本文選題：顆粒增強復(fù)合材料 + CUDA　； 參考：《昆明理工大學(xué)》2015年碩士論文

【摘要】：顆粒增強復(fù)合材料作為一種力學(xué)性能優(yōu)越的新型材料,已經(jīng)被廣泛地應(yīng)用于航空航天、機械制造和光學(xué),,逐步地替代了一些傳統(tǒng)的金屬材料。由于在基體中加入增強顆粒,顆粒增強復(fù)合材料的多項力學(xué)性能均優(yōu)于基體材料本身,與此同時,增強項的加入又降低了材料的疲勞斷裂性能。近年來,隨著顆粒增強復(fù)合材料越來越多地出現(xiàn)在日常生活的各個領(lǐng)域,國內(nèi)外大量的研究者針對該材料進(jìn)行了一系列的數(shù)值模擬工作。隨著科學(xué)和技術(shù)的發(fā)展,針對一些復(fù)雜的模型的精確模擬,研究人員需要解決計算耗時長和占用內(nèi)存量大這兩大主要問題。本文根據(jù)GPU的可編程能力和高速并行處理能力的優(yōu)勢,以實現(xiàn)并行計算加速為出發(fā)點,將CUDA編程融入到顆粒增強復(fù)合材料損傷演化的宏細(xì)觀跨尺度模擬。實現(xiàn)了CUDA Fortran程序和Fortran程序的混合編程。簡要分析了并行程序與串行程序的區(qū)別的同時,突出了GPU加速的合理性、可行性和優(yōu)越性。并且著重分析了CUDA編程要點以及充分利用共享存儲器Share Memory的重要性,不僅能夠很好的隱藏了數(shù)據(jù)傳輸延遲,而且也在一定程度上節(jié)省了存儲空間和時間,從而提高了效率。利用CUDA編程技術(shù),改進(jìn)了原來計算程序的算法流程。在原程序計算得到宏觀有限元位移解,整個構(gòu)建離散為細(xì)觀模型尺度與宏觀大范圍尺度之后,針對Voronoi單元的單元剛度矩陣計算時調(diào)用CUDA程序進(jìn)行計算,重新編寫了相應(yīng)的Voronoi單元的單元剛度矩陣的程序。最后將最終的結(jié)果與宏觀元計算結(jié)果耦合,得到整個模型的應(yīng)力應(yīng)變響應(yīng)。建立多個計算模型,模擬顆粒增強復(fù)合材料損傷演化的過程。并分析模型在不同實驗環(huán)境、不同高斯點數(shù)量、不同應(yīng)力參數(shù)情況下的計算速度,并通過與原程序計算結(jié)果對比進(jìn)行充分的驗證。其主要結(jié)論為計算的數(shù)據(jù)規(guī)模較小時,GPU并行計算得到的加速并不明顯；當(dāng)數(shù)據(jù)規(guī)模越大時,GPU的加速效果越明顯；CUDA核心越多,得到的提速效果越好。
[Abstract]:As a new material with superior mechanical properties, particle reinforced composites have been widely used in aerospace, mechanical manufacturing and optical materials, gradually replacing some traditional metal materials. Because of the addition of reinforced particles in the matrix, the mechanical properties of the particle reinforced composites are better than that of the matrix material itself, and at the same time, the fatigue fracture properties of the composites are reduced by the addition of the reinforced particles. In recent years, with the increasing emergence of particle reinforced composites in various fields of daily life, a large number of researchers at home and abroad have carried out a series of numerical simulation work on the material. With the development of science and technology, researchers need to solve the two major problems of long computation time and large amount of memory for the precise simulation of some complex models. According to the advantages of GPU's programmable ability and high speed parallel processing ability, this paper integrates CUDA programming into macro and mesoscale simulation of damage evolution of particle reinforced composite materials, taking the acceleration of parallel computing as the starting point. The mixed programming of CUDA Fortran program and Fortran program is realized. The difference between parallel program and serial program is briefly analyzed, and the rationality, feasibility and superiority of GPU acceleration are highlighted. The importance of CUDA programming and making full use of shared memory Share Memory is analyzed. It not only hides the data transfer delay, but also saves storage space and time to a certain extent, thus improving the efficiency. The algorithm flow of the original program is improved by using CUDA programming technology. After the macro finite element displacement solution was obtained by the original program, and the whole construction was discretized into mesoscopic model scale and macro large scale, the CUDA program was used to calculate the element stiffness matrix of Voronoi element. The program of element stiffness matrix of Voronoi element is rewritten. Finally, the stress and strain response of the whole model is obtained by coupling the final result with the result of macro element calculation. Several computational models were established to simulate the damage evolution of particle reinforced composites. The calculation speed of the model under different experimental conditions, different Gao Si points and different stress parameters is analyzed, and the results are compared with those of the original program. The main conclusion is that the acceleration of GPU parallel computation is not obvious when the data scale is small, and when the data scale is larger, the acceleration effect of GPU is more obvious than that of CUDA core, and the speedup effect is better.
【學(xué)位授予單位】：昆明理工大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2015
【分類號】：TB33

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本文編號：1889220