提出一种水下近场非接触爆炸载荷作用下舰船结构冲击响应峰值快速预报新方法。建立多舱段舰船模型，采用拉丁超立方抽样方法设计试验工况，每组工况在不同肋位横剖面处各层甲板、双层底内外底的典型位置布置样本测点，提取每组样本测点的冲击响应峰值，建立冲击响应峰值数据库。采用NSGA-II优化的BP神经网络方法建立船体结构冲击响应峰值快速预报模型。分别将炸药参数（TNT当量、爆距、攻角），船体结构形式参数（甲板、横纵舱壁数量和位置等）和测点与炸点相对位置作为神经网络输入，以峰值加速度、峰值速度、峰值位移作为神经网络输出进行训练。训练完成后对6组爆炸工况的冲击响应峰值进行精度验证。结果表明，与现有有限元仿真预报方法相比本文方法简单实用，与现有快速预报方法相比文本方法考虑了船体结构形式特征对水下爆炸冲击响应的影响，更符合对船体结构水下爆炸冲击响应快速预报的要求。
Abstract
A novel method for rapidly predicting the peak impact response of ship structures under underwater near-field non-contact explosion loads is proposed. A multi-compartment ship model is constructed, and the experimental conditions are designed using the Latin hypercube sampling method. Sample measurement points are arranged at typical positions of each layer of decks and double bottoms at different rib positions along the transverse sections for each set of conditions. The peak impact response of each sample measurement point is extracted to establish a database of peak impact responses. A rapid prediction model for the peak impact response of ship structures is established using the NSGA-II optimized BP neural network method. The parameters of the explosive (TNT equivalent, stand-off distance, angle of attack), parameters of the ship structure form (number and positions of decks, longitudinal and transverse bulkheads, etc.), and the relative positions of measurement points to the detonation point are separately taken as inputs to the neural network. Peak acceleration, peak velocity, and peak displacement are used as outputs for neural network training. After training, the accuracy of peak impact responses for 6 sets of explosion conditions is verified. The results indicate that compared to existing finite element simulation prediction methods, the method proposed in this paper is simpler and more practical. Compared to existing rapid prediction methods, this method takes into account the influence of ship structural features on underwater explosion impact response, thus better meeting the requirements for rapid prediction of underwater explosion impact response of ship structures.
关键词
舰船结构冲击响应 /
水下近场爆炸 /
冲击响应预报 /
NSGA-II /
BP神经网络
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Key words
ship structural impact response /
underwater near-field explosion /
impact response prediction /
NSGA-II /
BP neural network
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