An Inverse Design Framework for Solid Propellant Grain Based on Multi-Topology Modeling

Inverse design of solid propellant grains seeks geometries that reproduce prescribed internal-ballistic pressure–time profiles to enable rapid design iteration under complex performance requirements. Most surrogate-based methods rely on fixed-topology templates, limiting their ability to represent diverse burnback evolutions, while free-form topology optimization often lacks engineering interpretability and compatibility with template-based workflows. This study proposes a unified multi-topology inverse-design framework based on geometric-element-level parameterization within a shared design space. Grain geometries are represented by activatable geometric elements, enabling controlled topology switching while preserving compatibility with conventional parametric representations and engineering interpretability. A physics-consistent multi-topology dataset is constructed through a coupled forward-analysis pipeline integrating level-set burnback simulation with a zero-dimensional internal-ballistic model. A learning-based inverse strategy performs topology identification and topology-conditioned geometric parameter inversion directly from pressure–time inputs. Numerical results demonstrate reliable recovery of topology and key geometric parameters across multiple topologies and burnback scenarios. For the three representative pressure–time targets, the relative errors in total impulse are 0.26%, 1.89%, and 1.73%, with cosine similarities of 0.9853, 0.9938, and 0.9932, respectively. Uncertainty and noise analyses indicate stable topology identification, with variability concentrated near ignition peaks and stage transitions. The proposed framework provides an engineering-compatible pathway for multi-topology inverse grain design.