One of the more common questions we get on thermal expansion simulations in tech support for ANSYS Mechanical and ANSYS Mechanical APDL revolve around how the Coefficient of Thermal Expansion, or CTE. This comes in to play if the CTE of the material you are modeling is set up to change with the temperature of that material.
This detailed presentation goes in to explaining what the differences are between the Secant and Instantaneous methods, how to convert between them, and dealing with extrapolating coeficients beyond temperatures for which you have data.
This is the second installment in our review of all the different products and services PADT offers our customers. As we add more, they will be available here. As always, if you have any questions don’t hesitate to reach out to firstname.lastname@example.org or give us a call at 1-800-293-PADT.
The PADT sales and support team focused on simulation solutions is best known for our work with the full ANSYS product suite. What a lot of people don’t know is that we also represent a fantastic simulation tool called Flownex. Flownex is a system level 1-D program that is designed from the ground up to model thermal-fluid systems.
What does Flownex Do?
Flownex Simulation Environment is an interactive software program that allows users to model systems to understand how fluids (gas and/or liquid) flow and how heat is transferred in that same system due to that flow. the way it works is you create a network of components that are connected together as a system. The heat and fluid transfer within and between each node is calculated over time, giving a very accurate, and fast, representation of the system’s behavior.
As a system simulation tool, it is fast, it is easy to build and change, and it runs in real time or even faster. This allows users to drive the design of their entire system through simulation.
Need to know what size pump you need, use Flownex. Want to know if you heat exchanger is exchanging enough heat for every situation, use Flownex. Tasked with making sure your nuclear reactor will stay cool in all operating conditions, use Flownex. Making sure you have optimized the performance of your combustion nozzles, use Flownex. Time to design your turbine engine cooling network, use Flownex. Required to verify that your mine ventilation and fire suppression system will work, use Flownex. The applications go on and on.
Why is Flownex so Much Better than other System Thermal-Fluid Modeling Solutions?
There are a lot of solutions for modeling thermal-fluid systems. We have found that the vast majority of companies use simple spreadsheets or home-grown tools. There are also a lot of commercial solutions out there. Flownex stands out for five key reasons:
Breadth and depth of capability
Flownex boasts components, the objects you link together in your network, that spread across physics and applications. Whereas most tools will focus on one industry, Flownex is a general purpose tool that supports far more situations. For depth they have taken the time over the years to not just have simple models. Each component has sophisticated equations that govern its behavior and user defined parameters that allow for very accurate modeling.
Developed by hard core users
Flownex started life as an internal code to support consulting engineers. Experienced engineering software programmers worked with those consultants day-in and day-out to develop the tools that were needed to solve real world problems. This is the reason why when users ask “What I really need to do to solve my problem is such-and-such, can Flownex do that?” we can usually answer “Yes, and here are the options to make it even more accurate.”
Customization and Integration
As powerful and in-depth as Flownex is, there is no way to capture every situation for every user. Nor does the program do everything. That is why it is so open and so easy to customize and integrate. As an example, may customers have very specific thermal-pressure-velocity models that they use for their specific components. Models that they developed after years if not decades of testing. Not a problem, that behavior can be easily added to Flownex. If a customer even has their own software or a 3rd party tool they need to use, it is pretty easy to integrate it right into your Flownex system model.Very common tools are already integrated. The most common connection is Matlab/Simulink. At PADT we often connect Excel models from customers into our Systems for consulting. It is also integrated into ANSYS Mechanical.
Nuclear Quality Standards
Flownex came in to its own as a tool used to model the fluid system in and around Nuclear Reactors. So it had to meet very rigorous quality standards, if not the most stringent they are pretty close. This forced to tool to be very robust, accurate, and well documented. And the rest of us can take advantage of that intense quality requirement to meet and exceed the needs of pretty much every industry. We can tell you after using it for our own consulting projects and after talking to other users, this code is solid.
Ease of Use
Some people will read the advantages above and think that this is fantastic, but that much capability and flexibility must make it difficult to use. Nothing could be further from the truth. Maybe its because the most demanding users are down the hallway and can come and harangue the developers. Or it could be that their initial development goal of keeping ease of use without giving up on functionality was actually followed. Regardless of why, this simulation tool is amazingly simple and intuitive. From building the model to reviewing results to customization, everything is easy to learn, remember, and user. To be honest, it is actually fun to use. Not something a lot of simulation engineers say.
Why does buying and getting support from PADT for Flownex make a Difference?
The answer to this question is fairly simple: PADT’ simulation team is made up of very experienced users who have to apply this technology to our own internal projects as well as to consulting jobs. We know this tool and we also work closely with the developers at Flownex. As with our ANSYS products, we don’t just work on knowing how to use the tool, we put time in to understand the theory behind everything as well as the practical real world industry application.
When you call for support, odds are the engineer who answers is actually suing Flownex on a customer’s system. We also have the infrastructure and size in place to make sure we have the resources to provide that support. Investing in a new simulation tool can generate needs for training, customization, and integration; not to mention traditional technical support. PADT partners with our customers to make sure they get the greatest value form their simulation software investment.
Reach out to Give it a Try or Learn More
Our team is ready and waiting to answer your questihttp://www.flownex.com/flownex-demoons or provide you with a demonstration of this fantastic tool. . You can email us at email@example.com or give us a call at 480.813.4884 or 1-800-293-PADT.
Still want to learn more? Here are some links to more information:
Sometimes everything happens at once. This June 22nd was one of those days. Three key events were scheduled for the same time in three different states and we needed to be at all of them. So everyone stepped up and pulled it off, and hopefully some of you reading this were at one of these fantastic events. Combined they are a great example of PADT’s commitment to the local technology ecosystem, showing how we create true win-win partnerships across organizations and geographies. Since the beginning we wanted to be more than just a re-seller or just consultants, and this Thursday was a chance to show our commitment to doing just that.
Albuquerque: New Mexico Technology Council 3D Printing Peer Group Kickoff
Everyone talks about how they thing we should all work together, but there never seems to be someone who is willing to pull it all together. That is how the additive manufacturing committee in New Mexico was until the New Mexico Technology Council (NMTC) stepped up to host a peer group around 3D Printing. Even though it was a record 103f in Albuquerque, 35 brave 3D Printing enthusiasts ventured out into the heat and joined us at Rio Bravo Brewing to get the ball rolling on creating a cooperative community. We started with an introduction from NMTC, followed by an overview of what we want to achieve with the group. Our goals are:
Create stronger cooperation between companies, schools, and individuals involved in 3D Printing in New Mexico
Foster cooperation between organizations to increase the benefits of 3D Printing to New Mexico
Make a contribution to New Mexico STEM education in the area of 3D Printing
To make this happen we will meet once a quarter, be guided by a steering committee, and grow our broad membership. Anyone with any involvement in Additive Manufacturing in the state is welcome to join in person or just be part of the on-line discussion.
A nice facility
Rey Chu sharing his views on what is new in 3D Printing
NMTCs Nyika Allen kicking things off
These barrels did not have anything to do with our meeting, but they are cool.
Then came the best part, where we went around the room and shared our names, orginization, and what we did in the world of 3D Printing. What a fantastic group. From a K-12 educator to key researchers at the labs, we had every industry and interest representing. What a great start.
Here are the slides from that part of the presentation:
Once that was done PADT’s Rey Chu gave a presentation where it went over the most important developments in Additive Manufacturing over the last year or so. He talked about the three new technologies that are making an impact, new materials, and what is happening business wise. Check out his slides to learn more:
After a question and answer period we had some great conversations in small groups, which was the most valuable part.
If you want to learn more, please reach out to firstname.lastname@example.org and we will add you to the email list where we will plan and execute future activities. We are also looking for people to be on the steering committee and locations for our next couple of meetings. Share this with as many people as you can in New Mexico so that next event can be even better!
Denver: MSU Advance Manufacturing & Engineering Sciences Building Opening
Meanwhile, in Denver it was raining. In spite of that, supporters of educating the next generation of manufacturers and engineers gathered for the opening of the Advanced Manufacturing and Engineering Sciences Building at Metropolitan State University. This 142,000 sqft multi-disciplinary facility is located in the heart of downtown Denver and will house classes, labs, and local companies. PADT was there to not only celebrate the whole facility, but we were especially excited about the new 3D Printing lab that is being funded by a $1 million gift from Lockheed Martin. A nice new Stratasys Fortus 900 is the centerpiece of the facility. It will be a while before the lab itself is done, so watch for an invite to the grand opening. While we wait we are working with MSU, Lockheed Martin, Stratasys, and others to put a plan together to develop the curriculum for future classes and making sure that the engineers needed for this technology are available for the expected explosion of use of this technology.
Stratasys and PADT are proud to be partners of this fantastic effort along with many key companies in Colorado. If you want to learn more about how we can help you build partnerships between industry and academia, please reach out to email@example.com or give us a call.
The 113f high in Phoenix really didn’t stop anyone from coming to the AADM conference. This annual event was at ASU SkySong in Phoenix and is sponsored by the AZ Tech Council, AZ Commerce Authority, and RevAZ. PADT was proud to not only be a sponsor, but also have a booth, participate in the advanced manufacturing panel discussion, and do a short partner presentation about what we do for our Aerospace and Defense Customers.
Researchers and students at universities around the world are tackling difficult engineering and science problems, and they are turning to simulation more and more to get to understanding and solutions faster. Just like industry. And just like industry they are finding that ANSYS provides the most comprehensive and powerful solution for simulation. The ANSYS suite of tools deliver breadth and depth along with ease of use for every level of expertise, from Freshman to world-leading research professors. The problem in the past was that academia operates differently from industry, so getting to the right tools was a bit difficult from a lot of perspectives.
Now, with the ANSYS Academic program, barriers of price, licensing, and access are gone and ANSYS tools can provide the same benefits to college campuses that they do to businesses around the world. And these are not stripped down tools, all of the functionality is there.
Students – Free
Yes, free. Students can download ANSYS AIM Student or ANSYS Student under a twelve month license. The only limitation is on problem size. To make it easy, you can go here and download the package you need. ANSYS AIM is a new user interface for structural, thermal, electromagnetic, and fluid flow simulation oriented towards the new or occasional user. ANSYS Student is a size limited bundle of the full ANSYS Mechanical, ANSYS CFD, ANSYS Autodyn, ANSYS SpaceClaim, and ANSYS DesignXplorer packages.
That is pretty much it. If you need ANSYS for a class or just to learn how to use the most common simulation package in industry, download it for free.
Academic Institutions – Discounted Packages
If you need access to full problem sizes or you want to use ANSYS products for your research, there are several Academic Packages that offer multiple seats of full products at discounted prices. These products are grouped by application:
Structural-Fluid Dynamics Academic Products — Bundles that offer structural mechanics, explicit dynamics, fluid dynamics and thermal simulation capabilities. These bundles also include ANSYS Workbench, relevant CAD import tools, solid modeling and meshing, and High Performance Computing (HPC) capability.
Electronics Academic Products — Bundles that offer high-frequency, signal integrity, RF, microwave, millimeter-wave device and other electronic engineering simulation capabilities. These bundles include product such as ANSYS HFSS, ANSYS Q3D Extractor,ANSYS SIwave, ANSYS Maxwell, ANSYS Simplorer Advanced. The bundles also include HPC and import/connectivity to many common MCAD and ECAD tools.
Embedded Software Academic Products — Bundles of our SCADE products that offer a model-based development environment for embedded software.
Multiphysics Campus Solutions— Large task count bundles of Research & Teaching products from all three of the above categories intended for larger-scale deployment across a campus, or multiple campuses.
You can see what capabilities are included in each package by downloading the product feature table. These are fully functional products with no limits on size. What is different is how you are authorized to use the tool. The Academic licence restricts use to teaching and research. Because of this, ANSYS is able to provide academic product licenses at significantly reduced cost compared to the commercial licenses — which helps organizations around the globe to meet their academic budget requirements. Support is also included through the online academic resources like training as well as access to the ANSYS Customer Portal.
There are many options on price and bundling based upon need and other variables, so you will need to contact PADT or ANSYS to help sort it all out and find the right fit for your organization.
What does all this mean? It means that every engineer graduating from their school of choice should enter the workforce knowing how to use ANSYS Products, something that employers value. It also means that researchers can now produce more valuable information in less time for less money because they leverage the power of ANSYS simulation.The barriers are down, as students and institutions, you just need to take advantage of it.
Meshing is one of the most important aspects of a simulation process and yet it can be one of the most frustrating and difficult to get right. Whether you are using CAD based simulation tools or more powerful flagship simulation tools, there are different approaches to take when it comes to meshing complicated assemblies for structural or thermal analysis.
ANSYS has grown into the biggest simulation company globally by acquiring powerful technologies, but more importantly, integrating their capabilities into a single platform. This is true for meshing as well. Many of ANSYS’ acquisitions have come with several strong meshing capabilities and functionalities and ANSYS Workbench integrates all of that into what we call Workbench Meshing. It is a single meshing tool that incorporates a variety of global and local mesh operations to ensure that the user not only gets a mesh, but gets a good quality mesh without needing to spend a lot of time in the prep process. We’ll take a look at a couple examples here.
This is a Tractor Axle assembly that has 58 parts including bolts, gaskets and flanges. The primary pieces of the assembly also has several holes and other curved surfaces. Taking this model into Workbench Meshing yielded a good mesh even with default settings. From here by simply adding a few sizing controls and mesh methods we quickly get a mesh that is excellent for structural analysis.
Tractor Axle Geometry
Tractor Axle Default Mesh
Tractor Axle Refined Mesh
The assembly below, which is a model from Grabcad of a riveting machine, was taken directly into Workbench Meshing and a mesh was created with no user input. As you can see the model has 5,282 parts of varying sizes, shapes and complexity. Again without needing to make any adjustments, Workbench Meshing is able to mesh this entire geometry with 6.6 million elements in only a few minutes on a laptop.
Riveting Machine Default Mesh
Riveting Machine Default Mesh
The summary of the meshing cases are shown below:
# of Parts
# of Elements
# of Nodes
Tractor Axle Refined
5 Body Sizings
2 Local Mesh Methods
Characteristics of a robust meshing utility are:
Easy to use with enough power under the hood
Able to handle complex geometry and/or large number of parts
Quick and easy user specified mesh operations
Fast meshing time
ANSYS Meshing checks all of these boxes completely. It has a lot of power under the hood to handle large and/or complex geometry but makes it simple and easy for users to create a strong quality mesh for FEA analysis.
Engineering simulation has become much more prevalent in engineering organizations than it was even 5 years ago. Commercial tools have gotten significantly easier to use whether you are looking at tools embedded within CAD programs or the standalone flagship analysis tools. The driving force behind these changes are to ultimately let engineers and companies understand their design quicker with more fidelity than before.
Engineering simulation is one of those cliché items where everyone says “We want more!” Engineers want to analyze bigger problems, more complex problems and even do large scale design of experiments with hundreds of design variations – and they want these results instantaneously. They want to be able to quickly understand their designs and design trends and be able to make changes accordingly so then can get their products optimized and to the market quicker.
ANSYS, Inc. spends a significant amount of R&D in helping customers get their results quicker and a large component of that development is High Performance Computing, or HPC. This technology allows engineers to solve their structural, fluid and/or electromagnetic analyses across multiple processors and even across multiple computing machines. Engineers can leverage HPC on laptops, workstations, clusters and even full data centers.
PADT is fortunate to be working with Nimbix, a High Performance Computing Platform that easily allowed us to quickly iterate through different models with various cores specified. It was seamless, easy to use, and FAST!
Let’s take a look at four problems: Rubber Seal FEA, Large Tractor Axle Model, Quadrocopter CFD model and a Large Exhaust CFD model. These problems cover a nice spectrum of analysis size and complexity. The CAD files are included in the link below.
This model has several parts all with contact defined and has 51 bolts that have pretension defined. A very large but not overly complex FEA problem. As you can see from the results, even by utilizing 8 cores you can triple your analysis throughput for a work day. This leads to more designs being analyzed and validated which gives engineers the results they need quicker.
51 x Bolts with Pretension
928K Elements, 1.6M Nodes
Estimated Models Per 8 [hours]
RUBBER SEAL FEA
The rubber seal is actually a relatively small size problem, but quite complex. Not only does it need full hyperelastic material properties defined with large strain effects included, it also includes a leakage test. This will pressurize any exposed areas of the seal. This will of course cause some deformation which will lead to more leaked surfaces and so on. It basically because a pressure advancing solution.
From the results, again you can see the number of models that can be analyzed in the same time frame is signifcantly more. This model was already under an hour, even with the large nonlinearity, and with HPC it was down to less than half an hour.
Mooney Rivlin Hyperelastic Material
Seal Leakage with Advancing Pressure Load
42K Elements, 58K Nodes
Estimated Models Per 8 [hours]
QUADROCOPTER DRONE CFD
The drone model is a half symmetry model that includes 2 rotating domains to account for the propellers. This was ran as a steady state simulation using ANSYS Fluent. Simply utilizing 8 cores will let you solve 3 designs versus 1.
Multiple Rotating Domains
2M Elements, 1.4M Nodes
The exhaust model is a huge model with 33 million elements with several complicated flow passages and turbulence. This is a model that would take over a week to run using 1 core but with HPC on a decent workstation you can get that down to 1 day. Leveraging more HPC hardware resources such as a cluster or using a cloud computing platform like Nimbix will see that drop to 3 hours. Imagine getting results that used to take over 1 week that now will only take a few hours. You’ll notice that this model scaled linearly up to 128 cores. In many CFD simulations the more hardware resources and HPC technology you throw at it, the faster it will run.
K-omega SST Turbulence
33M Elements, 7M Nodes
As seen from the results leveraging HPC technology can be hugely advantageous. Many simulation tools out there do not fully leverage solving on multiple computing machines or even multiple cores. ANSYS does and the value is easily a given. HPC makes large complex simulation more practical as a part of the design process timeline. It allows for greater throughput of design investigations leading to better fidelity and more information to the engineer to develop an optimized part quicker.
If you’re interested in learning more about how ANSYS leverages HPC or if you’d like to know more about NIMIBX, the cloud computing platform that PADT leverages, please reach out to me at firstname.lastname@example.org
If you do CFD simulations then you know the struggle that is involved in meshing. It is a fine balance of accuracy, speed, and ease of set up. If you have complex geometry, large assemblies, or any difficulty meshing then this blog article is for you.
Why should I spend time making a good mesh?
The mesh is arguably one of the most important parts of any simulation set up. A good mesh can solve significantly faster and provide more accurate results. Conversely, a poor mesh can make the simulation have inaccurate results and be slow to converge or not converge at all. If you have done any simulation then you know that hitting the solve button can feel like rolling the dice if you don’t have a robust meshing tool.
When is it going to matter?
A good mesh is going to matter on a Friday afternoon when you need to get the simulation started before you leave for the weekend because it takes two days to run and you need to deliver results on Monday but you are up against the clock because you have to get to your kid’s soccer game by 5pm and the mesh keeps crashing.
A poor mesh can do more than just reorganizing you’re social agenda. A poor mesh can drastically change results like pressure drop in an internal flow passage or drag over a body. If you go into that meeting on Monday and tell your boss that the new design is going to perform 10% better than the previous design – you need to be confident that the design is 10% better not 10% worse.
What should I do when I need to create a good mesh?
If you’re the poor soul reading this on a Friday afternoon because you are trying to frantically fix you’re mesh so you can get your simulation running before the weekend – I pity you. Continue reading for my proprietary step by step approach titled “How to get you’re CFD mesh back on track!” (Patent pending).
Step 1) Know your tools
ANSYS has been developing its meshing technology since the beginning of time (not really but almost) – it’s no surprise that its meshing algorithms are the best in the business. In ANSYS you have a large number of tools at your disposal, know how to use them.
The first tool in your toolbox is the ANSYS automatic meshing technology. It is able to predictively apply settings for your part to get the most accurate automatic mesh possible. It has gotten so good that the automatic mesh is a great place to start for any preliminary simulations. If you want to get into the details, ANSYS meshing has two main groups of mesh settings – Global Meshing Parameters and Local Meshing Parameters. Global mesh parameters are great for getting a good mesh on the entire model without going into detailed mesh settings for each part.
But when you do have to add detailed meshing settings on a part by part basis then local mesh settings won’t let you down.
Step 2) Know your physics
What is your primary result of interest? Drag? Pressure drop? Max velocity? Stagnation? If you can quantify what you are most interested in then you can work to refine the mesh in that region so as to capture the physics accurately. ANSYS allows you set local sizing parameters on bodies, faces, lines, and regions which allow you to get the most accurate mesh possible but without having to use a fine mesh on the entire part.
Step 3) Know your mesh quality statistics
Mesh quality statistics can be a good way to gauge the health of your mesh. They are not a foolproof method for creating a mesh that will be accurate but you will be able to get an idea of how well it will converge. In ANSYS meshing there is a number of mesh quality statistics at your fingertips. A quick and easy way to check your mesh is to look at the Minimum Orthogonal Quality statistic and make sure it is greater than 0.1 and Maximum Skewness is less than 0.95.
Step 4) Know your uncertainty
Every test, simulation, design, process etc… has uncertainty. The goal of engineering is to reduce that uncertainty. In simulation meshing is always a source of uncertainty but it can be minimized by creating high quality meshes that accurately model the actual physical process. To reduce the uncertainty in meshing we can perform what is called a mesh refinement study. Using the concept of limits we can say that in the limit of the mesh elements getting infinitely small than the results will asymptotically approach the exact solution. In the graph below it can be seen that as the number of elements in the model are increased from 500 – 1.5million the result of interest approached the dotted line which we can assume is close to the exact solution.
By completing a mesh refinement study as shown above you can be confident that the mesh you have created is accurately capturing the physics you are modeling because you can quantify the uncertainty.
If you currently just skip over the meshing part of your CFD analysis thinking that it’s good enough or if your current meshing tool doesn’t give you any more details than just a green check mark or a red X then it’s time dig into the details of meshing and start creating high quality meshes that you can count on.
If you still haven’t figured out how to get your mesh to solve and its 5pm on Friday see below*
*Common pitfalls and mistakes for CFD meshing:
Choose your turbulence model wisely and make sure your mesh meets the quality metrics for that model.
Make sure you don’t have boundary conditions near an area of flow recirculation. If you are getting reverse flows at the boundary then you need to move your boundary conditions further away from the feature that’s causing the flow to swirl in and out of the boundary.
Sometimes you want to take two parts and and prepare them for meshing so that they either share a surface between them, or have identical but distinct surfaces on each part where they touch. In this simple How-To, we share the steps for creating both of these situations so you can get a continuous mesh or create a matching contact surface in ANSYS Mechanical.
By using the power of ANSYS SpaceClaim to quickly modify geometry, you can set up your surface models in ANSYS Mechanical to easily be connected. Take a look in this How-To slide deck to see how easy it is to extend geometry and intersect surfaces.
The below example demontrates how to couple Flownex and ANSYS mechanical using the Mechanical Generic Interface component.
For those that don’t know, Flownex is a thermal-fluid system modeling tool that is great for modeling heat, flow, pressure, etc… in systems. At PADT we often connect it to ANSYS Mechanical to do more detailed component level simulation when needed.
Why the need for the link in the fist place?
It is an automated workflow to couple Flownex and ANSYS through direct mapping of Flownex results (HTC and bulk temperatures) as boundary condition to an ANSYS thermal analysis.
Represents a conjugate heat transfer model with fluid calculations handled in Flownex
Allows one to easily/quickly investigate fluid flow and heat transfer properties under a wide range operating conditions.
First we will discuss the steady state thermal ANSYS Mechanical model that will be linked to Flownex.
We have a pipe Pipe with arbritraty geometry and material properties. Convection boundary conditions have been applied to both the internal and external pipe walls. The inernal Bulk Temperature will be supplied by Flownex.
HTC 100 w/m2K
Bulk Temperature 22C
HTC 1500 w/m2K
Bulk Temperature will be supplied by Flownex
A command snippet, which will calculate the total heat flow through the inner wall surface and write the value out into a text file called d_result, has been inlcuded in the ANSYS Mechanical model.
In order to achieve a bidirectional coupling, Flownex will execute the Mechanical APDL batch file. We can generate the Mechanical APDL batch file (ds.dat), from within Mechanical.
The soluiton procedure is as follows
Flownex modifies the ds.dat file
Flownex executes the modified ds.dat file
The modified ds.dat file generates the d_result.txt file
Flownex reads the d_result.txt file
Flownex executes an iteration, using value from d_result.txt
Repeat untill solutions are converged.
The next step after creating the ds.dat file is to set up your Flownex model.
The Flownex model comprises of a pipe component with arbritrary geomery, filled with air with an inlet temperature and pressure of 500˚C and 120 kPa respectilvy and a flow rate of approximatly 1kg/s.
We have connected the pipe component to the Mechanical Generic Interface using data transfer links.
The data transfer links pass the bulk fluid temperature form the pipe to the Mechanical Generic Interface component, and return the heat flow value calculated using ANSYS to the pipe.
Next we need place the ds.dat file in the AnsysMechanical_Files folder which is located in the Flownex project folder. It is necessary to create a copy of the ds.dat called ModifiedData.dat in the same location.
Let’s go over the inputs to the Mechanical Generic Interface component in Flownex:
This is the path to ANSYS executable. Pay particular attention to the version number (eg 180, 172), as this will be different depending on the version of ANSYS you have installed.
2) Command line parameters
-b -i ModifiedData.dat -o results
Flownex will launch ANSYS, and execute the ModifiedData.dat Mechanical APDL batch file from the command line, using the above command a detailed description of command line options can be found in another blog post here.
3) Project files folder, Data file name and Modified data file name
Here we specify location of the Mechanical APDL batch files
Here we will define where in ModifiedData.dat the value from Flownex, fluid temperature in this case, will be placed. This is done by determining what the boundary condition variable and ID is, and finding the prefix before the boundary condition value in the ds.dat file. Typically the variable for temperature is _loadvari and for HTC it is _convari.
It is possible to know the boundary condition ID by activating the appearance of Beta options in WB.
Here we will specify the location of the d_result.txt that ANSYS generates. It should appear in the same folder as the Mechanical APDL batch files after successful execution.
Flownex and ANSYS will pass data back and forth every time step of a transient Flownex run.
The simulation should continue to run up to, and beyond the point where the Flownex and ANSYS simulation have converged. If we plot out the heat input or temperature value vs time we should be able to visualize convergence, akin to residual plots when running a CFD simulation, and then manually stop the simulation after values have stabilized.
Below we increase the fluid inlet temperature form 500˚C to 1000˚C after 10 iterations, and observed a increase in heat flow from ~1.4kW to ~2.8kW.
A support request from one of our customers recently was for the ability to make Thermal Contact Conductance, which is sort of a reciprocal of thermal resistance at the contact interface, a parameter so it can be varied in a parametric study. Unfortunately, this property of contact regions is not exposed as a parameter in the ANSYS Mechanical window like many other quantities are.
Fortunately, with ANSYS there is almost always a way……in this case we use the capability of an APDL (ANSYS Parametric Design Language) command object within ANSYS Mechanical. This allows us to access additional functionality that isn’t exposed in the Mechanical menus. This is a rare occurrence in the recent versions of ANSYS, but I thought this was a good example to explain how it is done including verifying that it works.
A key capability is that user-defined parameters within a command object have a ‘magic’ set of parameter names. These names are ARG1, ARG2, ARG3, etc. Eric Miller of PADT explained their use in a good PADT Focus blog posting back in 2013
In this application, we want to be able to vary the value of thermal contact conductance. A low value means less heat will flow across the boundary between parts, while a high value means more heat will flow. The default value is a calculated high value of conductance, meaning there is little to no resistance to heat flow across the contact boundary.
In order to make this work, we need to know how the thermal contact conductance is applied. In fact, it is a property of the contact elements. A quick look at the ANSYS Help for the CONTA174 or similar contact elements shows that the 14th field in the Real Constants is the defined value of TCC, the thermal contact conductance. Real Constants are properties of elements that may need to be defined or may be optional values that can be defined. Knowing that TCC is the 14th field in the real constant set, we can now build our APDL command object.
This is what the command object looks like, including some explanatory comments. Everything after a “!” is a comment:
! Command object to parameterize thermal contact conductance
! by Ted Harris, PADT, Inc., 3/31/2017
! Note: This is just an example. It is up to the user to create and verify
! the concept for their own application.
! From the ANSYS help, we can see that real constant TCC is the 14th real constant for
! the 17X contact elements. Therefore, we can define an APDL parameter with the desired
! TCC value and then assign that parameter to the 14th real constant value.
! We use ARG1 in the Details view for this command snippet to define and enable the
! parameter to be used for TCC.
r,cid ! tells ANSYS we are defining real constants for this contact pair
! any values left blank will not be overwritten from defaults or those
! assigned by Mechanical. R command is used for values 1-6 of the real constants
rmore,,,,,, ! values 7-12 for this real constant set
rmore,,arg1 ! This assigned value of arg1 to 14th field of real constant
! Now repeat for target side to cover symmetric contact case
r,tid ! tells ANSYS we are defining real constants for this contact pair
! any values left blank will not be overwritten from defaults or those
! assigned by Mechanical. R command is used for values 1-6 of the real constants
rmore,,,,,, ! values 7-12 for this real constant set
rmore,,arg1 ! This assigned value of arg1 to 14th field of real constant
You may have noticed the ‘cid’ and ‘tid’ labels in the command object. These identify the integer ‘pointers’ for the contact and target element types, respectively. They also identify the contact and target real constant set number and material property number. So how do we know what values of integers are used by ‘cid’ and ‘tid’ for a given contact region? That’s part of the beauty of the command object: you don’t know the values of the cid and tid variables, but you alsp don’t need to know them. ANSYS automatically plugs in the correct integer values for each contact pair simply by us putting the magic ‘cid’ and ‘tid’ labels in the command snippet. The top of a command object within the contact branch will automatically contain these comments at the top, which explain it:
! Commands inserted into this file will be executed just after the contact region definition.
! The type number for the contact type is equal to the parameter “cid”.
! The type number for the target type is equal to the parameter “tid”.
! The real and mat number for the asymmetric contact pair is equal to the parameter “cid”.
! The real and mat number for the symmetric contact pair(if it exists)
! is equal to the parameter “tid”.
Next, we need to know how to implement this in ANSYS Mechanical. We start with a model of a ball valve assembly, using some simple geometry from one of our training classes. The idea is that hot water passes through the valve represented by a constant temperature of 125 F. There is a heat sink represented at the OD of the ends of the valve at a constant 74 degrees. There is also some convection on most of the outer surfaces carrying some heat away.
The ball valve and the valve housing are separate parts and contact is used to allow heat to flow from the hotter ball valve into the cooler valve assembly:
Here is the command snippet associated with that contact region. The ‘magic’ is the ARG1 parameter which is given an initial value in the Details view, BEFORE the P box is checked to make it a parameter. Wherever we need to define the value of TCC in the command object, we use the ARG1 parameter name, as shown here:
Next, we verify that it actually works as expected. Here I have setup a table of design points, with increasing values of TCC (ARG1). The output parameter that is tracked is the minimum temperature on the inner surface of the valve housing, where it makes contact with the ball. If conductance is low, little heat should flow so the housing remains cool. If the conductance is high, more heat should flow into the housing making it hotter. After solving all the design points in the Workbench window, we see that indeed that’s what happens:
And here is a log scale plot showing temperature rise with increasing TCC:
So, excluding the comments our command object is 6 lines long. With those six lines of text as well as knowledge of how to use the ARG1 parameter, we now have thermal contact conductance which varies as a parameter. This is a simple case and you will certainly want to test and verify for your own use. Hopefully this helps with explaining the process and how it is done, including verification.
One of the cool new features in CFX 18 is the ability to actively review results while the calculation is running. It is supported for steady state and transient calculations, and now includes support for rotating reference frames as well.
What follows are some tutorial-esque steps to get you started.
In a previous post, I laid out a structural classification of cellular structures in nature, proposing that they fall into 6 categories. I argued that it is not always apparent to a designer what the best unit cell choice for a given application is. While most mechanical engineers have a feel for what structure to use for high stiffness or energy absorption, we cannot easily address multi-objective problems or apply these to complex geometries with spatially varying requirements (and therefore locally optimum cellular designs). However, nature is full of examples where cellular structures possess multi-objective functionality: bone is one such well-known example. To be able to assign structure to a specific function requires us to connect the two, and to do that, we must identify all the functions in play. In this post, I attempt to do just that and develop a classification of the functions of cellular structures.
Any discussion of structure in nature has to contend with a range of drivers and constraints that are typically not part of an engineer’s concern. In my discussions with biologists (including my biochemist wife), I quickly run into justified skepticism about whether generalized models associating structure and function can address the diversity and nuance in nature – and I (tend to) agree. However, my attempt here is not to be biologically accurate – it is merely to construct something that is useful and relevant enough for an engineer to use in design. But we must begin with a few caveats to ensure our assessments consider the correct biological context.
1. Uniquely Biological Considerations
Before I attempt to propose a structure-function model, there are some legitimate concerns many have made in the literature that I wish to recap in the context of cellular structures. Three of these in particular are relevant to this discussion and I list them below.
1.1 Design for Growth
Engineers are familiar with “design for manufacturing” where design considers not just the final product but also aspects of its manufacturing, which often place constraints on said design. Nature’s “manufacturing” method involves (at the global level of structure), highly complex growth – these natural growth mechanisms have no parallel in most manufacturing processes. Take for example the flower stalk in Fig 1, which is from a Yucca tree that I found in a parking lot in Arizona.
At first glance, this looks like a good example of overlapping surfaces, one of the 6 categories of cellular structures I covered before. But when you pause for a moment and query the function of this packing of cells (WHY this shape, size, packing?), you realize there is a powerful growth motive for this design. A few weeks later when I returned to the parking lot, I found many of the Yucca stems simultaneously in various stages of bloom – and captured them in a collage shown in Fig 2. This is a staggering level of structural complexity, including integration with the environment (sunlight, temperature, pollinators) that is both wondrous and for an engineer, very humbling.
The lesson here is to recognize growth as a strong driver in every natural structure – the tricky part is determining when the design is constrained by growth as the primary force and when can growth be treated as incidental to achieving an optimum functional objective.
Even setting aside the growth driver mentioned previously, structure in nature is often serving multiple functions at once – and this is true of cellular structures as well. Consider the tessellation of “scutes” on the alligator. If you were tasked with designing armor for a structure, you may be tempted to mimic the alligator skin as shown in Fig. 3.
As you begin to study the skin, you see it is comprised of multiple scutes that have varying shape, size and cross-sections – see Fig 4 for a close-up.
The pattern varies spatially, but you notice some trends: there exists a pattern on the top but it is different from the sides and the bottom (not pictured here). The only way to make sense of this variation is to ask what functions do these scutes serve? Luckily for us, biologists have given this a great deal of thought and it turns out there are several: bio-protection, thermoregulation, fluid loss mitigation and unrestricted mobility are some of the functions discussed in the literature [1, 2]. So whereas you were initially concerned only with protection (armor), the alligator seeks to accomplish much more – this means the designer either needs to de-confound the various functional aspects spatially and/or expand the search to other examples of natural armor to develop a common principle that emerges independent of multi-functionality specific to each species.
1.3 Sub-Optimal Design
This is an aspect for which I have not found an example in the field of cellular structures (yet), so I will borrow a well-known (and somewhat controversial) example  to make this point, and that has to do with the giraffe’s Recurrent Laryngeal Nerve (RLN), which connects the Vagus Nerve to the larynx as shown in Figure 5, which it is argued, takes an unnecessarily long circuitous route to connect these two points.
We know that from a design standpoint, this is sub-optimal because we have an axiom that states the shortest distance between two points is a straight line. And therefore, the long detour the RLN makes in the giraffe’s neck must have some other evolutionary and/or developmental basis (fish do not have this detour) . However, in the case of other entities such as the cellular structures we are focusing on, the complexity of the underlying design principles makes it hard to identify cases where nature has found a sub-optimal design space for the function of interest to us, in favor of other pressing needs determined by selection. What is sufficient for the present moment is to appreciate that such cases may exist and to bear them in mind when studying structure in nature.
2. Classifying Functions
Given the above challenges, the engineer may well ask: why even consider natural form in making determinations involving the design of engineering structures? The biomimic responds by reminding us that nature has had 3.8 billion years to develop a “design guide” and we would be wise to learn from it. Importantly, natural and engineering structures both exist in the same environment and are subject to identical physics and further, are both often tasked with performing similar functions. In the context of cellular structures, we may thus ask: what are the functions of interest to engineers and designers that nature has addressed through cellular design? Through my reading [1-4], I have compiled the classification of functions in Figure 6, though this is likely to grow over time.
This broad classification into structural and transport may seem a little contrived, but it emerges from an analyst’s view of the world. There are two reasons why I propose this separation:
Structural functions involve the spatial allocation of materials in the construction of the cellular structures, while transport functions involve the structure AND some other entity and their interactions (fluid or light for example) – thus additional physics needs to be comprehended for transport functions
Secondly, structural performance needs to be comprehended independent of any transport function: a cellular structure must retain its integrity over the intended lifetime in addition to performing any additional function
Each of these functions is a fascinating case study in its own right and I highly recommend the site AskNature.org  as a way to learn more on a specific application, but this is beyond the scope of the current post. More relevant to our high-level discussion is that having listed the various reasons WHY cellular structures are found in nature, the next question is can we connect the structures described in the previous post to the functions tabulated above? This will be the attempt of my next post. Until then, as always, I welcome all inputs and comments, which you can send by messaging me on LinkedIn.
Occasionally when solid geometry is imported from CAD into ANSYS SpaceClaim the geometry will come in as solids, but when a mesh is generated on the solids the mesh will appear to “leak” into the surrounding space. Below is an assembly that was imported from CAD into SpaceClaim. In the SpaceClaim Structure Window all of the parts can be seen to be solid components.
When the mesh is generated in ANSYS Mechanical it appears like the assembly has been successfully meshed.
However, when you look at the mesh a little closer, the mesh can be missing from some of the surfaces and not displayed correctly on others.
Additionally, if you create a cross-section through the mesh, the mesh on some of the parts will “leak” outside of the part boundaries and will look like the image below.
Based on the mesh color, the mesh of the part in the center of the assembly has grown outside of the surfaces of the part.
To repair the part you need to go back to SpaceClaim and rebuild it. First you need to hide the rest of the parts.
Next, create a sketch plane that passes through the problem part.
In the sketch mode create a rectangle that surrounds the part. When you return to 3D mode in SpaceClaim, that rectangle will become a surface that passes through the part.
Now use the Pull tool in SpaceClaim to turn that surface into a part that completely surrounds the part to be repaired, making sure to turn on the “No Merge” option for the pull before you begin.
After you have pulled the surface into a solid, it should like the image below where the original part is completely buried inside the new part.
Now you will use the Combine tool to divide the box with the original part. Select Combine from the Tool Bar, then select the box that you created in the previous step. The cutter will be activated and you will move the cursor around until the original part is highlighted inside the box. Select it with the left mouse button. The Combine tool will then give you the option to select the part of the box that you want to remove. Select the part that surrounds the original part. After it is finished, close the combine tool and the Structure Tree and 3D window will now look like the following:
Now move the new solid that was created with the Combine tool into the location of the original part and turn off the original one and re-activate the other parts of the assembly. The assembly and Structure Tree should now look like the pictures below.
Now save the project, re-open the meshing tool, and re-generate the mesh. The mesh should now be correct and not “leaking” beyond the part boundaries.
What types of cellular designs do we find in nature?
Cellular structures are an important area of research in Additive Manufacturing (AM), including work we are doing here at PADT. As I described in a previous blog post, the research landscape can be broadly classified into four categories: application, design, modeling and manufacturing. In the context of design, most of the work today is primarily driven by software that represent complex cellular structures efficiently as well as analysis tools that enable optimization of these structures in response to environmental conditions and some desired objective. In most of these software, the designer is given a choice of selecting a specific unit cell to construct the entity being designed. However, it is not always apparent what the best unit cell choice is, and this is where I think a biomimetic approach can add much value. As with most biomimetic approaches, the first step is to frame a question and observe nature as a student. And the first question I asked is the one described at the start of this post: what types of cellular designs do we find in the natural world around us? In this post, I summarize my findings.
In a previous post, I classified cellular structures into 4 categories. However, this only addressed “volumetric”structures where the objective of the cellular structure is to fill three-dimensional space. Since then, I have decided to frame things a bit differently based on my studies of cellular structures in nature and the mechanics around these structures. First is the need to allow for the discretization of surfaces as well: nature does this often (animal armor or the wings of a dragonfly, for example). Secondly, a simple but important distinction from a modeling standpoint is whether the cellular structure in question uses beam- or shell-type elements in its construction (or a combination of the two). This has led me to expand my 4 categories into 6, which I now present in Figure 1 below.
Setting aside the “why” of these structures for a future post, here I wish to only present these 6 strategies from a structural design standpoint.
Volumetric – Beam: These are cellular structures that fill space predominantly with beam-like elements. Two sub-categories may be further defined:
Honeycomb: Honeycombs are prismatic, 2-dimensional cellular designs extruded in the 3rd dimension, like the well-known hexagonal honeycomb shown in Fig 1. All cross-sections through the 3rd dimension are thus identical. Though the hexagonal honeycomb is most well known, the term applies to all designs that have this prismatic property, including square and triangular honeycombs.
Lattice and Open Cell Foam: Freeing up the prismatic requirement on the honeycomb brings us to a fully 3-dimensionallattice or open-cell foam. Lattice designs tend to embody higher stiffness levels while open cell foams enable energy absorption, which is why these may be further separated, as I have argued before. Nature tends to employ both strategies at different levels. One example of a predominantly lattice based strategy is the Venus flower basket sea sponge shown in Fig 1, trabecular bone is another example.
Volumetric – Shell:
Closed Cell Foam: Closed cell foams are open-cell foams with enclosed cells. This typically involves a membrane like structure that may be of varying thickness from the strut-like structures. Plant sections often reveal a closed cell foam, such as the douglas fir wood structure shown in Fig 1.
Periodic Surface: Periodic surfaces are fascinating mathematical structures that often have multiple orders of symmetry similar to crystalline groups (but on a macro-scale) that make them strong candidates for design of stiff engineering structures and for packing high surface areas in a given volume while promoting flow or exchange. In nature, these are less commonly observed, but seen for example in sea urchin skeletal plates.
Tessellation: Tessellation describes covering a surface with non-overlapping cells (as we do with tiles on a floor). Examples of tessellation in nature include the armored shells of several animals including the extinct glyptodon shown in Fig 1 and the pineapple and turtle shell shown in Fig 2 below.
Overlapping Surface: Overlapping surfaces are a variation on tessellation where the cells are allowed to overlap (as we do with tiles on a roof). The most obvious example of this in nature is scales – including those of the pangolin shown in Fig 1.
What about Function then?
This separation into 6 categories is driven from a designer’s and an analyst’s perspective – designers tend to think in volumes and surfaces and the analyst investigates how these are modeled (beam- and shell-elements are at the first level of classification used here). However, this is not sufficient since it ignores the function of the cellular design, which both designer and analyst need to also consider. In the case of tessellation on the skin of an alligator for example as shown in Fig 3, was it selected for protection, easy of motion or for controlling temperature and fluid loss?
In a future post, I will attempt to develop an approach to classifying cellular structures that derives not from its structure or mechanics as I have here, but from its function, with the ultimate goal of attempting to reconcile the two approaches. This is not a trivial undertaking since it involves de-confounding multiple functional requirements, accounting for growth (nature’s “design for manufacturing”) and unwrapping what is often termed as “evolutionary baggage,” where the optimum solution may have been sidestepped by natural selection in favor of other, more pressing needs. Despite these challenges, I believe some first-order themes can be discerned that can in turn be of use to the designer in selecting a particular design strategy for a specific application.
This is by no means the first attempt at a classification of cellular structures in nature and while the specific 6 part separation proposed in this post was developed by me, it combines ideas from a lot of previous work, and three of the best that I strongly recommend as further reading on this subject are listed below.
As always, I welcome all inputs and comments – if you have an example that does not fit into any of the 6 categories mentioned above, please let me know by messaging me on LinkedIn and I shall include it in the discussion with due credit. Thanks!