Integrated Modelling Technology workshop June 8-10, 2011 Cadarache, France

1. Integrated Modelling Technology workshopJune 8-10, 2011Cadarache, France Strategies for collaborative Design and Validation S. Matteo:sophie.matteo@c-s.fr J Courquet: joel.courquet@c-s.fr CS Communication & Systèmes, Cite de la Grande Bastide Bat 914 13115 St Paul lez Durance Cedex France

2. Strategies for collaborative Design and Validation As Is Design context http://www.mscsoftware.com/aero/pdf/RomeInnovationA&D.pdf

3. Management Program Authorities Strategies for collaborative Design and Validation As Is: a silo oriented architecture Design/Engineering End User MFG Disciplines ILS Physics Internal DMU Electrical PLM MRO Extended Enterprise System architecture …

4. Strategies for collaborative Design and Validation Stakes, objectives & expectations Management Design/Engineering MFG End User DMU • Key stakes and objectives for an efficient collaborative Policy • Meet the needs across disciplines and activities • Reconcile Multiple Views and translation across areas and actors • Address all products from requirements to end of life • Manage multiple product configurations/adapted fidelity & representation • Reconcile vocabularies • Support processes execution (validation, deployment, change management) PLM

5. Strategies for collaborative Design and Validation First pillar: IS Alignment Management M&T Process Program MFG Simulation Electronics Design CAD DMU Software Electrical Partnership V&V Mechanical Manufacturing Physical Extendedenterprise Tests Sys Arch Business rq MRO Customer ILS

6. Strategies for collaborative Design and Validation Stakes, objectives & expectations • To support simulation-based design, the virtualization aims to develop a simulation and design environment in which design and modelling are tightly integrated. Multiple users can interact with the same design simultaneously, and design modifications introduced by one user can be propagated immediately to all other users. Two approaches can be applied to this formalization and development of the virtualization capabilities: • A top-down approach: • Based on a priori knowledge of the businesses; • Allowing the reduction of complexity by the federation and factorization of similar concepts; • But unable to elicit the semantic details specific to each CoC domain/discipline; • A bottom-up approach: • Based on domain/discipline experts detailed knowledge; • Allowing to elicit the details and specificities of the domain/discipline analysed; • But unable to elicit a global, human understandable, conceptual picture. • In order to cumulate the benefits of the two aforementioned approaches, the chosen methodological approach includes the two-fold approach.

7. Strategies for collaborative Design and Validation First pillar: IS Alignment • SLM (Simulation Lifecycle Management) • “SLM simplifies the capture and deployment of approved simulation methods and best practices, providing guidance and improved confidence in the use of simulation results for collaborative decision making. It improves quality by providing full traceability into simulation history and associated data. It also accelerates product development by providing timely access to the right information through secure storage, search, and retrieval with distinct functionality dedicated specifically to simulation scenarios and data.” www.simulia.com/products/slm.html) • Key wording • A Process (P) is a logical sequence of tasks performed to achieve a particular objective. A process defines “WHAT” is to be done, without specifying “HOW” each task is performed. The structure of a process provides several levels of aggregation to allow analysis and definition to be done at various levels of detail to support different decision-making needs • A Method (M) consists of techniques for performing a task, in other words, it defines the “HOW” of each task. (In this context, the words “method,” “technique,” “practice,” and “procedure” are often used interchangeably.) At any level, process tasks are performed using methods. However, each method is also a process itself, with a sequence of tasks to be performed for that particular method. In other words, the “HOW” at one level of abstraction becomes the “WHAT” at the next lower level. • A Tool (T) is an instrument that, when applied to a particular method, can enhance the efficiency of the task; provided it is applied properly and by somebody with proper skills and training. The purpose of a tool should be to facilitate the accomplishment of the “HOWs. In a broader sense, a tool enhances the “WHAT” and the “HOW.” Most tools used to support systems engineering are computer- or software-based, which also known as Computer Aided Engineering (CAE) tools.

8. Virtual Plateau End to End Process Transvers services Repository VT: Virtual Testing CCL Repository Repository DMU Simulation Factory Model Store Quality Lab MBSE MBSE VVA Process Modeling Process Simulation Transvers services MATRICS AIRBUS & COC IS Transvers services CCL DMU/I… Strategies for collaborative Design and Validation First pillar: IS Alignment Business • Virtual designGoals: Set up Competitive & complex engineering systems rely on: • a coherent IS exploiting mode • the synergism of mutually interacting phenomena (domain skills). • serviceability and overall life-cycle cost effectiveness, Domain A Program Request Program answer Domain B Domain X Domain Y Domain C SLM Collaborative services Enterprise Information Services:DMU…

9. Strategies for collaborative Design and Validation First pillar: IS Alignment Global IS ETL Design Domain x DMU Apply BC Post processing Extract data DMUs Filter data Cut back (all Section but xx) Create data Define attribute (material labels, thicknesses) Key domain Functional Data Program answer Extract data From functional DB Analyze BC Definition Build model Assembly model Idealization Simulate Post processing Apply BC Mesh xD model Methods Program Design Roadmap Analyze & decomposition Experience Plan Domain y Apply BC Post processing

10. Strategies for collaborative Design and Validation First pillar: IS Alignment Program Level Collaborative level Local domain

11. Strategies for collaborative Design and ValidationSecond Pillar: Virtual Testing • When is Virtual Testing Relevant? • Are you in an environment where it is worth investing in and developing a predictive capability? • Are you managing a code development project? • Do you operate in a mode, such as a Research and Development (R&D) or product maintenance engineer, where questions asked by your customers are often answered based on numerical simulations? • Do you have to support high-consequence decisions by examining a combination of experimental evidence, current knowledge, and numerical simulation results? • If the answer to one of these questions is “yes,” then Virtual Testing or some “level” of Virtual Testing , makes sense because it is how you demonstrate credibility.

12. Strategies for collaborative Design and ValidationSecond Pillar: Virtual Testing “Credibility” Scale 100% 0% ≈ 20% • It is your professional duty as engineers or scientists, and responsibility of citizen, to ask “what makes these simulation results credible?” • … And the answer “trust me, I’ve been doing this for 20 years” simply ain’t good enough! Proposed Revision of Diagrams for Verification and Validation in CSM Oberkampf • Virtual testing activities provide tools to help you make rigorous and scientifically defendable statements about yourcredibility as engineers or scientists.

13. Strategies for collaborative Design and ValidationSecond Pillar: Virtual Testing • PIRT: (PhenomenaIdentification & Ranking Tables) • The objective of a PIRT exercise is to identify phenomena associated with the intended scenario and to then rank the current state of knowledge relative to each identified phenomenon. • 1. Understand the given scenario and figure of merit, and ask clarifying questions as needed. • 2. Identify phenomena of interest and, as appropriate, key parameters associated withanyidentifiedphenomena. • 3. Rank the importance of each phenomenon in the context of the figure of merit. • 4. Rank the state of knowledge of phenomenon relative to the adequacy of existing modeling tools, the availability of supporting experimental data, and the prospects for gathering data if existing data were not ranked as “high”. • 5. Rank the importance and state of knowledge for any key parameters identified for anygivenphenomenon • PCMM: Predictive Capability Maturity Model

14. Strategies for collaborative Design and ValidationSecond Pillar: Virtual Testing • Code verification activities • Response feature extraction • Convergence of the numerical solution • Local sensitivity study (finite difference-based) • Design of computer experiments • Global sensitivity (variance-based), effect screening • Test-analysis comparison and correlation • Model revision and parameter calibration • Uncertainty propagation and assessment • Prediction accuracy assessment at specific settings • Extrapolation of prediction accuracy and uncertainty

15. Strategies for collaborative Design and ValidationThird Pillar: Multi Fidelity Simulation • Today Simulation-Based Engineering • Higher-fidelity models • Better approximation methods • Faster numerical algorithms • Larger scope applications • MDO • computer are more efficient • Democratization of Linux clusters • Ever faster processing systems • GPUs, … • Tunable Fidelity Disciplinary Analysis is Needed for Efficient Systems Design in order to • Capture dominant behavior of the system of interest • Enhance preliminary and trade off phases • Drastic CPU time reduction

16. Strategies for collaborative Design and Validation Third Pillar: Multi Fidelity Simulation • Steady parametric studies using POD: • Snapshots : S() where  are chosen in a set of parameters • POD basis computation :  and  using SVD • Prediction of S() for  by interpolation • Unsteady studies using POD-Galerkin: • Snapshots : S(x,ti) for chosen ti • POD basis computation : (x) by SVD • Prediction of S(x,) for ti by computing (t) solutions of Ode’s • Unsteady parametric studies using POD-Galerkin: • Snapshots = S(x,ti,j) for chosen ti and j • POD basis computation : (x) by SVD • Prediction of S(x,,) for ti and  by computing (t) solutions of Ode’s

17. Strategies for collaborative Design and Validation Third Pillar: Multi Fidelity Simulation Braconnier, Ferrier, Jouhaud (Cerfacs, 2008) Parametric POD for aeronautic design • HIRET benchmark : • Navier-Stockes • 12 000 000 cells • Computation on Cray XD1 using elsA • p = 64 snapshots • Prediction using 5 Pod vectors Energy Density E CFD computation: solid line POD prediction : dashed line max error < 6% and 2-norm error < 1,5 % Half aircraft + moving aileron with angle of deflection AoD Flight domain : [AoA,Mach,AoD][0°,3°]x[0.6,0.75]x[0°,2°] Snapshots : [, U, V, W, E] on section S1 Prediction at (1.8°,0.67,1.75°)

18. Questions