GrainArtist: A Physics-Informed Multi-Engine Framework for Solid Rocket Propellant Grain Inverse Design
Abstract
Abstract Solid rocket motor propellant grain inverse design requires synthesizing complex port geometries that fulfill target ballistic histories under stringent manufacturing and structural safety constraints. This study presents Grain Artist, a physics-informed multi-engine framework that integrates database retrieval, deep neural manifold inversion, closed-form analytical optimization, freeform generative sculpting, and surrogate-accelerated evolutionary search. Five analytical safety guards derived from NASA SP-8076 and SP-8064 principles purge 99.05% of infeasible candidate geometries in CPU memory prior to forward burn back simulation. A GPU-accelerated Euclidean distance transform solver executes burn back calculations with Sutton post-burnout gas blowdown in 0.59s. Across three canonical ballistic regimes progressive star, neutral horizontal-plateau, and dual-thrust boost-sustain the framework achieves active-regime pressure tracking accuracies exceeding 98% (98.81%, 98.61%, and 98.24%, respectively). Convergence runtimes range from 7.36s for analytical closed-form inversion to under two minutes for stochastic evolutionary synthesis. While 2D analytical safety guards guarantee planar casing clearance and fillet curvature, downstream draft-angle validation or additive manufacturing is required for physical 3D demolding.
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