BLOG | 02 June 2022
USING FLOWNEX SE TO DESIGN A REACTOR CAVITY COOLING SYSTEM (RCCS)
“The solution of the conservation equations together with the point kinetics equations means Flownex provides integrated thermal-hydraulics/neutronics results for system design.“
I had the privilege of working on the conceptual design of a Small Modular Reactor (SMR) Reactor Cavity Cooling System (RCCS). The RCCS is a safety-related system that is intended to operate passively to remove decay heat from the reactor cavity by means of natural circulation. The thermal-hydraulics involved are extremely complex and include time-dependent heat generated by the reactor, reactor materials thermal inertia, convection, conduction, thermal radiation, and of course heat-induced natural circulation flow. From the start, I knew that we faced an immensely challenging and important task ahead of us.
The first step to meeting this challenge was to choose the right software tools for the job. 3D modelling software like ANSYS Fluent is one of the first that came to mind, but we knew we had to investigate various geometry concepts and perform multiple optimization studies, all within a limited time frame. The answer, therefore, lies with systems simulation tools, that would save a lot of computational time. It was important, however, to also be able to model the neutronic behaviour of the reactor, natural circulation, phase change of water, and think ahead about nuclear licensing requirements.
Flownex SE is developed within a quality assurance framework that is ISO9001:2015 certified and complies with ASME NQA-1-2015 as well as 10 CFR Part 50 Appendix B. The software applies the conservation equations for mass, momentum and energy and has a comprehensive library of components, fluids and materials. In addition, the point kinetics model is used when solving neutron interactions of a specified reactor geometry. The combination of these methods means that an integrated reactor model can be simulated coupled to the RCCS, combining thermal-hydraulics and neutronic calculations in one model.
THE FLOWNEX MODEL
I’ll start off by sharing my experience in using Flownex, by showing a simplified representative geometry of the reactor and RCCS that I had to model (Figure 1).
The circular RCCS is positioned axis-symmetrically around the reactor. One of the concepts that we investigated is shown in Figure 2. In this design, the downcomers provide cooling fluid at a lower temperature to the risers, where the fluid in turn would be heated up before exiting the system, thereby removing a portion of the heat from the reactor cavity.
Figure 3 shows a part of the network that I added manually, which is one of the multiple ways in which an RCCS can be simulated by using Flownex. Complex models can be built up from the simple conduction, convection, radiation and flow elements. This model could then be further adjusted to investigate different geometries, different fluids and different materials.
Next, I had to link the RCCS with the reactor to allow interdependent performance changes with changing reactor conditions. Setting up the reactor model in Flownex was really quick and easy. The reactor geometry is specified in the Reactor Geometry Chart Editor (RGCE). Much like using LEGO blocks, different types of zones can be used to build complex geometries. Zones are available to simulate pebble-type or block-type fuel. Cavity zone types are chosen based on whether the flow is in the horizontal or vertical direction, and solid zones can have porosities to permit flow through them.
Flownex has a reactor builder script, which automatically generates the heat transfer components and pipe elements of the specified network in the RGCE. This enabled me to edit or add elements, based on the model requirements and monitor the thermal-hydraulic properties at any position within the reactor.
The runtime neutronics script is used in conjunction with the reactor builder script to solve the point kinetics model that is implemented in Flownex. The fuel, moderator and reflector feedback coefficients are specified as inputs, together with the decay heat parameters and control rod model.
The reactor builder script generates a new drawing page with all the relevant components to represent the network specified in the RGCE. A section of this network can be seen in Figure 5. The diagonal strings of conduction elements represent the conduction path from within the fuel spheres. The number of increments within the sphere zones can be specified by the user. The example shows five nodes for the fuel region in the pebble and 3 nodes for the pebble shell.
The model can also be refined further to include TRISO particles, as shown in Figure 6. I’ve decided not to include the TRISO particles in order to keep the number of nodes and elements to a minimum. The TRISO particle specification comes in handy when modelling accurate maximum fuel temperatures are important, but this was not my primary focus.
GETTING SOME RESULTS
One of the investigations entailed that I had to fully insert the control rods in a SCRAM scenario. A transient run with the system’s response to this scenario is shown below by plotting the reactor power as a function of time, with insertion of the rods:
Design case studies could now be done with the model and I set up three different cases as demonstrations. I chose the temperatures on the RPV wall, the inner wall and the enclosure surface as dependent variables. Firstly, the geometry was simulated with air as the RCCS coolant and with no insulating materials on the inner wall in Figure 2. A transient run was done in which the control rods were inserted at the onset of the simulation.
Secondly, since the temperatures in Figure 7 could be regarded as being too high, an insulation layer was placed on the inner wall surface to investigate the effects. The simulation was repeated and before long the following graph resulted (here the enclosure wall and inner wall temperatures are equal):
Thirdly, the influence of using a different fluid was then investigated. Water is often used in RCCSs for its high specific heat capacity compared with that of air. The model was thus quickly modified to use a different fluid which resulted in the graph in Figure 9.
A report was set up in Flownex that would extract the heat addition in each case. The results were written to a .csv file for further post-processing in Excel, from where the graph in Figure 10 was created. This makes it easy to compare different design options and choose the best option.
Flownex provides many other results that can be used for design purposes. I chose the wall temperatures during the design process, but Flownex provides the heat energy through components, Reynolds numbers, flow velocities, fluid properties, calculated convection heat transfer coefficients etc.
As a first approximation, I used the built-in Dittus Boelter correlation to calculate the convection heat transfer coefficient for the natural convection phenomena. Should the need arise, I could also use Flownex’s built-in scripting functionality to incorporate custom correlations for free convection or mixed convection.
Similarly to the RCCS design, Flownex could also be used to couple the balance of the plant to the reactor and RCCS model in order to do sizing calculations e.g. for the pumps and heat exchangers that are to be used. The whole process was simple and very time efficient.
NOT ALL PLAIN SAILING
As with any code, Flownex does have its drawbacks. The drawback of being a 1D code is that regions with strong 3D phenomena require simplifying assumptions to approximate the phenomena to 1D. Code coupling or co-simulation can be done between Flownex and ANSYS in which the system could be modelled by Flownex and the regions requiring more detail could be modelled by using ANSYS. Although this poses somewhat of a midway between 3D and systems codes, it does pose serious implications on the time frame and adjustability of the project. While still being in the concept phase, the results obtained from Flownex alone was more than adequate.
A FINAL THOUGHT
In closing, I found Flownex to be an invaluable tool especially when it comes to the system-level design. It combines thermal-hydraulic and neutronic phenomena for steady-state as well as transient conditions. It is easy to use, provides good accuracy for 1D phenomena and is time-efficient. This makes it possible for me to run through many different design iterations in a relatively short amount of time. The scripting functionality enables the implementation of the appropriate correlations for modelling natural circulation in the RCCS and integration between different parts of the system is easy. I’m definitely looking forward to the rest of the project with Flownex as part of my toolbox.