1. 1 ERIC WHITNEY (USYD) FELIPE GONZALEZ (USYD) Applications to Fluid Mechanics @ Inaugural Workshop for FluD Group : 28th Oct 2003. AMME Conference Room Supervisor: K. Srinivas Dassault Aviation: J. Périaux

2. 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. 3 CFD codes q 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. m HDASS : A time marching technique using a CUSP scheme with an iterative solver. m Vortex lattice method m Propeller Design q Requested to the author m 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 m ParNSS ( Parallel Navier--Stokes Solver) m FLO22 ( A three dimensional wing analysis in transonic flow suing sheared parabolic coordinates, Anthony Jameson) m MIFS (Multilock 2D, 3D Navier--Stokes Solver) q Free on the Web m nsc2kec : 2D and AXI Euler and Navier-stokes equations solver m vlmpc : Vortex lattice program

4. 4 Evolutionary Algorithms What are Evolutionary Algorithms?  Computers can be adapted to perform this evolution process. Crossover Mutation Fittest Evolution  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.  Populations of individuals evolve and reproduce by means of mutation and crossover operators and compete in a set environment for survival of the fittest.

5. Model 1 precise model Model 2 intermediate model Model 3 approximate model Exploration Exploitation qWe 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. qTime-consuming solvers only for the most promising solutions. qParallel Computing-BORGS Evolution Algorithm Evaluator HIERARCHICAL ASYNCHRONOUS PARALLEL EVOLUTION ALGORITHMS (HAPEA)

6. 6 Current and Ongoing CFD Applications Transonic Viscous Aerodynamic Design Multi-Element High Lift Design Propeller Design Formula 3 Rear Wing Aerodynamics Problem Two Element Aerofoil Optimisation Problem Transonic Wing Design Aircraft Design and Multidisciplinary Optimisation UAV Aerofoil Design 2D Nozzle Inverse Optimisation

7. 7 Outcomes of the research qThe new technique with multiple models: Lower the computational expense dilemma in an engineering environment (at least 3 times faster than similar approaches for EA) qThe new technique is promising for direct and inverse design optimisation problems. qAs developed, the evolution algorithm/solver coupling is easy to setup and requires only a few hours for the simplest cases. qA wide variety of optimisation problems including Multi-disciplinary Design Optimisation (MDO) problems could be solved. qThe benefits of using parallel computing, hierarchical optimisation and evolution algorithms to provide solutions for multi-criteria problems has been demonstrated.