Solid Rocket Motor Propellant Optimization with Coupled Internal Ballistic-Structural Interaction Approach


Tola C., Nikbay M.

JOURNAL OF SPACECRAFT AND ROCKETS, cilt.55, sa.4, ss.936-947, 2018 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 55 Sayı: 4
  • Basım Tarihi: 2018
  • Doi Numarası: 10.2514/1.a34066
  • Dergi Adı: JOURNAL OF SPACECRAFT AND ROCKETS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.936-947
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

This research aims to optimize the geometric design of slotted propellant grains for solid rocket motors with respect to coupled internal ballistic performance and structural strength criteria. In-house codes such as a zero-dimensional internal ballistic solver and an analytical burnback solver are implemented to compute the variation of chamber pressure and the rocket thrust transiently. Structural analysis of the solid propellant is achieved by using a parametric linear viscoelastic model and a parametric cooldown heat transfer model, both of which are based on the finite element method. The transient temperature distribution data derived from the cooldown process are required inputs for the material properties to be used in the viscoelastic structural analysis. To enable an efficient optimization process, a surrogate heat transfer model that predicts the cooldown time of the system by eliminating expensive iterations is also implemented and validated. Within a coupled analysis approach, the pressure data obtained from the internal ballistic performance analysis are used for the ignition step of the linear viscoelastic analysis. The structural analysis results are evaluated by using a deterministic approach based on the margin of safety with respect to the stress and strain criteria. Finally, the optimum geometrical parameters for a slotted grain subjected to both structural and internal ballistic performance constraints are investigated through multidisciplinary optimization techniques.