Abstract
As for the factors affecting the heat transfer performance of complex and nonlinear oscillating heat pipe (OHP), grey relational analysis (GRA) was used to deal with the relationship between heat transfer rate of a looped copper-water OHP and charging ratio, inner diameter, inclination angel, heat input, number of turns, and the main influencing factors were defined. Then, forecasting model was obtained by using main influencing factors (such as charging ratio, interior diameter, and inclination angel) as the inputs of function chain neural network. The results show that the relative average error between the predicted and actual value is 4%, which illustrates that the function chain neural network can be applied to predict the performance of OHP accurately.
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Foundation item: Project(531107040300) supported by the Fundamental Research Funds for the Central Universities in China; Project(2006BAJ04B04) supported by the National Science and Technology Pillar Program during the Eleventh Five-year Plan Period of China
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E, Jq., Li, Yq. & Gong, Jk. Function chain neural network prediction on heat transfer performance of oscillating heat pipe based on grey relational analysis. J. Cent. South Univ. Technol. 18, 1733–1737 (2011). https://doi.org/10.1007/s11771-011-0895-z
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DOI: https://doi.org/10.1007/s11771-011-0895-z