本文選題：多學科設計優(yōu)化 + 優(yōu)化過程��； 參考：《華中科技大學》2012年碩士論文

【摘要】：復雜產(chǎn)品的設計通常涉及到眾多學科，而且學科之間存在復雜的耦合關系，能夠解決復雜、強耦合設計問題的多學科設計優(yōu)化方法（MDO）的作用越來越突出。MDO優(yōu)化過程是多學科設計優(yōu)化中最核心、最重要的內(nèi)容，直接決定了MDO技術在具體的工程優(yōu)化問題中的應用可靠性。國內(nèi)外學者已經(jīng)對MDO優(yōu)化過程進行了較深入的研究，并取得了一定的成果。但是由于產(chǎn)品的多樣性和復雜性，還沒有形成一種能廣泛應用于各種MDO問題的方法。同時，近似模型技術因其既能保證一定的精度，又能大幅度降低重復調(diào)用精確仿真模型所需的計算成本，已成為研究復雜系統(tǒng)MDO問題時必不可少的工具。本文主要對MDO優(yōu)化過程和近似模型進行研究，尋求一種能更快更準找到全局最優(yōu)方案的高效、高可靠性的MDO優(yōu)化方法。 首先，本文對MDO優(yōu)化過程和近似模型技術的國內(nèi)外研究現(xiàn)狀進行了調(diào)研，從分析系統(tǒng)優(yōu)化與子系統(tǒng)優(yōu)化之間的關系出發(fā)，探討了一般分解-協(xié)調(diào)優(yōu)化過程的迭代流程，并對幾種主要的多級MDO優(yōu)化過程進行了研究，從分解技術、收斂性、求解效率等多方面分析比較了它們之間的優(yōu)劣。 其次，本文在已有的研究基礎之上，對其中綜合優(yōu)化性能較好的兩級集成系統(tǒng)協(xié)同優(yōu)化(BLISCO)方法進行了深入研究，針對該方法所存在的每次迭代需要調(diào)用高計算量的精確仿真分析、學科優(yōu)化易受數(shù)值噪聲影響等問題，對其進行了改進，提出了基于近似模型技術的BLISCO-AM方法，并給出了該方法的算法結(jié)構(gòu)和優(yōu)化流程。本文通過一個強耦合的非線性優(yōu)化問題對該改進方法的可行性進行了驗證。與原來的BLSCO方法相比，該改進方法大大減少了子系統(tǒng)分析次數(shù)，提高了求解效率。同時，本文還對改進方法中的一致性約束進行了研究，通過對比采用不同允許容差ε獲得的優(yōu)化結(jié)果，，證實了在算法中用不等式約束代替嚴格等式約束的可行性和有效性。 最后，在以上研究成果的基礎上，針對齒輪減速箱優(yōu)化問題，對改進的BLISCO-AM方法中采用不同近似模型技術的不同優(yōu)化效果進行了研究。試驗結(jié)果表明采用響應面模型的BLSCO-AM方法和采用Kriging模型的BLISCO-AM方法均獲得了較好的收斂效果，且大大降低了計算量，進一步驗證了該改進方法的可行性和高效性，并且構(gòu)造的近似模型精度越高，優(yōu)化效果越好。研究還表明并非對于所有的問題，Kriging模型的近似精度都高于響應面模型，因此應根據(jù)具體的問題選擇適合的近似技術。
[Abstract]:The design of complex products usually involves many disciplines, and there are complex coupling relationships between them, which can solve the problem of complexity. The role of multi-disciplinary design optimization method in strongly coupled design problem is more and more prominent. MDO optimization process is the core and most important content of multidisciplinary design optimization, which directly determines the reliability of the application of MDO technology in specific engineering optimization problems. Scholars at home and abroad have done more in-depth research on MDO optimization process, and have achieved certain results. However, due to the diversity and complexity of products, there has not been a method that can be widely used in various MDO problems. At the same time, the approximate model technology has become an indispensable tool to study the MDO problem of complex systems because it can not only guarantee a certain accuracy but also reduce the computational cost of repeating the accurate simulation model. In this paper, the optimization process and approximate model of MDO are studied to find an efficient and reliable MDO optimization method which can find the global optimal scheme more quickly and accurately. Firstly, this paper investigates the research status of MDO optimization process and approximate model technology at home and abroad. Based on the analysis of the relationship between system optimization and subsystem optimization, the iterative process of general decomposition-coordination optimization process is discussed. Several main multistage MDO optimization processes are studied and their advantages and disadvantages are analyzed and compared from decomposition technology convergence efficiency and so on. Secondly, on the basis of the existing research, this paper makes a deep research on the cooperative optimization method of two-level integrated system, which has better performance of integrated optimization. In order to solve the problems existing in this method, such as accurate simulation analysis with high computational load and subject optimization easy to be affected by numerical noise, the BLISCO-AM method based on approximate model technology is proposed. The algorithm structure and optimization flow of the method are also given. The feasibility of the improved method is verified by a strongly coupled nonlinear optimization problem. Compared with the original BLSCO method, the improved method greatly reduces the times of subsystem analysis and improves the efficiency of solution. At the same time, the consistency constraints in the improved method are studied in this paper. By comparing the optimization results obtained by using different allowable tolerances 蔚, the feasibility and effectiveness of replacing strict equality constraints with inequality constraints in the algorithm are proved. Finally, on the basis of the above research results, aiming at the problem of gear reducer optimization, the different optimization effects using different approximate model techniques in the improved BLISCO-AM method are studied. The experimental results show that both the BLSCO-AM method based on response surface model and the BLISCO-AM method using Kriging model have better convergence effect, and greatly reduce the computational complexity. The feasibility and efficiency of the improved method are further verified. And the higher the precision of the approximate model is, the better the optimization effect is. The results also show that not all the Kriging models have higher accuracy than the response surface model, so we should choose the appropriate approximation technology according to the specific problems.
【學位授予單位】：華中科技大學
【學位級別】：碩士
【學位授予年份】：2012
【分類號】：TH122

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本文編號：1990942