基于BLSOM神经网络的三维冰形描述方法OACSTPCD
3D Ice Shape Description Method Based on BLSOM Neural Network
在校核冰形计算软件时,根据计算冰形与典型试验冰形的接近程度来判断冰形计算软件是否准确,因此确定典型试验冰形就成为冰风洞试验的关键任务.在某大型水陆两栖飞机尾翼冰风洞试验中,为获得精确的典型试验冰形,使用Romer Absolute扫描仪获得了尾翼模型结冰冰形的三维点云数据,然后使用批学习自组织映射(Batch-learning self-organizing map,BLSOM)神经网络获得了冰形的三维点云数据沿模型展向的二维平均冰形,并使用概率统计方法获得了二维平均冰形的公差带.结果表明,二维平均冰形与其公差带相结合可准确代表试验冰形的三维特征信息,因此可作为一种典型试验冰形与计算冰形进行对比分析.
When checking the ice shape calculation software,its accuracy is judged based on the proximity between the calculated ice shape and the typical test ice shape.Therefore,determining the typical test ice shape becomes the key task of the icing wind tunnel tests.In the icing wind tunnel test of the tail wing model of a large amphibious aircraft,in order to obtain accurate typical test ice shape,the Romer Absolute Scanner is used to obtain the 3D point cloud data of the ice shape on the tail wing model.Then,the batch-learning self-organizing map(BLSOM)neural network is used to obtain the 2D average ice shape along the model direction based on the 3D point cloud data of the ice shape,while its tolerance band is calculated using the probabilistic statistical method.The results show that the combination of 2D average ice shape and its tolerance band can represent the 3D characteristics of the test ice shape effectively,which can be used as the typical test ice shape for comparative analysis with the calculated ice shape.
朱百六;左成林
中航通飞华南飞机工业有限公司,珠海 519040,中国中国空气动力研究与发展中心结冰与防除冰重点实验室,绵阳 621000,中国
结冰风洞试验冰形批学习自组织映射神经网络三维点云
icing wind tunnel testice shapebatch-learning self-organizing mapneural network3D point cloud
《南京航空航天大学学报（英文版）》 2024 (0z1)
70-80 / 11
This work was supported by the AG600 project of AVIC General Huanan Aircraft Industry Co.,Ltd.
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