Збережено в:
| Автори: | , , , , , , , , , |
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| Формат: | Recurso digital |
| Мова: | Англійська |
| Опубліковано: |
Zenodo
2025
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| Предмети: | |
| Онлайн доступ: | https://doi.org/10.5281/zenodo.18093210 |
| Теги: |
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Зміст:
- <p>The data were generated to support the development and validation of an inverse design framework for solid rocket motor grains under multi-topology conditions. The dataset is based on a physics-consistent numerical experimental campaign combining semantic multi-topology grain modeling, level-set-based burnback simulation, and zero-dimensional internal ballistic analysis. Solid propellant grains with different geometric topology combinations and material configurations were constructed using a unified semantic template representation. For each configuration, internal ballistic pressure–time responses were obtained through forward simulation under identical operating conditions. The resulting data include grain geometry parameters, semantic topology labels, and corresponding chamber pressure–time histories, and were generated to enable topology identification, geometric parameter inversion, and performance evaluation in data-driven inverse grain design studies.</p>