Research Article

Optimization of R245fa Flow Boiling Heat Transfer Prediction inside Horizontal Smooth Tubes Based on the GRNN Neural Network

Table 5

Some of the experimental data after being normalized.

Sequence numberMass flux rate (kg m−2 s−1)Heat flux (W m−2)Quality of vapor-liquid mixture (%)Evaporation temperature (K)Coefficient of heat transfer (W/(m2 K))

10.16670.29820.401100.3589
20.33340.36210.549200.4773
30.50010.36210.441100.5513
40.66680.34080.198800.6254
50.83350.27690.031500.3737
60.83350.48990.660800.7142
71.00000.44730.424100.8603
800.19170.79600.50000.2412
90.16670.21300.78910.50000.2774
100.33340.23430.59950.50000.3905
110.50010.21300.32020.50000.4652
120.66680.19170.14090.50000.4822
130.83350.14910.01590.50000.2769
140.83350.31950.48280.50000.6025
151.00000.27690.31000.50000.7592
1600.04260.49451.00000.1134
170.16670.04260.17601.00000.1686
180.33340.02130.02231.00000.0857
190.33340.08520.45171.00000.2652
200.50010.08520.30791.00000.3624
210.66680.06390.16151.00000.3457
220.83350.04260.06391.00000.3122
231.00000.0213−0.00451.00000.1521
241.00000.12780.26641.00000.5849