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Design search and optimization in aerospace engineering.

A J Keane1, J P Scanlan

  • 1University of Southampton, Highfield, Southampton SO17 1BJ, UK. andy.keane@soton.ac.uk

Philosophical Transactions. Series A, Mathematical, Physical, and Engineering Sciences
|May 24, 2007
PubMed
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This study optimizes civil aircraft wing design using computational tools. It balances aerodynamic performance, structural integrity, and manufacturing costs for competitive aerospace engineering.

Area of Science:

  • Aerospace Engineering
  • Computational Science
  • Design Optimization

Background:

  • Computational tools are increasingly vital in aerospace engineering.
  • Design Search and Optimization (DSO) is a key application area, especially for problems involving computational fluid dynamics (CFD).
  • Aerospace companies need advanced methods to maintain a competitive edge.

Purpose of the Study:

  • To explore the application of computational tools from a design-led perspective in the aerospace sector.
  • To present a multi-objective, multi-disciplinary example of DSO applied to civil aircraft wing design.
  • To demonstrate a method for balancing competing design goals in complex engineering problems.

Main Methods:

  • Review of state-of-the-art DSO in aerospace, focusing on CFD-intensive problems.

Related Experiment Videos

  • Development of a linked series of analysis codes for wing design, including CFD, structural assessment, and generative costing.
  • Application of a multi-objective probability of improvement formulation with stochastic process response surface models (Krigs) for optimization.

    • Main Results:

    • Successfully applied DSO to transonic civil transport wing design, considering aerodynamics, structure, weight, and manufacturing costs.
    • The chosen methods effectively mitigated long CFD computation times.
    • The approach provided an elegant way to balance competing objectives like low drag and cost.

    Conclusions:

    • DSO, particularly with advanced formulations like multi-objective probability of improvement and Krigs, is essential for modern aerospace design.
    • This integrated approach allows for efficient balancing of multiple, often conflicting, design criteria.
    • The methodology offers a pathway for companies to enhance competitiveness through optimized aircraft wing design.