Computational Fluid Dynamics for Centrifugal Pumps

Pumps are important for many common systems which deal with water, such as heating circulating flows, consumer or industrial water supply, fountains, and fire protection systems.

Pumps are classified into two major categories: Rotodynamic pumps and positive displacement pumps (piston pumps). Rotodynamic pumps can be further classified as axial pumps, centrifugal (radial) pumps, or mixed pumps.

Centrifugal pumps are the devices which impart energy to the fluid (liquid) by means of rotating impeller vanes, and the fluid exits radially from the pump impeller. Such pumps are simple, efficient, reliable, relatively inexpensive, and easily meet the pumping system requirements for filtration. This is a great pump choice for moving liquids from one place to another using pressure.

Types of Rotodynamic Pumps
Figure 1. Types of Rotodynamic Pumps

Centrifugal Pump Design

A centrifugal pump is a very common component in turbomachines, but as with any component, it still needs continual improvement in the design methodology, from conceptual level to the final product development including testing at different levels. The challenge is to design a pump with improved efficiency while minimizing the possibility of cavitation.

Need of Numerical Simulation

Years ago, engineers performed prototype testing at each level of design to check the performance (which was very costly and time consuming). Now with advancements in the computation technology and resources, it is comparatively easier to design high efficiency pumps within a short duration of time. These simulations can be done with a computer, so, the number of physical prototypes required is greatly reduced. The main advantage of numerical simulation is that it allows engineers to virtually test the CAD model early in the design process, and provides flexibility for engineers to iterate the design until getting the required performance.

Computational Fluid Dynamics for Centrifugal Pumps

Computational fluid dynamics (CFD) replaces the huge number of testing requirement. This not only shortens the design cycle time but also significantly reduces development cost.

In a CFD model, the region of interest, a pump impeller flow-path for example, is subdivided into a large number of cells which form the grid or mesh. The PDEs (partial differential equations) can be rewritten as algebraic equations that relate the velocity, pressure, temperature, etc. in a cell to those in all of the cell’s immediate neighbors. The resulting set of equations can then be solved iteratively, yielding a complete description of the flow throughout the domain.

To accomplish CFD simulations, there are several software programs available, but user must select a very well validated software that can provide and easy user interface, automatic mesh generation and flexibility to modify the geometry to perform optimization without needing to move to some other software platform.

In the current trend, automatic mesh generation tools like AxCFD™ are employed in the AxSTREAM® software platforms which reduces the turbomachines meshing complications and generate good quality mesh in considerably short timeframe which can capture the accurate flow features needed. Figure 2 shows the discretized impeller and pressure contour after CFD analysis.

Discretized Impeller and Pressure Contour After CFD Analysis
Figure 2. Discretized Impeller and Pressure Contour After CFD Analysis

AxCFD™, in AxSTREAM® platform, provides user an opportunity to perform CFD analysis by applying standard methods of full three-dimensional CFD, axisymmetric CFD (meridional), and blade-to-blade analysis. User can even perform optimization of the blade profiles and other geometrical parameters within the AxSTREAM® platform and perform CFD simulation without altering any CFD settings.

An Introduction to Accurate HVAC System Modeling

HVAC (Heat, Ventilation and Air Conditioning) is all about comfort, and comfort is a subjective feeling associated with many parameters like air quality, air temperature, surrounding surface temperature, air flow and relative humidity. For example, while it is easy to understand how the temperature of the air in your living impacts how good you feel, the surfaces with which you are in contact also strongly affect your comfort. For example, last night I got out of bed to clean up after my dog who thought it would be a good idea to swallow (and give back) her chew toy. If I was wearing my slippers, it would have been much easier to go back to sleep between the warm bed sheets without the discomfort of waiting my cold feet warm up to normal temperature.

Speaking of sleep discomfort, many stem from HVAC imbalances.  If you wake up in the middle of the night quite thirsty, then you should probably check how dry your bedroom is. The recommended range is 40-60% relative humidity. A higher humidity puts you at risk for mold while lower humidity can lead to respiratory infections, asthma, etc.

Now that we know how HVAC contributes to our comfort, let’s look at the HVAC unit as a system and see its role, functioning and simulation at a high level. The following examples provided are for a house, but similar concepts apply to residential buildings, offices, and so on.

Controlling Temperature

The easiest parameter to control is the air temperature. It can be set by a thermostat and regulated according to a heating or cooling flow distributed from the HVAC unit to the different rooms through ducting. Without the introduction of thermally-different-than-ambient air, the house will heat or cool itself based on a combination of outside conditions and how well the building is insulated. Therefore, to keep a constant temperature a certain amount of energy must be used to provide heating (or cooling) at the same rate the house is losing (or gaining) heat.  This is a match of the house load and heating/cooling capacity. Figure 1 provides a graph of the energy needed.

Illustration of dependency of house load and heating capacity on outside temperature
Figure 1 Illustration of dependency of house load and heating capacity on outside temperature

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Turbojets – Basics and Off-design Simulation

The Brayton cycle is the fundamental constant pressure gas heating cycle used by all air-breathing jet engines. The Brayton cycle can be portrayed by a diagram of temperature vs. specific entropy, or T–S diagram, to visualize changes to temperature and specific entropy during a thermodynamic process or cycle. Figure 1 shows this ideal cycle as a black line.  However, in the real world, the compression and expansion processes are never isentropic, and there is always a certain pressure loss in the combustor.  The real Brayton cycle looks more like the blue line in Figure 1.

Ts_Real_Brayton_Cycle
Figure 1 T-S diagram for ideal and real Brayton cycle
(Source: https://commons.wikimedia.org/wiki/File:Ts_Real_Brayton_Cycle_2.png)

The four stages of this cycle are described as:

1-2: isentropic compression

2-3: constant pressure heating

3-4: isentropic expansion

4-0: constant pressure cooling (absent in open cycle gas turbines)

The most basic form of a jet engine is a turbojet engine. Figures 2a and 2b provide the basic design of a turbojet engine. It consists of a gas turbine that produces hot, high-pressure gas, but has zero net shaft power output. A nozzle converts the thermal energy of the hot, high-pressure gas at the outlet of the turbine into a high-kinetic-energy exhaust stream. The high momentum and high exit pressure of the exhaust stream result in a forward thrust on the engine. Read More

Design of a Competitive Axial Turbine for Downsized Turbocharged IC Engine

The following article was written by Lorenzo Baietta a student at Brunel University London and presented at the International CAE Conference Poster Competition in Vicenza, Italy. Lorenzo’s work placed 6th overall and 1st among articles written by a single author. We’re thrilled for Lorenzo and excited to continue supporting universities and young engineers all over the world. 

The continue research for engine efficiency improvements is one of the major challenges of the last decades, leading to the design of highly downsized boosted engines. Among other boosting strategies, turbocharging allows to recover part of the exhaust gas energy, improving the overall efficiency of the power unit. However, turbochargers lead to less responsive power units because of the widely known turbo-lag effect due to the inertia of the rotating parts in the system. With engine manufacturers testing different concepts to reduce this effect, for both commercial and motorsport applications, the work is about the development of a low inertia turbocharger axial turbine, evaluating pro and cons of several design solution. The idea is to initially evaluate the performance (mainly efficiency) difference between prismatic and twisted blades turbine for different size ranges. In fact, as one of the issue of axial turbines compared to radial ones is the production cost, the use of low aspect ratios blades, in such a way to minimize the difference between the use of 3D optimized turbines and prismatic turbines, should allow for more cost-effective solutions to be implemented.

After selecting a specific engine to develop the axial turbine, several CAE techniques were used to verify the idea and to obtain the best possible solution. The OEM turbocharger was 3D scanned, with a blue light technology stereoscopic optical system, to acquire accurate geometry data and calculate several properties. A 1D engine model, calibrated on the dyno, was used to calculate the aerothermal boundary conditions for the design of the turbine every 1000rpm from 1000 to 6000 to have all the required boundary conditions data to design/test the turbine at different engine operating points.

Several turbines were preliminary designed and optimized with AxSTREAM® and their performances were evaluated considering many parameters, mainly focusing on the reduction of the turbocharger spool-up time. The AxSTREAM® preliminary design module resulted crucial to compare the performance of over 1 million turbines allowing the comparison of the results with different loss models and a wide number on flow boundary conditions and geometrical constraints.

Turbine Design Methodology – Preliminary Design in AxSTREAM

The generated turbine preliminary CAD and the scanned OEM turbine mesh were used along with CAM programs at an external company to estimate the production cost of different solutions. A final turbine design was chosen, among the pre-designed ones, to be validated with generation of complete maps within the AxSTREAM® streamline solver which allowed an initial verification of the suitability of the turbine for the desired application. A further optimization of the results was obtained with increasing precision CFD simulations in the AxSTREAM® Profiling and CFD modules. 2D cascade simulations were used to optimize the stator and rotor airfoils in the Profiling module. Then, in AxCFD™, axisymmetric CFD simulations were run at several operating points to quickly investigate the suitability of the generated design for the whole power unit operating range. To conclude, full 3D CFD and FEA simulations were conducted to obtain more accurate values and complete the design process of the turbine and finally compare the data of the newly designed turbine and the OEM one.

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Automotive Engine Lubrication System Modelling and Analysis

The lubrication system is one of the most important systems of an engine.

This system should ensure:

  1. Delivery of the required oil amount to the moving parts (e.g.-Bearings);
  2. Dissipation of the heat generated due to friction by circulation of lubricant throughout the system; and
  3. Cleaning of the oil from contamination and impurities introduced during engine operation.

 

To meet the above requirements, the lubricant circulation (lubricant reaching each component) should happen at appropriate pressure and mass flow rate throughout the system.  This is also required in order to avoid cavitation caused by adverse pressure, and excessive heat generation due to less mass flow rate, at any place or particularly at any component. However, sometimes lubricant does not circulate properly to each corner of the system or to the rotating components. In some cases, the rotation of the crankshaft can actually starve the bearings and increase the internal heat due to insufficient supply of lubrication.

To avoid such problems, simulation engineers must model the whole system at all operating modes. They can predict the best system by varying flow rates (volumetric or mass flow rates), system pressures, temperatures, heat flows, as well as by changing the system geometry itself. Such modelling can be performed easily and with sufficient accuracy in a 1D Thermal Fluid analysis tool, such as AxSTREAM NET™ developed by SoftInWay.

It is worthwhile to use a 1D-Analysis tool in this case, because it can be used at any stage of the system design process to explore more options for improving the final design and to reduce development cycle time. The simulation engineer can easily create a model of automotive engine lubrication system, using different elements (components) which are available in the element database of AxSTREAM NET™. The system configuration can also be easily changed at any stage in the design process without rebuilding the complex 3D models.

Let us try to understand how to build a 1D scheme for an automotive engine lubrication system in a 1D tool (AxSTREAM NET™). First, we need to identify the major elements (components) which are part of the automotive engine lubrication system as per their order or sequence in the scheme. A typical engine lubrication system involves components like Oil – sump, strainer, pump and filter, all of which are parts of the initial oil suction line. In addition, the main gallery involves components like flow passages within the connecting rods, crankshaft, and bearings. The typical connections among these elements are shown in Figure 1.

Figure 1 Main elements of a lubrication system
Figure 1 Main elements of a lubrication system

Now let’s see the arrangement of a few components with their specific purposes towards the construction of the whole model.

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Tidal Energy

If you’re looking for clean, free energy… a song comes to mind.

Tide after tide.
If you flow I will catch – I’ll be waiting.
Tide after tide.

With no particular link to Cyndi Lauper, waves just want to have fun so let’s allow them to do so while catching their drift as a potential energy source using tidal turbines.

Introduction:

Wave energy is a form of hydropower used to convert energy obtained from tides into mechanical and/or electrical power. Wave energy is produced when electricity generators are placed on the surface of the ocean. The energy provided is most often used in desalination plants, power plants and water pumps. Energy output is determined by wave height, wave speed, wavelength, and water density.

sea wave during storm
Figure 1 Ocean Waves

How are Tides Generated:

Tidal forces are periodic variations in gravitational attraction exerted by celestial bodies. It is these forces that are responsible for the currents in the world’s oceans. A local, strong attraction on a part of the ocean allied with moving celestial bodies and the rotation of the Earth leads this bulging part of water to meet the adjacent shallower waters of the shoreline which creates the tides.

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Turbine Blade Cooling – An Integrated Approach

It is a well-known fact in the turbomachinery community that the highest temperature achievable at the inlet of the turbine is a critical performance parameter for the turbine. For any given pressure ratio and adiabatic efficiency, the turbine specific work is proportional to the inlet stagnation temperature. Typically, a 1% increase in the turbine inlet temperature can cause a 2-3% increase in the engine output.

Increase in net power output of a gas turbine over a one percentage point rise in turbine inlet temperature
Figure 1 Increase in net power output of a gas turbine over a one percentage point rise in turbine inlet temperature

The major limitation for the maximum achievable value of the turbine inlet temperature comes from the material used for the turbine. The maximum material temperature has to be kept in check for multiple reasons, from the physical integrity to the structural reliability, and resulting temperature needs to be less than the turbine blade material’s maximum temperature.

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Computational Fluid Dynamics for Centrifugal Compressors

In today’s world where “time is money,” each and every industry involving turbomachinery wants to deliver their high performance products in the quickest time possible. Computational fluid dynamics (CFD) replaces the huge number of testing requirements thus not only shortening the design cycle time, but also reducing development costs.

Today with advancements in computational resources, numerical methods, and the availability of commercial tools, CFD has become a major tool for the design phase of a project. With a large number of validations and bench markings available on the applicability of CFD for centrifugal compressors, it has become an indispensable tool for the aerodynamic designer to verify the design and understand the flow physics inside a compressor’s flow path. However, CFD is still computationally expensive and requires a high level of user-knowledge and experience to get meaningful results. CFD analysis can be performed with and without considering viscous effects of the flow. The inclusion of viscosity into the flow introduces additional complexities for choosing the most appropriate turbulence closure model. CFD however, has some limitations due to:

  • – Errors created during modeling where the true physics are not well-known and are very complex to model.
  • – Multiple approximation and model errors created during the calculation process (such as mesh resolution, steady flow assumption, turbulence closure, geometric approximation, unknown boundary profile etc.). These approximations impact the calculations of local values of vital parameters.

In CFD for example, if the 1D design is not accurate, (stage loading and blade diffusion factors etc.), then CFD cannot turn out a good design. It is critical to use a design tool such as AxSTREAM®  which can generate optimized designs with less time and effort starting from the specification.

The preliminary design modules of AxSTREAM® uses inverse design tasks to generate the initial flow path for the centrifugal compressor. By choosing the right combination of geometrical and design parameters from the start, AxSTREAM® reduces the number of design cycle iterations required in generating an accurate design.

This initial design obtained is further analyzed and optimized using throughflow solvers in AxSTREAM® which considers various operating conditions. The throughflow solvers in AxSTREAM®  predict the performance parameters at different sections and stations, and presents the blade loading, flow distribution along the flow path, etc.

The generation of 3D geometry for the impeller and diffuser is another complex activity which is greatly simplified by using the radial profiler and 3D blade design module in AxSTREAM®. The geometry generated in AxSTREAM® is fully parameterized with complete control for the user to modify as and when required. Figure 1 shows a parameterized impeller geometry generated using seven spanwise sections with contours of the curvature.

Parameterized Impeller Geometry
Figure 1. Parameterized Impeller Geometry

In CFD analysis of turbomachines, grid generation becomes a very challenging task due to the geometries of complicated, twisted blades. To achieve reliable CFD results, the grid must resolve the topology accurately to preserve this geometric information. The quality of the grid should be in an acceptable range especially the angle, aspect ratio, and skewness of the grid elements. Automatic mesh generation tools are employed to reduce the turbomachines meshing complications. The AxSTREAM® platform uses AxCFD™ to generate a high quality mesh in considerably short time which captures the accurate flow features.

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Volute Design in AxSTREAM®

Volutes are a tangential part, resembling the volute of a snail’s shell, which collects the fluids emerging from the periphery of a pump/compressor impeller. As such, they are utilized ubiquitously in turbomachinery applications. The words “volute”, “scroll”, “spiral collector”, “housing”, “casing”, “collector chamber”, and “collector” are used interchangeably across different industries. This elegant geometry is also found in nature – the snail is just one example.

Volute in AxSTREAM with Snail Example
Figure 1 – Volute in AxSTREAM® on the left; on the right – Snail – Natural Occurrence of Such Shape

There is a large number of different volute types and applications: centrifugal pumps, axial pumps, centrifugal compressors, axial-flow compressors, radial-inflow turbines, radial fans, and multi-stage blowers, to name a few. Within each group, there is a narrower division on volute types and every application has its own unique features as well as specific properties that can be shared among the group members. The purpose of this post is not to have a detailed discussion of every possible scenario, but rather to show a robust and proven method of volute geometry construction working as a part of aerodynamic design and analysis in a system such as AxSTREAM®.

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Flexible, Fast and High Fidelity Approach to Gas Turbine Unit Part-Load and Off-Design Performance Predictions

Gas turbine (GT) engines are the primary engines of modern aviation. They are also widely used as power propulsion engines for power stations. The specificity of these engines implies they frequently work at off-design/part load modes that occur with:

  1. Different modes of aircrafts:
    1. Ground idle mode
    2. Take off
    3. Maximum continuous mode
    4. Cruising mode
  2. Different ambient conditions
  3. Grid demands (for power generation engines and gas pumping (compressor) stations)

 

Due to the off-design/part load operating conditions, the parameters of the engines might change significantly, which influences not only the engine efficiency, but also the reliable work of the turbine (high temperature at turbine inlet) and compressor (surge zone) at joint operational points. This is why accurate predictions of the gas generator parameters are crucial at every off-design mode.

To define the joint operational point, the compressor and turbine maps which are created for specified ambient conditions can be used. For example, pressure equal 101.3kPa, temperature – 288.15K. Maps method is widely used, relatively simple and allows you to find the needed engine parameters in the shortest time. However, when cooling is present, engine operation at low power modes (ground idle) impede the accurate determination of joint operational conditions based on maps. The significant drawback to the maps based approach is that it does not give the full picture of the physical processes in turbomachine flow paths which is critical for off-design calculations.

Compressor and Turbine Maps
Compressor and Turbine Maps [1]
Utilization of the digital twin concept allows significant increase of the off-design performance calculation accuracy. Use of the digital equivalent of object was introduced in 2003 [2]. Despite this, less 1% of machines that are in use today are modeled with digital twin technology [3]. Utilization of digital twin leads to a significant decrease in time and cost for developing and optimization of an object.

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