本文選題：燃燒反應(yīng)動(dòng)力學(xué) + 不確定性分析　； 參考：《中國(guó)科學(xué)技術(shù)大學(xué)》2017年博士論文

【摘要】：可靠的燃燒反應(yīng)動(dòng)力學(xué)模型有助于我們深入地理解燃燒反應(yīng)過程,進(jìn)而幫助我們?cè)O(shè)計(jì)更高性能的發(fā)動(dòng)機(jī),提高燃燒效率,降低污染物排放。燃燒反應(yīng)動(dòng)力學(xué)模型的研究主要圍繞著以下三個(gè)方面:(1)基元反應(yīng)的熱力學(xué)及動(dòng)力學(xué)理論計(jì)算;(2)基礎(chǔ)燃燒實(shí)驗(yàn),包括宏觀燃燒的屬性和微觀組分的測(cè)量;(3)基于前面兩方面的最新研究成果,發(fā)展和改進(jìn)燃燒反應(yīng)動(dòng)力學(xué)模型。這三部分既各自發(fā)展又相互影響。如單分子解離反應(yīng)的理論計(jì)算方法從Lindemann機(jī)理發(fā)展到RRKM理論,越來越接近物理化學(xué)過程的本質(zhì),這是其獨(dú)立發(fā)展的部分;利用某些條件下實(shí)驗(yàn)測(cè)量的速率系數(shù)修正RRKM理論中的部分參數(shù),從而得到更準(zhǔn)確的溫度壓力依賴的速率系數(shù),這是其相互影響的部分。燃燒反應(yīng)動(dòng)力學(xué)模型發(fā)展的最終目標(biāo)之一是降低模型預(yù)測(cè)的不確定性,得到能夠進(jìn)行準(zhǔn)確預(yù)測(cè)的燃燒反應(yīng)動(dòng)力學(xué)模型。燃燒反應(yīng)動(dòng)力學(xué)模型的發(fā)展過程就是不斷降低其預(yù)測(cè)不確定性的過程。本工作圍繞著如何更加高效地降低模型的不確定性,以靈敏性和不確定性分析為基本工具,探究不確定性在理論計(jì)算、模型和實(shí)驗(yàn)中的傳遞機(jī)制,進(jìn)而發(fā)展相關(guān)計(jì)算方法,最終構(gòu)造一條降低模型不確定性的快速通道。首先對(duì)傳統(tǒng)的全局靈敏性分析方法進(jìn)行了改進(jìn),提出了利用人工神經(jīng)網(wǎng)絡(luò)加速傳統(tǒng)的全局靈敏性分析的方法。此方法結(jié)合人工神經(jīng)網(wǎng)絡(luò)算法擬合能力強(qiáng)、收斂速度快的優(yōu)點(diǎn)以及高維模型表征法易于計(jì)算靈敏性系數(shù)的優(yōu)點(diǎn),構(gòu)造了雙層替代模型,大大加快了全局靈敏性分析的收斂速度,使之適用于更加復(fù)雜的模型。然后從理論計(jì)算、模型和實(shí)驗(yàn)三個(gè)方面深入探究如何利用靈敏性和不確定性分析高效地降低模型的不確定性。(1)在理論計(jì)算方面,利用不確定性分析評(píng)估RRKM/ME理論計(jì)算得到的速率系數(shù)的不確定性,發(fā)現(xiàn)了在有多條通道的反應(yīng)體系中,次要通道的速率系數(shù)理論計(jì)算結(jié)果的不確定性要遠(yuǎn)遠(yuǎn)高于主要通道的結(jié)果。同時(shí)提出了一種簡(jiǎn)單有效評(píng)估具有溫度壓力依賴效應(yīng)的速率系數(shù)不確定性的方法,從而大大降低了評(píng)估具有溫度壓力依賴效應(yīng)的速率系數(shù)不確定性的難度,提高了其實(shí)用性。(2)在燃燒實(shí)驗(yàn)方面,我們提出了利用模型輔助提高實(shí)驗(yàn)探測(cè)極限的方法,通過分析不同條件下模型預(yù)測(cè)不確定性以及尋找在不同條件下系統(tǒng)誤差之間的聯(lián)系,使得我們可以測(cè)量某些之前無法定量的實(shí)驗(yàn)?zāi)繕?biāo)。(3)在模型發(fā)展方面,為了得到對(duì)模型優(yōu)化更加有效的實(shí)驗(yàn)數(shù)據(jù),我們提出了靈敏性熵的概念來幫助我們?cè)O(shè)計(jì)實(shí)驗(yàn),并在甲醇燃燒體系中驗(yàn)證了這種方法的可行性。本論文的一系列工作表明,利用合適的方法,通過合理的分析不確定性在理論計(jì)算、動(dòng)力學(xué)模型和燃燒實(shí)驗(yàn)中的傳遞機(jī)制,可以有效地提高部分研究目標(biāo)的測(cè)量精度,也可以設(shè)計(jì)出對(duì)模型優(yōu)化最有效的實(shí)驗(yàn),進(jìn)而為降低動(dòng)力學(xué)模型的不確定性提供了一條快速通道。
[Abstract]:A reliable kinetic model of combustion reaction helps us to understand the combustion process in depth, and then help us to design a higher performance engine, improve combustion efficiency and reduce the emission of pollutants. The research on the kinetic model of the combustion reaction mainly focuses on the following three aspects: (1) the thermodynamic and kinetic theoretical calculation of the elemental reaction; (2 ) basic combustion experiments, including the properties of macroscopic combustion and the measurement of microscopic components; (3) developing and improving the kinetic model of combustion reaction based on the latest research results in the first two aspects. The three parts both develop and interact with each other. For example, the theoretical calculation method of single molecule dissociation reaction is developed from the Lindemann mechanism to the RRKM theory, and more and more connected. The essence of the physicochemical process is the part of its independent development; the rate coefficients in the RRKM theory are corrected by the rate coefficients measured experimentally under certain conditions, thus obtaining a more accurate rate coefficient of temperature and pressure dependence. This is part of its interaction. One of the ultimate goals for the development of the kinetic model of the combustion reaction is to reduce it. The development process of the combustion reaction dynamics model is to reduce the uncertainty of the prediction. This work focuses on how to reduce the uncertainty of the model more efficiently, and explore the sensitivity and uncertainty analysis as the basic tool. The transfer mechanism in the theoretical calculation, model and experiment is not determined, and then the relative calculation method is developed, and a fast path to reduce the uncertainty of the model is finally constructed. First, the traditional global sensitivity analysis method is improved and the method of using the artificial neural network to accelerate the traditional global sensitivity analysis is proposed. Method combining the advantages of artificial neural network algorithm with strong fitting ability, fast convergence speed and the advantages of high dimensional model characterization method which is easy to calculate the sensitivity coefficient, a double layer substitution model is constructed, which greatly accelerates the convergence rate of global sensitivity analysis and applies to more complex models. Then, the theoretical calculation, model and experiment three are used. How to use sensitivity and uncertainty analysis to effectively reduce the uncertainty of the model. (1) in theoretical calculation, using uncertainty analysis to evaluate the uncertainty of the rate coefficient calculated by the RRKM/ME theory, we find the theoretical calculation of the rate coefficient of the secondary channel in the reaction system with multiple channels. The uncertainty of the fruit is much higher than the result of the main channel. At the same time, a simple and effective method to evaluate the rate uncertainty of the temperature pressure dependence effect is proposed, which greatly reduces the difficulty of evaluating the rate uncertainty with the temperature pressure dependence effect and improves its practicality. (2) in the combustion experiment party On the other hand, we put forward the method of using model aided to improve the limit of experimental detection. By analyzing the uncertainty of model prediction under different conditions and finding the connection between system errors under different conditions, we can measure some experimental targets that can not be quantified before. (3) in the development of the model, we can get the model optimization more. With the effective experimental data, we propose the concept of sensitivity entropy to help us design the experiment and verify the feasibility of this method in the methanol combustion system. A series of work in this paper shows that the appropriate method is used to analyze the transfer of the kinetic model and the combustion experiment by reasonable analysis of the uncertainty in the theoretical calculation, the dynamic model and the combustion experiment. The mechanism can effectively improve the measurement accuracy of some research targets, and can also design the most effective experiment for the model optimization, and then provide a fast channel for reducing the uncertainty of the dynamic model.

【學(xué)位授予單位】：中國(guó)科學(xué)技術(shù)大學(xué)
【學(xué)位級(jí)別】：博士
【學(xué)位授予年份】：2017
【分類號(hào)】：TK16

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