validation gaining confidence in simulation darre odeleye ceng mimeche

Simulation and Modelling 2016 Conference Tuesday 13 and Wednesday 14 September 2016, Birmingham

Thinktank, Birmingham Science Museum Millennium Point Birmingham West Midlands B4 7XG

VALIDATION: GAINING CONFIDENCE

IN SIMULATION A Program Office perspective

Distinction must be made between

the verification and validation of the

key components that constitute a

simulation

• Verification - Does the code implement the physics of the

problem correctly and does the solution generated

compared favourably to exact analytical results ?

• Validation – Does the actual simulation agree with physical

reality? Is the level of uncertainty and error acceptable ?

From a Program Office perspective Validation

assessment generates credibility for decision

makers

Simulation verification may include analysis of:

• Discretization strategy,

• Application of boundary conditions,

• Grid convergence criteria,

• Handling non-linearity,

• Iterative convergence,

• Numerical stability ( relaxation factors etc.)

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–Validation is the process of determining the degree to which a model is an accurate representation of the real world from the perspective of the intended use of the model.

–High confidence simulations offers the promise of developing higher quality products with fewer resources in less time


• Generating the right data, not a lot of

data

• Understanding what is appropriate and

required for validating models

• Assessing the influencing factors that

need to be considered and tested

teg


• What does the Program need to

know and when? – This defines the problem and what CAE methods that can be

used

• Lean product creation concepts

require – Compatibility with Quality, Cost& Delivery before design maturity

– Front loading using all design options before commitment

– No late changes



data





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Correlate and compare the

components of simulation in

order to validate models i.e.

– Invest upfront resources to generate the most accurate inputs

for Simulation

– Compare results from different Simulation tools,

– Compare real world measurements to simulated results,

– Compare “controlled” test environment ( i.e. wind tunnel)

results to simulation results.


Extend legacy/baseline physical

tests to generate comprehensive

model input parameters

– Typical durability testing focuses on “ has it broken yet” .

providing little information on optimisation and distance from

failure modes

– Testing to failure provides quantitative data i.e. time to failure,

cycles to failure etc. and damage mode provides insight into

distance from failure mode and performance degradation

supports better predictive simulations.


Bogey Testing

Testing to Failure

Degradation testing

Trade off between different test schemes and implications

Delivery Cost Quality

Long test duration impact on engineering sign off timing

High Cost

Comprehensive data

Shortest test duration Most likely to meet program timing

Lowest relative Cost. Results of test cannot be used To predict performance with sufficient confidence


Bogey test

Test to failure


Predicted life Crankshaft #1

Predicted life for Crankshaft #3 ( revised #2 @125PS)

Predicted life for Crankshaft #3 ( revised #2 @150PS)

Crankshaft #1 Crankshaft #2 Crankshaft #3 ( revised #2) Crankshaft #3 @ 150PS

D.Odeleye CEng MIMechE

Original design is the purple part and the

green part is the revised part with the

width of web 6 increased

17

•Based on sampling at the extremes of either product strength, or level of

induced key stresses.

i.e. Selection of weakest parts, worst clearances, etc.

•If Input data is based on the weakest part test data, all stronger parts would

also pass the test ( for a given test cycle).



data





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Conduct studies to identify

sources of

– Variation in required simulation output, what input parameters

have what effect on the required results ?

– Conduct sensitivity analysis of input parameters ,

– Classify influencing factors and identify main effects

• Validate input data from experimental tests, does it

make engineering sense


Inputs CAD, Mesh

generation, space ( F.E, Volume,

elements) and time ( stability constraints)

discretization

Parameters, Boundary

Conditions.

Solver

Settings i.e. mesh refinement vs

solution convergence time

Different Simulation tools, iterative solver

Output

Simulation Solutions

Experimental data ( controlled noise

environment), real world data

Compare components , conduct sensitivity analysis

and Parametric studies to validate simulations

Simulation components

Confidence in Simulation is gained

by demonstrating value added to

– Quality – credible data necessary to make decisions at each stage of the

product development , fidelity of predictions over time.

– Cost – return on investment versus e.g. cost of testing prototypes

– Delivery – are simulations conducted and results available exactly when

needed ?


Further considerations and

opportunities for increasing

confidence • Faster post processing

• Use of virtual reality & augmented reality,

• Real time simulation ( running simulations on the ‘fly’ ),

• Cloud computing

• Artificial intelligence ( using techniques such as evolutionary

computations, artificial neural networks, fuzzy systems , general

machine learning and data mining methods

• Simulating Electric vehicles and EV powertrains

• Simulating Autonomous vehicle performance.


• Electric/hybrid vehicles present the next opportunities for the use

of simulation to further compress product development

timescales.


DISCUSS

The better the Validation the better

the prediction consequently the

more confidence customers have

in ongoing and future Simulations


validation gaining confidence in simulation darre odeleye ceng mimeche

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