Page 34 - 《爆炸与冲击》2026年第9期

第 46 卷 聂仲恒，等： 基于深度学习算法预测燃爆的临界淬熄直径 第 9 期

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15.2% 和 33.3%， 且 相 关 系 数 R 提 升 至 0.928（ CNN 的 R 为 2 0.906） 。 误 差 分 布 分 析 进 一 步 证 实 ，
Transformer 在全工况范围内保持窄带离散特性，这表明 Transformer 通过自注意力机制实现了对淬熄过
程临界淬熄直径的精准预测，为安全阈值的可靠判定提供了算法基础。
(3) 在突破归一化预处理的泛化能力验证中，Transformer 模型表现出卓越的尺度不变性，而 CNN 的 R 2
则显著下降。该差异源于 Transformer 模型内置的层归一化机制与特征解耦能力，使其可直接处理多物
理量纲混合的原始工程数据，对开发免预处理的实时安全防护系统具有重要应用价值。
评估了 CNN 与 Transformer 模型的预测性能，以确定预测多孔介质临界淬熄直径的最优模型。然
而，使用的机器学习算法存在一定的局限性，还能够引入其他模型进行对比。因此，后续工作应利用更
复杂的算法来构建多孔介质的关键参数预测模型。然后应将该模型与其他模型进行比较，以确定为最
佳模型，并将预测结果进行分析。此外，气体爆炸的火焰淬熄数据集相对有限，需要扩展数据集以提高
模型预测的精度。未来将对上述问题进行深入研究。

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