Applications to Fluid Mechanics

1. 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

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) Evolution Algorithm Evaluator • 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.