Applications to Fluid Mechanics

1
Applications to Fluid Mechanics

ERIC WHITNEY (USYD) FELIPE GONZALEZ (USYD)
_at_
Supervisor K. Srinivas Dassault
Aviation J. Périaux
Inaugural Workshop for FluD Group 28th Oct
2003. AMME Conference Room
2
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
• 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 .

3
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)
• Free on the Web
• nsc2kec 2D and AXI Euler and Navier-stokes
  equations solver
• vlmpc Vortex lattice program

4
Evolutionary Algorithms
What are Evolutionary Algorithms?
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.

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.

5
HIERARCHICAL ASYNCHRONOUS PARALLEL EVOLUTION
ALGORITHMS (HAPEA)

• 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-BORGS

Model 1 precise model
Exploitation
Model 2 intermediate model
Model 3 approximate model
Exploration

6
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
7
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.