Implementation of Multidisciplinary Multi-Fidelity Uncertainty Quantification Methods in Sonic Boom Prediction

Tekaslan, Huseyin E.; Yildiz, Sihmehmet; Demiroglu, Yusuf; Nikbay, Melike

doi:10.2514/6.2021-3100

Cited by 2 publications

(6 citation statements)

References 28 publications

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“…Using the combination of the correction functions, the multi-fidelity surrogate model, which shifts the low-fidelity prediction to high-fidelity data, is given in Equation (20).…”

Section: Multi-fidelity Extension Of Pce and Lramentioning

confidence: 99%

“…In Equation (20), the˜expression represents the surrogate model, while fl f (X), Ca (X), and Cm (X) represent the surrogate models established for the low fidelity, additive function, and multiplicative function results, respectively. Here, fm f (X) refers to the multi-fidelity surrogate model established by the combination of these surrogate models.…”

Section: Multi-fidelity Extension Of Pce and Lramentioning

confidence: 99%

“…As multi-fidelity surrogate modelling methods reduce the computational cost of simulations with an acceptable accuracy, this approach can be a remedy for uncertainty quantification processes [2]. In uncertainty quantification studies, orthogonal polynomial-based surrogate models such as multi-fidelity PCE [20] and SPCE [21] can be preferred since they successfully represent the general behaviour of computational models. In [20,21] studies, additive and multiplicative correction terms were used to extend the multi-fidelity versions of PCE and SPCE.…”

Section: Introductionmentioning

confidence: 99%

“…In uncertainty quantification studies, orthogonal polynomial-based surrogate models such as multi-fidelity PCE [20] and SPCE [21] can be preferred since they successfully represent the general behaviour of computational models. In [20,21] studies, additive and multiplicative correction terms were used to extend the multi-fidelity versions of PCE and SPCE. In addition, there are studies in which multi-fidelity extensions of these methods are made with the Gaussian process such as [22].…”

Section: Introductionmentioning

confidence: 99%

“…West and Philips [32] measured the uncertainty in the sonic boom level using the results of a small number of expensive analyses and based on three different fidelity levels with the multi-fidelity PCE method. Tekaslan et al [20] performed low-dimensional sonic boom uncertainty analysis using MFMC and MF-PCE methods. To the best of the authors' knowledge, the LRA method has not been applied to any engineering problems in the aviation field yet and the LRA method has not been implemented within a multi-fidelity modelling approach.…”

Section: Introductionmentioning

confidence: 99%

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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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“…Using the combination of the correction functions, the multi-fidelity surrogate model, which shifts the low-fidelity prediction to high-fidelity data, is given in Equation (20).…”

Section: Multi-fidelity Extension Of Pce and Lramentioning

confidence: 99%

Section: Multi-fidelity Extension Of Pce and Lramentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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

2022

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An efficient PanAir integrated framework for automated analysis

Shahid,

Gürkan

2024

Sci Rep

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The work proposed here is an automated pre and post-processor integrated to PanAir that is is a high-order aerodynamic panel method-based software for flow analysis developed in 70s but still in active use especially for preliminary aircraft design. With the integrated environment proposed in this work, manipulation of input and output data to and from PanAir is bypassed successfully that is otherwise requires manual manipulations and use of third party software. The integrated environment is validated over a Cessna 210 aircraft with a modified NLF (1)-0414 airfoil. The flow around the aircraft is analyzed using PanAir together with the integrated environment and results show that pre and post processing times reduced and ease in PanAir use is increased significantly.

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Implementation of Multidisciplinary Multi-Fidelity Uncertainty Quantification Methods in Sonic Boom Prediction

Cited by 2 publications

References 28 publications

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

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

An efficient PanAir integrated framework for automated analysis

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