CAD-Based 3D Grain Burnback Analysis for Solid Rocket Motors

Alhaj, Ahmed M.; Azam, Qummare; Abdullah, M. Z.; Mazlan, Nurul Musfirah

doi:10.1007/978-981-15-4756-0_28

Cited by 1 publication

(2 citation statements)

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“…To obtain faster optimization speed, an appropriate initial guess of vector p 0 needs to be selected according to specific prior knowledge to transform the problem into optimizing correction ∆p, as shown in Equation (20): φ(p, x, y) → ϕ(p 0 , ∆p, x, y), p = p 0 + ∆p (20) where the range of ∆p is chosen to be [−1, 1] to avoid holes in the grain. This technique is denoted as pre-training in this work.…”

Section: Pre-training Based On Supervised Learningmentioning

confidence: 99%

“…There are two types of burnback analysis methods: the non-meshing and meshing methods. 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].…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Pre-training Based On Supervised Learningmentioning

confidence: 99%