ANSYS 17.2 FLUENT External Flow Over a Truck Body Polyhedral Mesh

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

truck_poly_14m
ANSYS FLUENT 17.2 External Flow Over a Truck Body – Graph
ANSYS FLUENT External Flow Over a Truck Body with a Polyhedral Mesh (truck_poly_14m) Test Case
Number of cells 14,000,000
Cell type polyhedral
Models DES turbulence
Solver segregated implicit

The CPU Information

The AMD Opteron™ 6000 Series Platform:

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

  1. Intel® Xeon® Processor E5-2690 v4  (35M Cache, 2.60 GHz)
  2. Intel® Xeon® Processor E5-2667 v4  (25M Cache, 3.20 GHz)
  3. Intel® Xeon® Processor E5-2667 v3  (20M Cache, 3.20 GHz)
  4. 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.

Power consumption means money
CUBE HPC Compute Node Power Consumption as configured

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

  1. Linux 64-bit
  2. Windows 7 Professional 64-Bit
  3. Windows 10 Professional 64-Bit
  4. 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)

1 100.6 65.8 32.154 40.44 120.035 90.567
2 40.337 32.024 17.149 35.355 63.813 46.385
4 20.171 16.975 11.915 19.735 32.544 23.956
6 13.904 12.363 9.311 13.76 21.805 17.147
8 10.605 9.4 7.696 11.121 16.783 13.158
12 7.569 6.913 6.764 8.424 11.59 10.2
16 6.187 4.286 6.388 7.363 8.96 7.94
32 2.539 4.082 6.033 4.75
48 2.778 4.126 3.835
52 2.609 3.161 4.784
55 2.531 3.003 4.462
56 2.681 3.025 4.368
*64 3.871 5.004
64 2.688 2.746
96 2.433 2.202
128 0.625 2.112 2.367
256 1.461 3.531

* 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.

http://www.cube-hvpc.com/

Related Blog Posts

ANSYS FLUENT Performance Comparison: AMD Opteron vs. Intel XEON

Part 2: ANSYS FLUENT Performance Comparison: AMD Opteron vs. Intel XEON

ANSYS 17.2 CFX Benchmark External Flow Over a LeMans Car

Wow? yet another ANSYS Bench marking blog post? I know, but I have had four blog posts in limbo for months. There is no better time than now and since it is Friday. Time to knock out another one of these fine looking ANSYS 17.2 bench marking results of my list!

The ANSYS 17.2 CFX External Flow Over a LeMans Car Test Case

…dun dun dah!

On The Fast Track! ANSYS 17.2
On The Fast Track! ANSYS 17.2

The ANSYS CFX test case has approximately 1.8 million nodes

  • 10 million elements, all tetrahedral
  • Solves compressible fluid flow with heat transfer using the k-epsilon turbulence model.

ANSYS Benchmark Test Case Information

  • ANSYS HPC Licensing Packs required for this benchmark
    • I used (3) HPC Packs to unlock all 56 cores of the CUBE a56i.
    • The fastest solve time goes to the CUBE a56i – Boom!
      • From start to finish a total of forty-six (46) ticks on the clock on the wall occurred.
      • A total of fifty-five (55) cores in use between two twenty-eight (28) core nodes.
      • Windows 2012 R2 Standard Edition w/HPC update 3
      • MS-MPI v7.1
      • ANSYS CFX 17.2
  • 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 CFX benchmark data for the tetrahedral, 10 million elements External Flow Over a LeMans Car Test Case

ANSYS CFX Benchmark Data
ANSYS CFX Benchmark Data

ANSYS CFX Test Case Details – Click Here for more information on this benchmark

External Flow Over a LeMans Car
Number of nodes 1,864,025
Element type Tetrahedral
Models k-epsilon Turbulence, Heat Transfer
Solver Coupled Implicit

The CPU Information

The benchmark data is derived off of the running through the ANSYS CFX External Flow Over a LeMans Car test case. Take a minute or three to look at how these CPU’s perform with one of the very latest ANSYS releases, ANSYS Release 17.1 & ANSYS Release 17.2.

Wall Clock Time!

I have focused and tuned the numerical simulation machines with a focus on wall clock time for years now. What is funny if you ask Eric Miller we were talking about wall clock times this morning.

What is wall clock time? Simply put –> How does the solve time FEEL to the engineer…..yes, i just equated a feeling to a non-human event. Ah yes, to feel…oh and  I was reminded of old Van Halen song where David Lee Roth says.

Oh man, I think the clock is slow.

  I don’t feel tardy.

Class Dismissed!”

The CUBE phenomenon

CUBE a56i Appliance – Windows 2012 R2 Standard w/HPC
1U CUBE APPLIANCE (2 x 28)
4 x 14c @2.6GHz/ea – Intel® Xeon® e5-2690 V4
Dual Socket motherboard
256GB DDR4-2400 MHz LRDIMM
4 x 600GB SAS3 15k RPM
56Gbps Infiniband FDR CPU High Speed Interconnect
10Gbps Ethernet Low Latency
CUBE w32i Workstation – Windows 10 Professional
1 x 4U CUBE APPLIANCE
2 x 16c @2.6GHz/ea – Intel® Xeon® e5-2697a V4
Dual Socket motherboard
256GB DDR4-2400 MHz LRDIMM
2 x 600GB SAS3 15k RPM
NVIDIA QUADRO M4000

It Is All About The Data

 11/17/2016

PADT, Inc. – Tempe, AZ

ANSYS CFX 17.1 ANSYS CFX 17.1 ANSYS CFX 17.2
Total wall clock time Cores CUBE w32i CUBE a56i CUBE a56i
2 555 636 609
4 304 332 332
8 153 191 191
16 105 120 120
24 78 84 84
32 73 68 68
38 0 61 59
42 0 55 55
48 0 51 51
52 0 52 48
55 0 47 46
56 0 52 51

Picture Sharing Time!

Check out the pictures below of the Microsoft Server 2012 R2  HPC Cluster Manager.

I used the Windows Server 2012 R2  on both of the two compute nodes that make up the CUBE a56i.

Microsoft 2012 R2 w/HPC – is very quick, and oh so very powerful!

winhpc-cfx-56c-cpu

Windows 2012 HPC
Microsoft Windows 2012 R2 HPC. It is time…
INTEL XEON e5-2690 v4
The INTEL XEON e5-2690 v4 loves the turbo mode vrrooom It is time…

Please be safe out there in the wilds, you are all dismissed for the weekend!

ANSYS R17 Topological Optimization Application Example – Saxophone Brace

topo-opt-sax-a2What is Topological Optimization? If you’re not familiar with the concept, in finite element terms it means performing a shape optimization utilizing mesh information to achieve a goal such as minimizing volume subject to certain loads and constraints. Unlike parameter optimization such as with ANSYS DesignXplorer, we are not varying geometry parameters. Rather, we’re letting the program decide on an optimal shape based on the removal of material, accomplished by deactivating mesh elements. If the mesh is fine enough, we are left with an ‘organic’ sculpted shape elements. Ideally we can then create CAD geometry from this organic looking mesh shape. ANSYS SpaceClaim has tools available to facilitate doing this.

topo-opt-sax-a1Topological optimization has seen a return to prominence in the last couple of years due to advances in additive manufacturing. With additive manufacturing, it has become much easier to make parts with the organic shapes resulting from topological optimization. ANSYS has had topological optimization capability both in Mechanical APDL and Workbench in the past, but the capabilities as well as the applications at the time were limited, so those tools eventually died off. New to the fold are ANSYS ACT Extensions for Topological Optimization in ANSYS Mechanical for versions 17.0, 17.1, and 17.2. These are free to customers with current maintenance and are available on the ANSYS Customer Portal.

In deciding to write this piece, I decided an interesting example would be the brace that is part of all curved saxophones. This brace connects the bell to the rest of the saxophone body, and provides stiffness and strength to the instrument. Various designs of this brace have been used by different manufacturers over the years. Since saxophone manufacturers like those in other industries are often looking for product differentiation, the use of an optimized organic shape in this structural component could be a nice marketing advantage.

This article is not intended to be a technical discourse on the principles behind topological optimization, nor is it intended to show expertise in saxophone design. Rather, the intent is to show an example of the kind of work that can be done using topological optimization and will hopefully get the creative juices flowing for lots of ANSYS users who now have access to this capability.

That being said, here are some images of example bell to body braces in vintage and modern saxophones. Like anything collectible, saxophones have fans of various manufacturers over the years, and horns going back to production as early as the 1920’s are still being used by some players. The older designs tend to have a simple thin brace connecting two pads soldered to the bell and body on each end. Newer designs can include rings with pivot connections between the brace and soldered pads.

topo-opt-sax-01
Half Ring Brace

 

Solid connection to bell, screw joint to body
Solid connection to bell, screw joint to body
Older thin but solid brace rigidly connected to soldered pads
Older thin but solid brace rigidly connected to soldered pads
topo-opt-sax-04
Modern ring design
Modern Dual Degree of Freedom with Revolute Joint Type Connections
Modern Dual Degree of Freedom with Revolute Joint Type Connections

Hopefully those examples show there can be variation in the design of this brace, while not largely tampering with the musical performance of the saxophone in general. The intent was to pick a saxophone part that could undergo topological optimization which would not significantly alter the musical characteristics of the instrument.

The first step was to obtain a CAD model of a saxophone body. Since I was not able to easily find one freely available on the internet that looked accurate enough to be useful, I created my own in ANSYS SpaceClaim using some basic measurements of an example instrument. I then modeled a ‘blob’ of material at the brace location. The idea is that the topological optimization process will remove non-needed material from this blob, leaving an optimized shape after a certain level of volume reduction.

Representative Solid Model Geometry Created in ANSYS SpaceClaim. Note ‘Blob’ of Material at Brace Location.
Representative Solid Model Geometry Created in ANSYS SpaceClaim. Note ‘Blob’ of Material at Brace Location.

In ANSYS Mechanical, the applied boundary conditions consisted of frictionless support constraints at the thumb rest locations and a vertical displacement constraint at the attachment point for the neck strap. Acceleration due to gravity was applied as well. Other loads, such as sideways inertial acceleration, could have been considered as well but were ignored for the sake of simplicity for this article. The material property used was brass, with values taken from Shigley and Mitchell’s Mechanical Engineering Design text, 1983 edition.

topo-opt-sax-07
Applied Boundary Conditions Were Various Constraints at A, B, and C, as well as Acceleration Due to Gravity.

This plot shows the resulting displacement distribution due to the gravity load:

topo-opt-sax-08

Now that things are looking as I expect, the next step is performing the topological optimization.

Once the topological optimization ACT Extension has been downloaded from the ANSYS Customer Portal and installed, ANSYS Mechanical will automatically include a Topological Optimization menu:

topo-opt-sax-09

I set the Design Region to be the blog of material that I want to end up as the optimized brace. I did a few trials with varying mesh refinement. Obviously, the finer the mesh, the smoother the surface of the optimized shape as elements that are determined to be unnecessary are removed from consideration. The optimization Objective was set to minimize compliance (maximize stiffness). The optimization Constraint was set to volume at 30%, meaning reduce the volume to 30% of the current value of the ‘blob’.
After running the solution and plotting Averaged Node Values, we can see the ANSYS-determined optimized shape:

topo-opt-sax-10
Two views of the optimized shape.

What is apparent when looking at these shapes is that the ‘solder patch’ where the brace attaches to the bell on one end and the body on the other end was allowed to be reduced. For example, in the left image we can see that a hole has been ‘drilled’ through the patch that would connect the brace to the body. On the other end, the patch has been split through the middle, making it look something like an alligator clip.

 

Another optimization run was performed in which the solder pads were held as surfaces that were not to be changed by the optimization. The resulting optimized shape is shown here:

topo-opt-sax-11

Noticing that my optimized shape seemed on the thick side when compared to production braces, I then changed the ‘blob’ in ANSYS SpaceClaim so that it was thinner to start with. With ANSYS it’s very easy to propagate geometry changes as all of the simulation and topological optimizations settings stay tied to the geometry as long as the topology of those items stays the same.

Here is the thinner chunk after making a simple change in ANSYS SpacClaim:

topo-opt-sax-12

And here is the result of the topological optimization using the thinner blob as the starting point:

topo-opt-sax-13

Using the ANSYS SpaceClaim Direct Modeler, the faceted STL file that results from the ANSYS topological optimization can be converted into a geometry file. This can be done in a variety of ways, including a ‘shrink wrap’ onto the faceted geometry as well as surfaces fit onto the facets. Another option is to fit geometry in a more general way in an around the faceted result. These methods can also be combined. SpaceClaim is really a great tool for this. Using SpaceClaim and the topological optimization (faceted) result, I came up with three different ‘looks’ of the optimized part.

Using ANSYS Workbench, it’s very easy to plug the new geometry component into the simulation model that I already had setup and run in ANSYS Mechanical using the ‘blob’ as the brace in the original model. I then checked the displacement and stress results to see how they compared.

First, we have an organic looking shape that is mostly faithful to the results from the topological optimization run. This image is from ANSYS SpaceClaim, after a few minutes of ‘digital filing and sanding’ work on the STL faceted geometry output from ANSYS Mechanical.

topo-opt-sax-14

This shows the resulting deflection from this first, ‘organic’ candidate:

topo-opt-sax-15

The next candidate is one where more traditional looking solid geometry was created in SpaceClaim, using the topological optimization result as a guide. This is what it looks like:

topo-opt-sax-16

This is the same configuration, but showing it in place within the saxophone bell and body model in ANSYS SpaceClaim:

topo-opt-sax-17

Next, here is the deformation result for our simple loading condition using this second geometry configuration:

topo-opt-sax-18

The third and final design candidate uses the second set of geometry as a starting point, and then adds a bit of style while still maintaining the topological optimization shape as an overall guide. Here is this third candidate in ANSYS SpaceClaim:

topo-opt-sax-19

Here are is the resulting displacement distribution using this design:

topo-opt-sax-20

This shows the maximum principal stress distribution within the brace for this candidate:

topo-opt-sax-21

Again, I want to emphasize that this was a simple example and there are other considerations that could have been included, such as loading conditions other than acceleration due to gravity. Also, while it’s simple to include modal analysis results, in the interest of brevity I have not included them here. The main point is that topological optimization is a tool available within ANSYS Mechanical using the ACT extension that’s available for download on the customer portal. This is yet another tool available to us within our ANSYS simulation suite. It is my hope that you will also explore what can be done with this tool.

Regarding this effort, clearly a next step would be to 3D print one or more of these designs and test it out for real. Time permitting, we’ll give that a try at some point in the future.

ANSYS 17.1 FEA Benchmarks using v17-sp5

The CUBE machines that I used in this ANSYS Test Case represent a fine balance based on price, performance and ANSYS HPC licenses used.

Click Here for more information on the engineering simulation workstations and clusters designed in-house at PADT, Inc.. PADT, Inc. is happy to be a premier re-seller and dealer of Supermicro hardware.

  • ANSYS Benchmark Test Case Information.
  • ANSYS HPC Licensing Packs required for this benchmark
    • I used (2) HPC Packs to unlock all 32 cores.
  • 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 benchmark data from three excellent machines.

CUBE
CUBE by PADT, Inc. ANSYS Release 17.1 FEA Benchmark

BGA (V17sp-5)

BGA (V17sp-5)
Analysis Type Static Nonlinear Structural
Number of Degrees of Freedom 6,000,000
Equation Solver Sparse
Matrix Symmetric

Click Here for more information on the ANSYS Mechanical test cases. The ANSYS website has great information pertaining to the benchmarks that I am looking into today.

Pro Tip –> Lastly, please check out this article by Greg Corke one of my friends at ANSYS, Inc. I am using the ANSYS benchmark data fromthe Lenovo Thinkstation P910 as a baseline for my benchmark data.  Enjoy Greg’s article here!

  • The CPU Information

The benchmark data is derived off of the running through the BGA (sp-5) ANSYS test case. CPU’s and how they perform with one of the very latest ANSYS releases, ANSYS Release 17.1.

  1.  Intel® Xeon® e5-2680 V4
  2.  Intel® Xeon® e5-2667 V4
  3.  Intel® Xeon® e5-2697a V4
  • It Is All About The Data
    • Only one workstation was used for the data in this ANSYS Test Case
    • No GPU Accelerator cards are used for the data
    • Solution solve times are in seconds
ANSYS 17.1 Benchmark BGA v17sp-5
Lenovo ThinkStation P910 2680 V4 CUBE w16i 2667 V4 CUBE w32i 2697A V4
Cores Customer X  – 28 Core @2.4GHz/ea CUBE w16i CUBE w132i tS
2 1016 380.9 989.6 1.03
4 626 229.6 551.1 1.14
8 461 168.7 386.6 1.19
12 323 160.7 250.5 1.29
16 265 161.7 203.3 1.30
20 261 0 176.9 1.48
24 246 0 158.1 1.56
28 327 0 151.8 2.15
31 0 0 145.2 2.25
32 0 0 161.7 2.02
15-Nov-16 PADT, Inc. – Tempe, AZ –
  • Cube w16i Workstation – Windows 10 Professional
    1 x 4U CUBE APPLIANCE
    2 x 8c @3.2GHz/ea
    Dual Socket motherboard
    256GB DDR4-2400 MHz LRDIMM
    6 x 600GB SAS3 15k RPM
    NVIDIA QUADRO K6000
  • CUBE w32i Workstation – Windows 10 Professional
    1 x 4U CUBE APPLIANCE
    2 x 16c @2.6GHz/ea
    Dual Socket motherboard
    256GB DDR4-2400 MHz LRDIMM
    2 x 600GB SAS3 15k RPM
    NVIDIA QUADRO M4000
  • Lenovo Thinkstation P910 Workstation – Windows 10 Professional
    Lenovo P910 Workstation
    2 x 14c @2.4GHz/ea
    Dual Socket motherboard
    128GB DDR4-2400 MHz
    512GB NVMe SSD / 2 x 4TB SATA HDD / 512GB SATA SSD
    NVIDIA QUADRO M2000

As you will may have noticed above, the CUBE workstation with the Intel Xeon e5-2697A V4 had the fastest solution solve time for one workstation.

  • *** Using 31 cores the CUBE w32i finished the sp-5 test case in 145.2 seconds.

See 32 Cores of Power! CUBE by PADT, Inc.

cube-w32i-coresCUBE w32i

CUBE w32i

CUBE by PADT, Inc. of ANSYS 17.1 Benchmark Data for sp-5
CUBE by PADT, Inc. of ANSYS 17.1 Benchmark Data for sp-5

Thank you!

http://www.cube-hvpc.com/

ANSYS 17.2 Executable Paths on Linux


ansys-linux-penguin-1When running on a machine with a Linux operating system, it is not uncommon for users to want to run from the command line or with a shell script. To do this you need to know where the actual executable files are located. Based on a request from a customer, we have tried to coalesce the major ANSYS product executables that can be run via command line on Linux into a single list:

ANSYS Workbench (Includes ANSYS Mechanical, Fluent, CFX, Polyflow, Icepak, Autodyn, Composite PrepPost, DesignXplorer, DesignModeler, etc.):

/ansys_inc/v172/Framework/bin/Linux64/runwb2

ANSYS Mechanical APDL, a.k.a. ANSYS ‘classic’:

/ansys_inc/v172/ansys/bin/launcher172 (brings up the MAPDL launcher menu)
/ansys_inc/v172/ansys/bin/mapdl (launches ANSYS MAPDL)

CFX Standalone:

/ansys_inc/v172/CFX/bin/cfx5

Autodyn Standalone:

/ansys_inc/v172/autodyn/bin/autodyn172

Note: A required argument for Autodyn is –I {ident-name}

Fluent Standalone (Fluent Launcher):

/ansys_inc/v172/fluent/bin/fluent

Icepak Standalone:

/ansys_inc/v172/Icepak/bin/icepak

Polyflow Standalone:

/ansys_inc/v172/polyflow/bin/polyflow/polyflow < my.dat

Chemkin:

/ansys_inc/v172/reaction/chemkinpro.linuxx8664/bin/chemkinpro_setup.ksh

Forte:

/ansys_inc/v172/reaction/forte.linuxx8664/bin/forte.sh

TGRID:

/ansys_inc/v172/tgrid/bin/tgrid

ANSYS Electronics Desktop (for Ansoft tools, e.g. Maxwell, HFSS)

/ansys_inc/v172/AnsysEM/AnsysEM17.2/Linux64/ansysedt

SIWave:

/ansys_inc/v172/AnsysEM/AnsysEM17.2/Linux64/siwave

Modeling 3D Printed Cellular Structures: Challenges

In this post, I discuss six challenges that make the modeling of 3D printed cellular structures (such as honeycombs and lattices) a non-trivial matter. In a following post, I will present how some of these problems have been addressed with different approaches.

At the outset, I need to clarify that by modeling I mean the analytical representation of material behavior, primarily for use in predictive analysis (simulation). Here are some reasons why this is a challenging endeavor for 3D printed cellular solids – some of these reasons are unique to 3D printing, others are a result of aspects that are specific to cellular solids, independent of how they are manufactured. I show examples with honeycombs since that is the majority of the work we have data for, but I expect that these ideas apply to foams and lattices as well, just with varying degrees of sensitivity.

1. Complex Geometry with Non-Uniform Local Conditions

I state the most well-appreciated challenge with cellular structures first: they are NOT fully-dense solid materials that have relatively predictable responses governed by straightforward analytical expressions. Consider a dogbone-shaped specimen of solid material under tension: it’s stress-strain response can be described fairly well using continuum expressions that do not account for geometrical features beyond the size of the dogbone (area and length for stress and strain computations respectively). However, as shown in Figure 1, such is not the case for cellular structures, where local stress and strain distributions are non-uniform. Further, they may have variable distributions of bending, stretching and shear in the connecting members that constitute the structure. So the first question becomes: how does one represent such complex geometry – both analytically and numerically?

non-uniform-strain
Fig 1. Honeycomb structure under compression showing non-uniform local elastic strains [Le & Bhate, under preparation]

2. Size Effects

A size effect is said to be significant when an observed behavior varies as a function of the size of the sample whose response is being characterized even after normalization (dividing force by area to get stress, for example). Here I limit myself to size effects that are purely a mathematical artifact of the cellular geometry itself, independent of the manufacturing process used to make them – in other words this effect would persist even if the material in the cellular structure was a mathematically precise, homogeneous and isotropic material.

It is common in the field of cellular structure modeling to extract an “effective” property – a property that represents a homogenized behavior without explicitly modeling the cellular detail. This is an elegant concept but introduces some practical challenges in implementation – inherent in the assumption is that this property, modulus for example, is equivalent to a continuum property valid at every material point. The reality is the extraction of this property is strongly dependent on the number of cells involved in the experimental characterization process. Consider experimental work done by us at PADT, and shown in Figure 2 below, where we varied both the number of axial and longitudinal cells (see inset for definition) when testing hexagonal honeycomb samples made of ULTEM-9085 with FDM. The predicted effective modulus increases with increasing number of cells in the axial direction, but reduces (at a lower rate) for increasing number of cells in the longitudinal direction.

This is a significant challenge and deserves a full form post to do justice (and is forthcoming), but the key to remember is that testing a particular cellular structure does not suffice in the extraction of effective properties. So the second question here becomes: what is the correct specimen design for characterizing cellular properties?

sizeeffect
Fig 2. Effective modulus under compression showing a strong dependence on the number of cells in the structure [Le & Bhate, under preparation]

3. Contact Effects

In the compression test shown in the inset in Figure 2, there is physical contact between the platen and the specimen that creates a local effect at the top and bottom that is different from the experience of the cells closer the center. This is tied to the size effect discussed above – if you have large enough cells in the axial direction, the contribution of this effect should reduce – but I have called it out as a separate effect here for two reasons: Firstly, it raises the question of how best to design the interface for the specimen: should the top and bottom cells terminate in a flat plate, or should the cells extend to the surface of contact (the latter is the case in the above image). Secondly, it raises the question of how best to model the interface, especially if one is seeking to match simulation results to experimentally observed behavior. Both these ideas are shown in Figure 3 below. This also has implications for product design – how do we characterize and model the lattice-skin interface? As such, independent of addressing size effects, there is a need to account for contact behavior in characterization, modeling and analysis.

contact
Fig 3. Two (of many possible) contact conditions for cellular structure compression – both in terms of specimen design as well as in terms of the nature of contact specified in the simulation (frictionless vs frictional, for example)

4. Macrostructure Effects

Another consideration related to specimen design is demonstrated in an exaggerated manner in the slowed down video below, showing a specimen flying off the platens under compression – the point being that for certain dimensions of the specimen being characterized (typically very tall aspect ratios), deformation in the macrostructure can influence what is perceived as cellular behavior. In the video below, there is some induced bending on a macro-level.

5. Dimensional Errors

While all manufacturing processes introduce some error in dimensional tolerances, the error can have a very significant effect for cellular structures – a typical industrial 3D printing process has tolerances within 75 microns (0.003″) – cellular structures (micro-lattices in particular) very often are 250-750 microns in thickness, meaning the tolerances on dimensional error can be in the 10% and higher error range for thickness of these members. This was our finding when working with Fused Deposition Modeling (FDM), where on a 0.006″ thick wall we saw about a 10% larger true measurement when we scanned the samples optically, as shown in Figure 4. Such large errors in thickness can yield a significant error in measured behavior such as elastic modulus, which often goes by some power to the thickness, amplifying the error. This drives the need for some independent measurement of the manufactured cellular structure – made challenging itself by the need to penetrate the structure for internal measurements. X-ray scanning is a popular, if expensive approach. But the modeler than has the challenge of devising an average thickness for analytical calculations and furthermore, the challenge of representation of geometry in simulation software for efficient analysis.

Fig 4. (Clockwise from top left): FDM ULTEM 9085 honeycomb sample, optical scan image, 12-sample data showing a mean of 0.064″ against a designed value of 0.060″ – a 7% error in thickness

6. Mesostructural Effects

The layerwise nature of Additive Manufacturing introduces a set of challenges that are somewhat unique to 3D Printed parts. Chief among these is the resulting sensitivity to orientation, as shown for the laser-based powder bed fusion process in Figure 5 with standard materials and parameter sets. Overhang surfaces (unsupported) tend to have down-facing surfaces with different morphology compared to up-facing ones. In the context of cellular structures, this is likely to result in different thickness effects depending on direction measured.

Fig 5. 3D Printed Stainless Steel Honeycomb structures showing orientation dependent morphology [PADT, 2016]
For the FDM process, in addition to orientation, the toolpaths that effectively determine the internal meso-structure of the part (discussed in a previous blog post in greater detail) have a very strong influence on observed stiffness behavior, as shown in Figure 6. Thus orientation and process parameters are variables that need to be comprehended in the modeling of cellular structures – or set as constants for the range of applicability of the model parameters that are derived from a certain set of process conditions.

Figure
Fig 6. Effects of different toolpath selections in Fused Deposition Modeling (FDM) for honeycomb structure tensile testing  [Bhate et al., RAPID 2016]

Summary

Modeling cellular structures has the above mentioned challenges – most have practical implications in determining what is the correct specimen design – it is our mission over the next 18 months to address some of these challenges to a satisfactory level through an America Makes grant we have been awarded. While these ideas have been explored in other manufacturing contexts,  much remains to be done for the AM community, where cellular structures have a singular potential in application.

In future posts, I will discuss some of these challenges in detail and also discuss different approaches to modeling 3D printed cellular structures – they do not always address all the challenges here satisfactorily but each has its pros and cons. Until then, feel free to send us an email at info@padtinc.com citing this blog post, or connect with me on LinkedIn so you get notified whenever I write a post on this, or similar subjects in Additive Manufacturing (1-2 times/month).

ANSYS How To: Result Legend Customization and Reuse

ansys-mechanical-custom-legend-0A user was asking how to modify the result legend in ANSYS Mechanical R17 so Ted Harris put together this little How To in PowerPoint:

padt_mechanical_custom_legend_r17.pdf

It shows how to modify the legend to get just what you want, how to save the settings to a file, and then how to use those seettings again on a different model.  Very simple and Powerful.

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Jet Engines to Golf Clubs – Phoenix Area ANSYS Users Share their Stories

ansys-padt-skysong-conference-1There is nothing better than seeing the powerful and interesting way that other engineers are using the same tools you use.  That is why ANSYS, Inc. and PADT teamed up on Thursday to hold an “ANSYS Arizona Innovation Conference”  at ASU SkySong where users could come to share and learn.

The day kicked off with Andy Bauer from ANSYS welcoming everyone and giving them an update on the company and some general overarching direction for the technology.  Then Samir Rida from Honeywell Aerospace gave a fantastic keynote sharing how simulation drive the design of their turbine engines.  As a former turbine engine guy, I found it fascinating and exciting to see how accurate and detailed their modeling is.

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Next up was my talk on the Past, Present, and Future of simulation for product development.  The point of the presentation was to take a step back and really think about what simulation is, what we have padt-ansys-innovation-az-2016-pptbeen doing, and what it needs to look at in the future.  We all sort of agreed that we wanted voice activation and artificial intelligence built in now.  If you are interested, you can find my presentation here: padt-ansys-innovation-az-2016.pdf.

After a short break ANSYS’s Sara Louie launched into a discussion on some of the new Antenna Systems modeling capabilities, simulating multiple physics and large domains with ANSYS products.  The ability to model the entire interaction of an antenna including large environments was fascinating.

Lunchtime discussions focused on the presentations in the morning as well as people sharing what they were working on.

img_1632The afternoon started with a review by Hoang Vinh of ANSYS of the ANSYS AIM product. This was followed by customer presentations. Both Galtronics and ON Semiconductor shared how they drive the design of their RF systems with ANSYS HFSS and related tools.  Then Nammo Talley shared how they incorporated simulation into their design process and then showed an example of a projectile redesign from a shoulder launched rocket that was driven by simulation in ANSYS CFX.  They had the added advantage of being able to show something that blows up, always a crowd pleaser.

ping-ansysAnother break was followed by a great look at how Ping used CFD to improve the design of one of their drivers.  They used simulation to understand the drag on the head through an entire swing and then add aerodynamic features that improved the performance of the club significantly. Much of the work is actually featured in an ANSYS Advantage article.

We wrapped things up with an in depth technical look at Shock and Vibration Analysis using ANSYS Mechanical and Multiphysics PCB Analysis with the full ANSYS product suite.

The best part of the event was seeing how all the different physics in ANSYS products were being used and applied in different industries.  WE hope to have similar events int he future so make sure you sign up for our mailings, the “ANSYS – Software Information & Seminars” list will keep you in the loop.

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PADT Events – September 2016

PADT-Events-LogoSeptember is here and it is a jam packed month of events, many of them related to BioMedical engineering.  We are continuing with ANSYS webinars and talking about 3D Printing as well. See what we have below:


uma_new-sm2September 13: Salt Lake City, UT
Manufacturing Promotes Innovation Summit

The UMA Summit is a day long event filled with networking, guest speakers and informative information. In between speakers network with our vendor booths and see the latest products and services available for the Manufacturing Industry. PADT will be there with lots of example of 3D Printing and ready to engage on how manufacturing really does drive innovation. Check out the event page for times and an agenda.


Ansys-logo

September 15: Scottsdale, AZ
ANSYS Arizona Innovation Conference

ANSYS and PADT are pleased to announce that we be holding a user meeting in Scottsdale for the entire ANSYS use community.  Join us for an informative conference on how to incorporate various productivity enhancement tools and techniques into your workflow for your engineering department. ANSYS Applications Engineers and local customers like Honeywell, Galtronics, On Semi, Ping, and Nammo Talley, will discuss design challenges and how simulation-driven product development can help engineers rapidly innovate new products.  See the agenda and register here.


September 19: Phoenix, AZ
Seminar: Medical Device Product Development for Startups – The Bitter Pill

We will be kicking off our Arizona Bioscience Week with this a free seminar at CEI in Phoenix with a sometimes brutally honest discussion on the reality of medical device product development.

No one wants to discourage a good idea, and entrepreneurs make it a long way before someone sits them down and explains how long and expensive the engineering of a medical device product is. In this one hour seminar PADT will share the hard and cold realities of the process, not to discourage people, but to give them the facts they need.

Get the details and register here.


logo_MDM_Minn14_4c

September 21-22: Minneapolis, MN
Medical Design & Manufacturing Minneapolis

PADT Medical will have a booth with our partner Innosurg at this premier event for medical device development.  For 22 years, Medical Design & Manufacturing Minneapolis has been the medtech innovation, communication, and solution epicenter of the Midwest. Now over 600 suppliers strong, and with more than 5,000 industry professionals in attendance, the event provides the solutions, education, and partnerships you simply won’t find anywhere else.  Learn more here. And if you are attending, please stop by and say hello, we are in booth 1643.


azbio-logo-1September 21: Phoenix, AZ
AZBio Awards

Join PADT and others for this annual event that recognizes those that contribute to the growing AZ BioTech community.  The awards will be made by PADT’s 3D Printing team again this year.  Stop by our table to say hello. Register here.


AZ-Bioscience-Week

September 21 & 22: Phoenix, AZ
White Hat Investor Conference

The West was won by innovators, investors, and prospectors who understood the value of discovery and accepted the challenge of investing in new frontiers.  PADT will be joining others in the investment community to meet with and hear from companies (32 are signed up to present right now) in the Bioscience space and to also share ideas and network.  Registration for this special event can be found here.


exerience_it_nmSeptember 30: Albuquerque, NM
New Mexico Tech Council: Experience IT NM Conference

Geek out on all things technology. The New Mexico Tech community will gather the best and the brightest entrepreneurs, technicians, hackers, and tech fans for presentations, talks, meet-ups, and parties; all to highlight the vibrant tech community in our city. The Conference takes place on the final day of a week of events, and will focus on HR, CRM, Manufacturing, and Creative concerns of the tech community with panels and presentations.  PADT’s Eric Miller will be presenting in two “MakeIT” sessions.

Learn more here.


PADT-Webinar-LogoThis month’s webinars look at Signal Integrity and 3D Printing for Production

Wednesday, September 7, 2016 – 1:00 PM AZ/PDT, 12:00 PM MDT
Investigating Signal Integrity: How to find problems before they find you
Register
Thursday, September 29, 2016 – 4:00 PM AZ/PDT, 3:00 PM MDT
SAE Webinar: Additive Manufacturing: From Prototyping to Production Parts
Register

New Flownex Training Course Available Online

flownex-training-1

We are pleased to announce the new Flownex Training Course for Flownex SE, the world’s best (we think) thermal-fluid modeling tool.  The Flownex course is aimed at new users with a desire to quickly equip themselves in the basics of system modelling as well as enabling one to visually refresh one’s memory on the various capabilities and applications within the Flownex suite.

If you are not a user already but want to check this tool out by going through the training course, go to the login page and simply click “Don’t have an account?” and register. This will get you access and we will follow up with a temp key so you can try it out.  This is actually the best way for you to get a feel for why we like this program so much.

flownex-training-2

Here is a list of the sessions:

  • Session 1: Background to Flownex
  • Session 2: Page navigation
  • Session 3: Boundary values
  • Session 4: Pumps & Fixed mass flow functionality
  • Session 5: Flow restrictions
  • Session 6: Exercise 1
  • Session 7: Designer functionality
  • Session 8: Heat Exchangers
  • Session 9: Containers
  • Session 10: Exercise 2
  • Session 11: Excel component
  • Session 12: Visualization

As always, If you have any questions or want to know more, reach out to us at info@padtinc.com or 1.800.293.PADT.

Video Tips: Node and Element IDs in ANSYS Mechanical

This is a common question that we get, particularly those coming from APDL – how to get nodal and element IDs exposed in ANSYS Mechanical. Whether that’s for troubleshooting or information gathering, it was not available before. This video shows how an ANSYS developed extension accomplishes that pretty easily.

The extension can be found by downloading “FE Info XX” for the version XX of ANSYS you are using at  https://support.ansys.com/AnsysCustom…

Classification of Cellular Solids (and why it matters)

Updated (8/30/2016): Two corrections made following suggestions by Gilbert Peters: the first corrects the use of honeycomb structures in radiator grille applications as being for flow conditioning, the second corrects the use of the Maxwell stability criterion, replacing the space frame example with an octet truss.

~

This is my first detailed post in a series on cellular structures for additive manufacturing, following an introductory post I wrote where I classified the research landscape in this area into four elements: design, analysis, manufacturing and implementation.

Within the design element, the first step in implementing cellular structures in Additive Manufacturing (AM) is selecting the appropriate unit cell(s). The unit cell is selected based on the performance desired of it as well as the manufacturability of the cells. In this post, I wish to delve deeper into the different types of cellular structures and why the classification is important. This will set the stage for defining criteria for why certain unit cell designs are preferable over others, which I will attempt in future posts. This post will also explain in greater detail what a “lattice” structure, a term that is often erroneously used to describe all cellular solids, truly is.

1. Honeycomb

1.1 Definition
Honeycombs are prismatic, 2-dimensional cellular designs extruded in the 3rd dimension, like the well-known hexagonal honeycomb shown in Figure 1. All cross-sections through the 3rd dimension are thus identical, making honeycombs somewhat easy to model. Though the hexagonal honeycomb is most well known, the term applies to all designs that have this prismatic property, including square and triangular honeycombs. Honeycombs have a strong anisotropy in the 3rd dimension – in fact, the modulus of regular hexagonal and triangular honeycombs is transversely isotropic – equal in all directions in the plane but very different out-of-plane.

Figure 1. Honeycomb structure showing two-dimensional, prismatic nature (Attr: modified from work done by George William Herbert, Wikipedia)
honeycomb_bmwi3
Figure 2. Honeycomb design in use as part of a BMW i3 crash structure (Attr: adapted from youkeys, Wikipedia)

1.2 Design Implications
The 2D nature of honeycomb structures means that their use is beneficial when the environmental conditions are predictable and the honeycomb design can be oriented in such a way to extract maximum benefit. One such example is the crash structure in Figure 2 as well as a range of sandwich panels. Several automotive radiator grilles are also of a honeycomb design to condition the flow of air. In both cases, the direction of the environmental stimulus is known – in the former, the impact load, in the latter, airflow.

2. Open-Cell Foam

openfoam
Figure 3. Open cell foam unit cell, following Gibson & Ashby (1997)

2.1 Definition
Freeing up the prismatic requirement on the honeycomb brings us to a fully 3-dimensional open-cell foam design as shown in one representation of a unit cell in Figure 3. Typically, open-cell foams are bending-dominated, distinguishing them from stretch-dominated lattices, which are discussed in more detail in a following section on lattices.

2.2 Design Implications
Unlike the honeycomb, open cell foam designs are more useful when the environmental stimulus (stress, flow, heat) is not as predictable and unidirectional. The bending dominated mechanism of deformation make open-cell foams ideal for energy absorption – stretch dominated structures tend to be stiffer. As a result of this, applications that require energy absorption such as mattresses and crumple zones in complex structures. The interconnectivity of open-cell foams also makes them a candidate for applications requiring fluid flow through the structure.

Metal_Foam
Figure 4. SEM image of a metallic open-cell foam (Attr: SecretDisc, Wikipedia)
openfoam-deform
Figure 5. FEA simulation of open cell foam unit cell under compression, showing predominant mode of deformation is on account of bending

3. Closed-Cell Foam

closedfoam
Figure 6. Open cell foam unit cell representation [following Gibson and Ashby, 1997]
3.1 Definition
As the name suggests, closed cell foams are open-cell foams with enclosed cells, such as the representation shown in Figure 6. This typically involves a membrane like structure that may be of varying thickness from the strut-like structures, though this is not necessary. Closed-cell foams arise from a lot of natural processes and are commonly found in nature. In man-made entities, they are commonly found in the food industry (bread, chocolate) and in engineering applications where the enclosed cell is filled with some fluid (like air in bubble wrap, foam for bicycle helmets and fragile packaging).

3.2 Design Implications
The primary benefit of closed cell foams is the ability to encapsulate a fluid of different properties for compressive resilience. From a structural standpoint, while the membrane is a load-bearing part of the structure under certain loads, the additional material and manufacturing burden can be hard to justify. Within the AM context, this is a key area of interest for those exploring 3D printing food products in particular but may also have value for biomimetic applications.

Closed_cell_metal_foam_with_large_cell_size
Figure 8. Closed cell Aluminum foam with very large cells [Shinko Wire Company, Attr: Curran2, Wikimedia Commons]

 4. Lattice

4.1 Definition
Lattices are in appearance very similar to open cell foams but differ in that lattice member deformation is stretch-dominated, as opposed to bending*. This is important since for the same material allocation, structures tend to be stiffer in tension and/or compression compared to bending – by contrast, bending dominated structures typically absorb more energy and are more compliant.

So the question is – when does an open cell foam become stretch dominated and therefore, a lattice? Fortunately, there is an app equation for that.

Maxwell’s Stability Criterion
Maxwell’s stability criterion involves the computation of a metric M for a lattice-like structure with b struts and j joints as follows:

In 2D structures: M = b – 2j + 3
In 3D structures:
M = b – 3j + 6

Per Maxwell’s criterion, for our purposes here where the joints are locked (and not pinned), if M < 0, we get a structure that is bending dominated. If M >= 0, the structure is stretch dominated. The former constitutes an open-cell foam, the latter a lattice.

There are several approaches to establishing the appropriateness of a lattice design for a structural applications (connectivity, static and kinematic determinism etc.) and how they are applied to periodic structures and space frames. It is easy for one (including for this author) to confuse these ideas and their applicability. For the purposes of AM, Maxwell’s Stability Criterion for 3D structures is a sufficient condition for static determinancy. Further, for a periodic structure to be truly space-filling (as we need for AM applications), there is no simple rigid polyhedron that fits the bill – we need a combination of polyhedra (such as an octahedron and tetrahedron in the octet truss shown in the video below) to generate true space filling, and rigid structures. The 2001 papers by Deshpande, Ashby and Fleck illustrate these ideas in greater detail and are referenced at the end of this post.

Video: The octet truss is a classic stretch-dominated structure, with b = 36 struts, j = 14 joints and M = 0 [Attr. Lawrence Livermore National Labs]

4.2 Design Implications
Lattices are the most common cellular solid studied in AM – this is primarily on account of their strong structural performance in applications where high stiffness-to-weight ratio is desired (such as aerospace), or where stiffness modulation is important (such as in medical implants). However, it is important to realize that there are other cellular representations that have a range of other benefits that lattice designs cannot provide.

Conclusion: Why this matters

It is a fair question to ask why this matters – is this all just semantics? I would like to argue that the above classification is vital since it represents the first stage of selecting a unit cell for a particular function. Generally speaking, the following guidelines apply:

  • Honeycomb structures for predictable, unidirectional loading or flow
  • Open cell foams where energy absorption and compliance is important
  • Closed cell foams for fluid-filled and hydrostatic applications
  • Lattice structures where stiffness and resistance to bending is critical

Finally, another reason it is important to retain the bigger picture on all cellular solids is it ensures that the discussion of what we can do with AM and cellular solids includes all the possibilities and is not limited to only stiffness driven lattice designs.

Note: This blog post is part of a series on “Additive Manufacturing of Cellular Solids” that I am writing over the coming year, diving deep into the fundamentals of this exciting and fast evolving topic. To ensure you get each post (~2 a month) or to give me feedback for improvement, please connect with me on LinkedIn.

References

[1] Ashby, “Materials Selection in Mechanical Design,” Fourth Edition, 2011
[2] Gibson & Ashby, “Cellular Solids: Structure & Properties,” Second Edition, 1997
[3] Gibson, Ashby & Harley, “Cellular Materials in Nature & Medicine,” First Edition, 2010
[4] Ashby, Evans, Fleck, Gibson, Hutchinson, Wadley, “Metal Foams: A Design Guide,” First Edition, 2000
[5] Deshpande, Ashby, Fleck, “Foam Topology Bending versus Stretching Dominated Architectures,” Acta Materialia 49, 2001
[6] Deshpande, Fleck, Ashby, “Effective properties of the octet-truss lattice material,”  Journal of the Mechanics and Physics of Solids, 49, 2001

Notes

* We defer to reference [1] in distinguishing lattice structures as separate from foams – this is NOT the approach used in [2] and [3] where lattices are treated implicitly as a subset of open-cell foams. The distinction is useful from a structural perspective and as such is retained here.

New Second Edition in Paperback and Kindle: Introduction to the ANSYS Parametric Design Language (APDL)

APDL-Guide-Square-Advert-1After three years on the market and signs that sales were increasing year over year, we decided it was time to go through our popular training book “Introduction to the ANSYS Parametric

Introduction_to_APDL_V2-Kindle-Ipad-1
I’ll be honest, it was cool to see the book in print the first time, but seeing it on my iPad was just as cool.

Design Language (APDL)” and make some updates and reformat it so that it can be published as a Kindle e-book.   The new Second Edition includes two additonal chapters: APDL Math and Using APDL with ANSYS Mechanical.  The fact that we continue to sell more of these useful books is a sign that APDL is still a vibrant and well used language, and that others out there find power in its simplicity and depth.

This book started life as a class that PADT taught for many years. Then over time people asked if they could buy the notes. And then they asked for a real book. The bulk of the content came from Jeff Strain with input from most of our technical staff. Much of the editing and new content was done by Susanna Young and Eric Miller.

Here is the Description from Amazon.com:

The definitive guide to the ANSYS Parametric Design Language (APDL), the command language for the ANSYS Mechanical APDL product from ANSYS, Inc. PADT has converted their popular “Introduction to APDL” class into a guide so that users can teach themselves the APDL language at their own pace. Its 14 chapters include reference information, examples, tips and hints, and eight workshops. Topics covered include:

– Parameters
– User Interfacing
– Program Flow
– Retrieving Database Information
– Arrays, Tables, and Strings
– Importing Data
– Writing Output to Files
– Menu Customization
– APDL Math
– Using APDL in ANSYS Mechanical

At only $75.00 it is an investment that will pay for itself quickly. Even if you are an ANSYS Mechanical user, you can still benefit from knowing APDL, allowing you to add code snippets to your models. We have put some images below and you can also learn more here or go straight to Amazon.com to purchase the paperback or Kindle versions.

Introduction_to_APDL_V2-1_Cover

PADT-Intro-APDL-pg184-185 PADT-Intro-APDL-pg144-145 PADT-Intro-APDL-pg112-113 PADT-Intro-APDL-pg100-101 PADT-Intro-APDL-pg-020-021

 

Video Tips: Changing Multiple Load Step Settings in ANSYS Mechanical

ANSYS Mechanical allows you to specify settings for load steps one at a time. Most users don’t know that you can change settings for any combination of load steps using the selection of the load step graph. PADT’s Joe Woodward shows you how in this short but informative video.

The Additive Manufacturing Cellular Solids Research Landscape

I am writing this post after visiting the 27th SFF Symposium, a 3-day Additive Manufacturing (AM) conference held annually at the University of Texas at Austin. The SFF Symposium stands apart from other 3D printing conferences held in the US (such as AMUG, RAPID and Inside3D) in the fact that about 90% of the attendees and presenters are from academia. This year had 339 talks in 8 concurrent tracks and 54 posters, with an estimated 470 attendees from 20 countries – an overall 50% increase over the past year.

As one would expect from a predominantly academic conference, the talks were deeper in their content and tracks were more specialized. The track I presented in (Lattice Structures) had a total of 15 talks – 300 minutes of lattice talk, which pretty much made the conference for me!

In this post, I wish to summarize the research landscape in AM cellular solids at a high level: this classification dawned on me as I was listening to the talks over two days and taking in all the different work going on across several universities. My attempt in this post is to wrap my arms around the big picture and show how all these elements are needed to make cellular solids a routine design feature in production AM parts.

Classification of Cellular Solids

First, I feel the need to clarify a technicality that bothered me a wee bit at the conference: I prefer the term “cellular solids” to “lattices” since it is more inclusive of honeycomb and all foam-like structures, following Gibson and Ashby’s 1997 seminal text of the same name. Lattices are generally associated with “open-cell foam” type structures only – but there is a lot of room for honeycomb structures and close-cell foams, each having different advantages and behaviors, which get excluded when we use the term “lattice”.

CellularSolids
Figure 1. Classification of Cellular Solids [Gibson & Ashby, 1997]

The AM Cellular Solids Research Landscape

The 15 papers at the symposium, and indeed all my prior literature reviews and conference visits, suggested to me that all of the work in this space falls into one or more of four categories shown in Figure 2. For each of the four categories (design, analysis, manufacturing & implementation), I have listed below the current list of capabilities (not comprehensive), many of which were discussed in the talks at SFF. Further down I list the current challenges from my point of view, based on what I have learned studying this area over the past year.

AMcellular
Figure 2. AM Cellular Solid Research Landscape

Over the coming weeks I plan to publish a post with more detail on each of the four areas above, summarizing the commercial and academic research that is ongoing (to the best of my knowledge) in each area. For now, I provide below a brief elaboration of each area and highlight some important research questions.

1. Representation (Design)

This deals with how we incorporate cellular structures into our designs for all downstream activities. This involves two aspects: the selection of the specific cellular design (honeycomb or octet truss, for example) and its implementation in the CAD framework. For the former, a key question is: what is the optimum unit cell to select relative to performance requirements, manufacturability and other constraints? The second set of challenges arises from the CAD implementation: how does one allow for rapid iteration with minimal computational expense, how do cellular structures cover the space and merge with the external skin geometry seamlessly?

2. Optimization (Analysis)

Having tools to incorporate cellular designs is not enough – the next question is how to arrange these structures for optimum performance relative to specified requirements? The two most significant challenges in this area are performing the analysis at reasonable computational expense and the development of material models that accurately represent behavior at the cellular structure level, which may be significantly different from the bulk.

3. Realization (Manufacturing)

Manufacturing cellular structures is non-trivial, primarily due to the small size of the connecting members (struts, walls). The dimensions required are often in the order of a few hundred microns and lower, which tends to push the capabilities of the AM equipment under consideration. Additionally, in most cases, the cellular structure needs to be self-supporting and specifically for powder bed fusion, must allow for removal of trapped powder after completion of the build. One way to address this is to develop a map that identifies acceptable sizes of both the connecting members and the pores they enclose. For this, we need robust ways of monitoring quality of AM cellular solids by using in-situ and Non-Destructive techniques to guard against voids and other defects.

4. Application (Implementation)

Cellular solids have a range of potential applications. The well established ones include increasing stiffness-to-weight ratios, energy absorption and thermal performance. More recent applications include improving bone integration for implants and modulating stiffness to match biological distributions of material (biomimicry), as well as a host of ideas involving meta-materials. The key questions here include how do we ensure long term reliability of cellular structures in their use condition? How do we accurately identify and validate these conditions? How do we monitor quality in the field? And how do we ensure the entire life cycle of the product is cost-effective?

So What?

I wrote this post for two reasons: I love to classify information and couldn’t help myself after 5 hours of hearing and thinking about this area. But secondly, I hope it helps give all of us working in this space context to engage and communicate more seamlessly and see how our own work fits in the bigger picture.

A lot of us have a singular passion for the overlapping zone of AM and cellular solids and I can imagine in a few years we may well have a conference, an online journal or a forum of some sort just dedicated to this field – in fact, I’d love to assess interest in such an effort or an equivalent collaborative exercise. If this idea resonates with you, please connect with me on LinkedIn and drop me a note, or send us an email (info@padtinc.com) and cite this blog post so it finds its way to me.