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