Multipe NSSS (Nuclear Steam Supply System) modules use the common feeding-water system to drive the common turbine power generation set. The SSFFN (secondary side fluid flow network) of MHTGR plant has features i.e. strong-coupling and nonlinearity. A wide range of power switching operation will cause unsteady flow, which may destroy the working elements and will be a threat for normal operation. To overcome those problems, a differential-algebraic model and PI controllers are designed for the SSFFN. In MATLAB\SIMULINK environment, a simulation platform is established and used to make a simulation of SSFFN of a MHTGR plant with two NSSS modules, which uses feedwater valves to control the mass flow rate in each module instead of feedwater pump. Results reflect good robustness of controllers.
As the fourth generation of nuclear power system, the modular high-temperature gas-cooled reactor (MHTGR) plant receives extensive attention from the world’s nuclear power industry due to its inherent safety and economic efficiency. As characterized by modularization and miniaturization, MHTGR uses helium as coolant and uses graphite as moderator and structural materials. Its fuel elements contain thousands of ceramic mould package particles. Its core outlet helium temperature can reach 700˚C - 950˚C [
Fluid Flow networks (FFNs) widely exist in industrial engineering, e.g. mine ventilation, water supply and drainage system and oil gas industry, etc. [
In this paper, a differential-algebraic model of MHGTR plant’s SSFFN, based on Kirchhoff’s law and branch dynamics, and PI controller sensuring globally asymptotic stability are given. What’s more, a simulation platform was established, which can be used for simulation experiments on SSFFN, which has more NSSS modules.
From
Assuming that all flow dynamic parameters in pipes are evenly distributed on the radial cross section. Under the assumption, the flow in pipes can be seen as a one-dimensional flow. We can use the average values of dynamic parameters of the radial cross section and branch dynamic equation is deduced as Equation (1).
where Qj, Rj, Hj and Kj is the mass flow rate, the resistance, the pressure drop, the inertia coefficient of the branch j, j = 1, ∙∙∙, N and N is the branch number of network.
Making a topology analysis on the structure of the SSFFN of MHTGR plant shown in right in
There are n + 3 nodes and 2 × n + 2 branches (including 1 pump branch) in this network shown in
where
Based on Kirchhoff’s voltage law, we have
where
Hf is the pressure drop of the feedwater pump branch. Hf can be expressed as
where
From Equation (4), we can obtain the mass flow rates and pressure drops of the branch in the SSFFN, it is the differential-algebraic model of the SSFFN. If modules’ number n is given, it is clear that the topology of the SSFFN can be determined. So modules’ number n is important to determine the SSFFN dynamics.
In actual engineering, the mass flow rates and pressure drops should reach the expectation values. From Equation (4), a differential-algebraic model of the SSFFN has been established. R, K and Hd is the input of the model. We can adjust the opening of feedwater valves to change mass flow rates in the NSSS modules and control the main steam pressure drop of the steam turbine generator set by adjusting the pressuer head of feedwater pump. A PI control scheme based on above has been established and its structure can be seen in
To overcome the pipe sensor noise, a section of inertia link is added in controllers, which plays an role of low pass filter. Controllers shown in
In MATLAB\SIMULINK environment, the model has been implemented by using the programming language M code based on the principles of Level-2 S-Function. The model provides parameters interface in order to let users to define the number of NSSS modules and structure of the SSFFN. The users must input the parameters, such as the nodes’ number, the feeding-pump branches’ number, the rest branches’ number and the matrices of KCL, which depends on the number of NSSS modules, to initialize the model. The users can add other simulation modules to make real-time simulation.
In MATLAB\SIMULINK environment, the model has been implemented by using the programming language M code based on the principles of Level-2 S-Function (
This paper has made simulation of SSFFN of MHTGR plant, which has two NSSS modules and uses the common feedwater pump to feed for the two. In this network, the mass flow rate is controlled by the feeding valve, making topology analysis on the structure of the SSFFN and setting inertia coefficients of the branch excluding feeding-pump branch K as diag (0.1; 0.1; 0.1; 0.1; 0.1). To simulate the real operation ,conditions can be set as follow, the time of simulation t is 3000s,the initial value of pressure head of feedwater pump Hd is 25 Mpa, the resistance of branch R1, R2, R3, Rf is 0.00118, 0.00118, 0.0038, 0.00044, the initial value of Rv1, Rv2 is
0.00118, 0.00118. The main steam pressure drop of the steam turbine generator set remains 13.9 Mpa. A case study is given to show the performance of the model. In this case, the mass flow rate of 1#NSSS remains 96 kg/s in 1000 s, it will drop to 90 kg/s at 1000 s. Controller Hd: kp = 1, ki = 1; Controller Rv1: kp = 0.0001, ki = 0.0001; The mass flow rates, pressure drops and control inputs are respectively shown in
The mass flow rate of 1#NSSS module, Q1, is controlled by the feeding valves. The drop of mass flow rate is caused by the decrease of the valve opening, thus resulting in increase in the branches’ resistance and pressure
head of feedwater pump. Because of the controller, the main steam pressure drop, H3 remians the initial value. The results show good robustness of controllers.
The SSFFN of MHTGR plant has features, i.e. strong-coupling and nonlinear, because multiple NSSS modules use the common feedwater system to drive the same steam turbine generator set. A wide range of power switching operation may cause unsteady fluid flow in the network, which may destroy working components. This problem must be avoided in order to ensure the system’s safety. It is meaningful to make analysis on dynamics of the SSFFN. Branch dynamics and network’s structure features plays an important role in modeling SSFFN.
Base on KCL and KVL, this paper designed a differential-algebraic model for SSFFN. Based on the model, this paper designed PI controllers to promise the closed system globally asymptotically stable. In MATLAB\ SIMULINK environment, the paper made implementation of the model and designed a good human-machine interface for users to define the structure of the SSFFN at ease. To proof the model and the controller, this paper made simulation of SSFFN of a MHTGR plant which has two modules and uses one common feedwater pump feeding for the two. The results efficiently reflect the controllers’ good performance. The simulation platform established in this paper may help engineers to make integral coefficient test and choose the best one, which is actually based on engineers’ experiences. Also, the simulation platform can be used to control SSFFN of MHTGR plant which has more modules.
This work was supported in part by National S&T Major Project under Grant ZX06901, and in part by Natural Science Foundation of China (NSFC) under Grant 61374045.
Maoxuan Song,Zhe Dong, (2016) Dynamic Characteristics Analysis on MHTGR Plant’s Secondary Side Fluid Flow Network. Journal of Power and Energy Engineering,04,15-22. doi: 10.4236/jpee.2016.47003