An Analytical and Experimental Investigation of the Erosive Burning of Composite Propellants.

Abstract

Augmentation of solid propellant burning rate often occurs in the presence of strong product gas flow across the burning surface: This phenomenon is referred to as erosive burning. Increasing use of motors with low port-to-throat area ratios (including nozzleless motors) is leading to increased occurrence and severity of erosive burning. A first generation model based upon bending of columnar diffusion flames by a crossflow, permitting prediction of the effect of high-velocity crossflow on the burning rate of a composite propellant given only the zero-crossflow burning rate characteristics, is briefly summarized and compared with data. A second generation model (currently under development) which does not require even zero-crossflow burning rate data, using only composition and particle size as input, is outlined. In addition, a test device permitting extensive characterization of burning rate-pressure-crossflow velocity relationships for various propellants with direct continuous measurement of instantaneous burning rate by high-speed cinematography is described, and results of a series of tests with four propellants are presented. These tests indicate that the first generation composite propellant erosive burning model has reasonable good predictive capability, particularly in the higher pressure region where the propellant combustion is dominated by the propellant heterogeneity.

Document Details

Document Type
Technical Report
Publication Date
Oct 01, 1977
Accession Number
ADA050386

Entities

People

  • Merrill K. King

Organizations

  • ARCO

Tags

DTIC Thesaurus Topics

  • Burning Rate
  • Combustion
  • Composite Materials
  • Composite Propellants
  • Demographic Cohorts
  • Erosive Burning
  • Gas Flow
  • Measurement
  • Particle Size
  • Propellants
  • Solid Propellants

Fields of Study

  • Physics

Readers

  • Rocket Propulsion.
  • Theoretical Analysis.

Technology Areas

  • AI & ML
  • AI & ML - Bayesian Inference