本文關(guān)鍵詞： 油耗模型 BP神經(jīng)網(wǎng)絡(luò) 影響結(jié)構(gòu)分析 平均影響值算法 敏感度分析法　出處：《中國(guó)民航大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

【摘要】：隨著民航運(yùn)輸業(yè)的快速發(fā)展以及國(guó)際社會(huì)對(duì)環(huán)境問(wèn)題的關(guān)注,航空運(yùn)輸節(jié)能減排已成為熱點(diǎn)。因此,建立油耗模型,準(zhǔn)確預(yù)測(cè)航線飛行油耗量,是節(jié)油的重要環(huán)節(jié)之一。由于影響航線油耗的因素眾多且內(nèi)在關(guān)聯(lián)緊密,相對(duì)預(yù)測(cè)變量航線飛行油耗量呈顯著非線性,其難點(diǎn)在于表達(dá)各影響因素間的強(qiáng)耦合關(guān)系,關(guān)鍵在于模型的預(yù)測(cè)精度�；谝陨蠁�(wèn)題,本文的主要工作是:首先,對(duì)影響航線油耗的關(guān)鍵因素進(jìn)行研究,并完成QAR數(shù)據(jù)的飛行階段劃分和參數(shù)提取工作。然后,采用兩種方法構(gòu)建了基于BP神經(jīng)網(wǎng)絡(luò)的各階段油耗模型。第一種建模方法:以各階段影響參數(shù)為輸入,各階段油耗量為輸出,構(gòu)建了一個(gè)三層BP網(wǎng)絡(luò)油耗模型。第二種建模方法:以單參數(shù)油耗貢獻(xiàn)為加強(qiáng)因子的基礎(chǔ)上,采用重入設(shè)計(jì)方法,構(gòu)建了一種新型的BP網(wǎng)絡(luò)油耗模型。最后,基于模型采用平均影響值算法進(jìn)行影響結(jié)構(gòu)分析,給出各影響因素對(duì)相應(yīng)階段油耗量的影響方向和影響程度排序。接著,利用敏感度分析法對(duì)各階段影響因素進(jìn)行分析并量化其影響大小,進(jìn)而確定了各因素微小變化對(duì)相應(yīng)階段油耗量的貢獻(xiàn)程度。實(shí)驗(yàn)結(jié)果表明:實(shí)驗(yàn)建立的油耗模型精度均在自主加油量范圍之內(nèi),可為實(shí)際航線飛行油耗估計(jì)提供技術(shù)支持;平均影響值算法和敏感度分析法對(duì)影響因素的分析并量化對(duì)航線油耗影響因素評(píng)估具有參考價(jià)值。
[Abstract]:With the rapid development of civil aviation transportation and the attention of the international community to the environmental problems, the energy saving and emission reduction of air transportation has become a hot spot. It is one of the important links of fuel saving. Because there are many factors affecting the fuel consumption of the route and the internal relationship is close, the relative prediction variable flight fuel consumption is significantly nonlinear, the difficulty of which is to express the strong coupling relationship among the influencing factors. The key is the prediction accuracy of the model. Based on the above problems, the main work of this paper is as follows: first, the key factors affecting the fuel consumption of the route are studied, and the flight stage division and parameter extraction of the QAR data are completed. Based on BP neural network, the oil consumption model of each stage is constructed by two methods. The first modeling method is based on the input of each stage's influence parameter and the output of each stage's oil consumption. A three-layer BP network oil consumption model is constructed. The second modeling method: based on the single parameter fuel consumption contribution as a strengthening factor, a new BP network oil consumption model is constructed by using the re-entry design method. Finally, Based on the model, the average influence value algorithm is used to analyze the influence structure, and the influence direction and degree of the influence factors on the oil consumption in the corresponding stage are given. The influence factors of each stage are analyzed by sensitivity analysis method and the magnitude of the influence is quantified. The experimental results show that the accuracy of the fuel consumption models established by the experiment is within the range of self-refueling quantity, which can provide technical support for the actual flight fuel consumption estimation. The algorithm of average influence value and sensitivity analysis have reference value to the analysis of influencing factors and quantification to the evaluation of influencing factors of airline fuel consumption.
【學(xué)位授予單位】：中國(guó)民航大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：V323;TP183

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本文編號(hào)：1519094