Nuclear

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.

WHY FLOWNEX

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). 

Fig. 1 - Reactor geometry.

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. 

Fig. 2 - Top view of the reactor and RCCS.

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. 

Fig. 3 - A part of the RCCS network.

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.

Fig. 4 - The RGCE layout of the simplified reactor.

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.

Fig. 5 - A section of the Flownex network created by the builder script.

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.

Fig. 6 - TRISO particle modelling.

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.

Fig. 7 - Without insulation on the inner wall.

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):

Fig. 8 - With insulation on the inner wall.

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.

Fig. 9 - Water as fluid in the RCCS.

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. 

Fig. 10 - Comparison of heat removed by the RCCS.

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.

Peter Niemand

Peter Niemand

Peter is a nuclear engineer at Flownex, focussing on verification and validation activities.

FLOWNEX SE (2022)

FLOWNEX SE (2022)

The latest Flownex® 2022 release brings you a new transient solver, new mixture capabilities, our machine learning-powered reduced order model (ROM) builder and more!  Read our detailed release notes here.

 
The new transient solver is a non-iterative solver that can be selected on the fly and provides the capability of solving transient simulations up to 10 times faster. It is also now possible to create mixtures of mixtures specifically that a liquid and gas mixture can now be defined as consisting of a mixture of gases and a mixture of liquids. Through a new machine learning module, users can now create a ROM of their network which can be exported as an FMU. A built-in video recorder can now be used to record graphs and the screen synchronized with transient solving. Other enhancements include updates to the two-phase heat transfer correlations thereby improving the accuracy of two-phase heat transfer simulations. The latest Flownex® is also natively 4K compatible with an enhanced user experience related to graphs, scripts and other features.

LIST OF ENHANCEMENTS

MAJOR ENHANCEMENTS

New Non-Iterative Transient Solver

A new non-iterative transient solver has been implemented in Flownex®. Compared to the customary iterative solver, the non-iterative transient solver increases solve speed substantially during transient events by eliminating the need to iterate within time steps. This becomes very advantageous for networks of all sizes, but especially where large systems need to be modelled over a prolonged time span.

The user can switch between the iterative transient solver and the non-iterative transient solver via the dropdown provided in the Transient solver settings category on the Flow Solver input dialogue. For easy access, a direct toggle between the solvers was added as a tab to the Home ribbon as well.

Fig. 1 - Non-Iterative Transient Solver Toggle on the Home Ribbon.
Fig. 2 - Non-Iterative Transient Solver Selection Option in the Flow Solver.

The non-iterative transient solver retains the implicit pressure-velocity coupling in use for the iterative solver, thereby maximizing numerical stability in typical flow systems. Since the pressure-flow solution is not iterated with respect to the enthalpy solution, the method may be classified as semi-implicit.

Instead of using successive iteration with underrelaxation to obtain a converged solution, all governing equations are fully linearized with respect to the primary variables as well as the temporal variable, using exact and accurate gradients and derivatives without any relaxation. For this reason, all inputs within the Convergence, Relaxation Parameter as well as the Iterations categories become redundant when using the non-iterative transient solver option.

Mixture Generalization

The capability to create a mixture of fluids has been expanded to create mixtures of mixtures for all fluid types, with the exception of two-phase fluids. When a fluid mixture is created, the user now has the option to select more than one liquid and more than one gas when creating mixtures for each of those phases. The below figure shows a Gas and Liquid Mixture, where the user can create a liquid mixture and a gas mixture that consists of multiple liquids and gases within the liquid-gas mixture.

Fig. 3 - Example of a Gas and Liquid Mixture with a Mixture of Gases and a Mixture of Liquids.

Mixing rules for transport properties are applied to the individual phases separately and in the case of a liquid-gas mixture, additional liquid-gas mixing rules are applied when determining the transport properties of the liquid-gas mixture-of-mixtures.

To use the new capability a mixture is configured in the Fluid mixture specification dialog.  The remaining user interface experience has not changed.  Mixture mass fraction boundary conditions are specified as before, with the list of fluid components expanded to include all of the components of the mixture.

Fig. 4 - Specifying Mass Fractions of a Liquid-Gas Mixture which Contains a Liquid Mixture and Gas Mixture.

Similarly, the result property displays the fluid component mass fraction results for the expanded mixture of mixtures, as seen in Figure 5.

Fig. 5 - Mass Fraction Results of a Liquid-Gas Mixture which Contains a Liquid Mixture and Gas Mixture.

As phase transitions in two-phase fluids are significantly impacted by the presence of other two-phase fluids, the new mixture capability does not currently include the possibility to create a mixture of two-phase fluids.  It is however possible to create a Two-Phase Fluid and Gas Mixture where a mixture of gasses can be specified with a single two-phase fluid, as seen in Figure 6.

Fig. 6 - Two-Phase Fluid and Gas Mixture.

ROM Builder

The Flownex® ROM (Reduced Order Model) Builder generates a multi-platform enabled FMU (Functional Mock-up Unit) containing a Neural Network that was trained on sensitivity analysis data. The user is guided from specifying the input and result properties, creating sample data in a sensitivity analysis, specifying the Neural Network hyperparameters, evaluating the trained Neural Network to exporting the FMU ROM using one convenient dialog. The ROM Builder Configuration dialog can be seen in the image below:

Fig. 7 - ROM Builder Configuration.
Fig. 8 - ROM Builder training a Neural Network

Video Recorder

The capability has been added to record graphs and the screen synchronized with transient solving. The video recording options are added to the properties of each graph. When “Record graph as video” is set to “Yes”, a new video is recorded for each transient run. From the Video Recorder task properties (under Solvers), Flownex® can be configured to record the whole screen.

Fig. 9 - Recording a graph to the Videos folder.

Two Phase Flow Heat Transfer

The two-phase fluid generator has been updated to include Steiner and Taborek normalized coefficients and includes an updated radiation model specification. In previous versions the Steiner and Taborek normalized coefficients were effectively hardcoded and were only available for a limited number of two-phase fluids.  These coefficients have now been moved to the two-phase fluid data files and the fluid generator therefore required updating to allow the user to provide the appropriate values for generated fluids.  The latest two-phase data file format also provides for the selection of the radiation participation model to be used for the generated fluid.

Fig. 10 - Two-Phase Fluid Importer with Saturated Boiling and Radiation Model Specification.

MINOR ENHANCEMENTS

User Interface Framework

Higher resolution (4K) compatibility has been added to the Graphical User Interface.

Actions

The capability to specify a Ramp action has been added. When a Ramp action is created, the user can specify the duration and final value for the action rather than the coefficients for a straight line.

Fig. 11 - Ramp Action.

Units

  • The mils unit has been added that is used for vibration.
  • Added the capability for a user to reset the unit of a property to the current selected unit system default. This option is available on the context menu on a property, as seen below.
Fig. 12 - Reset Unit Option.

Graphs

  • Added a property to change point symbols to be solid or hollow.
  • Added a ThinCross symbol type.
Fig. 13 - ThinCross Symbol Type and Solid Symbol Option Properties added to Graphs.
  • Grouping line items with the same unit group onto one Y-axis is now possible. The “Display multiple Y-Axis” property has been replaced with ”Axes displayed” property.
Fig. 14 - Axes Displayed Property.
Fig. 15 -Axes Displayed Property.

Component Characteristic Graphs

  • An option to view all Angles on Compressor Component Characteristic Graph has been added.
  • Angles are now available to be checked/unchecked from the graph legend.
  • All four dimension’s values of the chart are displayed in the chart tooltip.
  • Only plotting the closest lines functionality is still available by setting a new property: “Show closest background lines” to “Yes”. Property is below “Background lines”.
  • By default, all lines with check boxes will be displayed for new graphs and graphs from older projects.

Scripting

  • The Script component has been updated such that the “Initialise” function is called only once before Steady State and “Cleanup” is called once after Steady State. This is done if any or all of the options are active for Before, During and After Steady State. Previously it was called multiple times during Steady State if more than one of the options were active.
  • The Iterative Script’s “Initialise” and “Cleanup” functions now works similar to a normal Script and is called before and after every steady state and called before and after every transient.
  • The font of the code editor was changed to a monotype font in order for spacing to align better.
  • A repository was added that allows for easy sharing of values between scripts. The repository of values can also be loaded and saved as needed.
    • The repository is used in the following manner:
      • To add or change a value:

IPS.Scripting.SharedValueRepository.AddOrUpdateDoubleValue(“MyVal”, 10.0);

      • To access a value from a different script:

double val = IPS.Scripting.SharedValueRepository.GetDoubleValue(“MyVal”);

    • The repository supports the following functions:
      • void AddOrUpdateDoubleValue(string Name, double Value);
      • void AddOrUpdateIntegerValue(string Name, int Value);
      • void AddOrUpdateBooleanValue(string Name, bool Value);
      • void AddOrUpdateStringValue(string Name, string Value);
      • void AddOrUpdateValue(string Name, System::Object^ Value);
      • double GetDoubleValue(string Name);
      • int GetIntegerValue(string Name);
      • bool GetBooleanValue(string Name);
      • string GetStringValue(string Name);
      • System::Object^ GetValue(string Name);
      • bool HasValue(string Name);
      • void SaveRepository(string FileName);
      • void LoadRepository(string FileName);

Drawing

  • An application setting has been added so that new pages have the viewport in the middle of the page. This setting is false by default.
Fig. 16 - Viewpoint in Center for New Pages Setting.

Snaps

  • Added an application wide setting: “Turn snap before run on by default”. This option is false by default, but a user can make it true, then it will be on for all new projects.
Fig. 17 - Turn Snap Before Run on by Default Option.

PCF Importing

  • Added a default import mapping that imports components other than only pipes as applicable.
Fig. 18 - Import Configuration Dialog.

Data Transfer Links

  • Implemented bi-directional data transfer capability for Data Transfer Links. The user can drag and drop from the left or the right side of the Data Transfer Link Setup dialog. The direction of the transfer is indicated by the arrows. Bi-directional transfers show arrows at both sides, as seen below.
Fig. 19 - Bi-Directional Data Transfer Capability for Data Transfer Links.
  • The letters F and C are displayed on a Data Transfer Link when a factor (F) or constant (C) is used.
Fig. 20 - F and C Displayed on Drawing Page.

Flow Path Graphs

  • Added the ability to plot Flow Path Graphs along the Rotating Annular Gap length and length increments of the Rotating Channel.

Two Phase Pressure Loss

  • Added output for Lockhart-Martinelli two-phase pressure loss calculations and the parameters that are used in its calculation as results.

Heat Transfer

  • Reynolds and Prandtl Number results have been added to convective subdivision element results.
  • Errors have been implemented to prevent Composite Heat Transfer element to Composite Heat Transfer element connection via a solid Node with non-adiabatic boundary conditions, since these are non-physical configurations.

Relap Coupling

  • Added an option to Relap simulation to save every transient step’s output file.
  • Fixed the problem where the minor edits were deleted in Relap files.
  • Allow users to add additional inputs or outputs to the Flow solver coupling. This is especially useful to extract additional results from the Relap simulation.

Command Logging

  • Flownex® logs many of the user actions now to a file. This log is useful to keep track of what was changed in a project and when.
  • The command log files are located in the project folder in the sub folder CmdRec\Logs.
  • Each new Flownex® session starts a new log with the date and time of the session. The user, operating system and computer name are recorded at the top of each file.
  • The following user actions are recorded to the file:
    • Interaction with the drawing canvas (e.g. adding, deleting, selecting components).
    • Interaction with pages (opening, closing, selecting pages).
    • Interaction with snaps (saving, loading).
    • Setting component inputs.
    • Solving commands (solve steady state, transient, stopping etc.).
Fig. 21 - Command Logging Text File.

FMI

  • Exported Flownex® FMUs now launch a separate console that communicates with the Flownex® instance that is launched. This is done to enable the FMU binary to be unloaded by the master simulator. The binary was previously locked until the master simulator process shuts down due to the CLR being loaded as part of the binary. All CLR code is now loaded in the separate console process.
  • The locked binary gave a warning or error when the FMU was unloaded, even though the FMU functioned correctly.

NIST Importer

  • The NIST fluid importer was updated to list all the available fluids and mixtures in NIST.

BUG FIXES

Result Layers

  • Fixed the bug where Result Layers did not update during transient in 3D view.
  • Result Layers are now updated after a Snap is loaded.
  • Fixed absolute value usage in Result Layers.
  • Changed mass flow, volume flow and velocity built in Result Layers to use absolute values.

Velocity PID

  • Fixed the bug where state of the PID was not saved to Snaps.

Drawing Texts

  • Fixed the problem when pressing the Cancel button when changing settings for the drawing texts, the operation was not fully cancelled.

Excel Component

  • Fixed bug where Snaps were not correctly loaded for Excel component if an editor wasn’t opened previously or network wasn’t solved previously.

Results Overview

  • Fixed the bug where the Solving On/Off state was not saved with the project. This meant that the user had to turn it off again every time the user opened a project.

API

  • Fixed a bug where an exception in the user interface was sometimes shown when using the API. This happened when using the API with a network with open graphs.

Ducting

  • Added tooltip results for all Junction component types.

Positive Displacement Pump

  • Fixed the unit for NPSH in the characteristic chart.

Excess Flow Valve

  • Added tooltip results for the Excess Flow Valve.

Nodes

  • Fixed the error where a user could connect multiple views of a node or element to a fiber that should only allow a single connection. This caused an error in the solver that was not very descriptive and hard to trace.

Warnings and Errors

  • Updated MATRIX_NOT_POSITIVE_DEFINITE error to also show the Node related to the error.
  • Added a warning when a large non-normalized energy residual is detected at a node and the energy equations may not have converged.

Neutronics Script

  • Implemented Total power result and Transient Fix power option correctly for the Neutronics Script.
  • Initialisation was not correctly called for the Neutronics Script and a Cleanup function has been added.

Boundary Conditions

  • If Mass source fraction is disabled after being specified, the mass fraction did not reset to 1 and 0, as with the normal mass fraction specification.
  • Fixed the problem where a Data Transfer Link could write and change the temperature of a Boundary Condition even if the option to specify temperature is not turned on.

Heat Transfer

  • Addressed Conduction results that went out of sync when changing from upstream Convection results to Conduction element results after an increment other than 1 is selected for the upstream Convection results.

Two Phase Flow Heat Transfer

  • The Steiner and Taborek reference coefficients are now specified in the two-phase fluid specification file and are no longer hard coded. Approximations have been provided for Flownex® fluids that are not featured in the original Steiner and Taborek paper, and a warning is issued.
  • The heat flux at the critical heat flux conditions is used when the wall temperature commensurate with the critical heat flux that is calculated. Previously the current heat flux result was used in the wall temperature calculation.
  • Updated the Groeneveld critical heat flux and film boiling lookup tables to the latest versions.

V&V

  • The Validation Runner was renamed to Verification Runner. The Verification Runner is now included in the Nuclear module and uses the normal Nuclear license and does not require a separate license anymore.

Actions

  • Fixed the problem where actions wrongly set integer values at the start of an action. The initial value of the integer property was always added as an offset. This was reported as a multiplexer problem but is a general problem.

sCO2

BLOG | 23 FEBRUARY 2022

UNDERSTANDING sCO2 CYCLE EFFICIENCY THROUGH SIMULATION

“The high specific heat near the critical point is the most significant factor for the increased thermal efficiency of the cycle and will be further explained in this post where we look at the compressor work for common power cycles.”

DIFFERENCES IN THE PERFORMANCE OF THE SCO2 POWER CYCLE COMPARED TO COMMON POWER CYCLES

As increased energy efficiency becomes more critical in all forms of power generation, new cycles are being considered by many industries to replace the traditional Rankine cycle. Supercritical carbon dioxide is currently being considered as a potential successor due to a wide range of advantages, such as a reduced physical footprint, the ability to respond faster to load changes and increased thermal efficiency. Of all these advantages, the most difficult point to understand in my opinion is the increased thermal efficiency. In this post I will highlight the key differences in the performance of the sCO2 power cycle compared to common power cycles to aid in understanding the contributing factors to an improved thermal efficiency of the sCO2 power cycle.

I will skip over an extensive definition of a supercritical fluid and simply mention that it behaves like a gas in the sense that it fills a space with no liquid level while having a similar density to that of a liquid. 

"The only other important characteristic about sCO2 is that the specific heat is about 5 times higher near the critical point when compared to the conditions at a higher temperature and entropy."

The high specific heat near the critical point is the most significant factor for the increased thermal efficiency of the cycle and will be further explained in this post where we look at the compressor work for common power cycles.

For this post I will be comparing sCO2 with standard air in the same cycle configuration to understand the advantages of using CO2 as the working fluid. I will also compare these results to a typical Helium Brayton cycle commonly used in nuclear power generation applications. The sCO2 cycle I have simulated is the recompression Brayton cycle (RCBC), this configuration is considered the ideal cycle for sCO2 and has a good balance of complexity and efficiency.

THE FIGURE BELOW SHOWS THE SCO2 RCBC MODEL BUILT IN FLOWNEX:

The cycle is configured for a turbine inlet temperature of 700°C and a cooler outlet temperature of 35°C. The maximum and minimum pressures are set to 20MPa and 7.5MPa respectively.

For direct comparison I built an identical cycle using air as the working fluid. Lastly, I built a Helium Brayton cycle with a recuperator and intercooler. This is a typical cycle configuration for Helium and uses the same number of compressors and heat exchangers as the RCBC cycle. 

THE FIGURE BELOW SHOWS THE HELIUM BRAYTON CYCLE MODEL BUILT IN FLOWNEX:

The turbine inlet temperature and cooler exit temperature for the Helium Brayton cycle were both set to the same values as those used in the sCO2 and air RCBC cycles. The maximum and minimum pressures were modified to 8MPa and 4.21MPa respectively, to be more representative of a typical Helium power cycle.

For all the cycles considered I made use of the optimiser tool available in Flownex to maximise the cycle efficiency using the unconstrained parameters in the cycles. For the RCBC cycles I varied the bypass flowrate fraction and found the optimal values for maximum cycle efficiency to be 26.9% for sCO2 and 3.9% for air. For the Helium cycle I varied the pressure at which the intercooler operates and found the optimal value to be 5.63MPa. Lastly, I used the designer to vary the mass flowrate for each cycle to achieve a heat input of 10MW. With the easy-to-use user interface of Flownex the 3 models were not only built but also optimised in under an hour. 

THE GRAPH BELOW SHOWS THE EFFICIENCY FOR EACH OF THE 3 CYCLES:

As hinted at in the introduction, it can be clearly seen that the efficiency of the sCO2 RCBC cycle is higher than the other cycles. To understand this a bit better, let’s look more closely at the differences in performance between the 3 cycles. Firstly, we can look at the total compressor power required by each cycle.

The first thing to note is the large difference in the compression power required by the sCO2 RCBC cycle and air RCBC cycle. Since the pressures in the 2 models are the same, the difference must be down to the difference in fluid properties. As I mentioned in the beginning of this blog, the specific heat for CO2 near the critical point is very high (±6.0 kJ/kg.K) and for the sCO2 RCBC cycle, this point coincides with inlet of the compressor. For comparison, the specific heat of air at the same point in the air RCBC cycle is ±1.1 kJ/kg.K. This difference in specific heat along with the increased density of CO2 (273 kg/m3 for CO2 compared to 85 kg/m3 for air) results in a large decrease in the amount of power required to compress the CO2. When looking at the Helium cycle, despite the pressure ratio being 1.9 while the pressure ratio for the sCO2 cycle is 2.66, the compression power is still significantly less for sCO2.

This large difference in compression power required can be considered the driving factor behind the high efficiency of the sCO2 RCBC cycle, provided that the turbine power output is not significantly less for this cycle. To investigate this, I’ve graphed the turbine power for each of the 3 cycles as well as the ratio of compressor power to turbine power below:

The graphs above prove that despite the lower turbine power output, the compression power required for the sCO2 RCBC cycle is significantly less than that for air or Helium when accounting for the decreased turbine power of sCO2.

To summarise, through simulation we have been able to learn that the efficiency gains of sCO2 are due to the reduced work required to compress the fluid. This reduced work can be attributed to the high specific heat and density of CO2 at the compressor inlet.

There are many more exciting problems that need to be resolved before sCO2 power generation cycles become commercialised such as the transient operation of these cycles. If you’re interested in solving some of these more challenging problems using Flownex please feel free to take a look at our sCO2 industry page and request a demo. My colleagues and I are excited to meet you and explain all the functionality of our software that makes it an ideal tool for sCO2 cycle design.

VINCENT BRITZ

VINCENT BRITZ

Vincent is a Thermofluids Solver Developer at Flownex who specialises in Gas Turbines and Power Cycle Analysis.

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Academic

BLOG | 14 December 2021

FLOWNEX AS A TEACHING TOOL

“Through simulation and experimentation, students are exposed to real systems and can gain a better understanding of fundamental concepts and relationships.”

Mastering your course

One of the great challenges of education, for both student and lecturer, is to ensure the knowledge gained is also retained past the last exam paper. This is especially difficult in the Engineering environment as class schedules are fully packed and the time to reflect and internalise key concepts become limited. Lecturers are faced with the challenge to convey complex subject matter in a short timeframe, while simultaneously ensuring students do not simply memorize but understand the material they are presented.  Often these fundamental concepts are both novel and confusing to students at first, and further subject matter only builds on this foundation.  If it is not firm and unyielding, students may find themselves at the end of a semester not having fully mastered their course.

A practical teaching element is usually incorporated in courses to facilitate discovery and understanding with subsequent tests and assignments to stimulate long-term memory recall of the material. Through simulation and experimentation students are exposed to real systems and can gain a better understanding of fundamental concepts and relationships. By incorporating simulation models, students can assess and experiment on simple and complex networks they would not have had access to otherwise.

“Flownex makes this experimentation possible without neglecting the mathematical grounds and fundamental principles that it is based upon.”

In a classroom environment, components are usually evaluated separately and in idealised conditions, while in practice this is almost never the case. The simulation of thermal flow components and networks facilitates the practical and realistic application of conditions to a 2-D representation.

Components are interconnected and dependent on the response of its neighbouring connections as well as the overall network. By giving students the ability to actively influence the conditions of an example, there exists a greater chance of them understanding the principles. Changing the pressure and seeing the system respond in real time solidifies the relationships drawn by the theoretical knowledge much more than simply being told they exist.

Using Flownex as an advantage

With material covering Thermodynamics, Fluid mechanics and Heat transfer, Flownex facilitates the understanding and experimentation of fundamental concepts. Lecturers can use this to their advantage by demonstrating different concepts and conditions whilst enabling students to easily learn on their own through experimentation. As Flownex is used in more than 40 countries around the world, students gain important experience in thermal flow simulation and evaluation.

Rankine cycle example

An essential cycle to understand in practice is the Rankine cycle, as most steam-based power plants are based on this closed cycle. The Rankine cycle, usually utilising water as a fluid, starts by pressurizing the fluid with a pump. The pressurized fluid is then heated beyond its boiling point in the boiler to produce steam which is expanded through a steam turbine that extracts mechanical energy from the system through the shaft. Finally, the steam is cooled back to a liquid state in the condenser and returned to the pump to start the cycle again.

To boil the fluid any heat source of adequate temperature can be used. Historically the combustion of fossil fuels was used, but more sustainable heat sources like nuclear and solar radiation are frequently used and becoming more prevalent.

The network and efficiency of the Rankine cycle is highly influenced by the temperature and pressure of the cycle and can be increased by:

  • Lowering the condenser pressure,
  • Increasing the pressure during heat addition, and
  • Superheating the steam.

Cycle variations of the basic Rankine like the reheat and regenerative cycles offer improved efficiency at the cost of simplicity.

In Thermodynamics the Temperature – Entropy diagram (T-s diagram) is most frequently used to analyse energy transfer, as the work done by or on, and the heat added or removed from the system can easily be visualised. Of course, the system response can be calculated from the first principles, but the real-time response of the system allows faster analysis with less complexity.

What becomes apparent in the simulation of such a system is the inherent energy losses of real thermodynamic cycles due to inefficiencies in the components. Power cycles are often evaluated on their thermal efficiency, as the ratio of the mechanical output to the thermal input gives a better sense of the real-world cycle performance. The track bars can be adjusted in the Rankine example to demonstrate the system response and the increase/decrease inefficiency.

An intuition of system and cycle response usually formed through years of experience can easily be developed by interacting with simulated examples. The visual and transient response presented by interacting with the simulated example gives students the opportunity to better understand the complex and sometimes abstract theoretical material.

“The visual and transient response presented by interacting with the simulated example gives students the opportunity to better understand the complex and sometimes abstract theoretical material.”

Creating a stimulating environment and equipping the students with the right tools to reach a point of deeper understanding and experience is the true calling of a lecturer. We encourage you to consider the engineers you send out into the world and how you can prepare them to be the best they can be.

Let Flownex help you in that journey!

Riani Wagner

Riani Wagner

Riani is the technical sales engineer for Flownex direct and has a passion for promoting education.

Hydrogen

BLOG | 29 October 2021

THERMAL AND WATER MANAGEMENT OF THE PROTON-EXCHANGE MEMBRANE FUEL CELL

“Flownex gives you several options and capabilities for modelling the system components and fuel cell stack of the PEM fuel cell.”

HYDROGEN AS A PATHWAY TO DECARBONISATION

As climate change has become a rising concern, more and more countries have dedicated themselves to a zero-emissions goal. Many have now joined the Paris Agreement on climate change and are aiming for net-zero by 2050.

“When using hydrogen within fuel cells, no CO2, or other hazardous emissions such as SOx’s or NOx’s are produced.”

Green hydrogen (hydrogen produced from renewable energy) has been one of the main topics of discussion as the world is shifting efforts to a zero-emissions solution. The combustion of hydrogen with pure oxygen will release only heat and water as by-products – without any direct emissions. Combustion of hydrogen in air at high temperatures will, however, result in NOx emissions: a dangerous group of gasses that can cause respiratory problems, headaches, eye irritation, and other impacts on human and animal life. When using hydrogen within fuel cells, no CO2, or other dangerous emissions such as NOx are produced. Due to this, fuel cell technology has been getting a lot of attention in the past few years.

THE PROTON-EXCHANGE MEMBRANE FUEL CELL

Fuel cells can be used in a wide variety of applications, from transportation to electrical systems on a space craft. Fuel cells also have several advantages when compared to conventional combustion-based technologies. One being that it has a much higher efficiency, even above 60%. Another, as mentioned, no harmful emissions are produced during the operation of the fuel cell. 

“This highlights the reason why so much focus is put on fuel cells: it is an energy source with the only emission being water and heat.”

The Proton-exchange membrane (PEM) fuel cell has the advantage that it operates at lower temperatures (at around 80oC) when compared to other fuel cells. This means that it has a quick start up time because less warmup time is needed. It also has a high power density and is low in weight. The fuel cells used in electric vehicles are most commonly the PEM fuel cell.

  • Hydrogen is supplied from a high-pressure tank into the anode side of the PEM fuel cell where it comes into contact with the electrolyte and splits up into Hydrogen ions (protons) and electrons.
  • The electrons cannot move through the membrane and is forced move through a conductor.
  • Air is suppled at the cathode side using a compressor.
  • When the electrons arrive at the cathode side through the conductor, the oxygen reacts with the hydrogen ions to form water and heat.

This highlights the reason why so much focus is put on fuel cells: it is an energy source with the only emission being water and heat. The fuel cell can be connected to a load, such as an electric motor in a hydrogen vehicle, and can be used in conjunction with a battery to accommodate for certain high demands.

PEM FUEL CELL SYSTEM COMPONENTS

Even though the PEM fuel cell has several advantages, compared to other conventional fuel cells. There are some technical challenges.

“So, to ensure that high performance is achieved, proper thermal and water management is required for the PEM fuel cell.”

  • The membrane used as an electrolyser must be kept at a specific humidity to allow for adequate hydrogen ion transfer.
  • If the membrane dries out, performance will be decreased, whereas when there is too much water content in the air, flooding will occur blocking the channels and preventing proton transfer.
  • To keep the humidity within a certain range has proven to be quite challenging. A humidifier is needed on the air supply line to increase the humidity as required.
  • A thermal management system is also added to extract the heat generated due to the chemical process at the cathode side.
  • If this heat is not removed, the fuel cell will heat up and damage the membrane, reducing the performance significantly.

So, to ensure that high performance is achieved, proper thermal and water management is required for the PEM fuel cell.

CAPABILITIES INCLUDED IN FLOWNEX

Flownex gives you several options and capabilities in modelling the system components and fuel cell stack of the PEM fuel cell.

Fluid capabilities

Flownex includes compressible gasses, two phase fluids, mixtures, etc. Custom fluids can be created from properties defined in literature. Higher incremented fluids or properties at different operating conditions can also be imported from NIST. The mass or mole fraction of mixtures can also specified and Flownex allows for changes in these fractions throughout the network. More important for the air side: Flownex allows for property calculation of humid air with detailed psychrometric charts available.

Chemical reaction models for the fuel cell stack 

The complex physics associated with the fuel cell stack can be included in the Flownex network. This allows for the analysis of how the entire fuel cell will behave at different conditions. Custom stack models can be implemented using scripting languages such as C#, Phyton and EES. Flownex can also be integrated with software packages such as Cantera and Matlab to include external stack models into Flownex. Reduced order models (ROMS) can also be imported using the Flownex FMI capabilities.   

With the ability of coupling Flownex to Ansys Fluent, the advanced fuel cell models in Fluent can be directly coupled to a network in Flownex. Ansys Fluent has specific addon modules to include the complex physics of the proton exchange membrane fuel cell, the solid oxide fuel cell and electrolysis. This means that the geometry of a fuel cell can be created, then imported into Ansys Fluent and the performance will then be calculated with these advanced fuel cell modules. The performance can then be coupled to Flownex using the Ansys Fluent coupling components or by importing a ROM exported from Fluent.

Pump and compressor models

Flownex includes a large library of turbos and pumps – including centrifugal and positive displacement compressors, centrifugal and positive displacement pumps, turbines, etc. Detailed compressor maps and pump charts can be included in these components.

Discretised heat exchange models

Flownex includes different heat exchange models, such as common geometries – finned tube heat exchangers, plate heat exchangers and so on. Flownex also allows for custom correlations for heat transfer to be included. Flownex also gives you the capability of coupling to Ansys Mechanical, allowing the user to integrate complex conduction problems into Flownex.

Typical pressure drop models

Pressure drop models are available such as pipes, valves, etc. The pressure drop models ranges from basic components to more detailed components to replicate real life components.

Steady state and transient solver

Flownex also allows for steady state as well as transient solving. Including an implicit transient solver which allows for very large timesteps for long transients.

Control models for transient simulations

Lastly, Flownex includes a large control library with analogue and digital controls such as PIDs, filters, switches, etc.

MODELLING THE PEM FUEL CELL

Pre-modelling setup

Flownex has a comprehensive list of standard components found in typical applications. Flownex also gives you the ability to create and use custom components.

Before modelling a network, it is custom to apply a background image on which the components can be placed. This will simplify the network building process and give the user a more structured approach.

Modelling the network

Flownex uses a drag and drop approach in the design of a thermal fluid systems. This user-friendly approach makes using Flownex easy for beginners.

Custom components

Custom components can be designed and used in Flownex. This allows the user to include non-standard components into the network.

One of the components that can be created using a custom component is the fuel cell stack. As mentioned previously, the physics of the fuel cell stack can be included using several methods. For this instance, a scripting component utilizing C# will be used in this example.

The script in the above fuel cell stack is used to calculate the hydrogen ion transfer rate from the anode (left) to the cathode (right), the stack current and voltage, heat generation, etc. The CEA Gibbs reactor calculates the chemical reaction of the hydrogen passed through the membrane and the oxygen in the air at the cathode side.

Anode and cathode side components

Custom components can be created for the humidifier as well as the condenser. The anode and cathode side components can then be dragged and dropped into the window.

The humidifier will ensure that the water content of the air stream is sufficient to prevent dry out of the membrane. The condenser is used to extract some of the water out of the stream which can then be introduced at the humidifier.

Adding thermal management

The heat being generated within the fuel cell can be extracted using a thermal management system. This can be quickly implemented in Flownex to ensure that this system is properly designed, preventing any damages to the membrane.

Adding control

A control system can be added to the network using components such as the PID controller. These components can be added to

  • Keep the fuel cell stack temperature consistent by changing the speed of the thermal management system’s fan.
  • Ensure the correct hydrogen flow rate according to the required power by changing the valve fraction opening.
  • Keeping the correct air to fuel ratio by changing the compressor speed at the cathode side.

In summary, Flownex is a tool that can be used by engineers to design, optimise, and evaluate thermal fluid systems. In the case of the PEM fuel cell where the thermal and water management of the stack is extremely important, Flownex provides a way of understanding the behaviour of these systems.

“It can be used to simulate the chemical reaction within a PEM fuel cell using different methods and allows for coupling with external software.”

Flownex gives you the ability to complete a full system design. It can handle any complex fluids and gives you the ability to include custom fluids. It can be used to simulate the chemical reaction within a PEM fuel cell using different methods and allows for coupling with external software. It includes a large library of components which ranges from basic to more complex models. It also gives you the ability to create your own custom components. Flownex also has numerous heat exchange models from basic to more advanced. It also includes various pressure drop models such as pipes, valves and so on. Flownex allows for steady state and transient solving allowing you to understand the behaviour of the system in its entirety.

Flownex is a useful tool for engineers in the designing and analysis process. As more emphasis is put on a cleaner zero emissions world, the design of greener energy solutions has become imperative. Using a systems simulation software such as Flownex will allow for rapid design of such systems, saving on cost and time.

Leander Kleyn

Leander Kleyn

Leander is a simulation design engineer at Flownex who specialises in Propulsion and Energy systems.