本文關(guān)鍵詞： 永磁調(diào)速器 三維有限元 支持向量機 混沌搜索 優(yōu)化　出處：《東北大學(xué)》2011年碩士論文　論文類型：學(xué)位論文

【摘要】：永磁調(diào)速器是一種先進的調(diào)速節(jié)能產(chǎn)品。除傳遞效率高外,還具備許多其他調(diào)速設(shè)備不具備的優(yōu)點,如高安全性和高可靠性、低故障率、長壽命、維護成本低、可在嚴(yán)酷條件下運行等。 在保證永磁調(diào)速器電磁性能的基礎(chǔ)上,優(yōu)化結(jié)構(gòu)參數(shù)、改進磁路及生產(chǎn)工藝、進一步提高產(chǎn)品性能具有重要的實際意義。永磁調(diào)速器的結(jié)構(gòu)參數(shù)優(yōu)化是一個復(fù)雜的非線性多維空間優(yōu)化問題,本文重點研究了基于智能算法的永磁調(diào)速器建模和優(yōu)化設(shè)計問題。 利用有限元軟件ANSYS建立了永磁調(diào)速器的三維有限元模型,對永磁調(diào)速器的電磁場分布進行了三維有限元仿真,分析了永磁調(diào)速器的磁場分布、渦流分布、熱分布,同時計算了永磁調(diào)速器在額定轉(zhuǎn)速下的輸出扭矩和損耗功率,并深入分析了永磁調(diào)速器結(jié)構(gòu)參數(shù)變化對其性能的影響。 為了降低智能優(yōu)化算法的計算成本,本文提出了基于智能非線性回歸算法的永磁調(diào)速器建模方法。利用BP神經(jīng)網(wǎng)絡(luò)、模糊自適應(yīng)神經(jīng)網(wǎng)絡(luò)和支持向量機建立了永磁調(diào)速器的非線性回歸模型,并將基于這三種非線性回歸模型的結(jié)果與有限元分析結(jié)果相比較,對三種方法的結(jié)構(gòu)、建模效率、訓(xùn)練精度、預(yù)測性能及在參數(shù)分析中的應(yīng)用作了對比分析。實驗結(jié)果表明,用支持向量回歸算法進行預(yù)測能夠取得比其他方法更好的效果。在此基礎(chǔ)上,應(yīng)用支持向量機為永磁調(diào)速器建立了數(shù)學(xué)模型,為永磁調(diào)速器的優(yōu)化設(shè)計奠定了基礎(chǔ)。 提出了基于智能優(yōu)化算法的永磁調(diào)速器優(yōu)化設(shè)計方法,分別應(yīng)用粒子群算法和混沌搜索算法對永磁調(diào)速器主要結(jié)構(gòu)參數(shù)進行了優(yōu)化設(shè)計,實驗結(jié)果表明,應(yīng)用混沌搜索算法優(yōu)化永磁調(diào)速器結(jié)構(gòu)參數(shù)更具有優(yōu)越性。最后在有限元軟件ANSYS環(huán)境下對優(yōu)化后的永磁調(diào)速器進行了電磁場仿真,并與樣機的仿真結(jié)果進行了對比分析,驗證了支持向量機建模和應(yīng)用混沌搜索算法優(yōu)化永磁調(diào)速器結(jié)構(gòu)參數(shù)的合理性與有效性。
[Abstract]:Permanent magnet speed governor is an advanced speed control and energy saving product. Besides the high transmission efficiency, it has many other advantages such as high safety and high reliability, low failure rate, long life and low maintenance cost, which can be run under harsh conditions.
Based on guaranteeing the performance of the permanent magnet electromagnetic governor, and optimize the structure parameters of magnetic circuit and improved production technology, improve product performance has important practical significance. The optimization of structure parameters of permanent magnet speed regulator is a complex nonlinear multidimensional optimization problem, this paper focuses on the research of permanent magnet governor modeling and optimization design of intelligent algorithm based on.
The establishment of a three-dimensional finite element model of permanent magnet speed by using finite element software ANSYS, the three-dimensional finite element simulation of the electromagnetic field distribution of the permanent magnet governor, analyzes the magnetic field distribution of permanent magnet eddy current governor distribution, heat distribution, permanent magnet speed in the rated speed of the output torque and power loss were calculated, and further analysis of the effect of permanent magnet governor structure parameters on its performance.
In order to reduce the computational cost of intelligent optimization algorithm, this paper proposed the permanent magnet governor modeling method based on nonlinear regression algorithm. Using BP neural network, fuzzy neural network and support vector machine to establish nonlinear permanent magnet governor regression model and regression model based on the three kinds of nonlinear finite element analysis results and the results in comparison, the structure of the three methods, modeling efficiency, precision of training, and compares the prediction performance and application in parameter analysis. The experimental results show that using support vector regression algorithm to forecast can be achieved better results than other methods. On this basis, the application of support vector machine to establish the mathematical model for the permanent magnet speed governor, which laid the foundation for the optimization design of permanent magnet governor.
The intelligent optimization algorithm of permanent magnet speed optimization design method based on the applied particle swarm algorithm and chaotic search algorithm is used to optimize the main structure parameters of permanent magnet speed, the experimental results show that the optimization of structure parameters of permanent magnet speed regulator has more advantages in application of chaotic search algorithm. Finally the electromagnetic simulation of permanent magnet speed governor after optimization in finite element software ANSYS, and simulation and prototype results were compared to verify the rationality and effectiveness of the support vector machine modeling and application of chaotic search algorithm to optimize the structure parameters of the permanent magnet governor.

【學(xué)位授予單位】：東北大學(xué)
【學(xué)位級別】：碩士
【學(xué)位授予年份】：2011
【分類號】：TH139

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本文編號：1506397