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Aiming at the thermoacoustic oscillation in the combustion process of a new generation of gas turbine, several time-domain analysis methods of high frequency data signals were retested and compared by constructing a complex network model. The results show that the two complex network models, node strength and network diameter, can give an earlier warning of thermoacoustic oscillations than the traditional time-domain analysis methods(root mean square and time kurtosis). Coupling prediction effect and data processing time, the node strength is preferred to construct the complex network model. Finally, the method was applied to the experimental data analysis of multi-nozzle micro-mixing burners, and it was found that the characteristic turning point of the method is about 2.3 s ahead of limit cycle analysis and the characteristic point of statistical analysis.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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