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A deep learning based model for aluminum agglomeration in solid propellant
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DOI：10.1016/j.rineng.2026.109963.png)
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Abstract
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• A GCN-attention model for aluminum agglomeration prediction is firstly proposed • The model greatly improves the computational efficiency by inputing local information • The model can adapt to different agglomeration criteria of aluminum through GCN • The error of size distribution between prediction and experimental data is less than 5%
Keywords:
Solid Propellant
Agglomeration Model
Deep learning
Graph Convolutional Networks
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