing, time-steps, assumed temperature range over which melting occurs - Modeling experimental set-up • Experimental uncertainties need to be better understood - Measurement; how to specify melt layer boundary - Heat flux changes when melting starts • Working with our LANL colleagues on how to produce experimental results more amenable for our model and on how to improve model to simulate a wider range of experimental conditions HAPL meeting, UCLA

  12. Please Refer to Brian Christensen’s Poster for More Details on our 1-D Target Thermomechanics Modeling (Including Phase Change) and DS2V Modeling • Brian has completed his MS Thesis on this - a summary of which will be submitted for journal publication • Thesis defense next week • His results has shed much light on the different processes affecting target survival • He has included recommendation on future work (2-D or quasi 2-D modeling + experiments) • We have identified a new student to continue this work as from the Fall (after the “Olympics”!) HAPL meeting, UCLA

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