基于神经网络液体横向晃动时罐体受力的预测

用Fluent软件,VOF(Volume of Fluid)模型,对基准罐体在不同充液比下受到横向加速度时的受力进行数值模拟;以充液比、前两时刻基准罐体受力、加速度及将要经历的加速度作为输入,以下一时刻受力作为目标输出,选用合理的计算结果作为训练样本,建立基于BP神经网络液体横向晃动时基准罐体受力的预测模型,用158个样本对完成训练的网络进行可靠性验证,横向力、垂向力和侧倾力矩最大预测误差分别为8.88%,0.36%,1.38%,符合精度要求.基准罐体的时间步长和受力进行修正后,与一般圆柱及椭圆形罐体受力的大小和规律基本一致;对于作横向运动的柱形罐体,受力大小与罐体长度成正比.通过修正基准罐体的时间步长和受力,对一般圆柱及椭圆形罐体的受力也可实现BP神经网络的预测,为罐车动力学分析快速有效地提供所需数据.

Prediction of lateral liquid sloshing force of tank based on neural networks

Numerical simulation of liquid sloshing force under lateral acceleration of reference tank was conducted with volume of fluid(VOF) model of fluent software.Liquid filling ratio,forces,acceleration were set as inputs,while forces after a few seconds as target outputs.Reasonable distributed results were selected as training samples to establish prediction model of liquid sloshing forces acting on the reference tank.This trained BP neural networks were tested by 158 various samples.The maximum prediction error...