本文選題：風切變 + 計算流體力學��； 參考：《中國民航大學》2014年碩士論文

【摘要】：低空風切變是一種嚴重影響飛機飛行的大氣現(xiàn)象,不同風切變類型有其各自不同的風場特征,對飛行的影響也大有不同。正確識別風切變類型,能為飛行員做出相應的操作帶來極大的幫助。因此,積極開展對低空風切變的識別具有重要的現(xiàn)實意義和實用價值。低空風氣變具有突發(fā)性、持續(xù)時間短、尺度小等特點,并與實際的地形、氣候等相關,使其實際數(shù)據(jù)探測比較困難。在忽略地形、氣象因素條件下,采用計算流體力學(Computational Fluid Dynamic,CFD)仿真軟件結合多普勒測風激光雷達的掃描方式,構造了微下?lián)舯┝�、低空急流、順逆風以及側風低空風切變的樣本,基于圖像特征對低空風切變進行分類識別。首先,根據(jù)微下?lián)舯┝鳌⒌涂占绷�、順逆風以及側風低空風切變的樣本特性,選擇對圖像形狀關系變化不敏感的矩方法提取風切變特征。主要研究了基于三次B樣條小波基的小波矩形狀特征提取方式,使得不僅在圖像徑向速度信息較全面時,較好地描述風切變的全局矩特征;同時,在圖像樣本徑向速度信息存在一定缺失度時,也能精確地刻畫其未缺失的局部特征,達到較好地識別性能。為驗證小波矩特征提取算法的有效性,先采用復雜度較低地Fisher線性判別方式(Linear Discriminative Analysis,LDA),最大化樣本可分性降低小波矩維數(shù),送入三階近鄰識別四種低空風切變。其次,在此基礎上進一步研究了小波矩特征的選擇,目的是再次提高風切變的識別性能。通過分析對比幾種已有的改進自適應遺傳算法(Improved Adaptive Genetic Algorithm,IAGA)的優(yōu)缺點,創(chuàng)建了一種新的改進自適應遺傳算法。該算法在均勻把握種群進化方向時,根據(jù)個體在當代群體中的作用豐富種群的多樣性,更適于選擇小波矩的最優(yōu)特征子集,使風切變達到了一個穩(wěn)定、較優(yōu)地識別效果。
[Abstract]:Low level wind shear is an atmospheric phenomenon which seriously affects the flight of aircraft. Different types of wind shear have their own characteristics of wind field and have different effects on flight. The correct identification of wind shear type can greatly help the pilot to make the corresponding operation. Therefore, it has important practical significance and practical value to actively carry out the identification of low-altitude wind shear. The low-altitude gas change is characterized by sudden occurrence, short duration, small scale and so on, which is related to the actual terrain and climate, which makes it difficult to detect the actual data. Under the condition of neglecting the terrain and meteorological factors, using computational fluid dynamics Fluid dynamic CFDs and the scanning mode of Doppler wind lidar, the samples of micro-downburst flow, low-altitude jet flow, headwind and crosswind low-altitude wind shear are constructed. Classification and recognition of low altitude wind shear based on image features. Firstly, according to the sample characteristics of micro-downburst, low-altitude jet, headwind and crosswind low-altitude wind shear, the moment method which is not sensitive to the change of image shape relationship is selected to extract the wind shear feature. Based on cubic B-spline wavelet basis, wavelet rectangular feature extraction method is studied in this paper, which not only describes the global moment feature of wind shear better when the radial velocity information of image is more comprehensive, but also, When the radial velocity information of the image sample has a certain degree of deficiency, it can also accurately describe the local features that are not missing, and achieve better recognition performance. In order to verify the validity of the wavelet moment feature extraction algorithm, linear Discriminative analysis is used to maximize the sample separability and the wavelet moment dimension is reduced by using the lower ground Fisher linear discriminant method. The wavelet moment dimension is reduced and the third order nearest neighbor is sent to identify four kinds of low-altitude wind shear. Secondly, the selection of wavelet moment features is further studied in order to improve the performance of wind shear recognition again. By analyzing and comparing the advantages and disadvantages of several existing improved Adaptive Genetic algorithms, a new improved adaptive genetic algorithm is proposed. This algorithm is more suitable for selecting the optimal feature subset of wavelet moments according to the function of individual in the contemporary population, which makes the wind shear reach a stable and better recognition effect.
【學位授予單位】：中國民航大學
【學位級別】：碩士
【學位授予年份】：2014
【分類號】：TN957.52

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本文編號：1922148