TOWARDS THE NEXT GENERATION …...Towards the next Generation MDO 3 •HPC capabilities and...
Transcript of TOWARDS THE NEXT GENERATION …...Towards the next Generation MDO 3 •HPC capabilities and...
This project has received funding from the European Union’s Horizon 2020 research and i n n o v a t i o n f ra m e wo r k p ro g ra m m e u n d e r g ra n t a gr e e m e n t N o 636 202
TOWARDS THE NEXT GENERATION COLLABORATIVE MDO THE AGILE PROJECT
ARTHUR RIZZI
AIRINNOVA AB & KTH ROYAL INST TECHNOLOGY
WITH SUPPORT OF
AGILE CONSORTIUM
MDO Challenges for current aircraft development applications
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Aircraft Product Development large number of parts and design sub-processes
Cross-organizational distributed and heterogeneous knowledge and expertise
Emerging novel technologies higher level integration of components
How to enable effective Collaborative MDO?
source: Lockheed P-80 Shooting Star
1943: 143 days!!!
Number of parts: 6 million Design changes per year: 150 000
source: Boeing
Today Yesterday Tomorrow
Towards the next Generation MDO
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• HPC capabilities and simulation distribution
• Automation of analysis capabilities
2) second (~00-today)
• Integration of expertise in the collaborative optimization
• Knowledge formalization of processes and disciplinary domains
3) third (next gen)
Optimization
Sensitivities
Iterati
on
Meta-Models Database
Requirements,
Targets
Parameter
Performance,
Properties
Analysis 2
Analysis n
Analysis 1
Central
Product Model
OAD process
Analysis Domains
Optimization
Sensitivities
Iterati
on
Meta-Models Database
Requirements,
Targets
Parameter
Performance,
Properties
Central
Product ModelAnalysis 2
Analysis 1
Analysis n
OAD process
Distributed Analysis
Competence 1
Competence 2
Competence n
Optimization
Sensitivities
Iterati
on
Meta-Models Database
Requirements,
Targets
Parameter
Performance,
Properties
Central
Product Model
OAD process
Distributed Competence
3rd Gen. MDO:
system of distributed competences 1) first (~80)
• Disciplinary Simulation and optimization capabilities
• Optimization Strategies for low computational power
Details
AIAA-2017-4137
MDO is not new!
3rd Generation MDO Enablers
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design competences
common language
process orchestration
knowledge integration
COM
COM
Power Equation LP Spool
Power Equ. HP Sp.
HPT Cooling
COM
COM
Power Equation LP Spool
Power Equ. HP Sp.
HPT Cooling
TLAR next generation of collaborative MDO
People Software Data Communication
N2 Functional Breakdown/Interface Chart
Functional Flow Diagram
AGILE Ambition
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PhysicsKnowledge
Abstraction
Knowledge
Abstraction
Design and Optimization Process
Time
AGILE
setup operational solution
Objectives: • Realize the 3rd generation MDO
• Reduce aircraft development time\costs • Enable Collaborative Aircraft Design
Accelerate the setup of large scale collaborative distributed processes
Support collaborative operation of design systems: people and tools
Efficient collaborative Optimization techniques
AGILE Use-Case Configurations
Design
1. Complexity - Preliminary aircraft design process
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All-at-Once Optimization X: Parametric Variables Xmin ≤ X ≤ Xmax
Xbaseline: As-Drawn Values U(X): Calculated Properties Maximize: F[U(X)]: Calculated MOM
Subject to constraints G[X, U(X)] ≥ 0 …
& busy work & errors
Intractable !
Decompose sequence of processes with some feedback
Orchestration N2-diagram shows: • Functional Breakdown • & Interface Flow
AGILE – Service Oriented Architecture
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Hierarchal workflow of “provided services” - Each service
- @ Partner site\network - collaborates across discipline & location - formalizes entire process - enabled by BRICS & RCE framework
Details
AIAA-2017-4138
MDO Process formalization
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MDO
Architecture?
Integration
Framework? Stored as CMDOWS Automated Translation
MDF
IDF
CMDOWS Common MDO Workflow Schema
MDO process description developed in AGILE project. Formalizes the MDO processes in a “neutral format” Enable to share MDO processes between MDO frameworks Provides the MDO process model which can be manipulated
cmdows-repo.agile-project.eu
Details
AIAA-2017-4139
AIAA-2017-3663
AGILE reference aircraft
Your
optimization
framework!
Orchestrate the workflow
Example DC1-MDA Aircraft
Target of AGILE Design Campaign 1 (DC 1):
Medium-range commercial airliner
• Baseline :
DC 0 by semi-empirical methods
• No experimental data available
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Parameter DC1-MDA Unit
Wing span 28.1 m
MAC 3.73 m
Wing area 82.8 m2
MTOW 39’750 kg
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Enhancing framework capabilities – Wing Design
Current limitations on wing shape optimization :
• Structural optimization loads constraints sequential aero / structure optimization process
Enhancement:
• Consider multi – objective approach for both aerodynamics and structure
Aero-structures optimization
Details
AIAA-2017-4140
Aero-elastic shape optimization loops
Shape optimization loop
Descartes Model
generation CPACS
Aero CAD
SUMO initial model generation
Descartes uv-mapping
Descartes Database
FEM model
SU2 model
Lagrange sizing
optimization Interface
Objective function evaluation
Punch file
Shape DVs
Fixed loads
SUMO volume mesh
update
Descartes
SUMO
Lagrange
SU2
Interface
Data
Legend:
Interface convergence
check
SU2 Aerodynamic
calculation
Lagrange Static analysis
FSI Matrix Interface convey
deformatio
Interface calculate
forces
Aero-elastic
loop
Descartes model update
Hierarchical schema definition Product and Process information xml based format
Developed since 2005 @ DLR Adopted by External Partners
Open source: https://cpacs.de Supporting Libraries available (visualization, handling, etc.)
CPACS – Data Schema Common Parametric Aircraft Configuration Schema
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Shape
Aerodynamics Low-Fi
DOC
Mission
Emissions
Systems
Structure
Aerodynamics High-Fi
Design Modules
Central Model
ad-hoc vs central interfaces
architecture
Data/Work Flow & Collaboration: DC-1 MDA
We are here!
: updated CPACS file
Flying Qualities
Step 1 – Create Required data
• Aerodynamic dataset – S & C derivatives
• Propulsion data • Mass breakdown data
– mass, cg, Inertia
• Geometric data
Store as CPACS file
AeroData Fusion
• Cannot compute every flight state
• Data fusion S&C aero-database
• CPACS compatible
• Steps include: 1. Initialization
2. Sampling
3. Co-Kriging surrogate model
4. Smart Sampling updates - hi-fi samples at suggested locations
5. Final surrogate model
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Step 2 – Create PHALANX Flight Simulator model
Performance, Handling Qualities, Loads Analysis Toolbox
• Automatic generation of simulation model
• Equations of motion based on multibody dynamics (nonlinear flight dynamics model)
• Selective fidelity (range of sub models is available)
Step 3 – Virtual flight test
• Trim and handling qualities analysis for range of flight conditions
Sample results • Trim attitudes and control settings including crosswind / engine out • Short period, Phugoid, Dutch roll, Spiral, Roll mode • Push-pull manoeuvre • Roll manoeuvre • Gibson criterion • Maximum pitch acceleration (take-off) • Response to turbulence and gusts
Example time domain simulation
Step 4 – Piloted simulation
Tex Johnson, Chief test pilot, Boeing
Movies
• Pitch maneuvers: – Angle of attack – Pitch rate – Pilot input (roll)
• Roll maneuvers: – Roll rate – Pilot input (roll) – Pilot input (pitch)
• Rollover: – Roll rate – Load factor – Pilot input (roll)
Questions ?
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