基于随机森林的风洞马赫数预测模型

doi:10.7527/S1000-6893.2015.0229

流体力学与飞行力学

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基于随机森林的风洞马赫数预测模型

王晓军¹, 袁平¹, 毛志忠¹, 杜宁²

1. 东北大学信息科学与工程学院, 沈阳 110819;
2. 中国空气动力研究与发展中心高速空气动力研究所, 绵阳 621000

收稿日期:2015-06-17 修回日期:2015-08-19 出版日期:2016-05-15 发布日期:2015-08-26
通讯作者: 袁平,Tel.:024-83680461 E-mail:yuanping@ise.neu.edu.cn E-mail:yuanping@ise.neu.edu.cn
作者简介:王晓军,女,博士研究生。主要研究方向:集成方法、风洞马赫数建模。 E-mail:wxjjessicaxj903@26.com;袁平,男,博士,副教授,博士生导师。主要研究方向:复杂工业过程建模、优化。Tel:024-83680461 E-mail:yuanping@ise.neu.edu.cn;毛志忠,男,博士,教授,博士生导师。主要研究方向:复杂工业过程建模、优化、控制。 E-mail:maozhizhong@ise.neu.edu.cn;杜宁,男,博士,高级工程师。主要研究方向:非定常空气动力学。 E-mail:tottidu@163.com
基金资助:
国家自然科学基金(61473073,61333006)

Wind tunnel Mach number prediction model based on random forest

WANG Xiaojun¹, YUAN Ping¹, MAO Zhizhong¹, DU Ning²

1. College of Information Science and Engineering, Northeastern University, Shenyang 110819, China;
2. High Speed Aerodynamics Institute, China Aerodynamics Research and Development Center, Mianyang 621000, China

Received:2015-06-17 Revised:2015-08-19 Online:2016-05-15 Published:2015-08-26
Supported by:
National Natural Science Foundation of China (61473073, 61333006)

摘要/Abstract

摘要：

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。

关键词: 风洞试验, 马赫数, 大数据集, 随机森林, 回归树

Abstract:

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

中图分类号:

V217⁺.21

王晓军, 袁平, 毛志忠, 杜宁. 基于随机森林的风洞马赫数预测模型[J]. 航空学报, 2016, 37(5): 1494-1505.

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.

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主管单位：中国科学技术协会主办单位：中国航空学会北京航空航天大学

基于随机森林的风洞马赫数预测模型

Wind tunnel Mach number prediction model based on random forest

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