We were pleased to note today that PADT Medical customer Ulthera Inc. filed for an $86M IPO with the SEC. We have truly enjoyed working with this company offer our congratulations to them on reaching this major milestone.
How do you turn a political defeat into a big win, you look at your options, decide where you want to go, and you do it. That is what a group of valley visionaries did in the early 1980’s when the state decided that only the University of Arizona could should have an agriculture program. That left Arizona State University with a large working farm that needed to be taken down. They could have sold the land for quick profit. But instead they looked at options that would provide the most long-term benefit to the school, the state, and the local community.
The result, thirty years ago, was the ASU Research Park. Located just west of the 101 Loop between Warner and Elliot roads, the Park is now a vibrant and thriving hot-spot of technical innovation and realization. This is not an incubator where people try to be successful in technology, this is where people who are successful with technology come to get stuff done.
PADT is pleased to own a building in the Park, the PADT Innovation Center, where our headquarters are located along with three other business that lease space from us. We have found the park to be a supportive place, centrally located, with great facilities for our employees.
The event was marked with a breakfast gathering of tenants, Tempe officials, Park board members, and representatives from ASU. Dr. Michael Crow, the President of ASU gave a great speech on how the park in particular helped move ASU towards being a true research university. He stressed that unlike in most places, ASU didn’t plan and study and move slowly. They wanted to become a research university and if you want to be a research university, you need a research park. So they built a research park, and in the end, a very successful one.
Some interesting facts about the park:
- Home to 49 companies with a total of over 4,500 employees
- Generates over $2,000,000 annually for ASU
- Has a $816,000,000 annual impact on the Arizona economy, generating 11,180 jobs
- 89% of the park is leased, 26 Acres still available
- 1,790,000 sqft of office space, with 350,000 sqft under construction.
The mayor of Tempe, Mark Mitchel, was also on hand to share with the audience the strong impact that the park and ASU have had on the city and how the ASU Research Park is a true university-city initiative. In fact, Mr. Mitchel’s father, Harry Mitchel, was the mayor of Tempe thirty years ago and was one of the visionaries that helped make the park happen.
This aerial view, taken a few months ago, shows the new GoDaddy tech center being built in the lower right hand corner. The PADT innovation center is the upside-down check mark in the upper right corner. PADT customers ViaSat and Amkor are both starting construction in the park right now.
To learn more, read the official press release: ASU Research Park Celebrates 30 Years – Press Release, or visit the park’s website: asuresearchpark.com.
It’s always nice when a customer gets a mention in the local press. PADT is helping NeruoEM in the development of a “a self-contained head device to prevent and treat Alzheimer’s Disease (AD) with electromagnetic waves.” Check out the write-up here.
This video is an introduction to ANSYS RBD – an add on module to ANSYS Mechanical for analyzing rigid mechanisms.
PADT held our 20th anniversary party at our primary offices in Tempe Arizona on April 10th. Despite the record high temperatures, around 400 people stop by to help us celebrate. There was good food, good entertainment, and most importantly, good people.
A highlight of the event is that April 10th was proclaimed PADT day in Tempe! That was an unexpected honor.
The only problem was not enough time to talk with everyone. If you could not make it, no worries. We have several events planned throughout the year.
Here are some images that we captured:
Most of these pictures were taken by Aaron Moncur from PipelineDesign.
(View part one of this series here.)
So, I’ve done a little of this Workbench customization stuff in a past life. My customizations involved lots of Jscript, some APDL, sweat and tears. I literally would bang my head against Eric Miller’s office door jamb wondering (sometimes out loud) what I had done to be picked as the Workbench customization guy. Copious amounts of alcohol on weeknights helped some, but honestly it still royally sucked. Because of these early childhood scars, I’ve cringed at the thought of this ACT thingy until now. I figured I’d been there, done that and I had zero, and I mean zero desire to relive any of it.
So, I resisted ACT even after multiple “suggestions” from upper management that I figure out something to do with it. That was wrong of me; I should have been quicker to given ACT a fair shot. ACT is a whole bunch better than the bad ol’ days of JScript. How is it better? Well, it has documentation… Also, there are multiple helpful folks back at Canonsburg and elsewhere that know a few things about it. This is opposed to the days when just one (brilliant) guy in India named Rajiv Rath had ever done anything of consequence with JScript. (Without him, my previous customization efforts would simply have put me in the mad house. Oh, and he happens to know a thing or two about ACT as well…)
Look Ma! My First ACT Extension!
In this post we are going to rig up the PID thermostat boundary condition as a new boundary condition type in Mechanical. In ACT jargon, this is known as adding a pre-processing feature. I’m going to refer you primarily to the training material and documentation for ACT that you can obtain from the ANSYS website on their customer portal. I strongly suggest you log on to the portal and download the training material for version 15.0 now.
Planning the Extension
When we create an ACT extension we need to lay things out on the file system in a certain manner. At a high level, there are three categories of file system data that we will use to build our extension. These types of data are:
- Code. This will be comprised of Python scripts and in our case APDL scripts.
- XML. XML files are primarily used for plumbing and for making the rest of the world aware of who we are and what we do.
- Resources. These files are typically images, icons, etc… that spice things up a little bit.
Any single extension will use all three of these categories of files, and so organizing this mess becomes priority number one when building the extension. Fortunately, there is a convention that ACT imposes on us to organize the files. The following image depicts the structure graphically.
We will call our extension PIDThermostat. Therefore, everywhere ExtensionName appears in the image above, we will replace that with PIDThermostat in our file structure.
Creating a UI for our Load
The beauty of ACT is that it allows us to easily create professional looking user experiences for custom loads and results. Let’s start by creating a user interface for our load that looks like the following:
As you can see in the above user interface, all of the controls and inputs for our little PID controller that we designed in Part 1 of this blog series are present. Before we discuss how we create these user elements, let’s start with a description of what they each mean.
The first item in the UI is named Heat Source/Sink Location. This UI element presents to the user a choice between picking a piece of geometry from the model and specifying a named selection. Internal to the PID controller, this location represents where in the model we will attach the control elements such that they supply or remove energy from this location. ACT provides us two large areas of functionality here. First, it provides a way to graphically pick a geometric item from the model. Second, it provides routines to query the underlying mesh associated with this piece of geometry so that we can reference the nodes or elements in our APDL code. Each of these pieces of functionality is substantial in its size and complexity, yet we get it for free with ACT.
The second control is named Temperature Monitor Location. It functions similarly to the heat source/sink location. It gives the user the ability to specify where on the model the control element should monitor, or sense, the temperature. So, our PID controller will add or remove energy from the heat sink/source location to try to keep the monitor location at the specified set point temperature.
The third control group is named Thermostat Control Properties. This group aggregates the various parameters that control the functionality of the thermostat. That is, the user is allowed to specify gain values, and also what type of control is implemented.
The forth control group is named Thermostat Setpoint Properties. This group allows the user to specify how the setpoint changes with time. An interesting ACT feature is implemented with this control group. Based on the selection that the user makes in the “Setpoint Type” dropdown, a different control is presented below for the thermostat setpoint temperature. When the user selects, “User Specified Setpoint” then a control that provides a tabular input is presented. In this case, the user can directly input a table of temperature vs time data that specifies how the setpoint changes with time. However, if the user specifies “Use Model Entity as Setpoint” then the user is presented a geometry picker similar to the controls above to select a location in the model that will define the setpoint temperature. When this option is selected, the PID controller will function more like a follower element. That is, it will try to cause the monitor location to “follow” another location in the model by adding or removing energy from the heat source/sink location. The offset value allows the user to specify a DC offset that they would like to apply to the setpoint value. Internally, this offset term will be incorporated into the constraint equation averaging method to add in the DC offset.
Finally, the last control group allows the user to visualize plots of computed information regarding the PID controller after the solution is finished. Normally this would be presented in the results branch of the tree; however, the results I would like to present for these elements don’t map cleanly to the results objects in ACT. (At least, I can’t map them cleanly in my mind…) More detail on the results will be presented below.
So, now that we know what the control UI does, let’s look at how to specify it in ACT
Specifying the UI in XML
As mentioned at the beginning, ACT relies on XML to specify the UI for controls. The following XML snippet describes the entire UI.
<extension version=“1” name=“ThermalTools”>
<script src=“thermaltools.py” />
<toolbar name=“thermtools” caption=“Thermal Tools”>
<entry name=“PIDThermostatLoad” icon=“ThermostatGray”
caption=“PID Thermostat Control”>
<load name=“pidthermostat” version=“1” caption=“PID Thermostat”
icon=“ThermostatWhite” issupport=“false” isload=“true” color=“#0000FF”>
<property name=“ConnectionGeo” caption= “Heat Source/Sink Location”
<attributes selection_filter=“vertex|edge|face” />
<property name=“MonitorGeo” caption= “Temperature Monitor Location”
<attributes selection_filter=“vertex|edge|face|body” />
<propertygroup name=“ControlProperties” caption=“Thermostat Control Properties”
<property name=“ControlType” caption=“Control Type”
control=“select” default=“Both Heat Source and Sink”>
<attributes options=“Heat Source,Heat Sink,Both Heat Source and Sink”/>
<property name=“ProportionalGain” caption=“Proportional Gain”
<property name=“IntegralGain” caption=“Integral Gain”
<property name=“DerivativeGain” caption=“Derivative Gain”
<propertygroup name=“SetpointProperties” caption=“Thermostat Setpoint Properties”
<propertygroup name=“SetpointType” display=“property” caption=“Setpoint Type”
control=“select” default=“User Specified Setpoint”>
<attributes options=“User Specified Setpoint,Use Model Entity as Setpoint”/>
<propertytable name=“SetPointTemp” caption=“Thermostat Set Point Temperature”
display=“worksheet” visibleon=“User Specified Setpoint”
<property name=“Time” caption=“Time” unit=“Time” control=“float”></property>
<property name=“SetPoint” caption=“Set Point Temperature”
<property name=“SetpointGeo” caption= “Setpoint Geometry”
visibleon=“Use Model Entity as Setpoint” control=“scoping”>
<attributes selection_filter=“vertex|edge|face|body” />
<property name=“SetpointOffset” caption=“Offset” control=“float” default=“0”/>
<propertygroup name=“Results” caption=“Thermostat Results” display=“caption”>
<property name=“ViewResults” caption=“View Results?” control=“select” default=“No”>
Describing this in detail would take far longer than I have time for now, so I’m going to direct you to the ACT documentation. The gist of it is fairly simple though. XML provides a structured, hierarchical mechanism for describing the layout of the UI. Nested structures appear as child widgets of their parents. Callbacks are used within ACT to provide the hooks into the UI events so that we can respond to various user interactions. Beyond that, read the docs!! And, hey, before I hear any whining remember that in the old days of Jscript customization there wasn’t any documentation! When I was a Workbench Customization Kid we had to walk uphill, both ways, barefoot, in 8’ of snow for 35 miles… So shut it!
Making the Magic Happen
So, the UI is snazzy and all, but the heavy lifting really happens under the hood. Ultimately, what ACT provides us, when we are creating new BCs for ANSYS, is a clever way to insert APDL commands into the ds.dat input stream. Remember that at its core all Mechanical is, is a glorified APDL generator. I’m sure the developers love me reducing their hard labor to such mundane terms, but it is what it is… So, at the end of the day, our little ACT load objects are nothing more than miniature APDL writers. You thought we were doing something cool…
So, the magic happens when we collect up all of the input data the user specifies in our snazzy UI and turn that into APDL code that implemented the PID controller. This is obviously why I started by developing the APDL code first outside of WB. The APDL code is the true magic. Collecting up the user inputs and writing them to the ds.dat file occurs inside the getcommands callback. If you look closely at the XML code, you will notice two getcommands callbacks. The first one calls a python function named: writePIDThermostatLoad. This callback is scheduled to fire when Mechanical is finished writing all of the standard loads and boundary conditions that it implements natively and is about to write the first APDL solve command. Our commands will end up in the ds.dat file right at this location. I chose this location for the following reason. Our APDL code for the PID thermostat will be generating new element types and new nodes and elements not generated by Workbench. Sometimes workbench implements certain boundary conditions using surface effect element types. So, these native loads and boundary conditions themselves may generate new elements and element types. Workbench knows about those, because it’s generating them directly; however, it has no idea what I might be up to in my PID thermostat load. So, if it were to write additional boundary conditions after my PID load, it very well might reuse some of my element type ids, and even node/element ids. The safer thing to do is to write our APDL code so that it is robust in the presence of an unknown maximum element type/real constant set/node number/etc… Then, we insert it right before the solve command, after WB has written all of its loads and boundary conditions. Thus, the likelihood of id collisions is greatly reduced or eliminated.
Note, too, that ACT provides some utility functions to generate a new element type id and increment the internal counter within Workbench; however, I have found that these functions do not account for loads and boundary conditions. Therefore, in my testing thus far, I haven’t found them safe to use.
The second getcommands callback is setup to fire when Workbench finishes writing all of the solve commands and has moved to the post processing part of the input stream. I chose to implement a graphing functionality for displaying the relevant output data from the PID elements. Thus, I needed to retrieve this data from ANSYS after the solution is complete so that I can present it to the user. I accomplished this by writing a little bit of APDL code to enter /post26 and export all of the data I wish to plot to a CSV file. By specifying this second getcommands callback, I could instruct Workbench to insert the APDL commands after the solve completed.
Viewing the Results
Once the solution has completed, clicking on the “View Results?” dropdown and choosing “Yes” will bring up the following result viewer I implemented for the PID controller. All of the graphing functionality is provided by ACT in an import module called “chart”. This result viewer is simply implemented as a dialog with a single child control that is the ACT chart widget. This widget also allows you to layout multiple charts in a grid, as we have here. As you can see, we can display all of the relevant output data for the controls cleanly and efficiently using ACT! While this can also be accomplished in ANSYS Mechanical APDL, I think we would all agree that the results are far more pleasing visually when implemented in ACT.
Where Do We Go from Here?
Now that I’ve written an ACT module, my next steps are to clean it up and try to make it a little more production ready. Once I’m satisfied with it, I’ll publish it on this blog and on the appropriate ANSYS library. Look for more posts along the way if I uncover additional insights or gotchas with ACT programming. I will leave you with this, however. If you have put off ACT programming you really should reconsider! Being mostly new to ACT, I was able to get this little boundary condition hooked up and functioning in less than a week’s time. Given the way the user interface turned out and the flexibility thus far of the control, I’m quite pleased with that. Without the documentation and general availability of ACT, this effort would have been far more intense. So, try out ACT! You won’t be disappointed.
PADT’s new Objet500 Connex3 is up and running, just in time for our 20th Anniversary party tonight. The latest machine from Stratasys is the first true 3D Color Printer that allows users to print accurate and durable parts in whatever combination of color they want, including tinted transparent material. The machine is comfortably nestled between our FORTUS 400 and FORTUS 250MC.
We are especially pleased to have several executives and support people from Stratasys, the manufacturer of this machine, here for our party tonight. They will be around to answer questions and will be offering a brief presentation on their technology as well.
Yesterday we successfully ran the standard “wrench” demo models:
And overnight we ran some more sample parts along with a printout of a 3D FEA result on a valve model:
The parts are still inside the support material, so you can’t see all the colors. Have no fear, we will be blogging about the FEA model very shortly.
PADT has been offering this machine for sale since its introduction in February and we have already sold one and have several other users about to purchase. The advantages of having a color part without having to paint on are significant. With our own machine we can now build benchmark parts for potential buyers and we can also offer color printing as part of our Rapid Prototyping services.
We will be showing off this machine, along with everything else PADT does, at our party tonight. But if you can’t make it and would like to learn more, just reach out to our sales team at firstname.lastname@example.org, our prototyping services team at email@example.com or just give us a call at 480.813.4884.
Everyone here at PADT is working hard on their day-to-day tasks and getting ready for the 20th Anniversary Party tomorrow night from 5:30-9:00. The refreshments have been procured, ice is on order, and an appointment has been set to go get the cake. In light of tomorrows high temps, we even ordered two coolers to keep everyone comfortable, so “it’s too hot” is no longer a valid excuse.
Tomorrow around noon, all of PADT’s employees will gather together for a special anniversary pictures, attend a company meeting, then set everything up.
All we need now is you!
Even though we have been suffering from construction delays, the new demo & server room is finished enough to show everyone around. In the new room, will be highlighting our CUBE HPC hardware and the new Objet 3D Color Printer in this new facility, along with the scanners and other Additive Manufacturing machines you have all come to know and count on:
Here are some reminders for those that can make it:
- Everyone is invited, no need to RSVP. Just come and bring a friend.
- If you are social network kind of person, please tag any posts: #padt20
- We will have plenty of food and drink
- Entertainment will be provided for the young (and young at heart) with a bouncy-house and a magician
- We will be featuring music from 1994… but it is an engineering event so don’t expect a lot of dancing.
- Come at any time you want. Speeches and door price drawings will be from 6:30-7:00
- We will be handing out some swag, first come first serve for t-shirts and slap bracelets.
As always, learn more on our website: www.padtinc.com/20
For those of you who could not make it, a big thank you for all the fantastic best wishes and congratulations. We really appreciate all the support everyone has given us over the past 20 years.
As part of our “Getting to know ANSYS” video series, this video is an introduction to ANSYS Icepak – an electronics thermal analysis package in the ANSYS Product Suite.
The ANSYS Product Suite contains a large number of modules that are each tailored for a particular area in the simulation and analysis world. We, at PADT, realize that many of our customers are not aware or are confused at where each of these modules fits in to the analysis spectrum.
The “Getting to know ANSYS” videos will hopefully help everyone to understand these modules a little better. Each video will focus on one module and will showcase the following in a mixture of presentations and mini-demos:
- What each module is
- What are its capabilities
- Why is it useful
- Who can benefit from using it
The videos will be in the “Getting to know ANSYS” playlist on PADT’s Youtube Channel.
Please feel free to let us know how the videos are and definitely let us know which module that you are interested in and that you’d like to see next. That will help us to plan these future videos accordingly.
You can reach out to me directly at firstname.lastname@example.org for questions or followups to these or the “Focus Video Tips” videos.
If you are interested in 3D Printing and you don’t follow Terry Wohlers, you should. He has been following this industry since it started and he is one of the best at separating hype from reality.
He brings up some very good points on where the technology is being used today and where growth may occur. Also some thoughts on the global growth of additive manufacturing, and the obstacles and challenges the industry faces.
My favorite take-away from the posting is Terry’s that “more and more effort is needed to not just take a traditional design and 3D Print it, but rather to re-think the entire part design to take into account the capabilities and limitations of AM.”
I’m going to embark on a multipart blog series chronicling my efforts in writing a PID Thermostat control boundary condition for workbench. I picked this boundary condition for a few of reasons:
- As far as I know, it doesn’t exist in WB proper.
- It involves some techniques and element types in ANSYS Mechanical APDL that are not immediately intuitive to most users. Namely, we will be using the Combin37 element type to manage the control.
- There are a number of different options and parameters that will be used to populate the boundary condition with data, and this affords an opportunity to explore many of the GUI items exposed in ACT.
This first posting goes over how to model a PID controller in ANSYS Mechanical APDL. In future articles I will share my efforts to refine the model and us ACT to include it in ANSYS Workbench.
PID Controller Background
Let’s begin with a little background on PID controllers. Full disclaimer, I’m not controls engineer, so take this info for what it is worth. PID stands for Proportional Integral Differential controller. The idea is fairly simple. Assume you have some output quantity you wish to control by varying some input value. That is, you have a known curve in time that represents what you would like the output to look like. For example:
The trick is to figure out what the input needs to look like in time so that you get the desired output. One way to do that is to use feedback. That is, you measure the current output value at some time, t, and you compare that to what the desired output should be at that time, t. If there is no difference in the measured value and the desired value, then you know whatever you have set the input to be, it is correct at least for this point in time. So, maybe it will be correct for the next moment in time. Let’s all hope…
However, chances are, there is some difference between what is measured and what is desired. For future reference we will call this the error term. The secret sauce is what to do with that information? To make things more concrete, we will ground our discussion in the thermal world and imagine we are trying to maintain something at a prescribed temperature. When the actual temperature of the device is lower than the desired temperature, we will define that as a positive error. Thus, I’m cold; I want to be warmer: that equals positive error. The converse is true. I’m hot; I want to be colder: that equals negative error.
One simple way we could try to control the input would be to say, “Let’s make the input proportional to the error term.” So, when the error term is positive, and thus I’m cold and wish to be warmer, we will add energy proportionate to the magnitude of the error term. Obviously the flip side is also true. If I’m hot and I wish to be cooler my negative error term would mean that remove energy proportionate to the magnitude of the error term. This sounds great! What more do you need? Well, what happens if I’m trying to hold a fixed temperature for a long time? If the system is not perfectly adiabatic, we still have to supply some energy to make up for whatever the system is losing to the surroundings. Obviously, this energy loss occurs even with the system is in a steady state configuration and at the prescribed temperature! But, if the system is exactly at the prescribed temperature, then the error term is zero. Anything proportionate to zero is… zero. That’s a bummer. I need something that won’t go to zero when my error term goes to zero.
What if I could keep a record of what I’ve done in the past? What if I accumulated all of the past error from forever? Obviously, this has the chance of being nonzero even if instantaneously my error term is zero. This sounds promising. Integrating a function of time with respect to time is analogous to accumulating the function values from history past. Thus, what if I integrated my error term and then made my input also proportional to that value? Wouldn’t that help the steady state issue above? Sure it would. Unfortunately, it also means I might go racing right on by my set point and it might take a while for that “mistake” to wash out of the system. Nothing is free. So, now I have kept a record of my entire past and used that to help me in the present, what if I could read the future? What if could extrapolate out in time?
Derivatives allow us to make a local extrapolation (in either direction) about a curve at a fixed point. So, if my curve is a function of time, which in our case the curves are, forward extrapolation is basically jumping ahead into the future. However, we can’t truly predict the future, we can only extrapolate on what has recently happened and make the leap of faith that it will continue to happen just as it has. So, if I take the derivative of my error term with respect to time, I can roll the dice a little a make some of my input proportional to this derivative term. That is, I can extrapolate out in time. If I do it right, I can actually make the system settle out a little faster. Remember that when the error term goes to zero and stays there, the derivative of the error term also goes to zero. So, when we are right on top of our prescribed value this term has no bearing on our input.
So, a PID controller simply takes the three concepts of how to specify an input value based on a function of the error term and mixes them together with differing proportions to arrive at the new value for the input. By “tuning” the system we can make it such that it responds quickly to change and it doesn’t wildly overshoot or oscillate.
Implementing a PID controller in ANSYS MAPDL
We will begin by implementing a PID controller in MAPDL before moving on to implementing the boundary condition in ANSYS Workbench via the ACT. We would like the boundary condition to have the following features:
- Ultimately we would like to “connect” this boundary condition to any number of nodes in our model. That is, we may want to have our energy input occur on a vertex, edge or face of the model in Workbench. So, we would like the boundary condition to support connecting to any number of nodes in the model.
- Likewise, we would like the “measured output” to be influenced by any number of nodes in our model. That is, if more than one node is included in the “measured value” set, we would like ANSYS to use the average temperature of the nodes in that set as our “measured output”. Again, this will allow us to specify a vertex, edge, face or body of the model to function as our measurement location. The measured value should be the average temperature on this entity. Averaging needs to be intelligent. We need to weight the average based on some measure that accounts for the relative influence of a node attached to large elements vs one attached to small elements.
- We would like to be able to independently control the proportional, integral and derivative components of the control algorithm.
- It would be nice to be able to specify whether this boundary condition can only add energy, only remove energy or if it can do both.
- We would like to allow the set point value to also be a function of time so that it too can change with time.
- Finally, it would be nice to be able to post process some of the heat flow quantities, temperature values, etc… associated with this boundary condition.
This is a pretty exhaustive list of requirements for the boundary condition. Fortunately, ANSYS MAPDL has built into it an element type that is perfectly suited for this type of control. That element type is the combin37.
Introducing the Combin37 Element Type
Understanding the combin37 element in ANSYS MAPDL takes a bit of a Zen state of mind… It’s, well, an element only a mother could love. Here is a picture lifted from the help:
OK. Clear as mud right? Actually, this thing can act as a thermostat whether you believe me from the picture or not. Like most/all ANSYS elements that can function in multiple roles, the combin37 is expressed in its structural configuration. It is up to you and me to mentally map it to a different set of physics. So, just trust me that you can forget the damping and FSLIDE and little springy looking thing in the picture. All we are going to worry about is the AFORCE thing. Mentally replace AFORCE with heat flow.
Notice those two little nodes hanging out there all by their lonesome selves labeled “control nodes”. I think they should have joysticks beside them personally, but ANSYS didn’t ask me. Those little guys are appropriately named. One of them, NODE K actually, will function as our set point node. That is, whatever temperature value we specify in time for NODE K, that same value represents the set point temperature we would like our “measured” location take on in time as well. So, that means we need to drive NODE K with our set point curve. That should be easy enough. Just apply a temperature boundary condition that is a function of time to that node and we’re good to go. Likewise, NODE L represents the “measured” temperature somewhere else in the model. So, we need to somehow hook NODE L up to our set of measurement nodes so that it magically takes on the average value of those nodes. More on that trick later.
Now, internally the combin37 subtracts the temperature at NODE K from NODE L to obtain an error term. Moreover, it allows us to specify different mathematical operations we can perform on the error term, and it allows us to take the output from those mathematical operations and drive the magical AFORCE thingy, which is heat flow. Guess what those mathematical operations are? If you guessed simply making the heat flow through the element proportional to the error, proportional to the time integral of the error and proportional to the time derivative of the error you would be right. Sounds like a PID controller doesn’t it? Now, the hard part is making sense of all the options and hooking it all up correctly. Let’s focus on the options first.
Key Option One and the Magic Control Value
Key option 1 for the combin37 controls what mathematical operation we are going to perform on the error term. In order to implement a full PID controller, we are going to need three combin37 elements in our model with each one keyed to a different mathematical operation. ANSYS calls the result of the mathematical operation, Cpar. So, we have the following:
|KEYOPT(1) Value||Mathematical Operation|
Thus, for our purposes, we need to set keyopt(1) equal to 1,4 and 2 for each of the three elements respectively.
Feedback is realized by taking the control parameter Cpar and using it to modify the heat flow through the element, which is called AFORCE. The AFORCE value is specified as a real constant for the element; however, you can also rig up the element so that the value of AFORCE changes with respect to the control parameter. You do this by setting keyopt(6)=6. The manner in which ANSYS adjusts the AFORCE value, which again is heat flow, is described by the following equation:
Thus, the proportionality constant for the Proportional, Integral and Derivative components are specified with the C1 variable. RCONST, C3 and C4 are all set to zero. C2 is set to 1. Also note that ANSYS first takes the absolute value of the control parameter Cpar before plugging it into this equation. Furthermore, the direction of the AFORCE component is important. A positive value for AFORCE means that the element generates an element force (heatflow) in the direction specified in the diagram. That is, it acts as a heat sink. So, assuming the model is attached to node J, the element acts as a heat sink when AFORCE is positive. Conversely, when AFORCE is negative, the element acts like a heat source. However, due to the absolute value, Cpar can never take on a negative value. Thus, when this element needs to act as an energy source to add heat to our model, the coefficient C1 must be negative. The opposite is true when the element needs to act as an energy sink.
Key Option Four and Five and when is it Alive?
If things weren’t confusing enough already, hold on as we discuss Keyopt 4 and 5. Consider the figure below, again lifted straight from the help.
The combination of these two key options controls when the element switches on and becomes “alive”. Let’s take the simple case first. Let’s assume that we are adding energy to the model in order to bring it up to a given temperature. In this case, Cpar will be positive because our set point is higher than our current value. If the element is functioning as a heat source we would like it to be on in this condition. Furthermore, we would like it to stay on as long as our error is positive so that we continue adding energy to bring the system up to temperature. Consider the diagram in the upper left. Imagine that we set ONVAL = 0 and OFFVAL = 0.001. Whenever Cpar is greater than ONVAL. So this sounds like exactly what we want when the element is functioning as a heat source. Thus, keyopt(4)=0 and keyopt(5)=0.001 with OFFVAL=ONVAL=0 is what we want when the element needs to function as a heat source.
What about when it is a heat sink? In this case we want the element to be active when the error term is negative; that is, when the current temperature is higher than the set point temperature. Consider the diagram in the middle left. This time let OFFVAL=0 and OFFVAL=-0.001. In this case, whenever Cpar is negative (less than OFFVAL) then the element will be active. Thus, keyopt(4)=0 and keyopt(5)=1 with OFFVAL=-0.001 ONVAL=0 is what we want when the element needs to function as a heat sink. Notice, that if you set ONVAL=OFFVAL then the element will always stay on; thus, we need to provide the small window to activate the switching nature of the element.
Thus, we see that we need six different combin37 elements, three for a PID controlled heat sink and three for a PID controlled heat source, to fully specify a PID controlled thermal boundary condition. Phew… However, if we set all of the proportionality constants for either set of elements defining the heat sink or heat source to zero, we can effectively turn the boundary condition into only a heat source or only a heat sink, thus meeting requirement four listed above. While we’re marking off requirements, we can also mark off requirements three and five. That is, with this combination of elements we can independently control the P, I and D proportionality constants for the controller. Likewise, by putting a time varying temperature constraint on control node K, we can effectively cause the set point value to change in time. Let’s see if we can now address requirements one and two.
How do we Hook the Combin37 to the Rest of the Model?
We will address this question in two parts. First, how do we hook the “business” end of the combin37 to the part of the model to which we are going to add or remove energy? Second, how do we hook the “control” end of the combin37 to the nodes we want to monitor?
Hooking to the Combin37 to the Nodes that Add or Remove Energy
To hook the combin37 to the model so that we can add or remove energy we will use the convection link elements, link34. These elements effectively act like little thermal resistors with the resistance equation being specified as:
In order to make things nice, we need to “match” the resistances so that each node effectively sees the same resistance back to the combin37 element. We do this by varying the “area” associated with each of these convective links. To get the area associated with a node we use the arnode() intrinsic function. See the listing for details.
Hooking the Combin37 to the Nodes that Function as the Measured Value
As we mentioned in our requirements, we would like to be able to specify more than one or more nodes to function as the measured control value for our boundary condition. More precisely, if more than one node is included in the measurement node set, we would like ANSYS to average the temperatures at those nodes and use that average value as the measurement temperature. This will allow us to specify, for example, the average temperature of a body as the measurement value, not just one node on the body somewhere. However, we would also like for the scheme to work seamlessly if only one node is specified. So, how can we accomplish this? Constraint equations come to our rescue.
Remember that a constraint equation is defined as:
How can we use this to compute the average temperature of a set of nodes, and tie the control node of the combin37 to this average? Let’s begin by formulating an equation for the average temperature of a set of nodes. We would like this average to not be simply a uniform average, but rather be weighted by the relative contribution a given node should play in the overall average of a geometric entity. For example, assume we are interested in calculating the average temperature of a surface in our model. Obviously this surface will have associated with it many nodes connected to many different elements. Assume for the moment that we are interested in one node on this face that is connected to many large elements that span most of the area of this face. Shouldn’t this node’s temperature have a larger contribution to the “average” temperature of the face as say a node connected to a few tiny elements? If we just add up the temperature values and divide by the number of nodes, each node’s temperature has equal weight in the average. A better solution would be to area weight the nodal temperatures based on the area associated with each individual node. Something like:
That looks a little like our constraint equation. However, in the constraint equation I have to specify the constant term, whereas in the equation above, that is the value (Tavg) that I am interested in computing. What can I do? Well, let’s add in another node to our constraint equation that represents the “answer”. For convenience, we’ll make this the control node on our combin37 elements since we need the average temperature of the face to be driving that node anyway. Consider:
Now, our constant term is zero, and our Ci’s are Ai/AT and -1 for the control node. Voila! With this one constraint equation we’ve compute an area weighted average of the temperature over a set of nodes and assigned that value to our control node. CE’s rock!
An Example Model
This post is already way too long, so let’s wrap things up with a little example model. This model will illustrate a simple PI heat source attached to an edge of a plate with a hole. The other outer edges of the plate are given a convective boundary condition to simulate removing heat. The initial condition of the plate is set to 20C. The set point for the thermostat is set to 100C. No attempt is made to tune the PI controller in this example, so you can clearly see the effects of the overshoot due to the integral component being large. However, you can also see how the average temperature eventually settles down to exactly the set point value.
The red squiggly represents where heat is being added with the PI controller. The blue squiggly represents where heat is being removed due to convection. Here is a plot of the average temperature of the body with respect to time where you can see the response of the system to the PI control.
Here is another run, where the set point value ramps up as well. I’ve also tweaked the control values a little to mitigate some of the overshoot. This is looking kind of promising, and it is fun to play with. Next time we will look to integrate it into the workbench environment via an actual ACT extension.
Part 2 is here
I’ve included the model listing below so that you can play with this yourself. In future posts, I will elaborate more on this technique and also look to integrate it into an ACT module.
keyopt,P_et,1,0 ! Control on UK-UL
keyopt,P_et,2,8 ! Control node DOF is Temp
keyopt,P_et,3,8 ! Active node DOF is Temp
keyopt,P_et,4,0 ! Wierdness for the ON/OFF range
keyopt,P_et,5,0 ! More wierdness for the ON/OFF range
keyopt,P_et,6,6 ! Use the force, Luke (aka Combin37)
keyopt,P_et,9,0 ! Use the equation, Duke (where is Daisy…)
keyopt,I_et,1,4 ! Control on integral wrt time
keyopt,I_et,2,8 ! Control node DOF is Temp
keyopt,I_et,3,8 ! Active node DOF is Temp
keyopt,I_et,4,0 ! Wierdness for the ON/OFF range
keyopt,I_et,5,0 ! More wierdness for the ON/OFF range
keyopt,I_et,6,6 ! Use the force, Luke (aka Combin37)
keyopt,I_et,9,0 ! Use the equation, Duke (where is Daisy…)
keyopt,D_et,1,2 ! Control on first derivative wrt time
keyopt,D_et,2,8 ! Control node DOF is Temp
keyopt,D_et,3,8 ! Active node DOF is Temp
keyopt,D_et,4,0 ! Wierdness for the ON/OFF range
keyopt,D_et,5,0 ! More wierdness for the ON/OFF range
keyopt,D_et,6,6 ! Use the force, Luke (aka Combin37)
keyopt,D_et,9,0 ! Use the equation, Duke (where is Daisy…)
keyopt,mass_et,3,1 ! Interpret real constant as DENS*C*Volume
!! S M A L L T E S T M O D E L !!
! Thickness of plate
! Plane55 element
! Make a block
! Make a hole
! Punch a hole
! create an nodal component for the
! ‘attachment’ location
! create a nodal component for the
! ‘monitor’ location
!! B E G I N P I D M O D E L !!
! Real constant and mat prop for the mass element
mp,qrate,mass_et,0 ! Zero heat generation rate for the element
r,mass_et,1e-10 ! Extremely small thermal capacitance
! Material properties for convection element
! make the convection “large”
! Real constant for the combin37 elements
! that ack as heaters
! build the PID elements
! Create the nodes. They can be all coincident
! as we will refer to them solely by their number.
! They will be located at the origin
! Put a thermal mass on the K and L nodes
! for each control element to give them
! thermal DOFs
! Proportional element
! Integral element
! Derivative Element
! Ground the base node
! Get a list of the attachment nodes
! Hook the attachment nodes to the
! end of the control element with
! convection links
! Hook up the monitor nodes
! We are going to need these areas
! so, hold on to them
! Write the constraint equations
! Create a transient setpoint temperature
! Constrain the temperature node to be
! at the setpoint value
! Apply an initial condition of
! 20 C to everything
! Plot the response temperature
! and the setpoint temperature
The merger of modern 2D printing and 3D printing is getting closer and stronger. As a sign of the convergence, this years Digital Printing Presses Conference has invited PADT to give a presentation to the digital printing press community on 3D Printing. The conference runs from April 30th through May 2nd in Scottsdale, Arizona. PADT will be presenting during the opening session on April 30th.
Learn more about the event here.
If you happen to be one of those people at the crossroads of 2D and 3D printing, this would be a great event to attend and see how both industries are progressing.
Here is the official press release with more details:
(Tempe, AZ) PADT is pleased to announce the release of a new product: The Detoxinator 1200. Based on their proven success with the Support Cleaning Apparatus used in the 3D Printing industry, PADT has modified the design for the medical market. A well known celebrity has also signed with PADT to promote the product.
The best way to learn more is to watch the new video, just released today:
Watch the Video here.
We were pleased to note that long time PADT customer Orbital Sciences Corporation is starting production of 81 satellites at its Gilbert, Arizona facility. This new constellation of satellites will replace the existing constellation of Iridium satellites now in low earth orbit. It is exciting to see this project moving to this next critical step, and we can’t wait to read about the launches in 2015. We know that many people at Orbital and at their suppliers have been working hard and long to get to this point.
AZCentral has a nice video of the ribbon cutting
Satnews Daily has a good article with details about the whole project.
And the Wall Street Journal has a more business look on the project in their article.
You can learn more about the Iridium NEXT project here
All of us tech-oriented people in Arizona should get excited about this major milestone. Although both Orbital Sciences and Iridium are headquartered in Virginia, both have extensive facilities here in Arizona. In fact the Iridium business operations is about one quarter of a mile from PADT’s Tempe office in the ASU Research Park. Satellite design, test, manufacturing and support are big business in the state of Arizona. Some of the other Arizona based companies involved in making or providing equipment for satellites are: ViaSat, Garmin, General Dynamics, Honeywell Aerospace, Lockheed Martin, Paragon Space Development, and many others. Most are PADT customers.
Congratulations to everyone involved and a big thanks to the people in the state, county, and city that help set up the Free Trade Zone that helped make it possible.