13 & 15 September, 2022Continue reading
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.
13 & 15 September, 2022Continue reading
BLOG | 31 August 2022
“Flownex offers the ability to simulate and analyse various scenarios and design conditions, specific to data centres.”
According to the data centre decade report of 2020, the worldwide data centre market has doubled from 2010 to 2019, and it is forecasted to grow even more rapidly. This growth is due to people opting more and more to use cloud-based data storing methods rather than paper-based. Going cloud-based brings advantages like reduced paper trails, lifelong data storage, encrypted data, and easy collaboration on projects.
With the increasing demand for data centres, it has become ever more critical for data centre design engineers to analyse the thermal behaviour of their designs with optimal accuracy and ease. One might want to consider various design conditions when designing a data centre which will require numerous parametric studies or one would like to know if the data centre can withstand a loss of mains power trip. Doing the before-mentioned analyses without engineering software is next to impossible.
Flownex is the industry leader in 1D CFD thermal fluid software for both steady state and transient analysis of large integrated systems. Flownex uses fundamental physics, mathematics and thermo-fluid principles to solve even the most complex thermo-fluid and heat transfer problems. The versatility that Flownex offers means that an engineer can apply it within the data centre industry.
Some of the typical applications of Flownex in the data centre space include the following:
Today I am summarising a thermal ride-through/short break study on a simplified data centre case study. The scenario compares the system’s reaction to a power mains failure at two different ambient temperatures, 39°C and 42°C. The chilled water (CHW) supply setpoint is 20°C, and the CRAH air supply setpoint is 25°C. The total IT load of the data centre is 1.2 MW with only the chillers not on UPS and with a generator start-up time of 20s.
A short break study is one of the more complex transient analyses on data centres and crucial in understanding the design behaviour during a power loss. The few seconds between a power trip and generator start-up can be critical in the uptime of a data centre.
The thermal inertia of the water in the piping system can be captured by importing a PCF file, which is a non-proprietary piping format, typically from Revit into Flownex. The image below displays the piping of the simplified data centre used for this study, imported from Revit into Flownex.
The simplified system consists of eight CRAHs per floor and two floors. The four chillers and HDACs are on the roof. A typical data centre would be more in the order of forty CRAHs per floor and around four to five floors.
At the heart of the CRAH is an incremented finned tube heat exchanger to characterise the heat transfer performance and pressure drop. This finned tube heat exchanger component calculates the change in water temperature through the tubes and the air temperature over the fins.
The CRAH fan is modelled with a variable speed component. The fan curves can be imported into Flownex and assigned to the variable speed component to capture the pressure rise at different fan speeds. The fan control system is also included to vary the fan speed to control the CRAH supply air temperature.
The pressure-independent control valve (PICV) is modelled on the water side. A valve opening or volume flow setpoint is provided to the component. The PICV is linked to a control loop that changes the valve opening or volume flow setpoint to control the CRAH air supply temperature.
For modelling the white space, two major approaches are possible when using Flownex. The first approach is a well-mixed approach, and the second is to couple the system modelled in Flownex with a full 3D CFD co-simulation.
The well-mixed approach assumes that the temperature in the hot and cold aisles is uniform throughout. Each CRAH component has one inlet temperature from the hot aisle and one outlet temperature for the cold aisle. This method substantially simplifies the system, leading to faster solving times. Still, it does, however, require a steady state CFD to calibrate white space recirculation and pressure drop.
For coupling the Flownex system model with a full 3D CFD co-simulation, Flownex can link with 6Sigma Room developed by Future Facilities. A dedicated component is available in Flownex to handle the data transfer between the two software packages and makes co-simulation convenient and hassle-free. This approach is well suited for data centres with a non-uniform load and has fundamental calculations for both white space recirculation and white space pressure drop. However, it comes with the penalty of longer solving times.
For this thermal ride-through study, the well-mixed approach is used. The image below displays the white space and CRAH modelling in Flownex.
Other equipment typically used in data centres are also modelled in Flownex, such as air and water-cooled chillers, HDACs, cooling towers, etc. The image below displays the cooling equipment used during this study: the water-cooled chillers, HDACs, CHW pumps and cooling ring pumps.
It can be pretty challenging to obtain the geometry of the internals of a chiller from the manufacturers. Hence, we have created a reduced-order model that captures the chiller’s performance using the steady state capacity data and chiller restart performance. The chiller’s reduced order model requires no internal geometry or compressor data. This method gives us a robust and accurate chiller model using data accessible from the manufacturer.
The HDAC is a heat exchanger that transfers heat to ambient and uses wetted pads to cool the air flowing over the tubes adiabatically. An incremented finned tube heat exchanger is used to model the heat transfer in the chilled water ring. The spray water used for cooling is modelled using an adiabatic saturation model to account for the humidity increase and temperature decrease fundamentally. The fan speed is controlled with a control loop to manage the cooling.
The chilled water pumps are controlled based on the pressure difference between the hot and cold rings. The pressure differences are measured between the risers on each floor on all four corners. The median pressure difference is then taken and fed to a master PID that calculates the flow setpoint needed to maintain the pressure difference setpoint. The flow setpoint from the master is provided to the slave PID of each pump to control the pump speed to achieve the required flow setpoint.
Flownex has a steady state and a transient solver built into the software and applied to the same components. Therefore, there is no need to create separate models. Once the model is set up, you can run the model. Flownex uses a non-iterative transient solver, a very efficient solver for transient simulations. It is, therefore, possible to have quick turnarounds on studies for different scenarios using the fast-solving capabilities of Flownex.
The key results we will look at today for the thermal ride-through study are the steady state temperature distribution, chiller heat transfer and the cold aisle data hall temperatures.
The below image displays the 3D steady-state temperature distribution of the system.
On the left-hand side of the image below is the heat transfer of the chillers for 39°C ambient, and on the right-hand side for 42°C ambient. At the beginning of the transient, we can see that the chiller capacity is around 320 kW. After 10 seconds, the power trips and after 20 seconds, the generator starts up. One can see that the chiller capacity does not fall to zero after the trip because of how we captured the thermal inertia inside the chiller. Sixty seconds after power is restored, the compressors of the chillers are all online, and cooling can commence according to the restart characteristics of the chiller.
In the above images, one can see the difference that the ambient temperature has on the cooling capacity of the chillers. At a lower ambient temperature, the chillers can do more cooling. For an ambient of 39°C, the chillers max out at 500 kW, but at 42°C, the chillers max out at 400 kW. The constrain is due to the temperature difference between the HDAC and the environment (ambient) being smaller at a higher ambient temperature, limiting the cooling the HDAC can provide to the chiller condenser.
The constraint will have a domino effect on the rest of the system and eventually affect the maximum data hall temperatures experienced.
The average data hall cold aisle temperatures at 39°C ambient are displayed on the left-hand side of the image below. Similarly, on the right-hand side is the results of 42°C ambient.
In the above images, one can see that the maximum data hall temperature occurs for the first floor at both 39°C and 42°C ambient. However, due to the limited chiller capacity at the higher ambient temperature of 42°C, the maximum data hall temperature at this ambient is higher. For an ambient of 39°C, the maximum data hall temperature is around 26.8°C, but at an ambient of 42°C, the data hall temperature sees a maximum of around 27.3°C.
Also, notice the time it takes for the system to reach steady state conditions again. For an ambient of 39°C, steady-state conditions are reached at around 9 minutes, but for an ambient of 42°C steady-state conditions are only reached at approximately 13 minutes. The reason is due to the limited capacity of the chillers when the ambient conditions are 42°C.
The above thermal ride-through/short break study showcases one of the many applications of Flownex within the data centre space. Flownex opens a world of possibilities for data centre design engineers. A complex system such as a data centre can be simulated in both steady state and transient using 1D CFD software. An engineer can now weigh up various scenarios and design conditions using Flownex. Flownex as a tool and the specialised consultation services is well equipped to be part of your journey.
If you are a data centre engineer looking to take your designs and analyses to the next level, look no further. Flownex is the tool for you!
BLOG | 02 June 2022
“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.
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.
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.
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.
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.
June 13 – 17, 2022Continue reading
May 24 – 25, 2022Continue reading
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.
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.
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.
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.
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.
Similarly, the result property displays the fluid component mass fraction results for the expanded mixture of mixtures, as seen in Figure 5.
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.
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:
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.
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.
Higher resolution (4K) compatibility has been added to the Graphical User Interface.
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.
double val = IPS.Scripting.SharedValueRepository.GetDoubleValue(“MyVal”);
March 02 – 03, 2022Continue reading
February 21 – 24, 2022Continue reading
BLOG | 23 FEBRUARY 2022
“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.”
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 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 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 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.
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.
BLOG | 14 December 2021
“Through simulation and experimentation, students are exposed to real systems and can gain a better understanding of fundamental concepts and relationships.”
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.
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.
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.
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:
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.
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!