A reduced order model for prediction of the burning rates of multicomponent pyrotechnic propellants. (5th February 2018)

• Record Type: Journal Article
• Authors: Ambekar, Anirudha; Yoh, Jack J.
• Is Part Of: Applied thermal engineering. Volume 130(2018)
• Page Start: 492
• Page End: 500
• Publication Date: 2018-02-05
• Subjects: Heat engineering -- Periodicals; Heating -- Equipment and supplies -- Periodicals; Periodicals; 621.40205
• DOI: 10.1016/j.applthermaleng.2017.11.008
• Languages: English
• ISSNs: 1359-4311

Abstract

Highlights: Reduced order analytical model for burning rate of pyrotechnic compositions. Linear burning rate of multi-component granular porous pyrotechnics predicted. Conductive combustion regime with primary reactions occurring on the surface. Technique accounts for the propellant conductivity, heat of reaction, and porosity. Case study predictions for KClO4 and KNO3 based pyrotechnics reasonably accurate. Abstract: This study reports a reduced order model for the prediction of the burning rate of pyrotechnic compositions. The combustion process of most pyrotechnics is primarily driven by condensed phase reactions. A priory estimation of the burning rate of pyrotechnics with multiple components may not be possible using the established methods. The study provides a simplified approach based on integral analysis of a proposed combustion wave structure for estimating the burning rate when the pyrotechnic composition, pure component thermo-physical properties, and thermo-kinetics parameter are known. The proposed combustion wave assumes a staged combustion process where the oxidizer undergoes decomposition in a broad reactive zone while fuel combustion occurs in a thin surface region. This approach takes account of the effective thermal conductivity as well as porosity of the pyrotechnic matrix. The pyrotechnic compositions studied here are expected to burn conductively at atmospheric pressure with little or no overpressure. The phenomenology of the combustion process of energetic materials is elucidated, and the reduced order model is validated through a case study.