multidisciplinary design optimisation (mdo) different mdo approaches but lack of robust and fast...
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Multidisciplinary Design Optimisation (MDO)
Different MDO approaches but lack of robust and fast design tools.
Evolutionary/genetic methods perform better with large number of
design variables. Example: Coupled problems in aeronautics and
aeroelastic wing deformations of smart structures.
What is MDO:
Methodology for the design of complex engineering systemsin which the strong interaction between the disciplines require the designer to manipulate simultaneously the
variables in each of the disciplines involved.
Evolution Algorithms
What are EAs.
Computers can be adapted to perform this evolution process.
Crossover Mutation
Fittest
Evolution Based on the Darwinian theory of evolution Populations of individuals evolve and reproduce by means of mutation and crossover operators and compete in a set environment for survival of the fittest.
Multiple Models & Parallel Computing
We use a technique
that finds optimum
solutions by using
many different models,
that greatly accelerates
the optimisation process.
Interactions of the 3
layers: solutions go up
and down the layers.
Time-consuming solvers
only for the most
promising solutions.
Parallel Computing
Model 1precise model
Model 2intermediate
modelModel 3
approximate model
Exploration
Exploitation
Evolution Algorithm Evaluator
Multi-Objective Optimisation and Pareto Front
Maximise/ Minimise
Subjected to constraints
Nixfi ,......,1),(
Kkxh
Jjxg
k
j
,.......,1,0)(
,......,1,0)(
Pareto Optimal Set
Design problems normally require a simultaneous optimisation of
conflicting objectives and associated number of constraints. They occur when two or more objectives that cannot be
combined rationally.
Technical Resources Analysis Tools
Aerodynamics/CFD
FLUENT
FLO22 (NASA Langley)
HDASS (In house Navier-Stokes Solver)
(2D Gridfree solver)
VLMpc ( Vortex lattice method)
MSES / XFOIL / NSC2ke
CAD
Solid Works, Autocad
Aircraft Design
Flight Optimisation System
(FLOPS) NASA Langley
AAA (DART corporation)
ADS (In House)
Structural Analysis / FEA
Strand 7, CalculiX
Capabilities
We are now confident of our ability to optimise real industrial / Aeronautical cases, which could be three- dimensional, having multi-objective criteria or related to Multidisciplinary Design Optimisation (MDO).
- Aerofoil (Inverse Design, Drag Minimization / Gridfree solvers/ SCB)
- Wing (Drag and Weight Minimisation)
- Whole Aircraft (Drag / weight / noise reduction)
- Nozzle (Inverse Design)
Aerofoil at Two Different Lift
s Property Flt. Cond. 1 Flt Cond.2
Mach 0.75 0.75
Reynolds 9 x 106 9 x 106
Lift 0.65 0.715
Constraints:• Thickness > 12.1% x/c (RAE 2822)• Max thickness position = 20% ® 55%
To solve this and other problems standard industrial flow solvers are being used.
Aerofoilcd
[cl = 0.65 ]
cd
[cl = 0.715 ]
Traditional Aerofoil
RAE28220.0147 0.0185
Conventional
Optimiser
0.0098
(-33.3%)
0.0130
(-29.7%)
New Technique0.0094
(-36.1%)
0.0108
(-41.6%)
For a typical 400,000 lb airliner, flying 1,400 hrs/year, a 3% drag reduction corresponds to 580,000 lbs (330,000 L) less fuel burned.
SCB (Shock Control Bump)
Cd = 0.01986 Cd = 0.01808 Cd = 0.01622> >
Without SCB Upper SCB Upper & Lower SCB
Delaying Upper Shock Delaying Upper & Lower Shock
Mach Contour
M = 0.8
= 10o
Re = 500
M1.113891.069341.024780.9802270.9356710.8911150.846560.8020040.7574480.7128920.6683370.6237810.5792250.5346690.4901130.4455580.4010020.3564460.311890.2673350.2227790.1782230.1336670.08911150.0445558
Gridfree Solvers
x-1 0 1
M0.588920.5653630.5418070.518250.4946930.4711360.4475790.4240230.4004660.3769090.3533520.3297950.3062390.2826820.2591250.2355680.2120110.1884540.1648980.1413410.1177840.09422720.07067040.04711360.0235568
M = 0.5
= 0o
Re = 5000
Features of Gridfree solvers
Gridfree solver require only a cloud of points in the computational domain and
connectivity, i.e., a set of neighbors for each point
Gridfree methods don’t care how the cloud of points are generated , i.e., the cloud
of points can be obtained from a structures grid or unstructured grid or from a
chimera grid.
Generation of good connectivity is critical for the successful application of gridfree
solvers, i.e., dense cloud of points is required near the regions of large flow
gradients and discontinuities for accurate simulation and good connectivity is
required for the solution convergence.
Future Research
Development of a random point generator to exploit the true nature of gridfree
flow solvers
Coupling Gridfree solvers with Evolutionary Algorithms
SCB on 3D Wing
Shock Distribution
Upper Surface Lower Surface
Without SCB All Section SCB Partial SCB
Wing Section Aerofoils
Without SCB
With All Section SCB
With Partial SCB
Mach Number 0.69
Cruising Altitude 10000 ft
Cl 0.19
Wing Area 2.94 m2
Minimisation of wave drag and wing weight
MOO of transonic wing design for an Unmanned Aerial Vehicle (UAV)
Aerofoil sections for
Pareto Member 0 12, 20
Top view of wings on
Pareto set
Results
Aircraft / UAV Design
Minimise two objectives Gross weight min(WG) Endurance min (1/E)
Subject to: Takeoff length < 1000 ft Alt Cruise > 40000 ROC > 1000 fpm, Endurance > 24 hrs
With respect to: External geometry of the aircraft
• Mach = 0.3• Endurance > 24
hrs • Cruise Altitude:
40000 ft
Pareto Optimal configurations
Current and Ongoing Industrial Applications
Transonic Viscous Aerodynamic Design
Multi-Element High Lift Design
Propeller Design
AF/A-18 FlutterModel Validation
F3 Rear Wing Aerodynamics
Problem Two Element Aerofoil Optimisation Problem
Transonic Wing Design
Aircraft Conceptual Design and Multidisciplinary Optimisation
UAV Aerofoil Design
2D Nozzle Inverse Optimisation
Conceptual design
Preliminary design
Detailed Design
CAD Integration
ApproximationTechniques
(RSM, Kriging),
Optimiser Set (EAS, gradient hybrid)
Higher Fidelity Models
Database of Case Studies)
Parallelization Strategies
Multidisciplinary Analysis
A Robust Framework for Aeronautical MDO
The results indicate that aircraft design optimisation and shape optimisation problem can be resolved with an evolutionary approach using a hierarchical topology.
The new method contributes to the development of numerical tools required for the complex task of MDO and aircraft design.
No problem specific knowledge is required The method appears to be broadly applicable to different analysis codes
A family of Pareto optimal configurations was obtained giving the designer a restricted search space to proceed into more details phases of design.
Conclusions