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Introduction to optimization methods for multidisciplinary design in aeronautics and turbomachinery - hardcover

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VKI LS 2008-07, Introduction to optimization methods for multidisciplinary design in aeronautics and turbomachinery, ISBN 978-2-930389-86-9

Introduction to optimization methods for multidisciplinary desig

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Introduction to optimization methods for multidisciplinary design in aeronautics and turbomachinery
June 2-6, 2008, edited by J. Périaux & H. Deconinck
Abstract

Innovative optimization and design techniques, both for aircraft (manned or unmanned UAV/UCAV) and engine systems, are now rapidly moving from research labs to industrial integrated platforms used by collaborative teams. These platforms aim at maximum performance in multidisciplinary context by combining several criteria like aerodynamic efficiency, safety, drag, losses, weight, strength, heat fluxes, emission, noise. To reach concurrently this level of excellence, emergent optimization and design methodologies require more and more robust and efficient associated software for daily use in industrial collaborative design environments.

These proceedings intend to provide the basic concepts and tools behind this technology, both in single discipline (single point or multi point design) and multidisciplinary (fluid-structure interaction, fluid-acoustics, conjugate heat transfer, …) context. Subjects which are treated in detail include: gradient based and steepest descent and quasi-Newton methods, methods based on adjoint equation, one shot or goal oriented methods, optimal control, evolutionary/differential evolution algorithms on parallel environments, game strategies like Pareto Fronts and Nash Equilibrium, parameterization, approximation methods (Radial Basis functions, Artificial Neural Networks, Kriging techniques …), distributed and collective design, collaborative optimization, robust design, self organizing map techniques, … . Numerical implementation is presented on model problems. Case study design problems are discussed in full detail (e.g. design of airfoils, UAV, conjugate heat transfer, cooling of turbine blades).

The content of these course notes is oriented towards junior and experienced engineers and
researchers involved in the field of multidisciplinary design, who are looking for innovative numerical solutions for complex multi criteria optimization problems. Also practically oriented design engineers in industry will strongly benefit from these course notes by better understanding of the existing and recently developed software tools that are rapidly becoming available to industry.

Table of content

Introduction

  • PIRONNEAU, O. – University of Paris VI, France
    Optimal shape design
  • PERIAUX, J.2; GONZALEZ, L.F.1; WHITHNEY E.J.; SRINIVAS K. – 1Queensland University of Technology (QUT), Australia, 2CIMNE/UPC, Barcelona, Spain & University of Jyvaskyla , Finland
    Part I: MOO methods for multidisciplinary design using parallel evolutionary algorithms, game
    theory and hierarchical topology: theoretical background
  • KROO, I. – Stanford University, USA
    Multidisciplinary design architectures and collaborative optimization

Part I Gradient based algorithms

  • VASSBERG, J.C. & JAMESON, A. – The Boeing Company, USA & Stanford University, USA
    Theoretical background for aerodynamic shape optimization
    Industrial applications of aerodynamic shape optimization
  • GAUGER, N.R. – German Aerospace Center (DLR), Germany & Humboldt University Berlin, Germany
    Adjoint approaches in aerodynamic shape optimization and MDO context I/II
  • SELMIN, V. – Alenia Aeronautica, Italy
    MDO systems for aeronautical applications

Part II Evolutionary and other Gradient-free algorithms

  • KROO, I  – Stanford University, USA
    Emerging methods for multidisciplinary design of complex systems
  • RAI, M.M. – NASA Ames Research Center, USA
    Single- and multiple-objective optimization with differential evolution and neural networks
    Towards robust designs via multiple-objective optimization methods
  • GONZALEZ, L.F.1; PERIAUX, J.2; LEE, D.S.11Queensland University of Technology (QUT), Australia, 2CIMNE/UPC, Barcelona, Spain & University of Jyvaskyla , Finland
    MOO methods for multidisciplinary design using parallel evolutionary algorithms, game theory  and hierarchical topology
    Part II: Numerical aspects and implementation of model test cases
    Part III: Practical application to the design and optimisation of UAV systems
  • GIANNAKOGLOU, K.C.; KAMPOLIS, I.C.; GEORGOPOULOU, C.A. – National Technical University of Athens, Greece
    Metamodel–assisted evolutionary algorithms (MAEAs)
    Hybridized adjoint methods/ evolutionary algorithms & applications to turbomachinery
  • VERSTRAETE, T. & VAN DEN BRAEMBUSSCHE, R.A. – von Karman Institute for Fluid Dynamics, Belgium
    Multidisciplinary optimization of turbomachinery components including heat transfer and stress predictions
  • PEDIRODA, V. & POLONI, C. – Università di Trieste, Italy
    Robust design, approximation methods and self organizing map techniques for MDO problems.
    Application in aeronautics and turbomachinery

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Manufacturer von Karman Institute for Fluid Dynamics

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