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

Table 4

Part of the experimental data.

Sequence number	Mass flux rate (kg m^−2 s^−1)	Heat flux (W m^−2)	Quality of vapor-liquid mixture (%)	Evaporation temperature (K)	Optical tube inner diameter (mm)
1	294.9	532.32	0.3722	313.15	4473.3
2	393.2	601.76	0.4996	313.15	5056.8
3	491.5	601.76	0.4066	313.15	5421.2
4	589.8	578.61	0.1981	313.15	5786.1
5	688.1	509.18	0.0541	313.15	4546.2
6	688.1	740.62	0.5957	313.15	6223.7
7	786.3	694.33	0.392	313.15	6943.3
8	196.6	416.60	0.712	323.15	3893.5
9	294.9	439.74	0.7061	323.15	4071.7
10	393.2	462.89	0.5429	323.15	4628.9
11	491.5	439.74	0.3026	323.15	4997.1
12	589.8	416.60	0.1483	323.15	5080.5
13	688.1	370.31	0.0407	323.15	4069.4
14	688.1	555.47	0.4425	323.15	5673.2
15	786.3	509.18	0.2938	323.15	6445.3
16	196.6	254.59	0.4526	333.15	3264
17	294.9	254.59	0.1785	333.15	3536
18	393.2	231.44	0.0462	333.15	3127.6
19	393.2	300.88	0.4157	333.15	4011.7
20	491.5	300.88	0.2920	333.15	4490.7
21	589.8	277.73	0.1660	333.15	4408.5
22	688.1	254.59	0.0820	333.15	4243.2
23	786.3	231.44	0.0231	333.15	3454.4
24	786.3	347.17	0.2563	333.15	5586.6
…	…	…	…	…	…