STAR CCM GLOBAL CONFERENCE UBERCLOUD
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Next-Generation Cloud Platform with optimization of rider & bicycle drafting formation analysis as a case study. UberCloud + Microsoft Azure + Trek Analysis https://www.theubercloud.com/star-ccm-cloud
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STAR CCM GLOBAL CONFERENCE UBERCLOUD
- 1. Next-Generation Cloud Platform with optimization of rider & bicycle drafting formation analysis as a case study UberCloud + Microsoft Azure + Trek Analysis
- 2. Nick Greising Cloud Channel Development Manager (HPC), Microsoft Corporation Burak Yenier Co-Founder & CEO, The UberCloud, Inc Mio Suzuki Analysis Engineer, Trek Bicycle Corporation
- 3. - One Click Deployment of a STAR-CCM+® Cluster on the Azure Marketplace - A8/A9VMs with QDR Infiniband - N-Series GPUVMsAnnounced - Latest Intel Processors - Power on Demand (PoD) Licensing "Our mission is to empower every person and every organization on the planet to achieve more.”
- 4. Passion. Power. Performance. Manufactured in Waterloo, WI USA.
- 6. Cloud Computing Scale up to win. More physics in less time. The new norm.
- 7. Current Cloud Platform Submit jobs. “Set it and forget it.” Done. ResultSubmit Jobs 0 1 2 3 4 5 6 7 Cloud: Rescale 128 cores, Intel Xeon Ivy Bridge Cloud: Rescale 96 cores, Intel Xeon Ivy Bridge Cloud: Rescale 64 cores, Intel Xeon Ivy Bridge Local HPC (old) 12 core, Intel Xeon Westmere Solver Time (hrs) Access via Terminal or web browser.
- 8. Multimodal Optimization Simultaneous analysis of competing objectives with HEEDS.
- 9. Optimization Workflow: Problem New workflow demands new type of hardware/software configuration. ResultSubmit Jobs Check The new process demands increased interactivity • Longer computation period • Interactively influence the optimization results • Data process while the runs are still executed
- 10. Optimization Workflow: Solution UberCloud Container. Remote Desktop on demand.
- 12. Log In Click.
- 13. Log In The remote environment looks just like your local environment.
- 16. • STAR-CCM+ v10.04 • RANS k-ω SST steady simulation • Riders/Mannequin body only (2M cells) • Rider + bike simulation is in progress (12M) • Each rider has a x & y coordinates which are exposed as design parameters • Initial 2-1-1 position • Allowed to form echelon shape • Curved boundary domain to which wind at certain yaw angle is subscribed (parameter in HEEDS) • Yaw = 0, 12.5, and 20 degrees Drafting Simulation STAR-CCM+ setup.
- 17. HEEDS Setup GUI driven, intuitive setup. • Solving nodes are pre-configured • Execution command with options are pre-loaded, but is customizable (#core, nodes) • In this case, using 8 cores x 8 nodes/core = 64 cores This is exactly what I would see on my own desktop
- 18. Drafting Simulation Looking for formation patterns that minimizes overall drag of four riders and minimizes drag of the middle rider.
- 19. Results – Scaled up operation Rider only simulation (per simulation): • 10 cores: 20 min • 64 cores: 6 minutes - x3.3 speed increase - because of the way simulation is set up, meshing consumes time - For 100+ iteration, ~ 12 hours of total run time (i.e. overnight) Rider + Bike simulation (per simulation, in progress): • 10 cores: 1 hr 30 min • 64 cores: 24 minutes - x 3.75 speed increase
- 20. Advantage of Container Correcting Error. Using HEEDS interactive feature. I noticed an error in my set up.
- 21. Created new study, and used the “good results” from the previous study. Advantage of Container Correcting Error. Using HEEDS interactive feature.
- 22. Quick Post Processing As if I’m using my own local desktop.
- 23. Results – Optimization Progression toward the “correct” echelon forms. Yaw = 0 deg (headwind) Yaw = 12.5 deg Yaw = 20 deg
- 24. Results – Optimization 0.6 0.7 0.8 0.9 1.0 1.1 1.2 1.3 1.4 1.5 0 20 40 60 80 100 Ratioofleading/draftingdrag Performance rank, according drag drag lead/draft ratio_0 drag lead/draft ratio_12.5 drag lead/draft ratio_20 Linear (drag lead/draft ratio_0) Linear (drag lead/draft ratio_12.5) Linear (drag lead/draft ratio_20) • The amount of drag reduction and its variation with yaw angle seem to coincide with previously collected field data (yaw 0-20 deg). • Optimized formations from extended study can offer a glimpse into configurations beyond the typical echelon forms. Initial 2-1-1 configuration
- 25. Conclusion: UberCloud Container. Cloud access simplified.
- 26. Thank You.