Local high strain in solid rocket motor grains is a primary cause of structural failure. However, traditional numerical simulations are computationally expensive, and existing surrogate models cannot explicitly establish geometric models and accurately capture high-strain regions. Therefore, this paper proposes an adaptive graph network, GrainGNet, which employs an adaptive pooling dynamic node selection mechanism to effectively preserve the key mechanical features of structurally critical regions, while concurrently utilising feature fusion to transmit deep features and enhance the model's representational capacity. In the joint prediction task involving four sequential conditions--curing and cooling, storage, overloading, and ignition--GrainGNet reduces the mean squared error by 62.8% compared to the baseline graph U-Net model, with only a 5.2% increase in parameter count and an approximately sevenfold improvement in training efficiency. Furthermore, in the high-strain regions of debonding seams, the prediction error is further reduced by 33% compared to the second-best method, offering a computationally efficient and high-fidelity approach to evaluate motor structural safety.

翻译：固体火箭发动机药柱的局部高应变是结构失效的主要原因。然而，传统的数值模拟计算成本高昂，且现有代理模型无法显式建立几何模型并精确捕捉高应变区域。为此，本文提出一种自适应图网络GrainGNet，该网络采用自适应池化动态节点选择机制，有效保留结构关键区域的力学特征，同时利用特征融合传递深层特征以增强模型表征能力。在包含固化冷却、贮存、过载与点火四种连续工况的联合预测任务中，GrainGNet相较于基准图U-Net模型，在参数量仅增加5.2%的情况下，均方误差降低62.8%，训练效率提升约七倍。此外，在脱粘缝高应变区域，其预测误差较次优方法进一步降低33%，为评估发动机结构安全性提供了一种计算高效且高保真的方法。