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Journal ArticleDOI

Two-Level Multifidelity Design Optimization Studies for Supersonic Jets

Seongim Choi, +2 more
- 01 May 2009 - 
- Vol. 46, Iss: 3, pp 776-790
TLDR
The results show that this particular combination of modeling and design techniques is quite effective for the design problem and the ones in general and that high-fidelity aerodynamic shape optimization techniques for complex configurations can be effectively used within the context of a truly multidisciplinary design environment.
Abstract
The conceptual/preliminary design of supersonic jet configurations requires multidisciplinary analyses tools, which are able to provide a level of flexibility that permits the exploration of large areas of the design space. High-fidelity analysis for each discipline is desired for credible results; however, the corresponding computational cost can be prohibitively expensive, often limiting the ability to make drastic modifications to the aircraft configuration in question. Our work has progressed in this area, and we have introduced a truly hybrid, multifidelity approach in multidisciplinary analyses and demonstrated, in previous work, its application to the design optimization of a low-boom supersonic business jet. In this paper, we extend our multifidelity approach to the design procedure and present a two-level design of a supersonic business-jet configuration, in which we combine a conceptual low-fidelity optimization tool with a hierarchy of flow solvers of increasing fidelity and advanced adjoint-based sequential quadratic programming optimization approaches. In this work, we focus on the aerodynamic performance aspects alone: no attempt is made to reduce the acoustic signature. The results show that this particular combination of modeling and design techniques is quite effective for our design problem and the ones in general and that high-fidelity aerodynamic shape optimization techniques for complex configurations (such as the adjoint method) can be effectively used within the context of a truly multidisciplinary design environment. Detailed configuration results of our optimizations are also presented.

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Citations
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Journal ArticleDOI

Survey of Multifidelity Methods in Uncertainty Propagation, Inference, and Optimization

TL;DR: In many situations across computational science and engineering, multiple computational models are available that describe a system of interest as discussed by the authors, and these different models have varying evaluation costs, i.e.
Journal ArticleDOI

Hierarchical Kriging Model for Variable-Fidelity Surrogate Modeling

TL;DR: It is observed that hierarchical kriging provides a more reasonable mean-squared-error estimation than traditional cokriging and can be applied to the efficient aerodynamic analysis and shape optimization of aircraft or anywhere where computer codes of varying fidelity are in use.
Dissertation

Surrogate-Based Optimization using Multifidelity Models with Variable Parameterization

TL;DR: In this article, the authors present a mapping between variable-parameterization spaces, the last three of which are new: Space mapping, corrected space mapping, a mapping based on proper orthogonal decomposition (POD), and a hybrid between POD and space mapping.
Journal ArticleDOI

Efficient aerodynamic shape optimization using variable-fidelity surrogate models and multilevel computational grids

TL;DR: The proposed variable-fidelity optimization approach is validated by analytical test cases and applied to aerodynamic shape optimization of a NACA0012 airfoil and an ONERA M6 wing in transonic flows, confirming that the proposed method can significantly improve the optimization efficiency and apparently outperforms the existing single-Fidelity or two-level-f fidelity method.
Journal ArticleDOI

Surrogate Modeling for Uncertainty Assessment with Application to Aviation Environmental System Models

TL;DR: A novel surrogate modeling methodology designed specifically for propagating uncertainty from model inputs to model outputs and for performing a global sensitivity analysis is presented, which characterizes the contributions of uncertainties inmodel inputs to output variance while maintaining the quantitative rigor of the analysis.
References
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Journal ArticleDOI

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Proceedings ArticleDOI

Multidisciplinary aerospace design optimization: Survey of recent developments

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