A Multi-Fidelity Prediction of Aerodynamic and Sonic Boom Characteristics of the JAXA Wing Body

Carpenter, Forrest L.; Cizmas, Paul G. A.; Bolander, Christian R.; Giblette, Ted N.; Hunsaker, Douglas F.

doi:10.2514/6.2019-3237

Cited by 9 publications

(1 citation statement)

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“…In this figure, the oscillations in the near field obtained from the PANAIR program are damped as they are propagated to the ground. Near-field pressure signatures are validated by SBPW-II participants' results [54], and the ground signature are validated by studies of Carpenter et al [55]. Aerodynamic coefficient and loudness values obtained from both fidelity levels are provided in Table 3.…”

Section: Application For Real-world Engineering Problemsmentioning

confidence: 94%

Multi-Fidelity Low-Rank Approximations for Uncertainty Quantification of a Supersonic Aircraft Design

2022

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Uncertainty quantification has proven to be an indispensable study for enhancing reliability and robustness of engineering systems in the early design phase. Single and multi-fidelity surrogate modelling methods have been used to replace the expensive high fidelity analyses which must be repeated many times for uncertainty quantification. However, since the number of analyses required to build an accurate surrogate model increases exponentially with the number of random input variables, most surrogate modelling methods suffer from the curse of dimensionality. As an alternative approach, the Low-Rank Approximation method can be applied to high-dimensional uncertainty quantification studies with a low computational cost, where the number of coefficients for building the surrogate model increases only linearly with the number of random input variables. In this study, the Low-Rank Approximation method is implemented for multi-fidelity applications with additive and multiplicative correction approaches to make the high-dimensional uncertainty quantification analysis more efficient and accurate. The developed uncertainty quantification methodology is tested on supersonic aircraft design problems and its predictions are compared with the results of single- and multi-fidelity Polynomial Chaos Expansion and Monte Carlo methods. For the same computational cost, the Low-Rank Approximation method outperformed both in surrogate modeling and uncertainty quantification cases for all the benchmarks and real-world engineering problems addressed in the present study.

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Section: Application For Real-world Engineering Problemsmentioning

confidence: 94%