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Evaluation of XHVrB for capturing transition to detonation as measured by embedded gauges

AIP Conference Proceedings

Tuttle, Leah W.; LaJeunesse, Jeffrey W.; Schmitt, Robert G.; Harstad, Eric N.

The Extended History Variable Reactive Burn model (XHVRB), as proposed by Starkenburg, uses shock capturing rather than current pressure for calculating the pseudo-entropy that is used to model the reaction rate of detonating explosives. In addition to its extended capabilities for modeling explosive desensitization in multi-shock environments, XHVRB's shock capturing offers potential improvement for single shock modeling over the historically used workhorse model HVRB in CTH, an Eulerian shock physics code developed at Sandia National Labs. The detailed transition to detonation of PBX9501, as revealed by embedded gauge data, is compared to models with both HVRB and XHVRB. Improvements to the comparison of model to test data are shown with XHVRB, though not all of the details of the transition are captured by these single-rate models.

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Evaluation of XHVRB for capturing shock desensitization

AIP Conference Proceedings

Tuttle, Leah W.; Schmitt, Robert G.; Kittell, David E.; Harstad, Eric N.

Explosive shock desensitization phenomena have been recognized for some time. It has been demonstrated that pressure-based reactive flow models do not adequately capture the basic nature of the explosive behavior. Historically, replacing the local pressure with a shock captured pressure has dramatically improved the numerical modeling approaches. A pseudo-entropy based formulation using the History Variable Reactive Burn model, as proposed by Starkenberg, was implemented into the Eulerian shock physics code CTH. Improvements in the shock capturing algorithm in the model were made that allow reproduction of single shock behavior consistent with published Pop-plot data. It is also demonstrated to capture a desensitization effect based on available literature data, and to qualitatively capture multi-dimensional desensitization behavior. This model shows promise for use in modeling and simulation problems that are relevant to the desensitization phenomena. Issues are identified with the current implementation and future work is proposed for improving and expanding model capabilities.

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21 Results
21 Results