基于随机森林的风洞马赫数预测模型

在风洞试验中,马赫数的稳定性和快速性对风洞流场品质有着重要影响。为了实现马赫数的精确控制,必须对马赫数进行快速、准确的预测。风洞试验积累了大量数据,大数据集包含了更多的有益信息,为实现马赫数的精确预测提拱了可能性,但也增加了建模的复杂度。通常高度复杂的模型会加重其在实际使用时的计算负担。针对大数据集问题,本文将随机森林方法应用于风洞马赫数建模。随机森林是一种集成模型建模方法,它从3方面降低模型的复杂度:产生多个样本子集,减少了子模型的训练样本个数;具有并行集成结构,子模型可在不同的CPU上运行,提高了运行速度;以简单学习算法回归树作为基学习机,降低了子模型的复杂度。试验证明基于随机森林的马赫数预测模型能够有效利用试验积累的大数据,满足工程上预测速度及精度的要求。

Wind tunnel Mach number prediction model based on random forest

In the measurements of wind tunnel, the stability and the rapidity of the Mach number produce an important effect on quality of the flow field. To realize precisely controlling of the Mach number, it is required that the Mach number prediction should be speed forecasting and accurate. Large-scale data ste are accumulated from measurements. Although large-scale data set contain more useful information to improve the accuracy on the Mach number prediction, it increases the complexity for modeling. In general, high complexity models also increase the computational burden at the phase of active use. To deal with the large-scale set issue, the random forest method is applied to predicting the Mach number in the wind tunnel. Suitable for large-scale problem, random forest reduces the complexity in the following three aspects:generating training subset and decreasing the size of training samples; with the parallel ensemble structure, running sub-models on different CPUs and saving the running time; selecting a simple base learner, thus reducing the complexity of sub-models. The test demonstrate that the random forest-based Mach number prediction model can successful utilize the large-scale data accumulated from measurements and meet the requirements of the forecasting speed and the accuracy.