Reasonable Design Space Approach to Response Surface Approximation

Balabanov, Vladimir; Giunta, Antony A.; Golovidov, Oleg; Grossman, Bernard; Mason, William H.; Watson, Layne T.; Haftka, Raphael T.

doi:10.2514/2.2438

Cited by 64 publications

(34 citation statements)

References 21 publications

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“…Mason et al (1998) present a review of work on the conceptual design of a high speed civil transport (HSCT) aircraft, in which DoE, variable complexity analysis using the 'reasonable design space' approach (Balabanov et al 1999), and polynomial response surface modelling are used to incorporate high-fidelity analysis into conceptual design. Knill et al (1999) make use of the same HSCT problem to investigate using a low-fidelity model to decrease the number of terms used to build a polynomial RSM and so reduce the number of high-fidelity simulations required.…”

Section: Introductionmentioning

confidence: 99%

Optimization using surrogate models and partially converged computational fluid dynamics simulations

2006

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Efficient methods for global aerodynamic optimization using computational fluid dynamics simulations should aim to reduce both the time taken to evaluate design concepts and the number of evaluations needed for optimization. This paper investigates methods for improving such efficiency through the use of partially converged computational fluid dynamics results. These allow surrogate models to be built in a fraction of the time required for models based on converged results. The proposed optimization methodologies increase the speed of convergence to a global optimum while the computer resources expended in areas of poor designs are reduced. A strategy which combines a global approximation built using partially converged simulations with expected improvement updates of converged simulations is shown to outperform a traditional surrogate-based optimization.

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Section: Introductionmentioning

confidence: 99%

Optimization using surrogate models and partially converged computational fluid dynamics simulations

2006

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“…All of these issues can be alleviated by intelligently examining the design space and reducing it accordingly. Balabanov et al applied the design space reduction technique to a high speed civil transport wing [1]. They discovered that 83% of the points in their original design space violated geometric constraints, while many of the remaining points were simply unreasonable.…”

Section: Design Space Refinementmentioning

confidence: 99%

“…A feasible design x (1) dominates another feasible design x (2) (denoted by x (1) < x (2) ), if both of the following conditions are true:…”

Section: Pareto Optimal Frontmentioning

confidence: 99%

Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design

Mack

Goel

Shyy

et al. 2007

Studies in Computational Intelligence

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Summary. Surrogate-based optimization has proven very useful for novel or exploratory design tasks because it offers a global view of the characteristics of the design space, and it enables one to refine the design of experiments, conduct sensitivity analyses, characterize tradeoffs between multiple objectives, and, if necessary, help modify the design space. In this article, a framework is presented for design optimization on problems that involve two or more objectives which may be conflicting in nature. The applicability of the framework is demonstrated using a case study in space propulsion: a response surface-based multi-objective optimization of a radial turbine for an expander cycle-type liquid rocket engine. The surrogate model is combined with a genetic algorithm-based Pareto front construction and can be effective in supporting global sensitivity evaluations. In this case study, due to the lack of established experiences in adopting radial turbines for space propulsion, much of the original design space, generated based on intuitive ideas from the designer, violated established design constraints. Response surfaces were successfully used to define previously unknown feasible design space boundaries. Once a feasible design space was identified, the optimization framework was followed, which led to the construction of the Pareto front using genetic algorithms. The optimization framework was effectively utilized to achieve a substantial performance improvement and to reveal important physics in the design. IntroductionWith continuing progress in computational simulations, computational-based optimization has proven to be a useful tool in reducing the design process duration and expense. Numerous methods exist for conducting design optimizations. Popular methods include gradient-based methods [4,14], adjoint methods [6,10], and surrogate model-based optimization methods such as the response surface approximation (RSA) [9]. Gradient-based methods rely on a

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“…Hence, it might be appropriate to identify the region where we expect improvements in the performance of the designs, and construct surrogate models by sampling additional design points in that region (reasonable design space approach [31]). To identify the region of interest, we used the surrogate models, constructed with Set A data (110 design points), to evaluate response at a large number of points in design space.…”

Section: Design Space Refinementmentioning

confidence: 99%

Improving the Hydrodynamic Performance of Diffuser Vanes via Shape Optimization

Goel

Dorney

Haftka

et al. 2007

43rd AIAA/ASME/SAE/ASEE Joint Propulsion Conference &Amp;amp; Exhibit

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The performance of a diffuser in a pump stage depends on its configuration and placement within the stage.The influence of vane shape on the hydrodynamic performance of a diffuser has been studied. The goal of this effort has been to improve the performance of a pump stage by optimizing the shape of the diffuser vanes. The shape of the vanes was defined using Bezier curves and circular arcs. Surrogate model based tools were used to identify regions of the vane that have a strong influence on its performance.

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Reasonable Design Space Approach to Response Surface Approximation

Cited by 64 publications

References 21 publications

Optimization using surrogate models and partially converged computational fluid dynamics simulations

Optimization using surrogate models and partially converged computational fluid dynamics simulations

Surrogate Model-Based Optimization Framework: A Case Study in Aerospace Design

Improving the Hydrodynamic Performance of Diffuser Vanes via Shape Optimization

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