A method has been developed to efficiently implement supersonic aerodynamic predictions from Euler solutions into a highly constrained, muItidisciplinary design optimization of a High-Speed Civil Transport. The method alleviates the large computational burden associated with performing computational fluid dynamics analyses through the use of variable-complexity modeling techniques, response surface (RS) methodologies, and coarse-grained parallel computing. Using information gained from lower-fidelity aerodynamic models, reduced-term RS models representing a correction to the linear theory RS model predictions are constructed using Euler solutions. Studies into 5-, 10-, 15-, and 20-variable design problems show that accurate results can be obtained with the reduced-term models at a fraction of the cost of creating the full-term quadratic RS models. Specifically, a savings of 255 CPU hours out of 392 CPU hours required to create the full-term RS model is obtained for the 20-variable problem on a single 75-MHz IP21 processor of a Silicon Graphics, Inc. Power Challenge. Nomenclaturec jk -response surface model coefficients g(x) = vector of optimization constraint values K = drag polar shape parameter m = number of design variables N = number of points used to evaluate response surface model error n = number of terms in the response surface model n p -number of processors used on a parallel computer Design Center for Advanced Vehicles. p = number of experimental design points q = number of candidate sample sites R LE = leading-edge radius parameter ffus, = fuselage radius at /th axial location 5 LE/ = inboard leading-edge length s TEj = inboard trailing-edge length (//c)break = thickness-to-chord ratio at leading-edge break (tlc\ ool = thickness-to-chord ratio at wing root 0/c) tip = thickness-to-chord ratio at wing tip WC-TOGW = corrected takeoff gross weight Wfue, = fuel weight WTOGW = takeoff gross weight Wwing = wing weight x = ra-dimensional vector of design variable values (*/c)max-r = chordwise location of maximum thickness Xj = jth design variable *max = vector of upper bounds on design variable values *min = vector of lower bounds on design variable values v = observed response value y = predicted response value Vnac = spanwise location of inboard nacelle AC Do = correction to linear theory value of the drag polar shape parameter A^f = correction to linear theory value of the drag polar shape parameter AW^i = correction to fuel weight A;y nac = distance between nacelles A LE/ = inboard leading-edge sweep angle ALE O = outboard leading-edge sweep angle A T E 7 = inboard trailing-edge sweep angle
The n-dimensional direct search algorithm DIRECT of Jones, Perttunen, and Stuckman has attracted recent attention from the multidisciplinary design optimization community. Since DIRECT only requires function values (or ranking) and balances global exploration with local refinement better than n-dimensional bisection, it is well suited to the noisy function values typical of realistic simulations. While not efficient for high accuracy optimization, DIRECT is appropriate for the sort of global design space exploration done in large scale engineering design. Direct and pattern search schemes have the potential to exploit massive parallelism, but efficient use of massively parallel machines is nontrivial to achieve. This paper presents a fully distributed control version of DIRECT that is designed for massively parallel (distributed memory) architectures. Parallel results are presented for a multidisciplinary design optimization problem-configuration design of a high speed civil transport.
V isualization has let scientists gain an understanding of their data that was not previously possible. However, lack of integration among the various software modules often separates the visualization process from the computation that generates the data.VizCraft is a problem-solving environment that aids designers during the configuration design of a high-speed civil transport (HSCT). VizCraft provides a graphical user interface to a widely used suite of simulation and analysis codes for HSCT design, 1 and it provides tools for visualizing the outputs of these codes. So, VizCraft provides an environment that combines visualization and computation, encouraging the designer to think in terms of the overall problem-solving task, not simply using the visualization to view the computation's results. The HSCT design problemWe want to minimize the takeoff gross weight (TOGW) for a 250-passenger HSCT with a range of 5,500 nautical miles and a Mach 2.4 cruise speed. The simplified mission profile includes takeoff, supersonic cruise, and landing. Typically, aircraft design comprises three distinct phases: conceptual, preliminary, and detailed design. The conceptual-design stage determines and sets major design parameters for the final configuration. It models an aircraft with a set of values for significant parameters relating to the aircraft geometry, internal structure, systems, and mission.Individual designs can be (and are) viewed as points in a multidimensional design space. The designer must determine that a proposed design point• is feasible (it satisfies a series of constraints) and • has a figure of merit determined by an objective function.The goal is then to find the feasible point with the smallest objective-function value. The multidisciplinary HSCT design problem uses TOGW as the objective function. TOGW is a nonlinear, implicit function of the 29 design variables that define the HSCT configuration and mission.
A method has been developed to efficiently implement supersonic aerodynamic predictions from Euler solutions into a highly constrained, multidisciplinary design optimization of a High-Speed Civil Transport (HSCT) configuration. The method alleviates the large computational burden associated with performing CFD analyses and eliminates the numerical noise present in the analyses through the use of response surface (RS) methodologies, a variation of the variable-complexity modeling (VCM) technique, and coarse grained parallel computing. Variablecomplexity modeling techniques allow one to take advantage of information gained from inexpensive lower fidelity models while maintaining the accuracy of the more expensive high fidelity methods. In this research, simple conceptual level aerodynamic models provide the functional form of the drag polar. Response surface models are therefore created for the intervening functions (drag polar shape parameters) revealed by the simple models instead of for the drag itself. Optimization results using linear theory RS models are used to select the allowable ranges of the design variables. Stepwise regression analysis, performed using data from linear theory aerodynamic results, provides information on the relative importance of each term in the polynomial RS models. With this information, reduced term RS models representing a correction to the linear theory RS model predictions are constructed using fewer Euler evaluations. Studies into five, ten, fifteen, and
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