用自适应图网络精准预测火箭燃料块高应变区，提速七倍且误差降六成。
Adaptive Fusion Graph Network for 3D Strain Field Prediction in Solid Rocket Motor Grains
- 通过动态节点选择机制保留关键结构特征
- 多工况联合预测下误差降低62.8%，训练效率提升七倍
- 特别擅长捕捉脱粘缝等高应变区域，适合航天结构安全评估
固体火箭发动机燃料块局部高应变是结构失效主因。传统数值模拟计算成本高，现有代理模型难以显式建模几何结构且无法准确捕捉高应变区。本文提出自适应图网络GrainGNet，采用自适应池化动态节点选择机制，有效保留关键结构区域的力学特征，同时结合特征融合增强模型表征能力。在包含固化冷却、存储、过载和点火四个工况的联合预测任务中，GrainGNet相较基线图U-Net模型均方误差降低62.8%，参数量仅增加5.2%，训练效率提升约七倍。在脱粘缝等高应变区域，预测误差较次优方法再降低33%，为发动机结构安全性评估提供高效高保真方案。
原文摘要 · Abstract (English)
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.
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