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Browsing by Author "Haim, Dan"

Now showing 1 - 3 of 3
  • Multidisciplinary Optimization of a Supersonic Transport Using Design of Experiments Theory and Response Surface Modeling
    Giunta, Anthony A.; Balabanov, Vladimir; Haim, Dan; Grossman, Bernard M.; Mason, William H.; Watson, Layne T.; Haftka, Raphael T. (Department of Computer Science, Virginia Polytechnic Institute & State University, 1997-07-01)
    The presence of numerical noise in engineering design optimization problems inhibits the use of many gradient-based optimization methods. This numerical noise may result in the inaccurate calculation of gradients which in turn slows or prevents convergence during optimization, or it may promote convergence to spurious local optima. The problems created by numerical noise are particularly acute in aircraft design applications where a single aerodynamic or structural analysis of a realistic aircraft configuration may require tens of CPU hours on a supercomputer. The computational expenses of the analyses coupled with the convergence difficulties created by numerical noise are significant obstacles to performing aircraft multidisciplinary design optimization. To address these issues, a procedure has been developed to create noise-free algebraic models of subsonic and supersonic aerodynamic performance qualities for use in the optimization of high-speed civil transport (HSCT) aircraft configurations. This procedure employs methods from statistical design of experiments theory and response surface modeling to create the noise-free algebraic models. Results from a sample HSCT design problem involving ten variables are presented to demonstrate the utility of this method.
  • Suitability of Optimization Packages for an MDO Environment
    Haim, Dan; Giunta, Anthony A.; Holzwarth, M.; Mason, William H.; Watson, Layne T.; Haftka, Raphael T. (Department of Computer Science, Virginia Polytechnic Institute & State University, 1996-10-01)
    An examination of the performance of several optimization packages and their suitability for inclusion in a realistic multidisciplinary design optimization (MDO) environment is conducted. The packages are incorporated into a High-Speed Civil Transport (HSCT) aircraft design code and are used with both a response surface (RS) model and a more detailed, noisy model. While most packages converge to similar designs, there are large variations in CPU times and usability. Results are reported for a SGI workstation.
  • Wing Design for a High-Speed Civil Transport Using a Design of Experiments Methodology
    Giunta, Anthony A.; Balabanov, Vladimir; Haim, Dan; Grossman, Bernard M.; Mason, William H.; Watson, Layne T.; Haftka, Raphael T. (Department of Computer Science, Virginia Polytechnic Institute & State University, 1996-07-01)
    The presence of numerical noise inhibits gradient-based optimization and therefore limits the practicality of performing aircraft multidisciplinary design optimization (MDO). To address this issue, a procedure has been developed to create noise free algebraic models of subsonic and supersonic aerodynamic performance for use in the MDO of high-speed civil transport (HSCT) configurations. This procedure employs methods from statistical design of experiments theory to select a set of HSCT wing designs (fuselage/tail/engine geometry fixed) for which numerous detailed aerodynamic analyses are performed. Polynomial approximations (i.e., response surface models) are created from the aerodynamic data to provide analytical models relating aerodynamic quantities (e.g., wave drag and drag-due-to-lift) to the variables which define the HSCT wing configuration. A multidisciplinary design optimization of the HSCT is then performed using the response surface models in lieu of the traditional, local gradient based design methods. The use of response surface models makes possible the efficient and robust application of MDO to the design of an aircraft system. Results obtained from five variable and ten variable wing design problems presented here demonstrate the effectiveness of this response surface modeling method.