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基于航迹分割的航空器噪声预测模型
引用本文:丁建立,周磊. 基于航迹分割的航空器噪声预测模型[J]. 航空学报, 2014, 35(4): 1102-1110. DOI: 10.7527/S1000-6893.2013.0474
作者姓名:丁建立  周磊
作者单位:1. 中国民航大学 计算机科学与技术学院, 天津 300300;2. 中国民航大学 中国民航信息技术科研基地, 天津 300300
基金项目:国家自然科学基金(61139002);国家“863”计划(2012AA063301);中国民用航空局科技项目(MHRD201006,MHRD201101);中央高校基本科研业务费专项资金(3122013P013)~~
摘    要:根据航空器的航迹来预测其影响区域的噪声情况,基于航迹的几何特性,将航迹分成直线段航迹和曲线段航迹两个基本航迹元素;确定了直线段和曲线段的各自影响范围之后,分别计算各航迹段对它们影响区域的噪声值贡献情况;统计了各个航迹段对监测点的噪声影响之后,综合得出整个航迹段对监测点的噪声影响,从而建立了航空器航迹噪声预测的模型。将该模型计算的结果分别和软件模拟数据、实际监测数据进行对比分析,发现该模型在航迹段内对监测点噪声均值的预测较为准确,监测点噪声峰值预测结果更接近实测数据。

关 键 词:飞行航迹  航空器噪声  噪声强度  预测模型  噪声预测  
收稿时间:2013-06-19
修稿时间:2013-11-26

Aircraft Noise Prediction Model Based on Flight Track Segmentation
DING Jianli,ZHOU Lei. Aircraft Noise Prediction Model Based on Flight Track Segmentation[J]. Acta Aeronautica et Astronautica Sinica, 2014, 35(4): 1102-1110. DOI: 10.7527/S1000-6893.2013.0474
Authors:DING Jianli  ZHOU Lei
Affiliation:1. School of Computer Science and Technology, Civil Aviation University of China, Tianjin 300300, China;2. Information Technology Research Base of Civil Aviation Administration of China, Civil Aviation University of China, Tianjin 300300, China
Abstract:To predict aircraft noise according to aircraft tracks, the aircraft tracks are divided into two basic track elements based on their geometric characteristics, i.e. the straight line tracks and the curve line tracks, and then the noise contribution of the straight line track and the curve line track are calculated respectively for their area of influence. After a statistical treatment of the noise impact on all segments of the track monitoring points, the noise finally comes together to affect the entire track segment monitoring points. Thus an aircraft track noise prediction model is established in this paper. Compared with similar prediction software and the actual monitoring data, the predicted average noise values of the present model are relatively accurate, and the predicted noise peak values are more accurate.
Keywords:flight track  aircraft noise  noise intensity  prediction model  noise prediction  
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