SRM 3D surface burnback computation using mixes stratification deduced from 3D grain filling simulation

Ribereau, D.; Breton, Prescott Le; Giraud, E.

doi:10.2514/6.1999-2802

Cited by 8 publications

(5 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Surface burnback simulation is divided into two parts: the grain configuration initialization and the propellant regression evolution. A number of algorithms and strategies for the simulation of grain evolution are now in use, amongst which the analytical method, [13][14][15] the generalized coordinate method 6,16 and the grid-based geometry method [17][18][19] are the dominating approaches.…”

Section: Introductionmentioning

confidence: 99%

An integrated framework for solid rocket motor grain design optimization

Wang

Yang

et al. 2013

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

Traditional grain designs, which identify the best combination of geometrical parameters to improve the grain performance and meet the flight-mission requirements, are often performed manually. In this article, an integrated framework is presented to perform the design optimization of solid rocket motor propellant grains. In the proposed framework, the level set method is adapted to solid propellant burnback analysis and this technique does not have any restriction on the grain configuration and is capable of handling grains of multi-stage and various burning forms. Along with the level set method, a dedicated algorithm is developed using application programming interfaces of commercial computer-aided design software to transform the initial grain shape into a special data file that can be fed to the level set codes to activate the burnback analysis. Moreover, a hybrid optimization method incorporating genetic algorithm and sequential quadratic programming is exploited to improve the grain design efficiency. Finally, two case studies have been performed to verify the feasibility and general-purpose characteristics of the proposed grain design optimization environment. The results obtained show that the proposed design framework facilitates the grain optimization process and various grain design requirements can be met. The design cycle has been remarkably reduced because of the introduction of hybrid optimization method.

show abstract

Section: Introductionmentioning

confidence: 99%

An integrated framework for solid rocket motor grain design optimization

Wang

Yang

et al. 2013

Proceedings of the Institution of Mechanical Engineers, Part G:

View full text Add to dashboard Cite

show abstract

“…The level set method (LSM) [3] is a widely used and welltested method for tracing general evolving surfaces. It has been proven to work well in SRM simulation [4][5][6][7][8][9][10]. The LSM processes geometry using implicit representation; that is, an ℝ-dimensional geometry is represented by an isosurface in ℝ + 1 dimensional space.…”

Section: Introductionmentioning

confidence: 99%

Combined Acceleration Methods for Solid Rocket Motor Grain Burnback Simulation Based on the Level Set Method

Wei

Bao

Liu

et al. 2018

International Journal of Aerospace Engineering

View full text Add to dashboard Cite

A detailed study of a set of combined acceleration methods is presented with the objective of accelerating the solid rocket motor grain burnback simulation based on the level set method. Relevant methods were improved by making use of unique characteristics of the grains, and graphical processing unit (GPU) parallelization is utilized to perform the computationally intensive operations. The presented flow traced the expansion of burning surfaces, and then Boolean operations were applied on the resulting surfaces to extract various geometric metrics. The initial signed distance field was built by an improved distance field generating method, and a highly optimized GPU kernel was used for estimating the gradient required by the level set method. An innovative Boolean operation method, thousands of times faster than ordinary ones, was ultimately proposed. Performance tests show that the overall speedup was close to 15 on desktop-class hardware, simulation results were proven to converge to analytical results, and the error boundary was 0.25%.

show abstract

“…• SRM internal ballistics evaluation with burn-back simulation [19,20] integrated with casting process effects [21,22]. • Simulation of random packings [23] and deflagration wave propagation through heterogeneous solid propellants [24][25][26][27][28] containing fine/ultrafine aluminum fuel [29,30].…”

Section: Reliability and Simulationmentioning

confidence: 99%