atw 2017-06

atw Vol. 62 (2017) | Issue 6 ı June
RESEARCH AND INNOVATION 412
| | Fig. 5.
Correlation degree in different resolution.
As seen from Fig. 5, using different
resolutions, the factors have different
effect on the maximum outlet temperature
of the coolant. There is a
little difference between the effects of
different factors, when the resolution
is 0.5. It shows that the above
parameters are not well distributed.
The individual characteristics of
the above parameters are gradually
distinguished, once the resolution is
reduced from 0.5 to 0.1. The order of
these parameters is established, based
on their degree of the importance, X 6 ,
X 1 , X 4 , X 5 , X 2 , X 3 . The initial power has
the strongest influence on the decay
heat after shutdown. This is followed
by temperature of IRWST, which is
the cooling source for the core. As
the height of the ascending pipe is
| | Fig. 6.
Error analysis.
increased, there is a greater density
difference between cold and hot
sections, further increasing the mass
flow rate of PRHRS. Other parameters
X5, X2 and X3 have a less pronounced
effect on the maximum outlet temperature
of the coolant.
4.3 GM(1,6) model
The Grey correlation has been used
to determine the influence of each
parameter on the maximum coolant
temperature at the reactor’s output.
Considering the relationship among
the parameters, the coolant fluid
temperature (X 0 ) is considered to
represent the main behavior of the
system. The temperature of IRWST
(X 1 ), the diameter of PRHR HX(X 2 ),
resistance coefficient (X 3 ), height of
ascending pipe(X 4 ), initial pressure(X
5 ) and the initial power level(X 6 )
are considered to represent the correlated
behavior factors. According to
Eq. (9)-(18), an in-house code has
been used to build GM (1,6) model.
The code randomly selects 90 groups
and the other 10 groups are used to
validate. The corresponding differential
equation is shown as Eq. (19).
(19)
Figure 6 shows the errors in the
coolant temperature as determined
by the results from GM(1,6) and
RELAP5.
As seen in Fig. 6, the results of
GM(1,6) agree well with RELAP5,
and the errors fall within 15%. The
Grey model can adequately predict
maximum coolant temperature at the
outlet of the reactor core using a small
amount of data, making up for the
deficiency of artificial neural network
(ANN), which becomes unstable with
a small amount of data. This is a
new way to replace thermal-hydraulic
model.
5 Conclusion
By taking the loss of normal feedwater
in AP1000 as an example, the behavior
of PRHRS has been analyzed with the
help of RELAP5, and the Grey system
method has been applied for calculating
the maximum coolant temperature
at the outlet of the reactor
core. The following conclusions are
drawn.
(1) The degree of Grey correlation is
used to analyze the importance of
influencing factors. Smaller the
resolution, more obvious is the
difference among these factors.
The behavior of the factors can be
distinguished very easily, when the
resolution is 0.1.
(2) The initial reactor power has the
greatest influence on the maximum
coolant temperature at the
reactor outlet. And the sequences
is followed by the temperature of
IRWST, height of ascending pipe,
initial pressure. Correspondingly,
the diameter and resistance coefficient
of PRHRS-HX have a lesser
effect.
(3) The GM(1,6) model is built to
predict maximum coolant temperature
at the reactor outlet. All
errors fall within 15 % range.
Research and Innovation
Reliability Analysis on Passive Residual Heat Removal of AP1000 Based on Grey Model ı Qi Shi, Zhou Tao, Muhammad Ali Shahzad, Li Yu and Jiang Guangming