A Type Design Method of Large Hydraulic Turbine Based on The Gaussian Mixture Model

• Autorzy: Lin L. , Zhong S. , Xie X.

Warianty tytułu

PL

Metoda projektowania dużych turbin hydraulicznych bazująca na mieszanym modelu Gaussa

• Języki publikacji: EN

Abstrakty

EN

Because of the complex fluid motion in hydraulic turbine and the imperfect design theory, the selection design of Large-scale hydraulic turbine is achieved based on the calculation and analysis on the synthetic characteristic curves, which is subjectivity and low efficiency. To solve this problem, the Gaussian mixture model is used to extract the geometric features from the synthetic characteristic curves so that the retrieval process of the model wheel can be achieved by these geometric features. The search model of the running area from the synthetic characteristic curves is build based on the contour curve similarity transformation method. In the paper, the Monte Carlo method is adopted to obtain the mean values of the synthetic characteristics in the running area so that the evaluation targets can be established by combining the mean values with the hydraulic turbine design experiences. Finally the validity of running area can be evaluated by the evaluation targets. The test results show that the accuracy and efficiency of the selection design of Large-scale hydraulic turbine can be improved by the proposed method.

PL

W artykule przedstawiono problemy projektowania dużych turbin hydraulicznych. Zaproponowano model matematyczny bazujący na mieszanym modelu Gaussa w celu wydobycia parametrów geometrycznych. Zaadaptowano metodę Monte Carlo do określania średnich wartości syntetycznych charakterystyk w obszarze pracy.

Słowa kluczowe

EN

hydraulic turbine; selection; Gaussian mixture model

PL

turbina hydrauliczna; metoda Monte Carlo; projektowanie; model Gaussa

• Wydawca: Wydawnictwo SIGMA-NOT
• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2012
• Tom: R. 88, nr 9b
• Strony: 153--156
• Opis fizyczny: Bibliogr. 7 poz., rys.

Twórcy

autor

Lin L.

• School of Mechanical Engineering, Harbin Institute of Technology, Harbin, China

autor

Zhong S.

• School of Mechanical Engineering, Harbin Institute of Technology, Harbin, China

autor

Xie X.

• School of Mechanical Engineering, Harbin Institute of Technology, Harbin, China

Bibliografia

• [1] Liangjun Cheng, Turbine, China Machine Press , (1981)
• [2] Shenglin Ji, Guozhu Liu. Turbine. China Hydraulic and Electric Press, 1985,5
• [3] Peng Yu, Xinwei Tong and Jufu Feng. Unsupervised classification based on penalized maximum likelihood of Gaussian mixture models, Chinese Journal of Applied Probability and Statistics, 24,(2008), No. 5, 475-482
• [4] Paul D.McNicholas, Model-based classification using latent Gaussian mixture models. Journal of Statistical Planning and Inference, 140, (2010), 1175–1181
• [5] Dempster,A., Laird,N. and Rubin,D., Maximum likehood estimation from incomplete data via the EM algorithm, J.Royal Statistical Soc.B, 39,1977, 1–38
• [6] Redner,R.A., and Walker,H.F., Mixtures densities, maximum likelihood and the EM algorithm, SIAM Review, 26, (1984), 195–239
• [7] Figueiredo, M.A.T., and Jain,A.K.. Unsupervised learning of finite mixture models, IEEE-PAMI, 24, 2002, No. 3, 381–396.