Predictive computer simulations of highly resolved large-scale 3D deflagrations and detonations are dependent on a robust reaction model embedded in a computational framework capable of running on massively parallel computer architectures. We have been developing such a model in the Uintah Computational Framework, which has exhibited good strong and weak scaling characteristics up to 512K cores [16]. Our focus is on predicting a Deflagration to Detonation Transition (DDT) when a large number of energetic devices are present. An example of this is a semi-tractor-trailer loaded with thousands of mining boosters that rolled over, ignited and went through a DDT. Our current reaction model adapts components from a) Ward, Son and Brewster[22] which incorporates the effects of pressure and initial temperature on deflagration, b) Berghout et al.[9] to model burning in cracks of damaged explosives, and c) Souers[20] for describing fully developed detonation. The reaction model has been subjected to extensive validation against experimental tests. Current efforts are focused on the effects of varying the grid resolution on multiple aspects of deflagration and the transition to detonation.

The Uintah software framework for the solution of a broad class of fluid-structure interaction problems has been developed by using a problem-driven approach that dates back to its inception. Uintah uses a layered taskgraph approach that decouples the problem specification as a set of tasks from the adaptive runtime system that executes these tasks. Using this approach it is possible to improve the performance of the software components to enable the solution of broad classes of problems as well as the driving problem itself. This process is illustrated by a motivating problem that is the computational modeling of the hazards posed by thousands of explosive devices during a Deflagration to Detonation Transition (DDT) that occurred on Highway 6 in Utah. In order to solve this complex fluid-structure interaction problem at the required scale, substantial algorithmic and data structure improvements were needed to Uintah. These improvements enabled scalable runs for the target problem and provided the capability to model the transition to detonation. The solution to the target problem from these runs provided insight as to why the detonation happened, as well as demonstrating a possible remediation strategy that may have avoided detonation.