本文選題：粒子群優(yōu)化　切入點：彈道優(yōu)化　出處：《北京理工大學(xué)》2015年碩士論文

【摘要】：現(xiàn)代飛行器設(shè)計是一個典型的多學(xué)科設(shè)計優(yōu)化的過程,廣泛存在著復(fù)雜、費時的仿真分析模型,如:基于有限單元法的結(jié)構(gòu)分析和氣動分析,以及學(xué)科間的耦合,如:結(jié)構(gòu)和氣動的耦合,因此計算量大成為現(xiàn)代飛行器優(yōu)化設(shè)計中一個顯著的難題。眾所周知,優(yōu)化算法的效率很大程度上決定了整個優(yōu)化設(shè)計的效率。作為一種性能較為優(yōu)越的全局優(yōu)化算法,粒子群優(yōu)化(Particle Swarm Optimization,PSO)由于算法結(jié)構(gòu)簡單、具有全局搜索能力,在飛行器設(shè)計中被廣為關(guān)注。但是,PSO通過在整個設(shè)計空間隨機進行搜索而保證最優(yōu)解的全局最優(yōu)性,計算時間較長,在現(xiàn)代飛行器設(shè)計中應(yīng)用十分受限。因此,迫切需要提高PSO算法的計算速度�，F(xiàn)階段從算法本身的層面對PSO進行改進,來實現(xiàn)加速的目的幾乎已達到一個瓶頸。近年來,圖像處理器(Graphics Processing Unit,GPU)因其具有良好的浮點計算能力、高并發(fā)度、以及相對廉價的特點,被廣泛應(yīng)用于通用計算領(lǐng)域,即GPGPU(General-Purpose Computing on Graphics Processing Units),在科研及工程領(lǐng)域皆具有巨大的潛力。2007年,NVIDIA推出統(tǒng)一計算架構(gòu)(Compute Unified Device Architecture,CUDA)并行計算平臺,大大推廣了GPU的應(yīng)用,GPU現(xiàn)已被廣泛應(yīng)用于流體力學(xué)、有限元仿真、分子動力學(xué)等領(lǐng)域。因此,本文首先針對PSO優(yōu)化費時的問題,提出CUDA平臺下基于GPU并行計算的PSO算法,充分利用PSO所具有的并行計算的基本構(gòu)架,采用GPU對粒子群算法進行細粒度并行化,即將每個粒子的速度位置初始化、適應(yīng)度估計及速度位置更新同時并行化,實現(xiàn)PSO算法的全面加速計算,大為縮減PSO的計算時間。其次,彈道優(yōu)化作為飛行器總體優(yōu)化設(shè)計中一個重要的學(xué)科,其所得結(jié)果的精度和效率對飛行器總體設(shè)計具有重要影響。因此,本文提出利用工程上廣為應(yīng)用的直接法——直接打靶法,來離散彈道優(yōu)化問題,首先將最優(yōu)控制問題轉(zhuǎn)換為非線性規(guī)劃問題,然后采用上述提出的并行PSO算法進行求解,將彈道仿真子程序所得的飛行距離作為適應(yīng)度函數(shù),大幅度縮短了計算時間,為工程及科研提供了新的彈道優(yōu)化解決方案。此外,基于有限單元法的結(jié)構(gòu)和氣動分析在現(xiàn)代飛行器設(shè)計中廣泛應(yīng)用,然而計算量大一直是其存在的顯著問題。因此,代理模型通常用于替代有限元仿真來進行氣動和結(jié)構(gòu)分析,但代理模型必然會引入誤差,甚至?xí)a(chǎn)生錯誤的估算結(jié)果,而且為了保證代理模型的精度也需要抽取大量的樣本,計算量依然很大。因此,本文從并行計算的角度,提出采用GPU來加速有限元仿真分析(ABAQUS和FLUENT)過程,在避免代理模型引入誤差的同時,大為提高了計算效率。通過諸多實例的仿真計算分析以及在飛行器設(shè)計中的應(yīng)用,得出本文提出和研究的諸多基于GPU并行計算的加速策略是有效的,相比于傳統(tǒng)的基于CPU(Central Processing Unit)串行計算的方式,能夠獲得非常可觀的加速比,因此GPU并行計算在飛行器設(shè)計中具有巨大的應(yīng)用潛力和廣闊的應(yīng)用前景。
[Abstract]:Modern aircraft design is a typical multidisciplinary design optimization process, there is a wide range of complex and time-consuming simulation models, such as: analysis and dynamic analysis of structure based on finite element method, and the coupling between the disciplines such as structural and aerodynamic coupling, so the computation has become a modern aircraft optimization a significant problem in the design. As everyone knows, the optimization efficiency largely determines the efficiency of the whole optimization design. As a kind of better performance of global optimization algorithm, particle swarm optimization (Particle Swarm Optimization, PSO) because the algorithm has the advantages of simple structure, has the global search ability in aircraft design, but has been widely concerned. To guarantee the optimal solution, the global optimality of PSO by random search in the design space, the computation time is long, ten points in limited application in modern aircraft design. Therefore, urgent To improve the calculation speed of the PSO algorithm. At this stage, PSO improved the algorithm itself in, to achieve the purpose of accelerating has almost reached a bottleneck. In recent years, the image processor (Graphics Processing Unit, GPU) computing ability because of its good floating point, and high degree, and relatively inexpensive, is widely used in the field of general-purpose computing, namely GPGPU (General-Purpose Computing on Graphics Processing Units), in scientific research and engineering field has great potential for.2007 years, NVIDIA launched a unified computing architecture (Compute Unified Device Architecture, CUDA) parallel computing platform, greatly extended the application of GPU, GPU has been widely used in fluid mechanics, finite element simulation, the field of molecular dynamics. Therefore, in this paper, PSO optimization time-consuming, GPU parallel PSO algorithm based on CUDA platform is proposed, taking full advantage of PSO has the parallel computing framework, using GPU on the particle swarm algorithm for fine-grained parallelism, speed and position of each particle is initialized, fitness and speed estimation and location update parallelization, PSO algorithm to achieve the overall acceleration of computation, for computing time reduced PSO. Secondly, as the aircraft trajectory optimization overall optimization is an important subject in the design, the efficiency and accuracy of the results has an important influence on the overall design of the vehicle. Therefore, this paper proposes the use of the wide engineering application of direct method -- direct shooting method to discrete trajectory optimization problem, the optimal control problem into a nonlinear programming problem, then using the proposed the parallel PSO algorithm to solve the trajectory simulation of the flight distance of the subroutine as fitness function, greatly shorten the calculation time, for engineering and Science The research provides a new solution scheme of trajectory optimization. In addition, analysis is widely used in modern aircraft design and dynamic structure based on finite element method, but the large amount of calculation has been significant problems. Therefore, the models are often used to make aerodynamic and structural analysis instead of finite element simulation model, but the agent must be the introduction of error, and even produce error estimation results, and in order to ensure the accuracy of surrogate models also need to extract a large number of samples, amount of calculation is still great. Therefore, this paper from the perspective of parallel computing, GPU is proposed to accelerate the simulation of finite element analysis (ABAQUS and FLUENT) in the process, to avoid the agent error model is introduced at the same time, to improve the computational efficiency. The simulation calculation and analysis of many examples and applications in aircraft design, it is proposed in this paper and the research of many parallel computing based on GPU The acceleration strategy is effective. Compared with the traditional way based on CPU (Central Processing Unit) serial computing, it can achieve a very substantial speedup. Therefore, GPU parallel computing has great potential and wide application prospects in aircraft design.

【學(xué)位授予單位】：北京理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：V221

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本文編號：1654111