2018
DOI: 10.1007/s00193-018-0847-8
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Effect of spatial inhomogeneities on detonation propagation with yielding confinement

Abstract: The propagation of detonation in layers of reactive gas bounded by inert gas is simulated computationally in both homogeneous and inhomogeneous systems described by the two-dimensional Euler equations with the energy release governed by an Arrhenius rate equation. The thickness of the layer is varied and the detonation velocity is recorded as the layer thickness approaches the critical value necessary for successful propagation. In homogeneous systems, as activation energy is increased, the detonation wave exh… Show more

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Cited by 31 publications
(11 citation statements)
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“…The use of GPU-accelerated computing platforms has been explored in several studies on gaseous detonations. [48][49][50][51] The current code is a further development of the gaseousdetonation code to simulate detonations in a multiphase energetic system. This code is of a hybrid nature, consisting of parts that are executed on both CPU (Central Processing Unit) and GPU.…”
Section: Numerical Methodologymentioning
confidence: 99%
“…The use of GPU-accelerated computing platforms has been explored in several studies on gaseous detonations. [48][49][50][51] The current code is a further development of the gaseousdetonation code to simulate detonations in a multiphase energetic system. This code is of a hybrid nature, consisting of parts that are executed on both CPU (Central Processing Unit) and GPU.…”
Section: Numerical Methodologymentioning
confidence: 99%
“…They found that the spatial heterogeneity enables the detonation wave to propagate at near limit conditions at greater velocities and in thinner layers than the corresponding homogeneous case. Mi et al [18][19][20] studied the effect of spatial discretization of energy on detonation propagation. They found that the average detonation wave speeds in a spatially inhomogeneous reactive medium is significantly greater than the corresponding CJ speed of the homogeneous reactive medium.…”
Section: Introductionmentioning
confidence: 99%
“…The solutions to the above equation systems are obtained numerically using a 2nd order MUSCL-Hancock scheme with an HLLC Riemann solver [67,68], with a CFL number of 0.90. To reduce the simulation run-time, the entire flow solver was implemented using NVIDIA CUDA programming language (NVIDIA Corp., Santa Clara, CA, USA) and run on a NVIDIA Tesla K40 General Purpose Graphics Processing Unit GPGPU [68][69][70]. Application of the GPU-CPU framework improves significantly the computational performance allowing high resolution simulations and parametric study to be performed efficiently.…”
Section: Methodsmentioning
confidence: 99%