Abonnement à la biblothèque: Guest
Portail numérique Bibliothèque numérique eBooks Revues Références et comptes rendus Collections
International Journal of Energetic Materials and Chemical Propulsion
ESCI SJR: 0.149 SNIP: 0.16 CiteScore™: 0.29

ISSN Imprimer: 2150-766X
ISSN En ligne: 2150-7678

International Journal of Energetic Materials and Chemical Propulsion

DOI: 10.1615/IntJEnergeticMaterialsChemProp.v4.i1-6.670
pages 719-733

INSTANTANEOUS REGRESSION BEHAVIOR OF HTPB SOLID FUELS BURNING WITH GOX IN A SIMULATED HYBRID ROCKET MOTOR

Martin J. Chiaverini
Department of Mechanical Engineering The Pennsylvania State University University Park, PA 16802 USA
Nadir Serin
Department of Mechanical Engineering The Pennsylvania State University University Park, PA 16802 USA
David K. Johnson
Department of Mechanical Engineering The Pennsylvania State University University Park, PA 16802 USA
Yeu-Cherng Lu
Mechanical Engineering Department, The Pennsylvania State University, University Park, PA 16802

RÉSUMÉ

An experimental investigation using a high−pressure hybrid motor analog has been conducted to provide detailed solid fuel regression rate data at realistic operating conditions for both model validation and correlation development. The 2−D motor operated at a maximum pressure of 9 MPa (1300 psi) with maximum injected gaseous oxygen mass fluxes of 420 kg/m2·s (0.6 lbm/in2·s). Hydroxyl−Terminated Polybutadiene (HTPB, R−45 M) cured with Isonate 2143L was used as the baseline solid fuel. Either carbon black powder or ultra−fine aluminum (UFAL) powder was added to the HTPB fuel during the processing stage for some tests. Both ultrasonic pulse−echo and real−time X−ray radiography techniques were used to determine the instantaneous regression rate. An array of fine−wire thermocouples was used to determine fuel surface temperatures and subsurface temperature profiles. The deduced instantaneous regression rates displayed a complex dependency on axial location and flow conditions. Close to the sample leading edge, the regression rate, governed by the mixing motion of the entering GOX flow, did not vary with time. Following this region, the regression rates exhibited strong dependency on both axial location and flow structure. When the GOX mass fluxes were below 140 kg/m2·s (0.2 lbm/in2·s), gas−phase radiative heat transfer to the fuel surface was found to play a more important role in the regression of solid fuel. Solid fuel regression rates were found to correlate well with the injected oxidizer mass flux, chamber pressure, axial location, and port height. The addition of carbon black had no effect on regression behavior. Addition of 20% of UFAL powder, however, was found to increase the mass burning rate by 70%.


Articles with similar content:

SURFACE HEAT RELEASE OF HTPB-BASED FUELS IN OXYGEN RICH ENVIRONMENTS
International Journal of Energetic Materials and Chemical Propulsion, Vol.5, 2002, issue 1-6
H. Stephen Jones, Grant A. Risha, George C. Harting, Joseph P. Arves, Arie Peretz, Donald E. Koch
PHYSICAL AND CHEMICAL PROCESSES GOVERNING THE COMBUSTION OF BINARY COMPOSITIONS OF AMMONIUM DINITRAMIDE WITH GLYCIDYLAZIDEPOLYMER
International Journal of Energetic Materials and Chemical Propulsion, Vol.6, 2007, issue 2
Anton I. Levshenkov, Valery P. Sinditskii, Maxim V. Berezin, Viacheslav Yu. Egorshev
CHARACTERIZATION OF SOLID FUEL MASS-BURNING ENHANCEMENT UTILIZING AN X-RAY TRANSLUCENT HYBRID ROCKET MOTOR
International Journal of Energetic Materials and Chemical Propulsion, Vol.6, 2007, issue 6
Eric Boyer, Brian Evans, Nicholas A. Favorito
COMBUSTION AND PERFORMANCE STUDIES OF GLYCIDYL AZIDE POLYMER AND ITS MIXTURES AS HYBRID ROCKET FUEL
International Journal of Energetic Materials and Chemical Propulsion, Vol.14, 2015, issue 3
Motoyasu Kimura, Hideo Nakayama, Akshay Garg, Po-Jul Chang, Keiichi Hori, Yutaka Wada
A NOVEL POLYETHYLENE PARTICLES/PARAFFIN-BASED SELF-DISINTEGRATION FUEL FOR HYBRID ROCKET PROPULSION
International Journal of Energetic Materials and Chemical Propulsion, Vol.17, 2018, issue 3
Wei Zhang, Yue Tang, Ruiqi Shen, Suhang Chen, Yinghua Ye, Luigi T. De Luca, Hongsheng Yu