Solid rocket motor grain burn back analysis using level set methods and Monte-Carlo volume integration

Sullwald, W; Gjf, Smit; Steenkamp, Anton J; Cw, Rousseau

doi:10.2514/6.2013-4087

Cited by 10 publications

(5 citation statements)

References 3 publications

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“…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%

“…For 3D computational fluid dynamics (CFD) simulation or visualization, the shape of the fluid boundary is required. In previous research [5,7], a method proposed in [22] is commonly used to retrieve the area and volume of geometries from implicit representations. While the method is easy to implement and works reliably, it is not adopted in this paper due to the fact that it is unable to provide explicit geometry in CFD simulations and that it introduces O 1 error in multidimensional cases [23].…”

Section: Introductionmentioning

confidence: 99%

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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

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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%.

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Section: Introductionmentioning

confidence: 99%

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

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“…In the present study, the level set method is employed to simulate the solid propellant burnback process. 1,[14][15][16] The main idea of level set method (LSM) is to use an implicit representation of surfaces during simulation. In this article, we use LSM following equation 1…”

Section: Performance Evaluationmentioning

confidence: 99%

Solid rocket motor design employing an efficient performance matching approach

Wang

et al. 2018

Proceedings of the Institution of Mechanical Engineers, Part G:

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Solid rocket motor design matching the thrust requirement of the overall vehicle has become an exigent task. In the present research, an efficient performance matching optimization approach is proposed and applied to the solid rocket motor design. To obtain reliable solid rocket motor designs, refined performance evaluation is accomplished successfully based on the coupling of the level set grain burnback analysis and one-dimensional ballistic simulation by the time-stepping algorithm. Due to the heavy computing burden of the coupled performance solver, a novel progressive matching optimization approach is proposed. This approach depends on partial but not full-burn thrust profile to eliminate inferior candidates, predict superior candidates, advance the simulation and drive the candidates converging to the specified performance. Validation of the proposed solid rocket motor design approach is carried out by two test cases possessing different thrust requirements, and results indicate that the proposed approach is practical and efficient.

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“…Non-meshing methods include the analytical method [15,16], general coordinate method [17,18], and CAD modeling method [19,20]. Meshing methods include the minimum distance method [21,22], moving grid method [23,24], level-set method [9,25,26], and fast marching method [27]. On the one hand, the non-meshing methods are accurate, computationally efficient, and reliable.…”

Section: Introductionmentioning

confidence: 99%

Reverse Design of Solid Propellant Grain for a Performance-Matching Goal: Shape Optimization via Evolutionary Neural Network

et al. 2022

Aerospace

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The reverse design of solid propellant grain for a performance-matching goal, one of the most challenging directions of the solid rocket motor designing work, is limited by the traditional semi-empirical parameter-driven optimization methods based on some predefined grain configurations. Grain designers call for a new method that can automatically provide brand-new grain shapes beyond the traditional ones. In this work, a shape optimization method based on the evolutionary neural network is proposed to achieve the reverse design of two-dimensional (2D) grains. Firstly, the modified ellipse-form eikonal equation is solved by using the finite element method to realize the burn-back analysis of 2D grains in any shape on a fixed unstructured mesh. Then, the neural network is introduced to determine the spatial distribution of the propellant to define the grain shape. The hyperparameters of the network are continuously evolved with the aid of the genetic algorithm. Finally, the optimal grain shape that matches the performance goal most is obtained. The method is verified in different scenarios. The result shows that the design can precisely match the given pressure-time curve of star grains and slotted-tube grains. Furthermore, the method can automatically evolve a new dendritic-shaped grain that matches the given dual-thrust pressure-time curve. Since the reverse design uses the concept of shape optimization, it does not require any pre-selection of the grain shape, and the designers shall be free from defining different kinds of geometric parameters for specific grain configurations. Consequently, the method has the potential to apply in the reconstruction of an actual grain and the conceptual design of innovative grain configurations.

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Solid rocket motor grain burn back analysis using level set methods and Monte-Carlo volume integration

Cited by 10 publications

References 3 publications

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

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

Solid rocket motor design employing an efficient performance matching approach

Reverse Design of Solid Propellant Grain for a Performance-Matching Goal: Shape Optimization via Evolutionary Neural Network

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