Needs for performance of Large Scale Modeling & Simulation

Transcription

1 Needs for performance of Large Scale ing & Simulation MODPROD 2010 Henric Andersson Saab Aeronautics / IEI-LiU This work is related to the NFFP5 and Crescendo research programs

2 MODEL BASED DEVELOPMENT Sensors Vehicle systems - Physical constraints - Behaviors Task 1 Task 2 Task Task n Embedded Controller Energy flows Actuators Information flows 1 In 1-25 Z Integer Delay K Ts z-1 Discrete-Time Integrator Lookup Table 1 Out 1 DriveSine Torque 1 Inertia 1 Gear = 2 Inertia2 Torque2 Load tau tau freqhz = { 1 } J= 1 J = 1. 5 duration = { 2 }

3 THE VALUES OF MBD / MBSE Maturity 100% 60% 40% 20% Based Development Defect discovered Defect Traditional Development Verbal definition Document Analysis Simulation Test Flight Review Prototype Usage ing, and analysis get the function to mature more rapidly compared to traditional document-centric methods

4 FLOW OF SIMULATION MODELS Origin of models : Deliverables from sub suppliers (e.g. Engine) Development of models of aircraft subsystems (e.g. Fuel system) Development of simulator specific models (e.g. Servo) Equipment & model development Desktop (e.g. ARES) Laboratory (e.g. MYSIM) System verification (e.g. SYSIM) Integration of training simulators Training via (Mission Trainer) l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l Sub system-team Simulator team Simulator team Subsupplier Aircraft Integrator (e.g. Saab or Airbus) Customer

5 FLOW OF SIMULATION MODELS s storage typical situation Equipment & model development Desktop Laboratory System verification Integration of training simulators Training via l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l Subsupplier Sub system-team Simulator team Simulator team Customer store store (Dimensions) store (svn) store (rcs) store (cvs) store

6 FLOW OF SIMULATION MODELS s storage in a common model store Equipment & model development Desktop Laboratory System verification Integration of training simulators Training via l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l l Subsupplier Sub system-team Simulator team Simulator team Customer store Common central model store store store (Dimensions) store (svn) store (rcs) store (cvs)

7 CHALLENGES Computer performance increases enables based approaches in product/systems development is used also for verification/certification and training activities Challenges in set-up and support of large-scale of a/c systems: Many models ~100 Different kinds of models, e.g. environment, mechanics, electronics, software Variants of the systems that the models represent Variants of the models, e.g. different levels of fidelity Versions of models Different sets of System Parameters for parametric models Reuse of models between simulator types, e.g. desktop, training, verification Different operating systems/computer platforms for execution Different modeling notations, languages & tools/techniques e.g. continuous, discrete, event-based, steady-state, dynamic

8 LARGE SCALE MODELING & SIMULATION In large scale M&S there is a need to support the activities between M and S the & & activities The process of creating s of the whole aircraft from a set of subsystem models

9 LARGE SCALE MODELING & SIMULATION What are the &.& activities? characterization/declaration Choice of models from model store Configuration of models integration Verification of models consistency Build integrated model (compilation & linking) Instantiation and Initialization

10 LARGE SCALE MODELING & SIMULATION For simplicity we call the & & activities the confintegration process selection, integration, instantiation & initialization during full configuration control

11 PARAMETRIC CONFIGURABLE MODELS PDM System Configuration Parameter (cp) Pump model Sets of System Parameters System Parameter (sp) Constant during a run A Input (u) Pump model Algorithm part Output (y) Varying during a run C State (x) interactions: Input (u), Output (y), State (x), System Parameter (SP) and Configuration Parameter (CP) used for parametric models. Configuration Parameters (CP) are created in the Product Data Management (PDM) system and used in the simulator prior to a run to instantiate the models with a consistent set of System Parameters (SP).

12 PARAMETRIC CONFIGURABLE MODELS PDM System Configuration Parameter (cp) Pump model Sets of System Parameters CP1 CP2... SP1 SP2... CNAstring System Parameter (sp) Constant during a run A Input (u) Pump model Algorithm part Output (y) Varying during a run C State (x) Typically, the CP are stored in PDM while SP are handled in a Software Configuration Management (SCM) tool, and in this work a proposal of connection between CP and SP on the conceptual level are suggested. A structured data storage object CNA-string (ConfiguratioN data string) for cross-referencing or mapping of CP onto SP is introduced. The various validated CNA-strings containing consistent configurations can be stored in the environment in e.g. XML format for easy access.

13 SIMULATION CONFINTEGRATION PROCESS Product configurations A specific instance reflects one product configuration Configuration parameters and settings is needed in all activities/steps to set up a SCM + PDM Simulation configuration parameters time switching Compile time switching Run time switching Build Generate code Compile code Link code Set up Simulate! model source code obj. code executable sim. instance

14 SIMULATION CONFINTEGRATION PROCESS Product configurations Simulation configuration parameters Run-time switches are most flexible and enables to change product configuration during flight / Type of switching technique depends on e.g. security and maintainability factors Structured configuration tables improve efficiency, quality and scalability time switching Compile time switching Run time switching Build Generate code Compile code Link code Set up Simulate! model source code source code obj. code obj. code obj. code executable executable executable sim. instance siminstance 2 siminstance 3

15 MODELING DOMAINS Usage, Needs Requirements Architecture Interfaces Control f(t) -> L (s) G(s) F(.) Information Class G Prop GetSpe Class H Number MMI Virtual surrounding s of information objects, needs, services Structure Physical systems Electronics Optronics F Embedded I/O U=1 OpAmp s of physical objects Diode Ground Integration and System Simulation

16 THE INTEGRATION OF MODELING DOMAINS ica UML CATIA Simulink / Stateflow VAPS Structure Physical systems Embedded Electronics Optronics I/O U=1 OpAmp Control f(t) -> L (s) G(s) Information Class G Prop GetSpe MMI Virtual surr. F(.) Class H Number Diode Ground s of physical objects s of information objects, needs, services

17 PRODUCT M&S DISCIPLINES Architecture - Responsibilities - Data dictionary - Definition of parts and interfaces Product Change - Product Data - Product Lifecycle - -Management parametric ing & Simulation Line configurator? Simulation Build Instantiation &Initiation

18 CONCLUSIONS Need for configuration and integration support when building large-scale s ( confintegration ) Development of method to integrate PDM/PLM and environment based on CNA-string approach

19 Thanks! This work is performed in scope of the Swedish National Aeronautics Research Programme, NFFP5, the Crescendo EC research project, and with application in Process and Systems Implementation for Aerospace products at Saab Aeronautics.