Dong Yi, Liu Jianmin, Liu Yanbin, Li Huaying, Zhang Xiaoming, Hu Xuesong
Vehicle Engineering Department, Army Academy of Armored Forces, Beijing 100072, China.
Department of Weapon and Control, Army Academy of Armored Forces, Beijing 100072, China.
Materials (Basel). 2021 Mar 13;14(6):1403. doi: 10.3390/ma14061403.
In order to improve the reliability and service life of vehicle and diesel engine, the fatigue life prediction of the piston in a heavy diesel engine was studied by finite element analysis of piston, experiment data of aluminum alloy, fatigue life model based on energy dissipation criteria, and machine learning algorithm. First, the finite element method was used to calculate and analyze the temperature field, thermal stress field, and thermal-mechanical coupling stress field of the piston, and determine the area of heavy thermal and mechanical load that will affect the fatigue life of the piston. Second, based on the results of finite element calculation, the creep-fatigue experiment of 2A80 aluminum alloy was carried out, and the cyclic response characteristics of the material under different loading conditions were obtained. Third, the fatigue life prediction models based on energy dissipation criterion and twin support vector regression are proposed. Then, the accuracy of the two models was verified using experiment data. The results show that the model based on the twin support vector regression is more accurate for predicting the material properties of aluminum alloy. Based on the established life prediction model, the fatigue life of pistons under actual service conditions is predicted. The calculation results show that the minimum fatigue life of the piston under plain condition is 2113.60 h, and the fatigue life under 5000 m altitude condition is 1425.70 h.
为提高车辆和柴油发动机的可靠性及使用寿命，通过对活塞进行有限元分析、铝合金实验数据、基于能量耗散准则的疲劳寿命模型以及机器学习算法，研究了重型柴油发动机活塞的疲劳寿命预测。首先，采用有限元方法计算并分析活塞的温度场、热应力场和热-机械耦合应力场，确定会影响活塞疲劳寿命的重热和机械载荷区域。其次，基于有限元计算结果，开展了2A80铝合金的蠕变疲劳实验，获得了材料在不同加载条件下的循环响应特性。第三，提出了基于能量耗散准则和双支持向量回归的疲劳寿命预测模型。然后，利用实验数据验证了这两种模型的准确性。结果表明，基于双支持向量回归的模型在预测铝合金材料性能方面更为准确。基于所建立的寿命预测模型，预测了活塞在实际服役条件下的疲劳寿命。计算结果表明，活塞在平原条件下的最小疲劳寿命为2113.60小时，在海拔5000米条件下的疲劳寿命为1425.70小时。
