It seems like the trend these days is for large companies to not do R&D in house. Instead the let StartUps develop innovation and then buy it when the market proves it out. I had to ask myself “Is acquiring disruptive innovation good for everyone?” I don’t think it is and explain why in this week’s blog post.
We have a problem. At some point it become not just OK, but prefered to count on amateurs to tackle difficult problems. In politics and in business it is a trend to go with people who have no background and no experience. Seeing the results, I a not a fan. In “When did we start thinking amateurs were a good idea?” I look at this disturbing reality, why it is a bad idea, and offer some suggestions on turning things around.
Welcome to 2017. We are all very excited about what we have planned for events this year. As we travel around the country, and the world, we hope to have to chance to meet many of you who follow PADT. 2017 will look a lot like 2016 except that, based on your feedback, we will be trying more on-line webinars and events. As always, contact us if you have any questions.
Launch of ASU Manufacturing Research and Innovation Hub
|PADT will be on-hand at ASU Polytechnic school for the launch of ASU’s new Manufacturing Research and Innovation Hub. Stop by to see their new facilities and meet the students and staff along with partners like PADT that helped make it happen.|
|— Learn more|
ANSYS Startup Program Webinar: The Significance of Simulation
|This seminar will discuss how ANSYS simulation software can be used by startups to shorten their time to market and reduce their manufacturing costs. We will discuss what simulation is and how to use it effectively, as well as go over the ANSYS Startup Program and how it gives early stage companies access to world class simulation.|
|— Learn more|
Invited Speaker at the 2017 Arizona Science Bowl (High School Event)
|PADT’s Dhruv Bhate, PhD will speak to students at the High School Science Bowl. This is a great event, and if you have never been, you should go. The level of technology and scientific rigour fo these Middle School and High School kids is amazing.|
|— Learn more|
Tesla Test Drive at PADT
|Yes, you read that right. We will be inviting customers to come to PADT and see how the simulation and 3D Printing technologin we sell, support, and use is applied to advanced automotive systems – Cool Cars! Tesla Motors has been kind enough to partner with us to allow a select few the oportunity to test drive a Tesla. Look for your invite via email and register quickly, space is limited.|
|— Learn more|
Metal AM Magazine publishes an article by PADT!
Our 10-page article on “Modeling the Mechanical Behavior of Cellular Structures for Additive Manufacturing” was published in the Winter 2016 edition of the Metal AM magazine. This article represents a high-level summary of the different challenges and approaches in addressing the modeling specific aspects of cellular structures, along with some discussion of the design, manufacturing and implementation aspects associated with AM.
Click HERE for link to the entire magazine, our article starts on page 51. Digital editions are free to download. Swing by PADT in the new year to pick up a hard copy or look for it at our table when you visit us at trade shows.
To stay in touch with the latest developments at the intersection of AM and Cellular Structures, connect with me on LinkedIn, where I typically post 1-2 blog posts every month on this, or related subjects in Additive Manufacturing.
Thermal Optimization for Energy Efficiency
Nearly everything has an optimal operating temperature and thermal condition. Millions of dollars each year are spent generating and transporting thermal energy to achieve thermal goals. Thermal optimization not only improves the economy of transporting energy, maintaining building temperatures, manufacturing processes and products, it improves their efficiency as well. Engineers use simulation to reveal detailed pictures of thermal processes, providing a deep understanding of all aspects of thermal management.
This webinar is presented by Richard Mitchell and Xiao Hu
Richard Mitchell is the Lead Product Marketing Manager for Structures. He joined ANSYS in 2006 working in pre-sales and support roles. Before this Richard was an ANSYS user working for a high tech company in the UK. He worked as an analyst on space and vacuum tube technologies.
Xiao is a principal engineer at ANSYS Inc. Xiao has spent a combined 12 years of his career at ANSYS and Fluent corporation working with customers in the modeling and simulation of powertrain related applications. Xiao spent his earlier years with Fluent working on engine CFD applications.
Keep checking back to the Energy Innovation Homepage for more updates on upcoming segments, webinars, and other additional content.
I have always had an issue with leaving well enough alone since the day I bought my Subaru. I have altered everything from the crank pulley to the exhaust, the wheels and tires to the steering wheel. I’ve even 3D printed parts for my roof rack to increase its functionality. One of the things that I have altered multiple times has been the shift knob. It’s something that I use every time and all the time when I am driving my car, as it is equipped with a good ol’ manual transmission, a feature that is unfortunately lost on most cars in this day and age.
I have had plastic shift knobs, a solid steel spherical shift knob, a black shift knob, a white shift knob, and of course some weird factory equipment shift knob that came with the car. What I have yet to have is a 3D printed shift knob. For this project, not any old plastic will do, so with the help of Concept Laser, I’m going straight for some glorious Remanium Star CL!
One of the great things about metal 3D printing is that during the design process, I was not bound by the traditional need for a staple of design engineering, Design For Manufacturing (DFM). The metal 3D printer uses a powder bed which is drawn over the build plate and then locally melted using high-energy fiber lasers. The build plate is then lowered, another layer of powder is drawn across the plate, and melted again. This process continues until the part is complete.
The design for the knob was based off my previously owned shift knobs, mainly the 50.8 mm diameter solid steel spherical knob. I then needed to decide how best to include features that would render traditional manufacturing techniques, especially for a one-off part, cost prohibitive, if not impossible. I used ANSYS Spaceclaim Direct Modeler as my design software, as I have become very familiar with it using it daily for simulation geometry preparation and cleanup, but I digress, my initial concept can be seen below:
I was quickly informed that, while this design was possible, the amount of small features and overhangs would require support structure that would make post-processing the part very tedious. Armed with some additional pointers on creating self supporting parts that are better suited for metal 3D printing, I came up with a new concept.
This design is much less complex, while still containing features that would be difficult to machine. However, with a material density of 0.0086 g/mm^3, I would be falling just short of total weight of 1 lb, my magic number. But what about really running away from DFM like it was the plague?
There we go!!! Much better, this design iteration is spec’d to come out at 1.04 lbs, and with that, it was time to let the sparks fly!
Here it is emerging as the metal powder that has not been melted during the process is brushed away.
The competed knob then underwent a bit of post processing and the final result is amazing! I haven’t been able to stop sharing images of it with friends and running it around the office to show my co-workers. However, one thing remains to make the knob functional… it must be tapped.
In order to do this, we need a good way to hold the knob in a vise. Lucky for us here at PADT, we have the ability to quickly design and print these parts. I came up with a design that we made using our PolyJet machine so we could have multiple material durometers in a single part. The part you need below utilizes softer material around the knob to cradle it and distribute the load of the vise onto the spherical lattice surface of the knob.
We quickly found out that the Remanium material was not able to be simply tapped. We attempted to bore the hole out in order to be able to press in an insert, and also found out the High Speed Steel (HSS) was not capable of machining the hole. Carbide however does the trick, and we bored the hole out in order to press in a brass insert, which was then tapped.
Finally, the shift knob is completed and installed!
Want to learn more, check out the article in “Additive Manufacturing Media.”
A bit of a twist for this weeks Phoenix Busines Journal blog post… “How far away are we from 3D Printing the androids on ‘Westworld?‘” In discussing this great new reboot of a classic, and yet another fantastic cautionary tale from Michael Crhichton, a couple people started wondering how far off the tech in the show is. The answer, well you will have to read the article.
Information regarding the next topic in the Breakthrough Energy Innovation Campaign has been released, covering Thermal Optimization and how ANSYS simulation software can be used to help solve a variety of issues related to this topic, as well as capture all thermal processes.
Additional content regarding thermal optimization can be viewed and downloaded here.
This is the next topic of a campaign that covers five main topics:
- Advanced Electrification
- Machine & Fuel Efficiency
- Thermal Optimization
- Effective Lightweighting
- Aerodynamic Design
Information on each topic will be released over the course of the next few months as the webinars take place.
Sign Up Now to receive updates regarding the campaign, including additional information on each subject, registration forms to each webinar and more.
We here at PADT can not wait to share this content with you, and we hope to hear from you soon.
How can the mechanical behavior of cellular structures (honeycombs, foams and lattices) be modeled?
This is the second in a two-part post on the modeling aspects of 3D printed cellular structures. If you haven’t already, please read the first part here, where I detail the challenges associated with modeling 3D printed cellular structures.
The literature on the 3D printing of cellular structures is vast, and growing. While the majority of the focus in this field is on the design and process aspects, there is a significant body of work on characterizing behavior for the purposes of developing analytical material models. I have found that these approaches fall into 3 different categories depending on the level of discretization at which the property is modeled: at the level of each material point, or at the level of the connecting member or finally, at the level of the cell. At the end of this article I have compiled some of the best references I could find for each of the 3 broad approaches.
1. Continuum Modeling
The most straightforward approach is to use bulk material properties to represent what is happening to the material at the cellular level [1-4]. This approach does away with the need for any cellular level characterization and in so doing, we do not have to worry about size or contact effects described in the previous post that are artifacts of having to characterize behavior at the cellular level. However, the assumption that the connecting struts/walls in a cellular structure behave the same way the bulk material does can particularly be erroneous for AM processes that can introduce significant size specific behavior and large anisotropy. It is important to keep in mind that factors that may not be significant at a bulk level (such as surface roughness, local microstructure or dimensional tolerances) can be very significant when the connecting member is under 1 mm thick, as is often the case.
The level of error introduced by a continuum assumption is likely to vary by process: processes like Fused Deposition Modeling (FDM) are already strongly anisotropic with highly geometry-specific meso-structures and an assumption like this will generate large errors as shown in Figure 1. On the other hand, it is possible that better results may be had for powder based fusion processes used for metal alloys, especially when the connecting members are large enough and the key property being solved for is mechanical stiffness (as opposed to fracture toughness or fatigue life).
2. Cell Level Homogenization
The most common approach in the literature is the use of homogenization – representing the effective property of the cellular structure without regard to the cellular geometry itself. This approach has significantly lower computational expense associated with its implementation. Additionally, it is relatively straightforward to develop a model by fitting a power law to experimental data [5-8] as shown in the equation below, relating the effective modulus E* to the bulk material property Es and their respective densities (ρ and ρs), by solving for the constants C and n.
While a homogenization approach is useful in generating comparative, qualitative data, it has some difficulties in being used as a reliable material model in analysis & simulation. This is first and foremost since the majority of the experiments do not consider size and contact effects. Secondly, even if these were considered, the homogenization of the cells only works for the specific cell in question (e.g. octet truss or hexagonal honeycomb) – so every new cell type needs to be re-characterized. Finally, the homogenization of these cells can lose insight into how structures behave in the transition region between different volume fractions, even if each cell type is calibrated at a range of volume fractions – this is likely to be exacerbated for failure modeling.
3. Member Modeling
The third approach involves describing behavior not at each material point or at the level of the cell, but at a level in-between: the connecting member (also referred to as strut or beam). This approach has been used by researchers [9-11] including us at PADT  by invoking beam theory to first describe what is happening at the level of the member and then use that information to build up to the level of the cells.
This approach, while promising, is beset with some challenges as well: it requires experimental characterization at the cellular level, which brings in the previously mentioned challenges. Additionally, from a computational standpoint, the validation of these models typically requires a modeling of the full cellular geometry, which can be prohibitively expensive. Finally, the theory involved in representing member level detail is more complex, makes assumptions of its own (e.g. modeling the “fixed” ends) and it is not proven adequately at this point if this is justified by a significant improvement in the model’s predictability compared to the above two approaches. This approach does have one significant promise: if we are able to accurately describe behavior at the level of a member, it is a first step towards a truly shape and size independent model that can bridge with ease between say, an octet truss and an auxetic structure, or different sizes of cells, as well as the transitions between them – thus enabling true freedom to the designer and analyst. It is for this reason that we are focusing on this approach.
Continuum models are easy to implement and for relatively isotropic processes and materials such as metal fusion, may be a good approximation of stiffness and deformation behavior. We know through our own experience that these models perform very poorly when the process is anisotropic (such as FDM), even when the bulk constitutive model incorporates the anisotropy.
Homogenization at the level of the cell is an intuitive improvement and the experimental insights gained are invaluable – comparison between cell type performances, or dependencies on member thickness & cell size etc. are worthy data points. However, caution needs to be exercised when developing models from them for use in analysis (simulation), though the relative ease of their computational implementation is a very powerful argument for pursuing this line of work.
Finally, the member level approach, while beset with challenges of its own, is a promising direction forward since it attempts to address behavior at a level that incorporates process and geometric detail. The approach we have taken at PADT is in line with this approach, but specifically seeks to bridge the continuum and cell level models by using cellular structure response to extract a point-wise material property. Our preliminary work has shown promise for cells of similar sizes and ongoing work, funded by America Makes, is looking to expand this into a larger, non-empirical model that can span cell types. If this is an area of interest to you, please connect with me on LinkedIn for updates. If you have questions or comments, please email us at firstname.lastname@example.org or drop me a message on LinkedIn.
References (by Approach)
Bulk Property Models
 C. Neff, N. Hopkinson, N.B. Crane, “Selective Laser Sintering of Diamond Lattice Structures: Experimental Results and FEA Model Comparison,” 2015 Solid Freeform Fabrication Symposium
 M. Jamshidinia, L. Wang, W. Tong, and R. Kovacevic. “The bio-compatible dental implant designed by using non-stochastic porosity produced by Electron Beam Melting®(EBM),” Journal of Materials Processing Technology214, no. 8 (2014): 1728-1739
 S. Park, D.W. Rosen, C.E. Duty, “Comparing Mechanical and Geometrical Properties of Lattice Structure Fabricated using Electron Beam Melting“, 2014 Solid Freeform Fabrication Symposium
 D.M. Correa, T. Klatt, S. Cortes, M. Haberman, D. Kovar, C. Seepersad, “Negative stiffness honeycombs for recoverable shock isolation,” Rapid Prototyping Journal, 2015, 21(2), pp.193-200.
Cell Homogenization Models
 C. Yan, L. Hao, A. Hussein, P. Young, and D. Raymont. “Advanced lightweight 316L stainless steel cellular lattice structures fabricated via selective laser melting,” Materials & Design 55 (2014): 533-541.
 S. Didam, B. Eidel, A. Ohrndorf, H.‐J. Christ. “Mechanical Analysis of Metallic SLM‐Lattices on Small Scales: Finite Element Simulations versus Experiments,” PAMM 15.1 (2015): 189-190.
 P. Zhang, J. Toman, Y. Yu, E. Biyikli, M. Kirca, M. Chmielus, and A.C. To. “Efficient design-optimization of variable-density hexagonal cellular structure by additive manufacturing: theory and validation,” Journal of Manufacturing Science and Engineering 137, no. 2 (2015): 021004.
 M. Mazur, M. Leary, S. Sun, M. Vcelka, D. Shidid, M. Brandt. “Deformation and failure behaviour of Ti-6Al-4V lattice structures manufactured by selective laser melting (SLM),” The International Journal of Advanced Manufacturing Technology 84.5 (2016): 1391-1411.
Beam Theory Models
 R. Gümrük, R.A.W. Mines, “Compressive behaviour of stainless steel micro-lattice structures,” International Journal of Mechanical Sciences 68 (2013): 125-139
 S. Ahmadi, G. Campoli, S. Amin Yavari, B. Sajadi, R. Wauthle, J. Schrooten, H. Weinans, A. Zadpoor, A. (2014), “Mechanical behavior of regular open-cell porous biomaterials made of diamond lattice unit cells,” Journal of the Mechanical Behavior of Biomedical Materials, 34, 106-115.
 S. Zhang, S. Dilip, L. Yang, H. Miyanji, B. Stucker, “Property Evaluation of Metal Cellular Strut Structures via Powder Bed Fusion AM,” 2015 Solid Freeform Fabrication Symposium
 D. Bhate, J. Van Soest, J. Reeher, D. Patel, D. Gibson, J. Gerbasi, and M. Finfrock, “A Validated Methodology for Predicting the Mechanical Behavior of ULTEM-9085 Honeycomb Structures Manufactured by Fused Deposition Modeling,” Proceedings of the 26th Annual International Solid Freeform Fabrication, 2016, pp. 2095-2106
We will take this oportunity to send a Happy Hollidays! to everyone and wishing all a very merry New Year! Come back in January and we will have lots to share, it’s going to be a busy year.
As a reminder, PADT is closed the week of December 26-30, 2016.
December 1: Phoenix, AZ
BioAccel Solutions Challenge for BioTech Startup in Arizona
This is a fantastic event that puts a nice cap to the year for Biotech startups in Arizona, and PADT is proud to be a sponsor. We will be at the “Scorpion Pit” competition as well as the networking event after. See you there.
The full agenda and all the details for this event are here.
December 6: Albuquerque, NM
Medical Device Product Development for Startups, The Bitter Pill
We will be in New Mexico for this lunch time event looking in to the harsh realities of doing a Medical Device startup. All are welcome! We hope this is the first of many regular seminars with the New Mexico Technology Council.
Get the details and register here.
The state of Arizona has made some great strides in creating a vibrant and growing startup community. Only a few things are missing and the big one right now is that “The Arizona startup market needs bridge funding for growth” Check out the article to get my feelings on the topic, what our problems are and how we can fix them.
A few years back PADT turned one of our training courses into a book, and even though it is about an obscure programming language for a software product that is only known to our industry, it has done well. In “Publishing your own book, technology makes it easy” I review how truly easy and affordable on-demand self-publishing can be. You can see the book here “Introduction to the ANSYS Parametric Design Language – Second Edition.”
● Understand the concept of a virtual prototype and how it reduces development costs while optimizing product performance.
● Identify seven essential features that must be included in a simulation in order to maximize the performance and efficiency.
● Learn how ZECO Hydropower used ANSYS simulation tools coupled with high performance computing to develop a new and optimal intake for a Kaplan turbine in half of the usual time. They were able to reduce civil engineering infrastructure costs to ensure they would be competitive in emerging markets.
● Walk through ZECO’s simulation process and results including CFD turbomachinery simulation, parallel computing, parametric modeling, and optimization tools.
Part 3: The ANSYS FLUENT Performance Comparison Series – CUBE Numerical Simulation Appliances by PADT, Inc.
November 22, 2016
External Flow Over a Truck Body with a Polyhedral Mesh (truck_poly_14m)
- External flow over a truck body using a polyhedral mesh
- This test case has around 14 million polyhedral cells
- Uses the Detached Eddy Simulation (DES) model with the segregated implicit solver
ANSYS Benchmark Test Case Information
- ANSYS HPC Licensing Packs required for this benchmark
- I used three (3) HPC Packs to unlock all of the cores used during the ANSYS Fluent Test Cases of the CUBE appliances shown on the Figure 1 chart.
- I did use four (4) HPC Packs for the two 256 core benchmarks shown on the data but only wanted the data for testing.
- The best average seconds per iteration goes to the 2015 CUBE Intel® Xeon® e5-2667 V3 with a 0.625 time using 128 compute cores.
- The 2015 CUBE Intel® Xeon® e5-2667 V3 outperformed the 256 core AMD Opteron™ series ANSYS Fluent 17.2 benchmarks.
- Please note that different numbers of CUBE Compute Nodes were used in this test. However straight across CPU times are also shown for single nodes at 64 cores.
- To illustrate this ANSYS Fluent test case as it relates to the real world. A completely new ANSYS HPC customer is likely to have up two (2) of the entry level INTEL CUBE Compute Nodes versus eight (8) CUBE compute nodes configuration.
- Please contact your local ANSYS Software Sales Representative for more information on purchasing ANSYS HPC Packs. You too may be able to speed up your solve times by unlocking additional compute power!
- What is a CUBE? For more information regarding our Numerical Simulation workstations and clusters please contact our CUBE Hardware Sales Representative at SALES@PADTINC.COM Designed, tested and configured within your budget. We are happy to help and to listen to your specific needs.
Figure 1 – ANSYS 17.2 FLUENT Test Case Graph
|ANSYS FLUENT External Flow Over a Truck Body with a Polyhedral Mesh (truck_poly_14m) Test Case|
|Number of cells||14,000,000|
The CPU Information
Yes, I am still impressed with the performance day after day, 24×7 of these AMD Opeteron CPU’s! After years of operation the AMD Opteron™ series of processors are still relevant and powerful numerical simulation processors. heavy sigh…For example, after reviewing the ANSYS Fluent Test Case data you can see for yourselves below. The 2012 AMD Opteron™ and 2013 AMD Opteron™ CPU’s can still hang in there with the INTEL XEON CPU’s. However one INTEL CPU node vs. four AMD CPU nodes?
I thought a more realistic test case scenario would be to drop the number of AMD Compute Nodes down to four. Indeed, I could have thrown more of the CUBE Compute Nodes with the AMD Opteron™ series CPU’s inside of them. That is why you can see one 256 core benchmark score where I put all 64 cores on each node to the test. As one would hopefully see in their hardware performance unleashing ANSYS Fluent with 256 core did drop the iteration solve time for the test case with the CUBE Compute Appliances.
Realistically a brand new ANSYS HPC customer is not likely to have:
a) Vast qualities of cores (AMD or INTEL) & compute nodes for optimal distributive numerical solving
b) ANSYS HPC licensing for 512 cores
c) The available circuit breakers to provide power
The Intel® Xeon® CPU’s used for this ANSYS Fluent Test Case
- Intel® Xeon® Processor E5-2690 v4 (35M Cache, 2.60 GHz)
- Intel® Xeon® Processor E5-2667 v4 (25M Cache, 3.20 GHz)
- Intel® Xeon® Processor E5-2667 v3 (20M Cache, 3.20 GHz)
- Intel® Xeon® Processor E5-2667 v2 (25M Cache, 3.30 GHz)
The Estimated Wattage?
No the lights did not dim…but here is a quick comparison with energy use by estimated maximum Watt’s used metric shows up in volumes (decibels) and dollars ($$$) saved or spent.
Less & More!
Overall CUBE Compute Node drops in average watts estimated consumption, indeed has moved forward in progress over the past four years!
- 2012 CUBE AMD Numerical Simulation Appliance with the Opteron™ 6278 – Four (4) Compute Nodes
- Estimated CUBE Configuration @ Full Power: ~8000 Watts
- 2013 CUBE AMD Numerical Simulation Appliance with the Opteron™ 6380
- Estimated CUBE Configuration @ Full Power: ~7000 Watts
- 2015 CUBE Numerical Simulation Appliance with the Intel® Xeon® e5-2667 V3 – Eight (8) Compute Nodes
- Estimated CUBE Configuration @ Full Power: ~4000 Watts
- 2016 CUBE Numerical Simulation Appliance with the Intel® Xeon® e5-2667 V4 – One (1) Compute Node.
- Estimated CUBE Configuration @ Full Power: ~900 Watts
- 2016 CUBE Numerical Simulation Appliance with the Intel® Xeon® e5-2690 V4 – Two (2) Compute Nodes
- Estimated CUBE Configuration @ Full Power: ~1200 Watts
Figure 2 – Estimated CUBE compute node power consumption as configured for this ANSYS FLUENT Test Case.
The CUBE phenomenon
|2012 AMD Opteron™ 6278||2015 CUBE Intel® Xeon® e5-2667 V3|
|4 x Compute Node CUBE HPC Appliance||8 x Compute Node CUBE HPC Appliance|
|4 x 16c @2.4GHz/ea||2 x 8c @3.2GHz/ea – Intel® Xeon® e5-2667 V3|
|Quad Socket motherboard||Dual Socket motherboard|
|DDR3-1866 MHz ECC REG||DDR4-2133 MHz ECC REG|
|5 x 600GB SAS2 15k RPM||4 x 600GB SAS3 15k RPM|
|40Gbps Infiniband QDR High Speed Interconnect||2016 CUBE Intel® Xeon® e5-2667 V4|
|2013 CUBE AMD Opteron™ 6380||1 x CUBE HPC Workstation|
|4 x Compute Node CUBE HPC Appliance||2 x 8c @3.2GHz/ea – Intel® Xeon® e5-2667 V4|
|4 x 16c @2.5GHz/ea||Dual Socket motherboard|
|Quad Socket motherboard||DDR4-2400 MHz LRDIMM|
|DDR3-1866 MHz ECC REG||6 x 600GB SAS3 15k RPM|
|3 x 600GB SAS2 15k RPM||2016 CUBE Intel® Xeon® e5-2690 V4|
|40Gbps Infiniband QDRT High Speed Interconnect||1 x 1U CUBE APPLIANCE – 2 Compute Nodes|
|2014 CUBE Intel® Xeon® e5-2667 V2||2 x 14c @2.6GHz/ea – Intel® Xeon® e5-2690 V4|
|1 x CUBE HPC Workstation||Dual Socket motherboard|
|2 x 8c @3.3GHz/ea – Intel® Xeon® e5-2667 V2||DR4-2400 MHz LRDIMM|
|Dual Socket motherboard||4 x 600GB SAS3 15k RPM – RAID 10|
|DDR3-1866 MHz ECC REG||56Gbps Infiniband FDR CPU High Speed Interconnect|
|3 x 600GB SAS2 15k RPM||10Gbps Ethernet Low Latency|
Operating Systems Used
- Linux 64-bit
- Windows 7 Professional 64-Bit
- Windows 10 Professional 64-Bit
- Windows Server 2012 R2 Standard Edition w/HPC
It Is All About The Data
Test Metric – Average Seconds Per Iteration
- Fastest Time: 0.625 seconds per iteration – 2015 CUBE Intel® Xeon® e5-2667 V3
- ANSYS FLUENT 17.2
|Cores||2014 CUBE Intel® Xeon® e5-2667 V2
(1 x Node)
|2015 CUBE Intel® Xeon® e5-2667 V3
(8 x Nodes)
|2016 CUBE Intel® Xeon® e5-2667 V4
(1 x Node)
|2016 CUBE Intel® Xeon® e5-2690 V4
(2 x Nodes)
|2012 AMD Opteron™ 6278
(4 x Nodes)
|2013 CUBE AMD Opteron™ 6380
(4 x Nodes)
* One (1) CUBE Compute Node with 4 x AMD Opteron™ Series CPU’s for a total of 64 cores was used to derive these two ANSYS Fluent Benchmark data points (Baseline).
PADT offers a line of high performance computing (HPC) systems specifically designed for CFD and FEA number crunching aimed at a balance between cost and performance. We call this concept High Value Performance Computing, or HVPC. These systems have allowed PADT and our customers to carry out larger simulations, with greater accuracy, in less time, at a lower cost than name-brand solutions. This leaves you more cash to buy more hardware or software.
Related Blog Posts
Our work on 3D printed honeycomb modeling that started as a Capstone project with students from ASU in September 2015 (described in a previous blog post), was published in a peer-reviewed paper released last week in the proceedings of the SFF Symposium 2016. The full title of the paper is “A Validated Methodology for Predicting the Mechanical Behavior of ULTEM-9085 Honeycomb Structures Manufactured by Fused Deposition Modeling“. This was the precursor work that led to a us winning an 18-month award to pursue this work further with America Makes.
Download the whole paper at the link below:
ULTEM-9085 has established itself as the Additive Manufacturing (AM) polymer of choice for end-use applications such as ducts, housings, brackets and shrouds. The design freedom enabled by AM processes has allowed us to build structures with complex internal lattice structures to enhance part performance. While solutions exist for designing and manufacturing cellular structures, there are no reliable ways to predict their behavior that account for both the geometric and process complexity of these structures. In this work, we first show how the use of published values of elastic modulus for ULTEM-9085 honeycomb structures in FE simulation results in 40- 60% error in the predicted elastic response. We then develop a methodology that combines experimental, analytical and numerical techniques to predict elastic response within a 5% error. We believe our methodology is extendable to other processes, materials and geometries and discuss future work in this regard.