ACTA AERONAUTICAET ASTRONAUTICA SINICA >
Wind tunnel Mach number prediction model based on random forest
Received date: 2015-06-17
Revised date: 2015-08-19
Online published: 2015-08-26
Supported by
National Natural Science Foundation of China (61473073, 61333006)
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.
Key words： wind tunnel test; Mach number; large-scale data set; random forest; regression tree
WANG Xiaojun , YUAN Ping , MAO Zhizhong , DU Ning . Wind tunnel Mach number prediction model based on random forest[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2016 , 37(5) : 1494 -1505 . DOI: 10.7527/S1000-6893.2015.0229
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