Porous media is widely used in CFD to reduce the computational expense of modeling things like filters, perforated plates, and tube banks. To accomplish this reduction in computational expense, the losses across the porous device are modeled mathematically using a simple equation rather than by geometrically resolving the flow obstruction.

Using the porous media model requires knowledge of the loss coefficients. These are referred to as the viscous and inertial loss coefficients. These coefficients can be derived from experimental data, empirical correlations in literature, or through CFD. The CFD models used to determine these coefficients are small sections of the full device, which makes the computational expense relatively small.

The pressure loss through a porous domain is represented by the following equation:

The unit cell model will be run at several flow rates and the pressure drop across the model will be recorded. The velocity vs pressure drop curve formed by this data will be curve fit to the form:

Coefficients a and b will then solved for using:

Knowing that the pressure loss will always follow a parabolic curve as described above, any tuning that is perceived to be needed means that the curve fit must be altered. Similarly, if experimental testing reveals that the pressure drop vs velocity curve follows any shape other than a parabola with a y-intercept of zero, then the porous loss model cannot represent this loss accurately across, though a curve fit could potentially be done for some limited range of velocities.

Reflecting on my Time at DRD Technology

Since I had exposure to DRD Technology at previous companies, I now have a seasoned view as a customer and as a team member of DRD Technology. Over the past 15 years, in roles including Senior Applications Engineer and now Chief Technologist, I have had the opportunity to work on some incredible projects.

First, since Ansys is a company that is constantly innovating, a large part of my job is to keep up with the latest developments and evaluate them for our team. When I see a new tool that could be useful, I evaluate it thoroughly to ensure it meets our needs and can integrate smoothly into our workflow. One focus area for me has been on a tool called HFSS, which has become a cornerstone of our company’s offerings, and my role has continued to evolve as I work to identify new Ansys technologies that enhance our company’s offerings.

In general, my role is 80 percent pre-sales work and 20 percent supporting technical questions and conducting training courses periodically. Pre-sales allows me the opportunity to work closely with the sales team in the early stages to understand the client’s needs and requirements, and then develop a solution that would meet those needs.

Over the years, I have worked on many different projects. One of the most memorable was a pre-sales project for uAvionix four years ago. uAvionix is a company that builds innovative products for aircraft many of which have integrated RF antennas and my project was to simulate one of their products as “proof-of-concept” effort. When I presented the results to the client, the output from HFSS and measured scope trace results on the same graph were practically identical! I was completely amazed at the accuracy of the simulation in this case. Most numerical simulations are good if they fall within 10 percent of measured data, but this HFSS solution was within <1% of the measured data. That was the first time I ever experienced a predictive physics simulation being that “predictive.” This specific project has always been stuck in my mind; it was quite amazing.

Today, most CAD program providers include an embedded option for conducting simulation. This option is appealing to many companies as their engineers can expand how they use the tools they already have available for low or no additional cost. For many companies, their existing CAD licenses provide a low-barrier-to-entry way to test drive simulation. As a company gets started with simulation, or if their simulation requirements will remain fairly simplistic, a CAD-embedded tool can be a great option. However, since the simulation technology included by many CAD vendors was acquired as a means to complete their product offerings, oftentimes, the level of simulation available is limited in scope and size of model and may not be a great long-term solution. 

So how do you know when you are outgrowing the capabilities of your CAD-embedded tool? Some examples of questions to ask your simulation team to determine if a CAD-embedded or standalone product is the best option for your needs include the following: 

• What are the limits to your simulation?
• Do you have large assemblies?
• Do you require advanced or coupled physics?
• When it comes to simple physics, do you have lots of parts and/or a complex model?

For a more detailed review of a few examples and additional related issues,  DRD encourages you to read our whitepaper “Six Considerations for Selecting Engineering Simulation Software”.

(link the whitepaper title to the registration page on our website to download it – https://www.drd.com/resources/engineering-simulation-software-wp/)

Bring Your Own License (BYOL) is a feature of Ansys Cloud Direct which allows you to use on-premises licenses in place of Ansys Elastic Credits (AECs) for the software cost of a Cloud Direct job.

What does BYOL do?

Without BYOL, the software cost of a Cloud job is covered by AECs, consumed over time during the job to cover the licenses checkouts that would otherwise occur. The rate of AEC consumption is dependent on which licenses are required to perform the job – see the rate tables here: https://www.ansys.com/legal/terms-and-conditions/elastic-licensing-terms

In addition to the software cost, the hardware cost of the Cloud job can be covered by either AECs or AHCs, Ansys Hardware Credits. The only difference between AHCs and AECs are that AHCs can only be used to cover the hardware cost, so they cannot be used for the software portion. The hardware rates are the same for both but are dependent on region and requested hardware configuration (also listed in the link above). BYOL does not interact with the hardware cost of a Cloud job, so this article won’t go any deeper on the hardware costs of Ansys Cloud.

If you have any questions about the details of AEC purchasing or consumption rates, please contact sales@drd.com.

Why use BYOL? Who should be interested?

BYOL is most attractive to customers who already run Ansys locally on-site with licenses they own or lease, but want to access the power and flexibility of the hardware available with Ansys Cloud while still utilizing the licenses they have already purchased. This can substantially reduce overall AEC consumption by covering some or all of the software cost for a given Cloud job. The remaining cost will be for the hardware and any licenses not owned or available to bring to Cloud.

A common example would be existing users who want to tackle problems which are numerically larger than is feasible or possible with their on-site computing environment. However, this need might be periodic or intermittent and so a long-term investment in hardware is harder to justify, but the work still needs to be done. In this way, Cloud provides increased hardware capability on-demand – the concept of “bursting to the Cloud”. Since this user would be using their existing licenses were they to run locally (and is used to operating that way), it makes sense that they’d want to use those same licenses on Cloud rather than incurring some extra AEC cost. This is exactly what BYOL provides.

Consider this job: a solve on 1 full node (60 cores) from the HB configuration in the US East region, using a CFD Solver license (plus required HPC), and lasting 6 hours. Using the AEC consumption rates at the time of writing this article, the software cost will be 204 AEC and the hardware 21 AEC. That’s a cost of 225 AECs without BYOL, but only 21 AEC if the job can check out the appropriate CFD and HPC licenses through BYOL instead – a reduction of more than 90% in this case!

Some example AEC consumption rates at the time of writing this article: note how on average, the software rate is multiple times that of the hardware rate per node – meaning that BYOL can dramatically reduce AEC usage!

Be aware that since BYOL has Cloud check out licenses from the local license server, it alone does not provide extra licensing “bandwidth”. However, BYOL can be used in conjunction with using AECs to cover software costs (as the Cloud job will “rollover” to using AECs automatically if licenses are not available on the license server to cover Cloud’s requests).

Requirements of BYOL and network security implications

BYOL first requires 1.) owning or leasing Ansys licenses and 2.) having an Ansys Cloud subscription (along with some amount of AECs or AHCs). BYOL does not require any additional product to be purchased, any add-on to Cloud to be switched on, or any extra software to be installed. However, BYOL does come with some very specific network/security considerations, so it’s important to understand these. We will also present some possible examples of accommodating these needs, though the specific way that your company implements a solution may vary, dependent on network configuration and security policies.

The most important technical aspect of BYOL to recognize is that the Ansys hardware running on Cloud will be making its licensing requests to your license server. That is, network communication will be initiated from outside your network and will need to reach the computer where you host your Ansys licenses. For many organizations, this means that they will need to change their license server setup to accommodate BYOL, since letting a foreign computer connect to a machine within your secure network will never be palatable, even with IP/port forwarding mitigating some of the risk.

Note the incoming arrow pointing at the on-prem license server, coming from the “Batch Service” which represents all the remote Ansys Cloud components. The padlock represents the IP forwarding and firewall rules which ensure that the request from a known, static IP on Cloud can be forwarded to the license server. 

Understanding BYOL from a technical standpoint

For the best source of technical details of implementing BYOL (including how it functions with IP forwarding), please refer to the Ansys Cloud Guide, found here: https://cloud.ansys.com/pdf/help. (Access to this page requires an account with an active Cloud subscription.) The chapter “Bring Your Own License (BYOL)” near the end covers some background of how BYOL works as well as detailed instructions on setting it up and testing it.

Especially note the sections “Prerequisites for IP Forwarding” and “Ensure Firewall Access” for network configuration information.

Possible solutions to accommodate BYOL

With the fact that Cloud hardware will need to initiate a connection to your license server in mind, many customers may opt to change their licensing configuration to accommodate BYOL. One possible solution would to be move the license server itself to some cloud solution. Since the license server is no longer within your network, nothing there needs to change with regards to security. Users on-site can send outgoing license requests from their computers running Ansys to this Cloud license manager, as can the Ansys Cloud hardware. There is no proprietary data on this Cloud-hosted license server, as its sole purpose is to serve Ansys licenses, so it offers little to no risk if compromised.

Another possible solution would be to use a network DMZ. This has the benefit of having the license server be a machine on-site, still managed by like other owned systems, but “compartmentalized” to a degree from a network/security standpoint.

DRD support engineers are not experts on network security, and we don’t offer any services to configure or manage customers’ networks for them. However, we’re more than happy to work with the group that handles network security for our customers, filling them in on the technical details of Ansys Cloud and BYOL specifically as needed.

A common technique in distributing ducted flow involves thin guiding vanes or baffles.  One of the biggest hurdles to modeling baffles is how thin they are relative to the rest of the model.  If you were to model their true thickness, you typically have a choice between poor quality skewed elements or an excessively high mesh count.  Instead, thin baffles are often approximated as infinitely thin.  

When using Fluent Meshing, the addition of surface bodies in SpaceClaim to represent each baffle is perhaps the quickest approach to adding in baffle geometry. In some cases it is also possible to split up the fluid volume to account for these baffles, but generation of non-manifold geometry is not a viable approach, so this is typically more challenging than the surface body approach.

It is also important to consider inflation on zero thickness baffles. If one was to resolve the actual thickness of a baffle, inflation could wrap around the baffle without issue, but this is not the case for a zero thickness baffle. Instead, inflation will be forced to stair-step wherever the baffle ends in the middle of the fluid volume. These stair-stepped elements are of poor quality and will negatively affect the convergence behavior of the solution.

To prevent stair-stepping, inflation must be allowed to wrap fully around an object, or extend to an external boundary. To accomplish this for the case of zero thickness baffles, additional surfaces must be added to the geometry. Inflation layers can be grown on surfaces that allow flow to pass through them. It would be typical to consider these as “meshing surfaces” as their only purpose in the model is to aid in mesh quality by preventing stair-stepping of inflation layers.

In the case of turning vanes in a duct as pictured in this article, it makes sense to create extensions to the turning vanes that terminate at the inlet and outlet. Note that these additional surfaces must be separate faces from the actual vanes themselves. This is so that the meshing surfaces can be set to interior type boundaries in the solver. This will allow flow to pass through the meshing surfaces unobstructed.

To achieve continuous inflation using these new meshing surfaces, there are two approaches in Fluent Meshing. First, you can intentionally set the meshing surfaces to the wall boundary type in the Update Boundaries task. This will allow you to grow inflation using the default Add Boundary Layers control. When choosing this option it is important to set the meshing surfaces to “Interior” type boundaries once inside the solver otherwise flow will not be able to pass through these surfaces. The second option while meshing is to set the meshing surfaces to “internal” boundaries in the Update Boundaries task. This will then require that you manually scope your boundary layers for these surfaces, but avoids the need to change the boundary type later in the solver. Note that the terminology for a conformal boundary that allows flow to pass through it is different for the mesher and the solver. In the mesher this is “internal” and in the solver this is “Interior.”

Regardless of approach, the final mesh should have no stair-stepping of inflation as shown below. 

If in your final model you have no need for multiple cell zones, then you can merge the multiple cell zones together inside of the solver. To do so, go to the Domain Tab > Combine > Merge and select the relevant cell zones. This will remove the workflow information from the case file, so be sure that you have saved the .msh.h5 file separately before performing this operation.

When solving CFD models using Ansys products, there are a number of ways to determine model convergence.  DRD recommends monitoring a combination of holistic (like individual equation residual values) and local quantities (like surface and point monitors) to ensure a stable, converged solution. Residuals are representative of the average error across all control volumes in the model, while local quantities can help you better focus on key parameters in your model.

Sometimes you might encounter a situation where one or more residual does not reach its convergence target, but important characteristic features being monitored have steadied to a particular value.  Often this is sufficient for approximating the behavior you are simulating, but other times you may need to strive for more precision.  To do so, it is helpful to be able to determine where in the model there are locally high residuals that are preventing the overall residual value from reaching its target.

By default, Fluent only exposes mass-imbalance (related to continuity residual) to the user.  When post-processing, this can be found in the Residuals group of variables.

To access the other residuals, you need to enable an expert parameter using the console prior to solving.  Expert parameters can be accessed using the TUI command ‘/solve/set/expert’.  Beyond that, the prompts you receive will depend on your Fluent environment, so we can’t dictate a specific list of responses.  However, to enable the remaining residuals you will need to answer “yes” to the prompt “Save cell residuals for post-processing?”.

After solving you should then see more options for plotting residuals, which will allow you to better evaluate where local error is preventing the overall residual from reaching its convergence target.

Often you will find this is associated with a mesh quality issue that requires resolution via remeshing, or even a geometric modification in order to improve mesh quality.