This post discusses latest developments and enhancements in ANSYS Fluids R19 applications. Maximize your RoI and productivity with the latest ANSYS release.
How we use simulations has changed drastically since its inception. A couple of decades back simulations were majorly used for research purpose. But today it is used for various applications ranging from airplanes to microfluidics. Simulations have also evolved to handle more complex problems in smaller run times. ANSYS, a leading simulation software company is constantly innovating to make simulation easier to use and at the same time making them more robust. With every release, the GUI is getting better and the solver is getting smarter. Hence, without further ado, let’s take a dive into a few enhancements in ANSYS Fluids R19.
In this article, I will discuss some of these developments however, I recommend you to join the upcoming webinar that I will deliver on April 17.
Enhancements for Spray Modelling
The new feature in ANSYS Fluids R19 would significantly reduce the computational effort needed for spray nozzle designers to optimize product performance. CFD has been used for modelling sprays for a while now. Multiple approaches are available for spray modelling namely, full resolution (resolving all the length scales in the spray), semi-empirical (uses empirical correlation for droplet break up and stability analysis to generate droplet data), etc. ANSYS Fluids R19 has significantly enhanced spray modelling using VOF (volume of fluid)-to-DPM (discrete phase modelling) approach. As a result, you can directly track interface instabilities and surface tension effects that result in ligament and droplet formation. Due to this, you’ll get fast, accurate spray breakup and droplet distribution with minimal effort.
Accurate Preventive Maintenance
Engineers seeking to maximize up time and optimize preventive maintenance programs need to reliably predict the location and extent of erosion in pipelines that are carrying particle-laden flows. Previously, static meshes could not account for structural changes in the pipe caused by erosion and its subsequent impact on fluid flow, thereby reducing prediction accuracy. New technologies in Fluent R19 automatically couple structural changes due to erosion with a dynamic mesh so that the simulation more fully captures the degradation arising from erosion.
More Computational Power
To empower the users with more computational power, significant changes have been made to the High-Performance Computing (HPC) solution.
High-Performance Meshing Technologies that help in meshing the complex geometries at lightning speed. Higher Productivity.
All core solver technologies utilize four (4) cores without HPC License Checkout. HPC products add on top of these four cores. Hence, this gives you more value for money.
ANSYS Fluids R19: Other Noteworthy Enhancements
Blade flutter modelling
Risk assessment for Urea Solid Deposition for SCR
Lagrangian wall film
Local residual scaling for multiphase
Shar/Dispersed discretization schemes with Mixture Multiphase
FSI: Accurate leakage flows through narrow gaps
Species mass transport improvements
Cavitation modelling improvements
Native rolling ball fillets
Variable shroud gap
New Workbench templates
In conclusion, this article has only covered the tip of the iceberg. There is much more to learn about ANSYS Fluids R19. Join us on April 17 for the ANSYS Fluids R19 Update Webinar to get the details! Register now.
So, how does Tesla make it possible? Porous media cane help them achieve this. By porous, we can infer a substance to have minute interstices through which fluid may pass through it. Porous material is permeable if the interstices are interconnected or continuous thereby making a fluid to flow through them. Massive amount of consolidated energy wastage (due to improper combustion and left of un-burnt particles) happens due to this impure air. For efficient fuel burning, there is the pressing need to filter air before passing it through any combustion device. Another application that is quite relevant to this topic is of air conditioners – all pervasive at homes and our workplaces. In all these applications, impure air passes through a series of filters. The interstices present in these porous zone filter holds off solid dust particles and parses clean air.
These concepts are ubiquitous in nano- and micro-scale applications, oil reservoirs and geophysics applications, electronics cooling, thermal insulation engineering, nuclear waste repository, biomedical, biological and environmental applications, grain storage and enhanced recovery of petroleum reservoirs among many others. Today we need to explore innovative approaches to effectively apply existing porous media technologies to these applications. These porous media play a vital role in gas turbine inlet filtration systems. A typical pollution eliminator contains different type of filters such as bag filters, cartridge filters, EPA (Efficient Particulate Air), HEPA (high efficiency particulate air) and ULPA (Ultra Low Particulate Air) filters etc.: each filter has a specific purpose and level of efficiency.
Fluid Flow Effects in Porous Media
Design and shape of the filter plays a crucial role in evading compressor surge and improving the performance of a system as a whole. It is very crucial to keep the flow conditions at a minimum total pressure drop by adopting a filtration system that suits the operational environment.
During filtration, fluid experiences certain changes such as:
static pressure rise due to diffusion,
reduction in the flow energy, thereby making it more laminar based on the porous medium’s permeability,
heat transfer effects through the porous zone, etc.
Today simulation plays a significant role in understanding filter performance and filter housing design to deliver adequate air flow distribution by translating a physical scenario into a math-based numerical model. As simulation engineers, we will need to model porous media to recreate these effects.
Using ANSYS FLUENT interface, I will explain the process here onward. In ANSYS FLUENT, porous media model adds a momentum sink in the governing momentum equations. You can model this in two ways:
Using cell zone conditions
Porous jump boundary conditions, especially if our only concern is about the pressure drop.
The approach to model porous media using porous jump boundary conditions is useful when we don’t have all the necessary flow transport properties. With this approach, however, you can expect a decline in accuracy because you need to assign the boundary conditions only on the surfaces. This makes it critical for the solver to understand a sudden rise in the pressure value at the imposed location.
Modeling Porous Media using Cell Zone Conditions
Once you import your meshed model into ANSYS FLUENT, you can edit the fluid cell zone condition. Here you will find options like Frame Motion, 3D Fan Zone, Source Terms, Laminar Zone, Fixed Values and Porous Zone. By selecting the Porous Zone feature, you will find input options mainly related to Inertial and Viscous resistances and direction vectors.
Inertial and Viscous resistances are the coefficients combined with other parameters of the Hagen-Darcy’s equation. This equation calculates pressure drop across the porous zone. This zone provides the capability to model pressure drop inside the fluid volume in the axial direction. The pressure drop in this medium is contributed due to viscous and inertial resistances; we can define it as:
∆p = ∆pViscous + ∆pInertial
where the pressure drop due to viscous resistance is given as the product of viscous resistance coefficient, thickness of the porous zone, viscosity of the fluid and the velocity of the flow. Since we provide viscosity, thickness (from the geometric model), velocity of the fluid (as calculated by the solver at the corresponding place in the domain through iterations) and the coefficient (user input values), solver calculates the pressure drop attributed due to this viscous effect loss.
Similarly, the pressure drop due to inertial losses is given as the half product of inertial resistance coefficient, square of the velocity of the fluid, thickness of the porous zone and density of the fluid. Take sufficient care while entering coefficient values into the software; sometimes the values may be given of the negative exponential order. Confusion arises because coefficient is represented as C¹= 1/K. In the software, you need to enter the value of K to accurately account for the right coefficient value.
Achieve Faster Convergence
Occasionally, the convergence rate slows down when the pressure drop is relatively large in the flow direction. For example, when the coefficient value of C² is large or permeability (alpha) is low, convergence rate is slower. You can resolve this by providing a good initial guess for the pressure drop across the medium. You can obtain the initial guess from two ways:
by performing standard initialization, or
by supplying an initial flow field without the effect of the porous region by temporarily disabling the porous media model.
Frequently-Asked Questions: The Top Three
Direction vectors, especially for conical or cylindrical faces, are automatically calculated by ANSYS FLUENT. Engineers fail to check if the direction vectors are normal to the surface. If the direction vectors are not normal to the surface, then results will be incorrect. Be careful, there!
Does every porous flow application have pressure losses due to the combination of both the viscous and inertial effects? The CADFEM’s Support Hotline gets this question quite often. The answer is no. For laminar flows, you’ll not find any inertial effects. Whereas for flows through a planar porous media (not a standard industrial use case though), you’ll not find viscous effects as well.
I don’t have the either of the viscous or inertial coefficient values. With information about pressure drop across the porous zone, can I simulate the fluid flow? This one is tricky because the pressure drop is due to the combined effect of both the inertial and viscous effects. Without knowledge about the significant contribution to the pressure loss due to either effect, it’s impossible to accurately model the flow. However if you are willing to ignore one of the two effects, then you can utilize the information about pressure drop to model the flow.
It’s not difficult to model porous flow problems, however you need to right software and the right partner to guide you through the solution. Talk to us, and we’ll glad to help you!
In contemporary Computational Fluid Dynamics, for practicing engineers and students, there lies an essential need for the know-how of “making a mesh better” to capture gradient information especially at the fluid-surface boundaries. Modeling the boundary layer becomes extremely important. Visualization of the mainstream flow is, of course, vital to understand the flow behavior. However to obtain a fairly accurate solution for a fluid flow problem, appropriate discretization or meshing of the fluid domain at the boundary layer holds the key.
What is Boundary Layer?
From theoretical fluid mechanics, we know that gradients of velocity and temperature exist within the boundary layer (Wikipedia). Obviously the fluid that is immediately in contact with the boundary will have the same velocity as the boundary. As we move away from the boundary, the velocity of the successive layers of the fluid will increase. Within the boundary layer, shear stresses are developed between layers of fluid moving at different velocities because of viscosity and the interchange of momentum as a result of turbulence. This can cause movement of fluid particles from one layer to the other. In all such flows where “the wall” participation brings considerable changes in the fluid flow, we observe that there is a non-linear variation in the velocity profile normal to the flow direction.
Without accurately capturing these effects at the boundary, you wouldn’t have an accurate solution to such fluid flow problems. Hence, to ensure that you get a fairly accurate result, I will provide recommendations for meshing at boundaries.
Boundary Layer – Key Meshing Recommendations
Typically, the best way to capture effects in the boundary layer is by accommodating higher number of cells in the direction normal to the fluid flow. For mainstream flow, I wouldn’t expect gradients to change much. Hence I recommend reducing the mesh intensity in the flow direction. Within the boundary layer, I would suggest you to have elements with high aspect ratios (up to 100-1000); you can stack them in the direction normal to the wall.
You will need to choose element types that can be stacked one over the other. By doing so, you can marginally save the number of grid cells and time required for the computation. Apart from the conserving the mesh count, it is extremely important to model the boundary layer with sufficiently high quality of meshing elements. You will agree that a poor quality mesh will obviously result in a commensurate accuracy of the solution.
Modeling the Boundary Layer in ANSYS
In ANSYS Fluent, you can achieving cell/element stacking in the direction normal to the boundary using a feature called Inflation. Essentially, you can inflate the mesh with several layers from the surface of the boundary until you cover the boundary layer thickness fully. Tetrahedral elements, when subjected to high aspect ratios, suffer from poor geometric quality. In contrast, Prism elements, due to very high geometric anisotropy, even if they are subject to high aspect ratios, show no deterioration in the geometric quality.
Now, I will compare using prism elements to model the boundary layer instead of tetrahedral elements. Towards the end, I will draw comparison between these two types of elements.
For a sample geometry, I have utilized the inflation feature to setup the growth of five inflation layers from the surface of the boundary. As you can see, prism elements are stacked over one another (inflated) in order to capture the boundary layer effects.
If the number of layers are specified as three, the meshing tool grows three layers of prism elements. Beyond the inflation layers, the rest of the fluid domain is meshed with tetrahedral elements. Therefore, the end result will be a hybrid mesh of prism and tetrahedral elements. You can control the inflation layers with parameters like growth rate.
In the velocity contour plots, you can see the solution to the fluid flow problem with and without use of inflation. If you notice, the velocity gradients at the boundary are captured quite well when inflation is used. Do you work with applications that involve highly turbulent flows? In such cases, mesh inflation at the boundaries becomes extremely crucial.
In addition to capturing the boundary layer effects accurately, inflation also contributes to lesser element count and computational time. Considering this, I would advise you to use inflation for any wall bounded flow.
In this article, I explained the importance and the approach the use Inflation in the boundary layer. In my next article, I will describe ways to control the growth of the inflation layers using specific application(s).