Airbus Group Innovation - CSDM 2015 · Airbus Group Innovations CSDM 2015 Workshop HIRF coupling on Helicopter cables TECHNICAL OBJECTIVES Computation of constraints on a cable located

Airbus Group InnovationPrepared by Gilles Peres E. Duceau & F. Mangeant

CSDM-IEE 2015 Workshop

From Worst Case Analysis to Margin Management

Outline

- General context = From simulation to understand to simulation to decide = the real value of simulation

- Two test cases in detail = • “robust” HIRF coupling on Helicopter• “risk “analysis linked to fuel

- Lessons learnt and way forward…

© Airbus Group. All rights reserved. Confidential & proprietary document26.11.2015 3

General context

We are in engineering not in Systems of System areaProduct are optimized each modification create potential (strong) “couplings”Value is in innovation innovation is at “systems” levelReal world as a constrain Physics always in the loop

Thanks to our computational capabilities, can we provide new value? by revisiting some existing solutions? by de-risking early stage decisions? by proposing alternative architectures? by enabling more collaborative decision making?

Lots of computations/simulation today provide knowledge/understanding Do we really perform the right ones wrt value?

Airbus Group Innovations CSDM 2015 Workshop

© Airbus Group. All rights reserved. Confidential & proprietary document

Integration &assembly

Assembly &Systems

Validation & Verification

Verification &Certification

@ aircraft level

Componentsdevelopment

Detaileddefinition

Preliminarydesign

Conceptsdefinition

SCADE

MATLAB/Simulink

Expectations and Value

Models

Operational support+ services

Difficulties

+

Airbus Group Innovations – MATHIAS 2015 WorkshopLots of computations/simulation today in each domain! Value is when full collaborative scenarios can be simulated


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General context for “ robust ” design

Robust design target a solution is already identified margins are taken to cover all “unknowns” computations are conducted to help margins assessments Know-how reinforce experts in the domain

Thanks to our computational capabilities, can we provide better results ? “unknowns” to be qualified with their dependencies clarified models of systems, of physics AND of data have to be consistent uncertainty propagation through cascade of models quantification vs accurate computation !


HIRF coupling on Helicopter cables

TECHNICAL OBJECTIVES

Computation of constraints on a cable located in helicopter :- influence of the metalisation of the fairing with

rivets placed every 25 to 50 cm)- Influence of the orientation the impinging field

R&T PROJECT : UMEPS : Uncertainty Management of Electromagnetic Protection on Systems

Quantify and analyse sources of uncertainty influencing the design of Electromagnetic constraints and aeronautic and space domain.

Develop reduction model methods in order to statistically address uncertainties linked to geometry and materials.

Demonstrate the operating capability of the probabilistic approach in the field of lightning indirect effects, High Intensity Radiated Fields (HIRF) and Electromagnetic Compatibility (EMC) on a representative sub system.

Establish and promote a probabilistic analysis approach of electromagnetic protection of systems

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Hazards in margin management at system levelPhenomenology and system hazards

Phenomenology of Electromagnetic environment with respect to system is often of random nature : difficulty to control system exposure with respect to high intensity fied source (orientations, relative speed, duration, ...) ; lightning : occurrence and caracteristics clearly of random nature or probabilistic.

The system under study in term of complexity, materials , shapes and components, associated with a multicale physics (from structure to electronic component) and which can adopt a large number of physicalstates (the on board software for example) makes this problem of constraints evaluation by test/computation probabilistic . For the test, the hazard comes from the impossibility to adress all the states of the system and all the relative configuration system/threat. For the analysis/simulation this hazard is due to the simplification process (choice due to complexity) and the choice of relative configuration system/threat to study.

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Hazard on sources

Hazard withrespect to complexity


Identification of uncertain parameters

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EC175ATV


Propagation des incertitudesOpenturns Platform, http://trac.openturns.org

OPEN source Treatment of Uncertainty, Risks aNd Statis tics- C++ library to directly integrate computational chains : - Python module for a scripting usage- Available under an Open Source LGPL

Openturns documentation :InstallationArchitecture, interfacingUser manual GuideUse Case GuideTheoritical Guide

Research Community and Industrial GroupsIMDR : Institut de Maitrise des Risques , ww.imdr.frGDR Mascot NumEsREDA

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High Intensity Radiated Field (HIRF) studyObjectives

Compuation of constraints on cables located in fairing z one :- influence of the metalisation of the fairing via fasteners (spaced every 25 to

50cm)- Influence of the orientation of the field

Numerical model (Aseris BE) - The slot is modelled by refining the mesh size (to about 1mm )- 140 000 elements, 120 processors and 137 frequencies CPU time : 14 jours

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Maillage 1cm autour de la fente

Numerical Modèle (Aseris BE) of EC175 (10kHz – 300MHz)

Câble sous capotCapot fermé


High Intensity Radiated Field (HIRF) studyinfluence of the metalisation of a fairing with fasteners

Fairing : floating (R_vis = 1 MΩ) Fairing : bonded with ideal connexions (R_vis = 1 µΩ)Side illumination

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100MHz

100MHz

Icc câble- Rj fixe - Rj = Gauss[10m ΩΩΩΩ ; 1ΩΩΩΩ]

Zoom en BF


High Intensity Radiated Field (HIRF) studyinfluence the incidence and polarisation of the threat

The initial computation is realised for 19 incident plane waves (x2 polarisations). It is then easy to know the impact on internal fields and/or current/voltge on internal cables of any linearcombination of those fields

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19 ONDES PLANES (POLAR H et V)

Icc

SE (dB)


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General context for “ risk ” analysis

Worst case analysis the feared event shall not happen margins are taken to cover all “unknowns” computations are conducted to help margins decisions Know-how spread over experts in the company

Thanks to our computational capabilities, can we provide better results ? the feared event: what’s the scenario behind? “unknowns” to be qualified uncertainty quantification right decision vs accurate computation !

Airbus Group Innovations – MATHIAS 2015 Workshop


The fuel tank analysis: risk assessment

TECHNICAL OBJECTIVES

Better understanding of physical phenomena

Development of virtual predictive capabilities

Analysis of driving factors on the performances

Consolidation of a risk methodology taking intoaccount flammability & ESD

R&T PROJECT

Impact of composite materials on Electro StaticDischarge (ESD) phenomena and mitigation of associated risks

Better management of operationalperformances (effect of additives in fuel, management of fuel filling rates)

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Risk analysis: the fire triangle

The is a risk when all the components are simultaneously in a risky state !

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Different views

RISK VIEWWhat are the most critical scenarii ?What is the top-level feared event ?

PHYSICAL VIEWWhich assumptions to

make in the electrostatic models ?How to take into account the CFD ?

How to link the differentphysical phenomena ?

MODELLING VIEWWhich numerical approximation

to compute the physical models ?How to quantify uncertainties ?

How to quantify the global risk approach ?

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Presentation of the risk methodology

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As-is situation: a two steps approach conservative by n ecessity !

STEP 2: ESD ANALYSIS

Goal : Inexistence of ESD ignition source demonstrated by WORST-CASE approach

DEMONSTRATE MAX Physical Indicators < Threshold

Physical indicator: maximum electric field, maximum voltage at the surface of the fuel.

STEP 1: FLAMMABILITY ASSESSMENT

Goal : Flammability assessed by global PROBABILISTIC approach

COMPUTE Risk_Flammability = Probability(« Tank is Flammable »)

Physical indicator: Ullage temperature between Lower and Upper Flammability Limit, Minimum Ignition Energy

If Risk_Flammability > Threshold , SPECIFY NO ignition source allowed

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R&T study: a combined approach to assess the degree of conservatism

•The objective is to develop a combined approach between flammability analysis and ES D analysis to assess the statistical coverage that could result from it.

• The proposed risk index is defined by:

Risk = Probability(« Tank is Flammable » AND « ESD ignition source exists »)

This requires the identification of the common variables between the two disciplines and the consistent analysis in each discipline compared to the other one.

• TODAY, the computation is done as follows due to the organisat ional constraints:

Bayes formula: Risk = Probability(Event_ESD) x Probability(« Tank is Flammable »)

With the definition of the eventEvent_ESD = « ESD ignition source exists » knowing th at « Tank is Flammable » during refueling phase

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Link with Probabilistic Risk Assessment (PRA standa rds)

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Simulation process and proof of concept

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Description of the scenario

•Aircraft type: A350 use-case

•Type of fuel: Jet A1

•Central Wing Tank : CTL, CTC, CTR

•Phase: refuelling phase

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The following results are wrong as the numericalmodels did not integrate the representativephenomenology at the time of the study.


Data analysis of flammability results

• We studied where the flammability risk areas were in termsof Flight phases, Fuel quantity, range and external conditions.• We were able to quantify a probability of being flammable• There is a shared variable between Flammability and ESD:the level of fuel.The sampling has to take that into accountthis aspect.

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Toy result !


Computation of ESD elementary probabilities with Op en TURNS

The computation of the leaf probabilities requires the use of Open TURNS and ASERIS-Tank solver for prototyping.

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ESD numerical results: sensitivity analysis

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Scatter plot

Sensitivity Analysison the probability


Conclusion

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Conclusion and lessons learnt

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• No panic it’s a R&T project aiming at highlighting what can be done!

• Scenario definition: The definition of the scenario is key and requires a priori expertise revisiting processes through organization creates knowledge !

• Risk index : The formalization of the combined approach requires skills in risk, probability and statistics.

tools are availablebut training “deterministic centric” people remains a challenge

• Simulation : The computational burden is overcome by adequate surrogate modelling and advanced Monte Carlo techniques.

warning: coherency within this methodology is key

• Next step : prove that the approach valuable (internally), then present it to the authorities internal processes are being updated to support this evolution

Documents

Airbus Group Innovation - CSDM 2015 · Airbus Group Innovations CSDM 2015 Workshop HIRF coupling on Helicopter cables TECHNICAL OBJECTIVES Computation of constraints on a cable located