Transcript No Slide Title

Applications to Fluid Mechanics
ERIC WHITNEY
(USYD)
FELIPE GONZALEZ (USYD)
@
Supervisor:
K. Srinivas
Dassault Aviation: J. Périaux
Inaugural Workshop for FluD Group : 28th Oct 2003. AMME Conference Room
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Overview
 Aim:
Develop modern numerical and evolutionary optimisation
techniques for number of problems in the field of Aerospace,
Mechanical and Mechatronic Engineering.
 In Fluid Mechanics we are particularly interested in optimising
fluid flow around different aerodynamic shapes:
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Single and multi-element aerofoils.
Wings in transonic flow.
Propeller blades.
Turbomachinery aerofoils.
Full aircraft configurations.
 We use different structured and unstructured mesh generation
and CFD codes in 2D and 3D ranging from full Navier Stokes to
potential solvers .
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CFD codes

Developed at the school
MSES/MSIS - Euler + boundary layer interactive flow solver. The external solver is based on a structural quadrilateral
streamline mesh which is coupled to an integral boundary layer based on a multi layer velocity profile representation.
 HDASS : A time marching technique using a CUSP scheme with an iterative solver.
 Vortex lattice method
 Propeller Design

Requested to the author
 MSES/MSIS - Euler + boundary layer interactive flow solver. The external solver is based on a structural
quadrilateral streamline mesh which is coupled to an integral boundary layer based on a multi layer velocity
profile representation
 ParNSS ( Parallel Navier--Stokes Solver)
 FLO22 ( A three dimensional wing analysis in transonic flow suing sheared parabolic coordinates, Anthony
Jameson)
 MIFS (Multilock 2D, 3D Navier--Stokes Solver)
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Free on the Web
 nsc2kec : 2D and AXI Euler and Navier-stokes equations solver
 vlmpc : Vortex lattice program
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Evolutionary Algorithms
What are Evolutionary Algorithms?
Populations of individuals evolve and
reproduce by means of mutation and
crossover operators and compete in a set
environment for survival of the fittest.
Evolution
Crossover
Mutation
Fittest
 Computers can be adapted to perform
this evolution process.
 EAs are able to explore large search spaces and are robust
towards noise and local minima, are easy to parallelise.
 EAs are known to handle approximations and noise well.
 EAs evaluate multiple populations of points.
 EAs applied to sciences, arts and engineering.
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HIERARCHICAL ASYNCHRONOUS PARALLEL
EVOLUTION ALGORITHMS (HAPEA)
We use a technique that
Model 1
precise model finds optimum solutions by
Exploitation
using many different
Model 2
models, that greatly
intermediate accelerates the optimisation
model
process. Interactions of the
Model 3
3 layers: solutions go up
approximate and down the layers.
Exploration
model
Time-consuming solvers
only for the most promising
solutions.
Parallel Computing-BORGS
Evolution Algorithm
Evaluator
Current and Ongoing CFD Applications
Problem Two Element
Aerofoil Optimisation
Problem
Formula 3 Rear
Wing
Aerodynamics
2D Nozzle Inverse
Optimisation
Multi-Element High
Lift Design
Transonic Viscous
Aerodynamic
Design
Transonic Wing
Design
Aircraft Design and
Multidisciplinary
Optimisation
Propeller Design
UAV Aerofoil
Design
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Outcomes of the research
 The new technique with multiple models: Lower the computational
expense dilemma in an engineering environment (at least 3 times
faster than similar approaches for EA)
 The new technique is promising for direct and inverse design
optimisation problems.
 As developed, the evolution algorithm/solver coupling is easy to
setup and requires only a few hours for the simplest cases.
 A wide variety of optimisation problems including Multi-disciplinary
Design Optimisation (MDO) problems could be solved.
 The benefits of using parallel computing, hierarchical optimisation
and evolution algorithms to provide solutions for multi-criteria
problems has been demonstrated.
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