An artificial neural network (ANN) for predicting critical heat flux (CHF) of concentric-tube open thermosyphon has been trained successfully based on the experimental data from the literature. The dimensionless input parameters of the ANN are density ratio, ρl/ρv, the ratio of the heated tube length to the inner diameter of the outer tube L/Di, the ratio of frictional area, di/(Di + do), and the ratio of equivalent heated diameter to characteristic bubble size, Dhe/[σ/g(ρl−ρv)]0.5, the output is Kutateladze number, Ku. The predicted values of ANN are found to be in reasonable agreement with the actual values from the experiments with a mean relative error (MRE) of 8.46%. For a particular outer tube, the CHF increases initially and then decreases with increasing inner tube diameter, and has a maximum at an optimum diameter of inner tube (do,opt). The do,opt is correlated with the working fluid and may decrease with the increase of ρl/ρv. CHF decreases with the increase of L/Di, and the decreasing rate decreases as L/Di increases. In the influence scope of pressure, the CHF decreases with increasing pressure for R22, while increases with increasing pressure for R113.
- Nuclear Engineering Division
Analysis of CHF Characteristics of Concentric-Tube Open Thermosyphon by Using Artificial Neural Network
- Views Icon Views
- Share Icon Share
- Search Site
Chen, R, Su, G, & Qiu, S. "Analysis of CHF Characteristics of Concentric-Tube Open Thermosyphon by Using Artificial Neural Network." Proceedings of the 18th International Conference on Nuclear Engineering. 18th International Conference on Nuclear Engineering: Volume 4, Parts A and B. Xi’an, China. May 17–21, 2010. pp. 689-696. ASME. https://doi.org/10.1115/ICONE18-29707
Download citation file: