Study of the dynamic behavior of a pump with Code_ASTER on Simulagora

Collaboration:Aster Pronet – March 27, 2015

Study of the dynamic behavior of a pump with Code_ASTER on Simulagora

Collaboration:

Florent CayréKarina MacoccoFelipe Aguirre

Aster PronetMarch 27, 2015


… solutions for robust engineering

SICODYN BenchmarkSurrogate modeling


SICODYN BenchmarkStudy of the pump casing – variables & definition domain

Uncertain parameters are the radius of the connecting screws of the frame with the other elements.

Uniform distributions

Radius type 1 [m] : [0,0088 ; 0,0132]

Radius type 2 [m] : [0,011088 ; 0,016632]

Radius type 3 [m] : [0,011088 ; 0,016632]

● 400 training points and 100 testing points

● 1h30 minutes per simulation with high RAM consumption (10 GB).

● Total simulation time :

– 25 days for the training points (Distributed with Simulagora)

– 6,25 for the testing points (Distributed at Phimeca over 3 cores)

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Input of the design of experiment

Sobol Sequence

• 400 points for the training dataset.

LHS• 100 points for the testing

dataset. Highlighted in the graph.

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Output of the design of experimentFrequencies

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MAC matrix of the VGP©

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Adjustment graph of the VGP©

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Validation of the VGP©

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Validation of the VGP©

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Simulagora in few words...

Public cloud based

– Computation and storage resources

– Enormous power

– Super-fast computation resources ramp-up

Server

Cloud

Simulagora

Cloud

Simulagora

Browser

WWW

A platform using Web technology to ease grid computation by leveraging cloud resources

HPC in the cloud


Implementation on Simulagora

Modeling Results analysis

● Modeling and results analysis performed on Phimeca infrastructure● Could have been done on Simulagora too

Single computation setup

Complete study setup and monitoring

Simulagora


Simulagora - single computation setup

● Transfer input data : using the Web interface

Technicalities

● Data integrity check

● Resume on failure



● Transfer input data : using the Web interface

Technicalities

● Data integrity check

● Resume on failure

No install

No setup


● Transfer input data : using the Python API

– Simple config file

– Simple Python script

Technicalities

● Open Source

● Easy install, easy setup

● Web services (no firewall pb)



● Transfer input data : using the Python API

– Simple config file

– Simple Python script

Technicalities

● Open Source

● Easy install, easy setup

● Web services (no firewall pb)


Fast install

Easy setup


● Launch an interactive session


Technicalities

● Full root access

● Max session time

● File modifications saved



Technicalities

● Requires a high-bandwidthconnection

● Full-featured lightweightwindow manager

● Connect to your machine in your browser



No install

No setup

Technicalities

● Requires a high-bandwidthconnection

● Full-featured lightweightwindow manager

● Connect to your machine in your browser



● Connect to your machine in text-mode

Technicalities

● Uses the SSH protocol(rarely blocked by firewalls)

● Fast even with low-bandwidth connections

● Requires a simple setup(a bit more on Windows)



● Connect to your machine in text-mode

Technicalities

● Uses the SSH protocol(rarely blocked by firewalls)

● Fast even with low-bandwidth connections

● Requires a simple setup(a bit more on Windows)

Fast install

Easy setup


Simulagora – prepare to take-off...

● Write the parametrized launch script and check that :

– it runs smoothly to the end– it produces the expected results

● Prepare the 400 runs

Technicalities

● Also possible to store run parameters into a file per run

● Runs are recorded but notstarted until explicitlyrequested

● Main difficulty is to generatethe parametrized command


Simulagora – launch the complete study

● In this 1st medium-scale experiment, we had to use a conservative computation power ramp-up (more on this later)

● Real elapsed-time for the 400 computations was 2,5 days

(total of 33 CPU days on the chosen servers - 8Go RAM, 4 CPU)

● We can now achieve the same within ≃ 5 hours (and soon a bit less)

Number of running computations over time


Phimeca's feedback (1/2)Code Aster:

● 1h30 per simulation● RAM 10 GB consumption● 400 simulations to launch

Simulagora vs. clusters:

● Phimeca's internal resources may be temporarily exhausted like during the Sicodyn project

● Simulagora's pros vs. usual external clusters approach = simplicity

– No configuration or install

– No job manager

– Powerful Python API

For this kind of projects, we usually distribute our calculations on external clusters.


Phimeca's feedback (2/2)Pros:

● Highly and skilled technical support● Machine image customization to come● Python API to distribute the computations and retrieve results (still in

development but very promising)● Web interface to manage the studies

To improve:● Need an even simpler Python API:

– 'Simulagora run script.py' to create and launch a study– 'Simulagora download' to retrieve results

● No commercial offer yet


Lessons learned by Logilab

● Logilab faced and solved or worked-around real-world problems

– A bug in disk resource freeing● Warned by Amazon because of a 20To limit reach● Resources first freed manually● Quick code fix

– Some computations crashed due to a high load on the web server● Worked-around by staggering the launches● Crashed runs re-init not easy, but a quick work-around

→ clean fix and API improve to come


Near future of Simulagora

● The Python client will shortly provide higher level operations(suggestion from Phimeca)

● Logilab will now work on a commercial offerWe now think the service has a commercial value

● Beta-testers are still welcome !


Thanks for your attention!

Florent Cayré[email protected]

+33 1 45 32 03 12www.logilab.fr

Karina [email protected]

Felipe Aguirre [email protected]

+33 1 58 51 18 11www.phimeca.com

mailto:[email protected]

http://www.logilab.fr/



http://www.phimeca.com/

Date post:	15-Jul-2015
Category:	Data & Analytics
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Study of the dynamic behavior of a pump with Code_ASTER on Simulagora

Data & Analytics