Determination of detonation front curvature radius of ANFO explosives and its importance in numerical modelling of detonation with the Wood-Kirkwood model

Authors

DOI:

https://doi.org/10.17794/rgn2022.2.9

Keywords:

ANFO, nonideal detonation, detonation front curvature radius, detonation velocity, numerical modeling

Abstract

Unlike most military high explosives, which are characterized by an almost plane detonation front, ammonium nitratebased commercial explosives, such as ANFO (ammonium nitrate/fuel oil mixture) and emulsion explosives, are characterized by a curved detonation front. The curvature is directly related to the rate of radial expansion of detonation products in the detonation driving zone and the rate of chemical reactions, and it is one of the characteristics of nonideal explosives. The detonation theories used to model the nonideal behaviour of explosives require both reaction rate and rate of radial expansion to be known/specified as input data. Unfortunately, neither can be measured and what is mostly used is a link between these rates and parameters which can be more easily measured. In this paper, the Wood-Kirkwood approach of determination of radial expansion through the radius of detonation front curvature and the electro-optical technique for experimental determination of detonation front curvature of ANFO explosives is applied. It was shown that an experimentally determined radius of detonation front curvature vs charge diameter, incorporated in the Wood-Kirkwood detonation theory, can satisfactorily reproduce experimental detonation velocity-charge diameter data for ANFO explosives, especially when the pressure-based reaction rate law is also calibrated (D=1.3 and k=0.06 1/(μs/GPaD)).

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Published

2022-03-15

How to Cite

Štimac Tumara, B., Dobrilovic, M., Skrlec, V., & Suceska, M. (2022). Determination of detonation front curvature radius of ANFO explosives and its importance in numerical modelling of detonation with the Wood-Kirkwood model. Rudarsko-geološko-Naftni Zbornik, 37(2), 97–107. https://doi.org/10.17794/rgn2022.2.9

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Section

Mining