Smart Manufacturing: CAE in the Cloud
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The document discusses the concept of smart manufacturing and the role of CAE (Computer-Aided Engineering) as a service in the cloud, highlighting the benefits and challenges associated with various computing platforms: workstations, servers, and clouds. It emphasizes the advantages of cloud computing such as scalability and cost-effectiveness while noting ongoing challenges like security and market complexity. The document also details the UberCloud experiments, showcasing case studies that illustrate successful applications of high-performance computing in various engineering fields.
Smart Manufacturing: CAE in the Cloud
- 1. SMART MANUFACTURING: CAE AS A SERVICE IN THE CLOUD Wolfgang Gentzsch President, The UberCloud NAFEMS Conference, Manchester, October 20-22, 2014 Courtesy Time Magazine What is Smart Manufacturing?
- 2. Summary Workstations / servers / clouds: benefits and challenges Exploring cloud challenges: CAE experiments Case studies Lessons learned CAE Cloud Marketplace Finally, the ‘secrete’ sauce: application containers 2
- 3. Wolfgang Gentzsch President,The UberCloud Experiment and Marketplace => Product innovation requires computing Want to build the best bike in the world? Build it in the computer first!
- 4. Engineers & scientists computing tools: workstations, servers, and clouds
- 5. Stay competitive with computing Desktop: 94% of engineers Server: 5% of engineers Cloud : less than 1%
- 6. However: Workstations have limited capacity Computing: too slow Memory: too small 57 % of users are dissatisfied with their desktop computing capacity* * Source: US Council of Competitiveness: http://www.compete.org/
- 7. Benefits of servers More compute power and memory Higher quality design and products Reducing product failure Shorten time to market
- 8. However: Servers: expensive and complex $70,000 server is $1 millionTCO over 3 years
- 9. Benefits of clouds More computing, on demand, pay per use Scaling resources up and down Low risk with multiple providers Result: better, faster, cheaper.
- 10. However: Clouds: still many challenges Security, licensing, control, data transfer, expertise, … … a crowded cloud market, difficult to find your ideal cloud service
- 11. UberCloud Experiments SMBs & research organizations to explore the end-to-end process of using remote computing resources, as a service, on demand, at your finger tip Since July 2012: 2000+ participants, 155 experiments Learning how to resolve the roadblocks !
- 12. Experiment teams End-User Team Expert SoftwareVendor Cloud Resource Provider Finally, writing the Case Study 42 case studies in Compendium I & II
- 13. The UberCloud HPC Experiments Example: AmazonAWS in the UberCloud: Team 2: Team 20: Team 30: Team 40: Team 65: Team 70: Team 116: Team 142: Team 147: 13 Simulation of a Multi-resonant Antenna System Turbo-machinery Application Benchmarks HeatTransfer Use Case Simulation of Spatial Hearing Weather Research with WRF Next Generation Sequencing Data Analysis Quantitative Finance Historical Data Modeling VirtualTesting of Severe Service ControlValve Compressor Map Generation Using Cloud-Based CFD
- 14. The UberCloud HPC Experiments Started July 2012, 1500 participants, 72 countries Example: Bull extreme factory in the UberCloud: Team 5: Team 8: Team 32: Team 52: Team 85: Team 89: Team 120: 14 2-phase Flow Simulation of a Separation Column Flash Dryer with Hot Gas to EvaporateWater from a Solid 2-phase flow simulation of a separation columns Simulations of Blow-off in Combustion Systems Combustion simulations of power plant equipment Simulations of Enzyme-Substrate reactions Simulation of water flow around self-propelled ship Courtesy: Marc Levrier, Bull
- 15. © 2013 ANSYS, Inc. October 23, 201415 Some Lessons Learned - UberCloud HPC Experiment Team 8: Flash Dryer Simulation (ANSYS Fluent) Simulation throughput criterion was met ‼ Remote visualization solution required ‼ Time for downloading results ‼ IP concern Team 9: Irrigation Simulation (ANSYS CFX) Timely, high fidelity results were obtained ‼ Windows above Linux preferred ‼ HPC workshop services for SMEs requested Ability to conduct parametric simulations ‼ Sufficient number of licenses needed ‼ Remote visualization solution required ‼ Disappointing hardware performance results Team 34: Wind Turbine Simulation (ANSYS Fluent) Courtesy: Wim Slagter, ANSYS
- 16. © 2013 ANSYS, Inc. October 23, 201416 Some Lessons Learned - UberCloud HPC Experiment Team 36: IC-Engine Simulation (ANSYS Fluent) Smooth setup of environment and sw ‼ Appropriate cloud licensing required ‼ Network bandwidth not good for graphics ‼ Customized sw needs to be recompiled Team 54: Pool Plant Simulation (ANSYS CFX) Ability to easily burst into the Cloud Accelerated file transfer and 3D graphics ‼ Cost of the commercial CFD licenses Ease of use Good remote visualization ‼ File uploading time ‼ Stress test with multiple users required Team 56: Axial Fan Simulation (ANSYS Fluent) Courtesy: Wim Slagter, ANSYS
- 17. TEAM 118: Coupling in-house FE code with ANSYS Fluent CFD in the Cloud End user - Marius Swoboda, Hubert Dengg, Rolls-Royce Deutschland Software Provider:Wim Slagter, René Kapa, ANSYS Cloud Provider: Matthias Reyer, CPU 24/7 Team Expert: Alexander Heine, CPU 24/7 Team Mentor: Wolfgang Gentzsch, UberCloud
- 18. Team 118: Temperature predictions for jet engine components CFD Model of a High Pressure Turbine Interstage Cavity HPT Rotor 1 HPT Rotor 2 Nozzle Guide Vane Seal Carrier Nozzle Inlet (Mass Flow Inlet) Nozzle Outlet (Pressure Outlet) Seal Outlet (Pressure Outlet) Annulus Outlet (Pressure Outlet) © Rolls-Royce The Jet Engine
- 19. Team 118: Temperature predictions for jet engine components Jet engine high pressure turbine assembly Transient aero-thermal analysis FEA/CFD coupling achieved through iterative loop with exchange of information between the FEA and CFD at each time step, Ensuring consistency of temperature & heat flux on the coupled interfaces between metal and fluid domains Temperature contours for a Jet Engine Component
- 20. Team 118: The aim of this experiment To couple ANSYS Fluent with in-house FE code. Done by extracting heat flux profiles from Fluent model and applying FE model. FE model provides metal temperatures in the solid domain. Conjugate heat transfer needs a lot of computing, especially when 3D-CFD-models with more than 10 mio cells are required. Using cloud resources is beneficial regarding computing time. Contours of heat flux
- 21. Team 118: Benefits of CAE in the Cloud Keep on using your workstation for daily design while using Cloud resources for bigger jobs An HPC system at your finger tip, on demand Pay per use (cost savings by reducing CAPEX) Scaling resources up and down (business flexibility) Low risk by working with multiple providers. Maintaining control: Cloud provider was around the corner
- 22. The ProblemToday: Crowded and ineffective cloud ‘market’ Supply Cloud providers ISVs Consultants Demand Engineers Scientists Data analysts . . . . . Complexity Data Transfer SecurityLicensing Uncertain Cost Roadblocks
- 23. Solution: The UberCloud Marketplace Supply Cloud providers ISVs Consultants … Demand Engineers Scientists Data analysts UberCloud Marketplace
- 25. Builder Launcher Controller ISV DataTools Stackable units with tools (ex: encryption), ISV application codes (ex: OpenFOAM). Just add your own codes and data. Run anywhere with UberCloud Run Time. Scale up or down the compute power as needed. Collect granular usage data, logs. Monitor, alert, report. Any Workstation Any Cluster Any Cloud Run Time Run Time Run Time Containers: Build once, run anywhere
- 26. Builder Launcher Controller ISV DataTools Stackable units with tools (ex: encryption), ISV application codes (ex: OpenFOAM). Just add your own codes and data. Run anywhere with UberCloud Run Time. Scale up or down the compute power as needed. Collect granular usage data, logs. Monitor, alert, report. Any Workstation Any Cluster Any Cloud Run Time Run Time Run Time Containers: Build once, run anywhere
- 27. Builder Launcher Controller ISV DataTools Stackable units with tools (ex: encryption), ISV application codes (ex: OpenFOAM). Just add your own codes and data. Run anywhere with UberCloud Run Time. Scale up or down the compute power as needed. Collect granular usage data, logs. Monitor, alert, report. Any Workstation Any Cluster Any Cloud Run Time Run Time Run Time Containers: Build once, run anywhere
- 28. Builder Launcher Controller ISV DataTools Stackable units with tools (ex: encryption), ISV application codes (ex: OpenFOAM). Just add your own codes and data. Run anywhere with UberCloud Run Time. Scale up or down the compute power as needed. Collect granular usage data, logs. Monitor, alert, report. Any Workstation Any Cluster Any Cloud Run Time Run Time Run Time Containers: Build once, run anywhere
- 29. Containers: Reducing / Removing Cloud Challenges CAE Cloud Challenges UberCloud *) Security Portability Compliance DataTransfer Standardization Software Licenses Resource Availability Transparency of Market Cost & ROITransparency No Cloud Expertise Needed *)When UberCloud is fully developed one year from now
- 30. It’s your turn now Download 2013 Compendium of case studies Download 2014 Compendium of case studies Register at TheUberCloud.com Register for The UberCloudVoice newsletter Check The UberCloud Marketplace
- 31. www.nafems.org Thank You ! Please register at TT http://www.TheUberCloud.com