基于数值模拟和机器学习的汽车碰撞代理模型

建立某型皮卡车三维有限元实体模型,模拟汽车对刚性墙碰撞和两车相向对撞的动态过程,得到不同初速度下汽车的碰撞力-位移曲线.将汽车初始速度作为输入,碰撞力-位移曲线作为输出,采用BP神经网络机器学习算法建立全速度域下汽车碰撞代理模型.将数值模拟样本分为训练集和测试集,对代理模型进行训练,并利用测试集样本验证了模型的精度.利用代理模型可以快速预测任意碰撞速度下该汽车的力-位移曲线,为其安全性设计提供依据.

A vehicle collision surrogate model based on numerical simulation and machine learning

By building a three-dimensional finite element model of a pickup truck, the dynamic processes of a truck colliding against a rigid wall and the collision between two trucks are numerically simulated, and the corresponding collision force-displacement curves of the truck with different initial speeds are obtained. Taking the initial speed of the truck as the input and the collision force-displacement curve as the output, the BP neural network machine learning algorithm is used to establish the vehicle collision surrogate model in the full speed domain. The numerical simulation samples are divided into a training set and a test set. The surrogate model is trained with the training set and its accuracy is verified by the test set samples. The model can be used to predict the force-displacement curve of a vehicle at an arbitrary collision speed quickly, providing an instruction for its safety design.