基于显式有限元的薄壁结构吸能特性预测

谢素超;田红旗;周 辉

振动与冲击 ›› 2010, Vol. 29 ›› Issue (5) : 183-186.

PDF(1360 KB)
PDF(1360 KB)
振动与冲击 ›› 2010, Vol. 29 ›› Issue (5) : 183-186.
论文

基于显式有限元的薄壁结构吸能特性预测

  • 谢素超;田红旗;周 辉
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Energy-absorbing characteristic prediction of thin-walled structure based on explicit finite element

  • XIE Su-chao;TIAN Hong-qi;ZHOU Hui
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摘要

为了探讨轴向载荷冲击作用下试验参数对薄壁结构吸能特性的影响规律,预测和分析某一类型的薄壁结构吸能特性,基于显式有限元技术建立了这一类型薄壁结构的BP人工神经网络吸能特性预测模型。以方形薄壁结构为例,通过改变结构质量、冲击速度、横截面尺寸、壁厚等影响结构吸能特性的因素对这一类型的薄壁结构进行了大量的数值试验,获得不同试验条件下的结构吸能特性参数,然后建立了这一类型薄壁结构的吸能特性预测模型,并进行自主训练学习, 当训练步数为2035时,网络模型达到误差要求。结果表明:该BP神经网络模型的输出样本与目标值十分接近, 比吸能误差值为-2.53%,有效撞击力误差值为4.67%,有效撞击行程误差值为-3.90%,说明该模型具有较好的精度。

Abstract

In order to discuss the influence rule of experimental parameters on energy-absorbing characteristic of thin-walled structure, predict and analyze energy-absorbing characteristic of thin-walled structure of the same type, the prediction model of thin-walled structure of the same type was set up based on BP(Back Propagation) artificial neural network and explicit finite element. Take the square thin-walled structure for example, to obtain various energy-absorbing characteristic parameters under different experimental conditions, plentiful numerical experiments were performed on thin-walled structures of the same type by changing the influence factors of energy-absorbing characteristic such as structural mass, impact velocity, cross section size, wall thickness and so on, then the energy-absorbing characteristic prediction model of thin-walled structure of the same type was set up, and the network model was trained till getting the desired network error by 2035 steps. The results show that the output samples of the BP artificial neural network match the aim well, the difference of specific energy absorption(SEA) is -2.53%, the difference of effective impact force is 4.67% and the difference of effective impact journey is -3.90%, which indicates that the model has relatively high accuracy.

关键词

薄壁结构 / 显式有限元 / BP神经网络 / 吸能特性 / 预测

Key words

thin-walled structure / explicit finite element / BP artificial neural network / energy-absorbing characteristic / prediction

引用本文

导出引用
谢素超;田红旗;周 辉. 基于显式有限元的薄壁结构吸能特性预测[J]. 振动与冲击, 2010, 29(5): 183-186
XIE Su-chao;TIAN Hong-qi;ZHOU Hui. Energy-absorbing characteristic prediction of thin-walled structure based on explicit finite element[J]. Journal of Vibration and Shock, 2010, 29(5): 183-186

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