【作者】 杨玥；

【导师】 王继新；

【作者基本信息】 吉林大学 ， 机械设计及理论， 2013， 硕士

【摘要】 通风盘式制动器的热—结构耦合分析过程复杂，而且单次仿真分析所需的时间很长，加之制动器仿真参数复杂，仿真工况繁多。所以，想通过一次或几次热—结构耦合仿真完全了解制动器的性能几乎不太可能。针对该问题，本文以通风盘式制动器为研究对象，对热—结构耦合仿真原理、正交试验设计、BP神经网络进行研究，建立起制动器的变参数工况下的热—结构耦合有限元仿真快速预测程序和方法。本文的主要研究内容如下：1)盘式制动器的热－结构有限元仿真。在Abaqus软件中建立制动器的几何及有限元模型，对制动初速度为=70Km/h，制动平均压为为=10MPa，盘／片摩擦系数为0.3的典型工况进行了数值仿真。2)通过对制动器制动过程关键参数的研究，确定了影响盘式制动器热－结构有限元仿真结果的主要仿真参数，并利用正交试验法建立四因数三水平仿真试验模型。3)利用Abaqus软件中的Python脚本语言及参数化处理的办法，对正交试验安排的既定典型工型进行批处理仿真及结果输出，缩短了多个工况逐个仿真所需要的时间，提高了多工况任务数值仿真效率。4)在Matlab中编写了遗传算法的BP神经网络预测程序，以典型工况下的仿真参数和计算结果作为样本对BP网络进行训练，并最终通过工况对比，检验了本文作建立的BP神经网络快速预测系统的有效性。研究结果表明：通过正交试验设计的制动器热—结构耦合仿真方案，其获得的人工神经网络训练样本有很强的代表性。BP神经网络可通过高效的样本训练、学习，可以较高的精度逼近未知有限元计算模型。更多还原

【Abstract】 The method of the thermal structure coupled simulation of the vented disc brakeis complicated, mainly because that there are too many cases and parameters need tobe validated in the simulation process, and a long computational time will be cost ineach simulation case. So it is almost impossible to comprehensively know theperformance of the disc brake by taking just one or a few times of finite elementsimulations. To solve this problem, disc brake is studied as the research object in thispaper, and a rapid predicting system, which can quickly give the approximate resultsof FEM simulation, is founded based on the combination of the thermal structuralanalysis technology of disc brake, the principal of the orthogonal experimental designand the algorithm of the BP architecture neural network. The main content of thispaper is listed bellow:1) The thermal structural analysis of disc brake is conducted in Abaqussoftware based on the principal of the thermoelasticity, which worked with the initialbraking velocity70Km/h, and the average braking pressure10MPa, and the frictioncoefficient0.3between the disc and pad.2) The major parameters of the thermal structural analysis of disc brake aredetermined based on the research of the braking procedure and the analysis of theFEM result of disc simulation. The simulation experiment with three level for eachfour factors are determined by the orthogonal design.3) The time costed in the finite element simulations are greatly shorten by thePython scripts and parametric batch methods in Abaqus/CAE developmentenvironment.4) The validity of the predicting system are verified by combination of codingthe BP neural network in Matlab and training the BP neural network which extract itstraining samples in Abaqus software with the typical computational cases.The final result prove that artificial neural network can efficiently approximate and predict the finite element result by training the neural network with typical FEMcomputational results which was arranged by the orthogonal design.更多还原