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  本文選題：VCE數(shù)學模型　切入點：部件級建模　出處：《山東大學》2015年碩士論文　論文類型：學位論文

【摘要】：求解變循環(huán)發(fā)動機(Variable Cycle Engines,VCE)數(shù)學模型問題是近幾年國內(nèi)航空和軍工領域開始重點研究的課題,其核心問題是首先通過建立合理的數(shù)學模型模擬其工作狀態(tài),然后提出適當?shù)乃惴ㄇ蠼饩S持其工作狀態(tài)的非線性平衡方程組。對于數(shù)學模型建立問題,部件級建模方法一直占據(jù)主流,本文通過分析氣體流動和能量消耗兩個方向,整理各部件的特性量及關系,建立了VCE部件級模型；對于提出算法求解非線性平衡方程組問題,近些年來許多學者提出了很多非常重要的方法,例如,使用傳統(tǒng)的牛頓-拉夫森(Newton-Raphson)迭代算法、遺傳算法(Genetic Algorithm, GA)、混合遺傳算法以及BP神經(jīng)網(wǎng)絡算法解決此類問題等等,在本文中,我們在目前解決此問題較為前沿的方法——粒子群算法(Particle Swarm Optimization,PSO)基礎上,提出了混合混沌粒子群算法(Hybrid Chaos-Particle Swarm Optimization,HCPSO),即,在解決本問題時,針對PSO算法對初始值較為依賴的特點,采用帶隨機初始值修正的窮舉搜索法確定初始值的具體范圍；針對PSO算法初期收斂快后期陷入局部最優(yōu)的特點,將混沌的思想引入模型,使PSO算法在后期局部收斂后能夠跳出穩(wěn)定狀態(tài),繼續(xù)尋找更優(yōu)解；同時在混沌與穩(wěn)定狀態(tài)之間加入半混沌狀態(tài),使得算法在混沌程度相對較低時也能進行很好地計算。最后我們將通過實際數(shù)據(jù)實驗驗證該方法的可行性與有效性。從數(shù)值實驗中我們可以看到,上述方法的全局尋優(yōu)能力很好。
[Abstract]:Solving the mathematical model of variable Cycle engine (VCE) is an important research topic in the field of aviation and military industry in China in recent years. The core problem is to simulate the working state of VCE by establishing reasonable mathematical model. Then an appropriate algorithm is proposed to solve the nonlinear equilibrium equations that maintain its working state. For the problem of mathematical modeling, the part-level modeling method has been the mainstream. In this paper, gas flow and energy consumption are analyzed in two directions: gas flow and energy consumption. In recent years, many scholars have put forward a lot of important methods for solving nonlinear equilibrium equations, for example, in recent years, many scholars have put forward many very important methods, for example, The traditional Newton-Raphson iterative algorithm, genetic algorithm, hybrid genetic algorithm and BP neural network algorithm are used to solve these problems. On the basis of particle Swarm optimization algorithm (PSO), a hybrid chaotic particle swarm optimization algorithm (hybrid Chaos-Particle Swarm optimization) is proposed. In order to solve this problem, we propose a hybrid Chaos-Particle Swarm optimization algorithm, which is based on the characteristic that the PSO algorithm depends on the initial value. The exhaustive search method with random initial value correction is used to determine the specific range of initial value, and the idea of chaos is introduced into the model in view of the characteristic that PSO algorithm falls into local optimum at the beginning and late stage of convergence. After local convergence, the PSO algorithm can jump out of the stable state and continue to find a better solution. At the same time, the semi-chaotic state is added between the chaos and the stable state. Finally, we will verify the feasibility and effectiveness of the method through the actual data experiments. We can see from the numerical experiments, The global optimization ability of the above method is very good.
【學位授予單位】：山東大學
【學位級別】：碩士
【學位授予年份】：2015
【分類號】：V231

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本文編號：1600673