Browsing by Author "Balabanov, Vladimir"

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  • A Coarse Grained Parallel Variable-Complexity Multidisciplinary Optimization Paradigm
    Burgee, Susan L.; Giunta, Anthony A.; Balabanov, Vladimir; Grossman, Bernard M.; Mason, William H.; Narducci, Robert; Haftka, Raphael T.; Watson, Layne T. (Department of Computer Science, Virginia Polytechnic Institute & State University, 1995-10-01)
    Modern aerospace vehicle design requires the interaction of multiple discipines, traditionally processed in a sequential order. Multidisciplinary optimization (MDO), a formal methodology for the integration of these disciplines, is evolving towards methods capable of replacing the traditional sequential methodology of aerospace vehicle design by concurrent algorithms, with both an overall gain in product performance and a decrease in design time. A parallel MDO paradigm using variable-complexity modeling and multipoint response surface approximations is presented here for the particular instance of the design of a high speed civil transport (HSCT). This paradigm interleaves the disciplines at one level of complexity, and processes them hierarchically at another level of complexity, achieving parallelism within disciplines, rather than across disciplines. A master-slave paradigm manages a coarse grained parallelism of the analysis and optimization codes required by the disciplines showing reasonable speedups and efficiencies on an Intel Paragon.
  • 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.
  • Optimization in Aircraft Design
    Burgee, Susan L.; Watson, Layne T.; Giunta, Anthony A.; Balabanov, Vladimir; Grossman, Bernard M.; Haftka, Raphael T.; Mason, William H. (Department of Computer Science, Virginia Polytechnic Institute & State University, 1995)
    A parallel variable-complexity modeling approach, permitting the efficient use of emerging parallel computing in multidisciplinary optimization (MDO) technology, is presented for the particular instance of High Speed Civil Transport (HSCT) design. In this method simple analyses are used to limit the approximation domain based on the D-optimality criterion through the use of a genetic alogorithm. At the D-optimal points, a refined analysis is performed. The optimization code is composed of a sequence of analysis cycles between aerodynamic and structural calculations. Results for coarse grained parallelization of the aerodynamic and structural codes on an Intel Paragon are presented for an example HSCT design problem involving only two variables. The full HSCT design problem employs twenty-eight design variables.
  • Variable-Complexity Response Surface Approximations for Wing Structural Weight in HSCT Design
    Kaufman, Matthew; Balabanov, Vladimir; Burgee, Susan L.; Giunta, Anthony A.; Grossman, Bernard M.; Haftka, Raphael T.; Mason, William H.; Watson, Layne T. (Department of Computer Science, Virginia Polytechnic Institute & State University, 1996)
    A procedure for generating and using a polynomial approximation to wing bending material weight of a High Speed Civil Transport (HSCT) is presented. Response surface methodology is used to fit a quadratic polynomial to data gathered from a series of structural optimizations. Several techniques are employed in order to minimize the number of required structural optimizations and to maintain accuracy. First, another weight function based on statistical data is used to identify a suitable model function for the response surface. In a similar manner, geometric and loading parameters that are likely to appear in the response surface model are also identified. Next, simple analysis techniques are used to find regions of the design space where reasonable HCST designs could occur. The use of intervening variables along with analysis of variance reduce the number of polynomial terms in the response surface model function. Structural optimization is then performed by the program GENESIS on a 28-node Intel Paragon. Finally, optimizations of the HSCT are completed both with and without the response surface.
  • 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.