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南京信息工程大学 自动化学院,江苏 南京 210044
陆军军医大学 大坪医院,重庆 400042
创伤与化学中毒国家重点实验室,重庆 400042
南京理工大学 机械工程学院,江苏 南京 210094
Received:01 July 2025,
Online First:25 December 2025,
Published:2026-04
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XU Tongtong, SONG Yang, GUI Binqi, et al. Fast Prediction of Human Blunt Impact Response Characteristics Based on Surrogate Model[J]. Acta Armamentarii, 2026, 47(4): 250590.
XU Tongtong, SONG Yang, GUI Binqi, et al. Fast Prediction of Human Blunt Impact Response Characteristics Based on Surrogate Model[J]. Acta Armamentarii, 2026, 47(4): 250590. DOI: 10.12382/bgxb.2025.0590.
非穿透性弹道冲击引起的钝性损伤对个体防护效果和伤情评估提出了高时效性的响应预测需求。传统有限元法虽然具有高精度,但计算成本高、响应滞后,难以适应快速决策场景。为此提出一种多物理场区域感知的代理建模方法,用于快速预测人体上躯干在软防护条件下的钝击响应特征。通过构建人体上躯干有限元模型,模拟9mm手枪铅芯弹以340m/s初速度冲击软防护下人体上躯干的过程,并提取关键部位的等效应力与位移数据构建训练集。该模型采用区域特化结构和物理约束,显著增强了对复杂组织响应模式的建模能力。实验结果表明,该模型在多区域、多指标预测中均达到相关指数
R
2
≥0.96的高精度,可实现快速响应预测。所得成果为钝击损伤的高效建模、个性化防护装备设计与突发伤情智能评估提供了可推广、可部署的技术路径。
The blunt trauma caused by non-penetrative ballistic impact poses a demand for high-efficiency response prediction in individual protection effectiveness and injury assessment. Although the traditional finite elementmethod
offers high accuracy
it suffers from high computational cost and delayed response
making it difficult to adapt to fast decision-making scenarios. This paper proposes a surrogatemodelingmethod based onmulti-physical domain awareness
which is used to rapidly predict the blunt impact response characteristics of human torso under soft protection conditions. A finite elementmodel of human torso is established to simulate the process of a 9mm handgun fullmetal jacketed bullet impacting the human torso at an initial velocity of 340m/s under soft protection. The equivalent stress and displacement data of key parts of human body
are extracted to construct a training dataset. Themodel adopts a regionspecific structure and physical constraints
significantly enhancing themodeling capability for complex tissue response patterns. The experimental results indicate that themodel achieves high accuracy with
R
2
≥0.96 inmulti-region andmulti-metric predictions. This study provides a scalable and deployable technical approach for efficient blunt traumamodeling
personalized protective equipment design
and intelligent assessment of sudden injuries.
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