Browsing by Author "Burgee, Susan L."
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- A Coarse Grained Parallel Variable-Complexity Multidisciplinary Optimization ParadigmBurgee, 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.
- A coarse-grained variable-complexity MDO paradigm for HSCT designBurgee, Susan L. (Virginia Tech, 1995-10-06)Modern aerospace vehicle design requires the interaction of multiple disciplines, traditionally processed in a sequential order. Multidisciplinary optimization (MDO), a formal methodology for the integration of these disciplines, is evolving toward 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
- Optimization in Aircraft DesignBurgee, 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.
- The Promise (and Reality) of Multidisciplinary Design OptimizationBurgee, Susan L.; Watson, Layne T. (Department of Computer Science, Virginia Polytechnic Institute & State University, 1995-08-01)Modern aerospace vehicle design requires the interaction of multiple disciplines, 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. This paper discusses the obstacles to MDO, and presents a parallel MDO paradigm using variable-complexity modeling and multipoint response surface approximations 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.
- Variable-Complexity Response Surface Approximations for Wing Structural Weight in HSCT DesignKaufman, 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.