|Published on:||December 19, 2017|
|With:||Ted Harris, Joe Woodward, Alex Grishin, Jim Peters, Tom Chadwick, Ahmed Fayed, Eric Miller|
|Description:||In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s Alex Grishin, Jim Peters, Joe Woodword, Tom Chadwick, Ahmed Fayed, and Ted Harris, for a discussion on predictions of what the future holds for ANSYS and simulation in general, covering topics such as 3D Printing, Acquisitions, The Cloud, IOT, and Artificial Intelligence.|
Before joining PADT last July, I have worked on FEA and CFD analyses but my exposure to ANSYS was limited and I was concerned about the transition. To my delight, the software was very easy to learn; most often than not intuitive and self-explanatory (e.g. mechanical wizard), the setup time was minimized after learning couple simple features (e.g. named selection, object generator etc.) and the resources on the ANSYS portal were very instrumental in the learning process. Furthermore, the colleagues at PADT proved to be very knowledgeable and experienced and more importantly responsive and eager to jump for help.
One of the most attractive features that caught my attention was the streamline of the Multiphysics nature that ANSYS has. I have been satisfied with the performance of standalone CFD packages in the past, and same goes for structural ones. But never have I dealt with an extensive software that maintained the quality of a specialized one. The importance of this attribute is showing more and more its powers in recent years given the development of new convoluted products of Multiphysics nature e.g. medical applications.
Using ANSYS to simulate medical applications is one of the most rewarding experience I personally enjoy. Even though, it is definitely satisfying to be able to help accelerate innovation in the aerospace, automotive, and a myriad of other industrial areas…the experience in the medical area has a more refreshing taste, probably due to the clear and direct link to human lives. From intravascular procedures to shoulder implants and microdevices, there is one common factor: ANSYS is decreasing the risks of catastrophic failures, improving the product capabilities and shortening the innovation cycle.
Editors Note: Ziad is part of PADT’s team in Southern California. He is a graduate of USC and has worked at Boeing, Meggit, and Pacific Consolidated Industries before joining PADT. He works with the rest of our ANSYS technical staff to make sure our users are getting the most from their ANSYS investment.
|Published on:||December 4, 2017|
|With:||Ted Harris, Joe Woodward, Eric Miller|
|Description:||In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s Senior Mechanical Engineer Joe Woodward, and Simulation Support Manager Ted Harris for a look into recent announcements regarding simulating 3D Printing with ANSYS and 3DSIM as well as a discussion about what users can do when their models are taking too long to solve.|
One of the key outputs from any random vibration analysis is determining the response of the object you are analyzing in terms of reaction forces. In the presentation below. Alex Grishin shares the theory behind getting accurate forces and then how to do so in ANSYS Mechanical.PADT-ANSYS-Random-Vib-Reaction-Forces-2017_11_22-1
As always, please contact PADT for your ANSYS simulation, training, and customization needs.
IEEE Day celebrates the first time in history when engineers worldwide and IEEE members gathered to share their technical ideas in 1884. Events were held around the world by 846 IEEE Chapters this year. So, to celebrate, I attended a joint chapter meeting in at The Museum of Flight in Seattle with technical presentations focused on “Smart Antennas for IoT and 5G”. There were approximately 60 in attendance, so assuming this was the average attendance globally results in over 50,000 engineers celebrating IEEE Day worldwide!
The Seattle seminar featured three speakers that spanned theory, design, test, integration, and application of smart antennas. There was much discussion about the complexity and challenges of meeting the ambitious goals of 5G, which extend beyond mobile broadband data access. Some key objectives of 5G are to increase capacity, increase data rates, reduce latency, increase availability, and improve spectral and energy efficiency by 2020. A critical technology behind achieving these goals is beamforming antenna arrays, which were at the forefront of each presentation.
Anil Kumar from Boeing focused on the application of mmWave technology on aircraft. Test data was used to analyze EM radiation leakage through coated and uncoated aircraft windows. However, since existing regulations don’t consider the increased path loss associated with such high frequencies, the integration of 5G wireless applications may be restricted or delayed. Beyond this regulatory challenge, Anil discussed how multipath reflectors and absorbers will present significant challenges to successful integration inside the cabin. Although testing is always required for validation, designing the layout of the onboard transceivers may be impractical to optimize without an asymptotic EM simulation tool that can account for creeping waves, diffraction, and multi-bounce.
Considering the test and measurement perspective, Jari Vikstedt from ETS-Lindgren focused on the challenges of testing smart antenna systems. Smart or adaptive antenna systems will not likely perform the same in an anechoic chamber test as they would in real systems. Of particular difficulty, radiation null placement is just as critical as beam placement. This poses a difficult challenge to the number and location of probes in a test environment. Not only would a large number of probes become impractical, there is significant shadowing at mmWave frequencies which can negatively impact the measurement. Furthermore, compact ranges can significantly impact testing and line of sight measurements become particularly challenging. While not a purely test-oriented observation, this lead to considering the challenge of tower hand off. If a handset and tower use beamforming to maintain a link, if is difficult for an approaching tower to even sense the handset to negotiate the hand-off.
In contrast, if the handset was continuously scanning, the approaching tower could be sensed to negotiate the hand-off before the link is jeopardized.
The keynote speaker, who also traveled from Phoenix to Seattle, was ASU Professor Dr. Constantine Balanis. Dr. Balanis opened his presentation by making a distinction between conventional “dumb antennas” and “smart antennas”. In reality, there are no smart antennas, but instead smart antenna systems. This is a critical point from an engineering perspective since it highlights the complexity and challenge of designing modern communication systems. The focus of his presentation was using an adaptive system to steer null points in addition to the beam in an antenna array using a least mean square (LMS) algorithm. He began with a simple linear patch array with fixed uniform amplitude weights, since an analytic solution was practical and could be used to validate a simulation setup. However, once the simulation results were verified for confidence, designing a more complex array with weighted amplitudes accompanying the element phase shift was only practical through simulation. While beam steering will create a device centric system by targeting individual users on massive multiple input multiple output (MIMO) networks, null steering can improve efficiency by minimizing interference to other devices.
Whether spatial processing is truly the “last frontier in the battle for cellular system capacity”, 5G technology will most certainly usher in a new era of high capacity, high speed, efficient, and ubiquitous means of communication. If you would like to learn more about how PADT approaches antenna simulation, you can read about it here and contact us directly at email@example.com.
|Published on:||November 6, 2017|
|With:||Tom Chadwick, Joe Woodward, Jim Peters, Ahmed Fayad, Eric Miller|
|Description:||In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s Senior CFD Engineer Tom Chadwick, Senior Mechanical Engineer Joe Woodward, Senior Staff Technologist Jim Peters, and IT Operations and Support Engineer Ahmed Fayad for the first part of an in depth look at Explicit Dynamics in ANSYS along with a review of the various CFD tools available in the ANSYS family of products.|
|Published on:||October 23, 2017|
|With:||Ted Harris, Matt Sutton, Eric Miller|
|Description:||In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s Simulation Support Manager Ted Harris and Senior Analyst and Lead Software Developer Matt Sutton for an introduction into the various advantages available thanks to training, along with a discussion on the increased functionality available through the customization of ANSYS software.|
Nerdtoberfest, PADT’s annual fall open house is coming up soon!
This year our fall open house will offer attendees a glimpse at some of our core offerings, introductions to a few new additions, and free food and drinks! Come experience this innovative technology first-hand, including:
|Published on:||October 9, 2017|
|With:||Tom Chadwick, Joe Woodward, Manoj Mahendran, Ahmed Fayed, Eric Miller|
|Description:||In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s Senior CFD Engineer Tom Chadwick, Senior Mechanical Engineer Joe Woodward, and IT Operations and Support Engineer Ahmed Fayad, for a discussion on their thoughts on the recent ANSYS Arizona Innovation Conference along with an interview with PADT’s Southern California Lead Application Engineer, Manoj Mahendran, featuring a look at what it’s like being an engineer in Southern California.|
ANSYS Meshing is a general-purpose, intelligent, automated high-performance product that helps engineers to produce the most appropriate mesh for accurate, efficient multi-physics solutions.
With the release of ANSYS version 18 earlier this year, engineers were introduced to a variety of new and innovative enhancements that help improve the quality of their meshing, and speed up the simulation process.
Join PADT’s Simulation Support Manager Ted Harris, for an in depth look at new mechanical meshing capabilities made available in ANSYS 18.0, 18.1 and 18.2!
This free webinar will cover a variety of new and improved capabilities within the latest version of ANSYS, including:
Don’t miss this informative presentation – Secure your spot today!
If this is your first time registering for one of our Bright Talk webinars, simply click the link and fill out the attached form. We promise that the information you provide will only be shared with those promoting the event (PADT).
You will only have to do this once! For all future webinars, you can simply click the link, add the reminder to your calendar and you’re good to go!
|Published on:||September 25, 2017|
|With:||Tom Chadwick, Ted Harris, Eric Miller|
|Description:||In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s Senior CFD Engineer Tom Chadwick, and Simulation Support Manager Ted Harris for a discussion on convergence with both FEA and CFD solutions, as well as a look at some of their favorite hidden gems in the ANSYS tools set. Learn about some beneficial ANSYS capabiliites you may not be aware of!|
It is no mystery that I love my Subaru. I bought it with the intention of using it and I have continually made modifications with a focus on functionality.
When I bought my roof crossbars in order to mount ski and/or bike racks, I quickly realized I needed to get a fairing in order to reduce drag and wind noise. The fairing functions as designed, and looks great as well. However, when I went to install my bike rack, I noticed that the fairing mount was in the way of mounting at the tower. As a result, I had to mount the rack inboard of the tower by a few inches. This mounting position had a few negative results:
These issues could all be solved if the fairing mount was simply inboard a few more inches. If only I had access to the resources to make such a concept a reality…. oh wait, PADT has all the capabilities needed to take this from concept to reality, what a happy coincidence!
First, we used our in-house ZEISS Comet L3D scanner to get a digital version of the standard left fairing mount bracket. The original bracket is coated with Talcum powder to aid in the scanning process.
The output from the scanning software is a faceted model in *.STL format. I imported this faceted CAD into ANSYS SpaceClaim in order to use it as a template to create editable CAD geometry to use as a basis to create my revised design. The standard mounting bracket is an injection molded part and is hollow with the exception of a couple of ribs. I made sure to capture all this geometry to carry forward into my redesigned parts, which would make the move to scaled manufacturing of this design easy.
Continuing in ANSYS SpaceClaim, as it is a direct modeling software instead of traditional feature-based modeling, I was able to split the bracket’s two function ends, the crossbar end and fairing end, and offset them by 4.5 inches, in order to allow the bike rack to mount right at the crossbar tower. I used the geometry from the center section CAD to create my offset structure. A mirrored version allows both the driver and passenger side fairing mount to be moved inboard to enable mounting of two bike racks in optimal positions. The next step is to turn my CAD geometry back into faceted *.STL format for printing, which can be done directly within ANSYS SpaceClaim.
After the design has been completed, I spoke with our 3D printing group to discuss what technology and material would be good for these brackets, as the parts will be installed on the car during the Colorado summer and winter. For this application, we decided on our in-house Selective Laser Sintering (SLS) SINTERSTATION 2500 PLUS and glass filled nylon material. As this process uses a powder bed when building the parts, no support is needed for overhanging geometry, so the part can be built fully featured. Find out more about the 3D printing technologies available at PADT here.
Finally, it was time to see the results. The new fairing mount offset brackets installed just like the factory pieces, but allowed the installation of the bike rack right at the tower, reducing the movement that was present when mounted inboard, as well as making it easier to load and unload bikes!!
I am very happy with the end result. The new parts assembled perfectly, just as the factory pieces did, and I have increased the functionality of my vehicle yet again. Stay tuned for some additional work featuring these brackets, and I’m sure the next thing I find that can be engineered better! You can find the files on GrabCAD here.
As the world of manufacturing continues to grow and change, engineers are being challenged to design, test, and evaluate products in increasingly complex environments. In such a time it is necessary to rely on an all-encompassing simulation platform that can handle a variety of physics efficiently, operating as a one stop shop for complete virtual prototyping. ANSYS is that platform!
Join us for this informative seminar including presentations from customers and ANSYS technical experts, focusing on how to effectively implement the ANSYS platform and productivity enhancement tools into your work-flow.
Through this free event we hope to inform you on how a single consolidated platform for complete virtual prototyping can help to drive efficiency across your company!
Date: October 4, 2017
Time: 9:00 AM – 4:30 PM MST AZ
Location: ASU SkySong – Building 3
1365 N. Scottsdale Rd.
Scottsdale, AZ 85257
Check out the full agenda, with presentations covering a plethora of topics including:
This event will include presentations from customers and ANSYS technical experts alike, focusing on how to effectively implement the ANSYS platform and productivity enhancement tools into your work-flow.
We are pleased to publish this very useful post from Nicolas Jobert from Synchrotron SOLEIL in France. Nicolas is a Mechanical Engineer with more than 20 years of experience using ANSYS for engineering design and analysis in academia and industry. He currently is Senior Mechanical Engineer at Synchrotron SOLEIL, the French synchrotron radiation facility. He also teaches various courses on Design and Validation in the field of structural and optomechanics. He graduated from the Ecole Centrale Marseille, France, and is a EUSPEN member.
Do you remember the moment you first heard about ANSYS introducing APDL Math?
I, for one, do, and I have a vivid memory of thinking “Wow, that can be a powerful tool, I’m dead sure it won’t be long before an opportunity arises and I’ll start developing pretty useful procedures and tools”. Well, that was half a decade ago, and to my great shame, nothing quite like that has happened so far. Reasons for this are obvious and probably the same for most of us: lack of time and energy to learn yet another set of commands, fear of the ever present risk of developing procedures that are eventually rejected as nonstandard use of the software and therefore error-prone (those of you working under quality assurance, raise your hand!), anxiety of working directly under the hood on real projects with little means to double check your results, to name a few.
That said, finally an opportunity presented itself, and before I knew it, I was up and running with APDL Math. The objective of this article is to showcase some simple yet insightful applications and hopefully remove the prevention one can have regarding using these additional capabilities.
For the sake of demonstration, I will begin with a somewhat uncommon analysis tool that should nevertheless ring a bell for most of you, that is: modal analysis (and yes, the pun is intended). You may wonder what is the purpose of using APDL Math to perform a task that is a standard ANSYS capability since say revision 2.0, 40 years ago? But wait, did I mention that by modal analysis, I mean thermal modal Analysis?
Although scarcely used, thermal modal analysis is both an analysis and a design validation tool, mostly used in the field of precision engineering and or optomechanics. Specifically, it can serve a number of purposes such as:
Q: Will my system settle fast enough to fulfill design requirements?
A: Compute the system Thermal Time Constants
Q: Where should I place sensors to get information rich / robust measurements?
A: Compute Thermal modes and place your sensors away for large thermal gradients
Q: Can I develop a reduced model to solve large transient thermal mechanical problems?
A: Modal basis allows for the construction of such reduced problem effectively converting a high-order coupled system to a low order, uncoupled set of equations.
Q: How to develop a reduced order state-space matrices representation of my thermal system (equivalent to SPMWRITE command)?
A: Modal analysis provides every result needed to build those matrices directly within ANSYS.
Although you might only be vaguely familiar with many or all of those topics, the idea behind this article is really to show that APDL Math does exactly what you need it to do: allow the user to efficiently address specific needs, with a minimal amount of additional work. Minimal? Let’s see what it looks like in reality, and you will soon enough be in a position to make your own opinion on the matter.
To begin with, it is worth underlining the similarities and differences between structural (vibration) modes and thermal modes.
Mathematically, both look very much the same, i.e. modes are solutions of the dynamics equation in the absence of forcing (external) term:
[K] is the stiffness matrix
[K] is the conductivity matrix
Now, the fundamental difference is that the eigenvalues have completely different physical interpretations (This is a direct consequence of the fact that dynamical systems are 2nd order systems, whereas thermal systems a 1st order systems. While after being disturbed the former will oscillate around equilibrium position, the latter will return to its initial state via exponential decay. Mind you, there is no such thing as thermal resonances!) :
No big deal, right? Hence, the APDL Math code for Thermal Modal Analysis should be a straightforward adaption of the original. As it turns out, the modifications are quite small. Below is a table comparing input codes to perform both type of analyses, using APDL Math.
! Setup Model … ! Make ONE dummy transient solve ! to write stiffness and mass ! matrices to .FULL file /SOLU ANTYPE,TRANSIENT TIME,1 WRFULL,1 SOLVE ! Get Stiffness and Mass *SMAT,MatK,D,IMPORT,FULL,,STIFF *SMAT,MatM,D,IMPORT,FULL,,MASS ! Eigenvalue Extraction Antype,MODAL modopt,Lanb,NRM,0,Fmax *EIGEN,MatK,MatM,,EiV,MatPhi ! No need to convert eigenvalues ! to frequencies, ANSYS does ! it automatically ! Done !
! Setup Model … ! Make TWO dummy transient solve ! to separately write conductivity ! and capacitances matrices to .FULL file /SOLU ANTYPE,TRANSIENT TIME,1 NSUB,1,1,1 TINTP,,,,1 WRFULL,1 ! Zero out capacitance terms … SOLVE ! Get Conductivity Matrice *SMAT,MatK,D,IMPORT,FULL, Jobname.full,STIFF ! Restore capacitance and zero out ! conductivity terms … SOLVE ! Get Capacitance Matrice *SMAT,MatC,D,IMPORT,FULL,,STIFF ! Eigenvalue Extraction Antype,MODAL modopt,Lanb,NRM,,0,1/(2*PI*SQRT(Tmin)) *EIGEN,MatK,MatC,,EiV,MatPhi ! Convert Eigenvalues for Frequency ! to Thermal time Constants ! *do,i,1,EiV_rowDim Eiv(i)=1/(2*PI*Eiv(i))**2 *enddo ! Done !
The only data requested from the users is the number of requested modes (NRM) as well as the upper frequency (or for that matter, the shortest time constant of interest). Also, note that in the thermal case, one needs to perform two separate dummy analyses to store the conductivity and capacitance matrices, since internally those are merged into an equivalent stiffness (conductivity) matrix:
If you are familiar with APDL, some important differences are apparent here:
Now that we have some procedure and results, we would like to be able to show this to the outside world (and to be honest, some graphical results would also help getting confidence in results).
The additional task to do so is really minimal. What we need to do is simply to put back those numerical results into the ANSYS database so that we can use all the conventional post-processing capabilities. This can be made using the appropriate POST1 commands, essentially: DNSOL. And, while we are at it, why not do a hardcopy to an image file? Here is the corresponding input.
… User should place all nodes with non-prescribed temperatures in a component named MyNodeComponent … First, convert Eigenvectors from solver to BCS ordering ! Conversion needed *SMAT,Nod2Bcs,D,IMPORT,FULL,Jobname.full,NOD2BCS *MULT,Nod2Bcs,TRAN,MatPhi,,MatPhi ! Then, read in mapping vector to convert to user ordering *VEC,MapForward,I,IMPORT,FULL,Jobname.full,FORWARD ! Put the results in ANSYS database /POST1 *do,ind_mode,1,NRM cmsel,s,MyNodeComponent curr_node=0 *do,i,1,ndinqr(0,13) curr_node=ndnext(curr_node) curr_temp=MatPhi(MapForward(curr_node),ind_mode) dnsol,curr_node,TEMP,,curr_temp *enddo Tau=1/(2*3.14*EiV(ind_mode))**2 To=NINT(Tau*10)/10 ! compress to 1 digit after comma /title,Mode #%ind_mode% - Tau=%To%s plnsol,temp ! Hardcopy to BMP file /image,SAVE,JobName_Mode%ind_mode%,bmp *enddo
This way, modes can be displayed, or even written to a conventional .RTH file (using RAPPND), and used as any regular ANSYS solver result.
Now you may wonder what the results look like in reality. To remain within the field of precision engineering, let’s use a support structure typically designed for high-stability positioning. From a structural point of view, it must have a high dynamic stiffness and a low total mass so that a Delta shaped bracket is appropriate. Since we want the system to rapidly evacuate any heat load, we choose aluminum as candidate material. We do know from first principles that any applied disturbance will exponentially vanish and the system will go back to equilibrium state. Now, what will be the time constants of this decay?
For the sake of simplicity we restrict the analysis to a highly simplified, 2D model of such a support. PLANE55 elements are used to model the structural part while the heat sink is accounted for using SURF151. Boundary conditions are enforced using an extra node.
After applying boundary conditions, we execute the modal solution to obtain say – the first 8 modes.
|Index||Time Constant [s]||Comment|
|1||535.9||Quasi-uniform temperature field (i.e. “rigid body” mode)|
|2||32.1||1st order (one wavelength along perimeter)|
|3||23.8||1st order (one wavelength along perimeter)|
|4||8.1||2nd order (two wavelengths along perimeter)|
|5||6.8||2nd order (two wavelengths along perimeter)|
|6||3.5||3rd order (three wavelengths along perimeter)|
|7||3.1||3rd order (three wavelengths along perimeter)|
|8||2.2||4th order (four wavelengths along perimeter)|
The output is strictly the same as the one a standard modal analysis, except for the two additional lines at the end of the solving sequence.
Allocate a  Vector : EIV Allocate a  Dense Matrix : MatPhi
Please note that the solution has 227 DOFs whereas the entire problem has 228 DOFs. This is the consequence of having introduced the boundary conditions as an enforced temperature on a node, which DOF is therefore removed from the DOF set to be obtained by the solver.
Also, we might want to use the modal shapes information to decide which locations are best suited to capture the entire temperature field on the structure. Without knowledge of the excitation source, one straightforward way to do so is to retain for each mode the node that has the largest amplitude. This is made even easier in this situation, since we have normalized each mode to have unit maximum amplitude we just need to select nodes having modal amplitude equal to 1 (or -1). On the figure below, each temperature sensor location is marked with a ‘TSm’ label where m is the mode index.
Doing so, we reach a pretty satisfactory distribution for the sensors locations, completely consistent with intuition. In numerical terms, we can also check that the modal matrix [Φ]_sensors, i.e. the original full matrix restricted to the selected DOF, has an excellent condition number. But there are many other things we could do starting from this. For example, with additional information, such as the location and the frequency content of the temperature fluctuations, one could further restrict the set of needed temperature sensors by running a dummy transient analysis and choosing locations where the correlation between sensors readings is as low as possible (using *MOPER,,,CORR). Even better, one can estimate the thermally induced displacements and select locations best suited to build an empirical model (typically using AR or ARMA), allowing one to predict structural displacements induced by temperature fluctuations using just a couple of sensors. This in turn can be used to select control strategies, check modal controllability… all within ANSYS.
APDL Math was presented as an alternate route for users who need to include specialized steps in an otherwise standard FE process, and in my opinion it does just that. The benefits can be immense and the learning curve is steep but short. As long as the user knows what he/she is doing, there is little possibility to get lost: after all, APDL Math only comprises 18 additional commands.
What hindered me so far was the necessity to account for internal, BCS and user ordering, but it really is not a big deal, as seen from the above example.
What is more, the possibility to store the created results in the Mechanical APDL database (DNSOL and RAPPND are your friends!) provides every means to control your results and finally to build confidence in your developments.
And for those of us who prefer to stay within Workbench environment, there is nothing preventing from including APDL Math procedures into Workbench command snippets.
This was just an introductory example, since many other applications could be found, to name a few just in the fields of precision engineering and/or opto-mechanics:
Let us know your opinion on the matter, and if further introductory articles on APDL Math could be of use to the ANSYS users community.
|Published on:||September 11, 2017|
|With:||Jim Peters, Tom Chadwick, Ted Harris, Eric Miller|
|Description:||In this episode your host and Co-Founder of PADT, Eric Miller is joined by PADT’s Senior CFD Engineer Tom Chadwick, Senior Staff Technologist Jim Peters, and Simulation Support Manager Ted Harris for a look at the CFD updates available within ANSYS 18.2, along with a discussion on why they love the new disruptive simulation tool from ANSYS; Discovery Live.|