Abstract
A series of experiments on steam injected into owing water in a vertical pipe were carried out. The inlet steam flow rate and the owing water temperature were in the range of 0~800 kg•m-2•s-1and 30~70℃, respectively. The features of condensation pressure signals at different scales over a time-frequency plane were revealed by the method of wavelet multiresolution analysis. Absolute mean and relative energy of condensation pressure signals at levels 2~5 were selected as the characteristic parameters. Principal component analysis was applied to extract four principal component parameters, and the accumulative contribution rate exceeds 85%. A regime intelligent recognition system is constructed based on the neural network model. The recognition rates of Chugging, Ocil-I, Ocil-II and stable condensation in the pipe flow system were all over 91.7%.
| Original language | English |
|---|---|
| Pages (from-to) | 328-334 |
| Number of pages | 7 |
| Journal | Kung Cheng Je Wu Li Hsueh Pao/Journal of Engineering Thermophysics |
| Volume | 40 |
| Issue number | 2 |
| State | Published - 1 Feb 2019 |
Keywords
- Neural network
- Regime recognition
- Steam jet condensation
- Wavelet transform
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