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?

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

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!

Engineers & scientists computing tools: workstations

, servers, and clouds

Stay competitive with computing

Desktop: 94% of engineers

Server: 5% of engineers

Cloud : less than 1%

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/

Benefits of servers

More compute power and memory

Higher quality design and products

Reducing product failure

Shorten time to market

However: Servers: expensive and complex

$70,000 server is $1 million TCO over 3 years

Benefits of clouds

More computing, on demand, pay per use

Scaling resources up and down

Low risk with multiple providers

Result: better, faster, cheaper.

However: Clouds: still many challenges

Security, licensing, control, data transfer, expertise, …

… a crowded cloud market, difficult to find your ideal cloud service

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 !

Experiment teams

End-User

Team Expert

Software Vendor

Cloud Resource Provider

Finally, writing the Case Study

42 case studies in Compendium I & II

The UberCloud HPC Experiments

Example: Amazon AWS 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

Heat Transfer Use Case

Simulation of Spatial Hearing

Weather Research with WRF

Next Generation Sequencing Data Analysis

Quantitative Finance Historical Data Modeling

Virtual Testing of Severe Service Control Valve

Compressor Map Generation Using Cloud-Based CFD

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 Evaporate Water 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

© 2013 ANSYS, Inc. October 23, 2014 15

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

© 2013 ANSYS, Inc. October 23, 2014 16

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

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

Team 118: Temperature predictions for jet engine components

CFD Model of a High Pressure Turbine Interstage Cavity

18

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

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

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

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

The Problem Today: Crowded and ineffective cloud ‘market’

Supply

Cloud providers ISVs Consultants

Demand

Engineers Scientists Data analysts

.

.

.

.

.

Complexity

Data Transfer

Security Licensing

Uncertain Cost

Roadblocks

Solution: The UberCloud Marketplace

Supply

Cloud providers ISVs Consultants …

Demand

Engineers Scientists Data analysts

UberCloud Marketplace

UberCloud marketplace, sample

Builder

Launcher

Controller ISV Data Tools

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

Builder

Launcher

Controller ISV Data Tools

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

Builder

Launcher

Controller ISV Data Tools

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

Builder

Launcher

Controller ISV Data Tools

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

Containers: Reducing / Removing Cloud Challenges

CAE Cloud Challenges UberCloud *)

Security

Portability

Compliance

Data Transfer

Standardization

Software Licenses

Resource Availability

Transparency of Market

Cost & ROI Transparency

No Cloud Expertise Needed

*) When UberCloud is fully developed one year from now

It’s your turn now

Download 2013 Compendium of case studies Download 2014 Compendium of case studies

Register at TheUberCloud.com

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Check The UberCloud Marketplace

www.nafems.org

Thank You !

Please register at

TT http://www.TheUberCloud.com