本文關(guān)鍵詞：基于群體智能優(yōu)化理論的輻射換熱腔體幾何反設(shè)計(jì)　出處：《哈爾濱工業(yè)大學(xué)》2015年碩士論文　論文類型：學(xué)位論文

  更多相關(guān)文章： 幾何反設(shè)計(jì) 磷蝦群算法 微粒群優(yōu)化算法 輻射換熱 群體智能優(yōu)化算法

【摘要】：輻射換熱設(shè)備廣泛存在于工業(yè)鍋爐、紅外反射爐、材料加工成型等多種工業(yè)領(lǐng)域,腔體設(shè)計(jì)的合理與否直接影響到安全問(wèn)題。幾何反設(shè)計(jì)是近些年新興的一種設(shè)計(jì)方法,基本思路是首先根據(jù)設(shè)計(jì)要求建立相應(yīng)的目標(biāo)函數(shù),然后采用優(yōu)化方法對(duì)其優(yōu)化,最終達(dá)到設(shè)計(jì)目的。反設(shè)計(jì)方法具有過(guò)程簡(jiǎn)單、設(shè)計(jì)周期短、求解精度高等優(yōu)點(diǎn),因而得到越來(lái)越廣泛的關(guān)注和應(yīng)用。目前輻射腔體的幾何反設(shè)計(jì)多采用基于對(duì)目標(biāo)函數(shù)求導(dǎo)的方法,這類方法具有收斂速度快、結(jié)果穩(wěn)定性好等優(yōu)點(diǎn),但其梯度求解過(guò)程繁瑣復(fù)雜,而且對(duì)初值依賴性較大。群體智能優(yōu)化算法隨機(jī)地從某一個(gè)解出發(fā),然后根據(jù)相應(yīng)算法的搜尋原則,尋找下一個(gè)使目標(biāo)函數(shù)更小的解,逐步搜索直至得到最優(yōu)解,這種求解方式邏輯簡(jiǎn)單,易于編程實(shí)現(xiàn),而且算法本身具有并行性、自組織性、靈活性等優(yōu)點(diǎn),因而越來(lái)越得到人們的重視。本文基于群體智能優(yōu)化理論求解了二維輻射換熱腔體的幾何反設(shè)計(jì)問(wèn)題,主要研究?jī)?nèi)容包括以下幾個(gè)方面:(1)采用貼體坐標(biāo)系下離散坐標(biāo)法求解不規(guī)則幾何形狀介質(zhì)內(nèi)的輻射換熱問(wèn)題,并與有限體積法計(jì)算結(jié)果進(jìn)行對(duì)比,驗(yàn)證正問(wèn)題模型計(jì)算程序的可靠性,根據(jù)求得的邊界熱流密度建立相應(yīng)的目標(biāo)函數(shù)。(2)概述群體智能優(yōu)化算法的主要特點(diǎn)及其分類,簡(jiǎn)單介紹群搜索方法、人工魚(yú)群算法、蟻群優(yōu)化算法以及蛙跳算法等四種常見(jiàn)群體智能優(yōu)化算法的求解基本思路,并重點(diǎn)闡述磷蝦群算法和微粒群優(yōu)化算法的基本原理及其求解過(guò)程,為后續(xù)幾何反設(shè)計(jì)研究提供理論支撐。(3)分別采用磷蝦群算法、標(biāo)準(zhǔn)微粒群優(yōu)化算法和隨機(jī)微粒群優(yōu)化算法求解輻射換熱腔體幾何反設(shè)計(jì)問(wèn)題,通過(guò)幾何優(yōu)化,達(dá)到設(shè)計(jì)表面指定區(qū)域熱流密度均勻分布的設(shè)計(jì)目的,并比較了三種求解方法在輻射腔體幾何反設(shè)計(jì)問(wèn)題中的計(jì)算性能;分析了幾何反設(shè)計(jì)中設(shè)計(jì)表面控制點(diǎn)數(shù)量、介質(zhì)物性及邊界黑度等參數(shù)對(duì)設(shè)計(jì)結(jié)果的影響。
[Abstract]:Radiant heat exchanger widely exists in many industrial fields, such as industrial boiler, infrared reflector, material processing and molding. Whether the cavity design is reasonable or not directly affects the safety problem. Geometric inverse design is a new design method in recent years. The basic idea is to set up the corresponding objective function according to the design requirements, and then use the optimization method to optimize it, and ultimately achieve the purpose of design. The anti design method has the advantages of simple process, short period of design and high precision of solving, so it has been paid more and more attention and application. At present, the geometric inverse design of radiation cavities is based on the derivation method of objective function. This method has the advantages of fast convergence and good stability, but its gradient solution is complicated and complex, and it depends on initial value. Swarm intelligence optimization algorithm randomly from the point of view of a solution, and then according to the principle of the corresponding search algorithm, to find a better solution to search until the optimal solution, this solution logic is simple, easy in programming, and the algorithm has the advantages of self organization, parallelism and flexibility therefore, more and more people's attention. The geometry of swarm intelligence optimization theory to solve the two-dimensional heat radiation cavity based on inverse design problems, the main research contents include the following aspects: (1) using body fitted coordinates discrete coordinate method of irregular geometry solving radiation heat transfer problems in medium, and the results are compared with the finite volume method, reliability calculation program verification is the problem of model, according to heat flux boundary obtained the corresponding objective function is established. (2) an overview of swarm intelligence optimization algorithm the main characteristics and classification, introduces the swarm search method, artificial fish swarm algorithm, ant colony optimization algorithm and SFLA four kinds of swarm intelligence optimization algorithm for solving the basic ideas, and focuses on the krill swarm algorithm and particle swarm optimization algorithm is the basic principle and solving process, theory support for the subsequent research of inverse geometry design. (3) using krill swarm algorithm and standard particle swarm optimization algorithm and stochastic particle swarm optimization algorithm for solving radiative heat transfer cavity geometry inverse design problem, the geometric optimization, achieve the goal of the design design of surface heat flux distribution of the designated area, and compare the three method calculation of performance in the radiation cavity inverse geometry design problems the analysis of the influence of inverse geometry design; design of surface control point number, dielectric properties and boundary emissivity parameters on the design results.
【學(xué)位授予單位】：哈爾濱工業(yè)大學(xué)
【學(xué)位級(jí)別】：碩士
【學(xué)位授予年份】：2015
【分類號(hào)】：TK172

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本文編號(hào)：1346085