Section Extraction from Numerical Simulations of Proving Ground DataFRANÇOIS QUIN – RENAULT GROUP

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Agenda

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1. Introduction

2. "OK-Measure" development with nCode

3. Numerical simulation quality control

4. Conclusion

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Intro to François QUIN, ProjectLeader in numerical simulationfor reliability & durability atRenault GroupGraduated both in Mathematics and

Physics, I have been asked to acceleratenumerical transformation of tests for vehiclereliability and durability for Renault Group.

• RENAULT GROUP provide 6 brands worldwidefor 3,8 million vehicles in 2019.

• Vehicle Test Department is responsible forglobal vehicle validation except Power Unitinternal management.

• Reliability & Durability Service provides on-bench endurance result and proving groundrecords.

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25 years ago, Renault shifted from full on-ground driving durability validation to on-benchsimulation with on-ground measurement. From months of driving on proving grounds we’vekept only few hours to acquire signals to repeat on benches.

Quality for Numerical Simulation Project

Now Renault progressively frees itselffrom ground to go to the cloud, replacingmeasurement by numerical simulation ofdriving.

Once OCM (OK-Measure) had ensuredphysical measurement quality, we’vebeen asked to extend the use ofautomated quality check to numericalsimulation.

Summary

It gave to measurement team a good reputation in several services which align themselves (some of them havedone, some are still ongoing) to our methods and standard to profit from OK-M quality tool.For example, numerical simulation team were requested to adopt our signal naming and unit standards to beenabled in bench feeding.

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We‘ve firstly done a fast development with ready-to-useglyphs from nCode.

Once functional design has been fully validated bysome months of use, we processed to change internalmethods to a more easy-to-maintain solution withexplicit and commented python code combined withnCode glyphs.

Users were both asking for fast deployment, easyevolution and strong maintainability.

A good example is given by new needs brought bynumerical simulation that come along with severalcomputing-related troubles we hadn’t heard before inProving Ground Measurement teams.

Challenge Solution

Benefit

Using nCode GlyphWorks for OK-M

We’ve made the OK-M system very easyto adapt by using human-readable excelfiles and maintainable due to pythonscripting.

It was a first step of development we’vegone through and today we’re able to usemore integrated scripting using CSV files.

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Looping feature

From simple xls to data-based xls on csv

At first, we’ve set an Excel-Input glyph andtwo standards lines of code to get a tablewith all data about sections.

It’s fast and understandable for few inputs

We went to two files : one formatted xlsreferring to one python-readable csv.

Formated xls calling csv database

And 5 times more code, fitted to data

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0 excel-input whateverwould be the numer of

excel files to load

From simple xls to data-based xls on csv

Benefits of progressive development

• First implementation very easy to adaptduring program development with glyph-based-language.

• Progressive transferring to python codewith simultaneous support of thesefeatures by nCode and its ability to linkthem.

Benefits of python scripting solution

• Simpler process view gathering all functionsin code with 0 “excel-input” glyph whateverwould be the number of excel files to load.

• Possible to isolate the load function as asubfunction called wherever you want withsingle line.

• Between 2 and 5 seconds for each avoidedExcel-Input.

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From direct execution to looping

For treatment development purpose, glyphs are very easy-to-use and it gives you a clearview of each steps.

It’s emphasized by the step execution feature which stop execution after each glyph.

The lone feature I found not very easy is the looping feature. It needs 2 levels of embeddingwith production of a table with the looping values beforehand…

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From direct execution to looping

for i in range(len(fichiers))

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Presentation of riding numerical simulation for durability tests

Section extraction for numerical simulation

Control of computing crash

Control of computing divergence

Numerical simulation quality control

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Presentation of riding numerical simulation for durability tests

Digitalized road with coordinatesEach section of proving ground is given withits specific riding parameters (trajectory,speed). The coordinates are given withlongitudinal and orthogonal values.

Gravity settlingThe car is dropped on the road as 4 wheelsand the modelling of each component isworking to settle the car on right heightaccording to their properties. It gives somedecreasing sinusoidal movement of the body.

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Presentation of riding numerical simulation for durability tests

Incoming additionTo ensure gravity settling is done before toreach the section to record, an incomingaddition of right road is added to digitalizedsection.

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Outgoing additionTo allow simulation to continue up to the rearwheels got off the section, a similar right roadaddition is made after the section.

Section extraction for numerical simulation

One section in each fileWhile in large record on proving ground one needs to found several sections in one record. Innumerical simulation, the cutting has been made beforehand. This allow less memoryconsumption during simulation.

Furnished coordinatesStart and end points of section are obviously well known in coordinates and numerical simulationcan record them. Thus the section extraction is possible at any precision requested.

Method for section extractionAs I remind you, these simulation feed durability benches. Bench mechanic asks for small delayfrom excitation start to full power so beginning of each section is defined with a coordinate pre-trigger related to speed. A similar function with length of the car is used for ending of sections.

The section extraction is no longer a big challenge but is still needed.P U B L I C14

Control of computing crash

Presentation of the problemDriving numerical simulation is a complex computing work with some crash hazards. With ourcurrent simulation solution we found that some crashes brought the simulation files to be closed“as it” at the time of crash. It gives us a shorter file.

Method fittingThe section extraction was fed with excel files in which we found the standard length of eachsection. We just had to use this information in a different way. We develop a test function onnCode metadata to check that each file has requested length.

Control of computing crash is very easy and reliable.

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Control of computing divergence

Presentation of the problemFor same causes of complexity as for computer crash, computing divergence appends and givesabsurd values such as 200kN on vertical force on wheels. Being determinist, same drivingnumerical simulation always diverges in the same way.

Solution seekingAs for differential equation, initial parameters define a lot on the whole computing. So we’veproposed to numerical simulation team to do two simulations on each section with differentincoming addition length.

Final control methodThese different driving allow us to check constituency of computing after section extraction to fitstart time.

With a bit of mathematic mindset, computing divergence is well-checkedP U B L I C16

Conclusion

nCode solution choice was made upon its allowance to mix python and glyph processes. It allows to check eachfunction implementation with full process during the translation from glyph to python.

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The numerical simulation have different features tocheck from physical records. But the previouslydeveloped tool brought a good structure to add specificnumerical checking functions.

These additions were also implemented by samedevelopment progressive way.

Glyph developing was first used to enable fastimplementation of straight processes with easydebugging.

To fasten the process execution and to enable easyloops, the code was afterwards progressivelytranslated to python code.

Development Numerical Simulation

Focus on nCode choice

Thank You

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