Multi-fidelity Aerodynamic Dataset Generation of a Fighter Aircraft

Kurt, Huseyin B.; Millidere, Murat; Gomec, Fazil S.; Uğur, Ömür

doi:10.2514/6.2021-0544

Cited by 7 publications

(2 citation statements)

References 9 publications

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“…The fusion of multiple information sources into a single surrogate model has readily been applied to a very wide range of industrial design problems, from ship component [28,41,43] and civil infrastructure [13,40] design to traffic state estimation [1] and inter-satellite calibration [10]. Aeroplane component design in particular often relies on multi-fidelity surrogate models [6,26,32,37,50,52], where https://doi.org/10.1017/S1446181124000087 Published online by Cambridge University Press [3] Mf-EBB modelling: methodology and challenges 3 expensive wind tunnel data might be supplemented with much more abundant but less reliable computational fluid dynamics (CFD) data. When using surrogate modelling techniques in Mf-EBB problems, many high-level decisions need to be made.…”

Section: Introductionmentioning

confidence: 99%

Methodology and Challenges of Surrogate Modelling Methods for Multi-Fidelity Expensive Black-Box Problems

ANDRÉS-THIÓ,

MUÑOZ,

SMITH-MILES

2024

ANZIAM J.

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Many industrial design problems are characterized by a lack of an analytical expression defining the relationship between design variables and chosen quality metrics. Evaluating the quality of new designs is therefore restricted to running a predetermined process such as physical testing of prototypes. When these processes carry a high cost, choosing how to gather further data can be very challenging, whether the end goal is to accurately predict the quality of future designs or to find an optimal design. In the multi-fidelity setting, one or more approximations of a design’s performance are available at varying costs and accuracies. Surrogate modelling methods have long been applied to problems of this type, combining data from multiple sources into a model which guides further sampling. Many challenges still exist; however, the foremost among them is choosing when and how to rely on available low-fidelity sources. This tutorial-style paper presents an introduction to the field of surrogate modelling for multi-fidelity expensive black-box problems, including classical approaches and open questions in the field. An illustrative example using Australian elevation data is provided to show the potential downfalls in blindly trusting or ignoring low-fidelity sources, a question that has recently gained much interest in the community.

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Section: Introductionmentioning

confidence: 99%

Methodology and Challenges of Surrogate Modelling Methods for Multi-Fidelity Expensive Black-Box Problems

ANDRÉS-THIÓ,

MUÑOZ,

SMITH-MILES

2024

ANZIAM J.

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“…The flight simulator needs a high-fidelity aerodynamic database to replicate the actual aircraft behaviour for the pilots. There are several methods for developing an aerodynamic database: semiempirical datasheet methods, linear flow solvers, nonlinear flow solvers, wind tunnel tests, and flight tests, in increasing order of fidelity [2][3][4]. Semiempirical methods and linear flow approaches yield lower fidelity databases, and the accuracy notably decreases as the nonlinearity of the flow increases [5].…”

Section: Introductionmentioning

confidence: 99%

Development of Aerodynamic and Propulsion Models Using the Iterative Equation Error Method

Millidere,

Akgül,

Leblebicioğlu

et al. 2023

Aerospace

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For developing high-fidelity flight simulations, an accurate and complete representation of the aerodynamic characteristics of the aircraft is necessary. To obtain a realistic aerodynamic database, system identification methods can be used to describe the applied forces and moments acting on the aircraft. This study is based on simulated flight test data from a nonlinear simulation of the F-16 aircraft. It is demonstrated that the complete set of aerodynamic coefficients can be reconstructed from the flight test data. Thrust forces and moments are obtained from ground tests. A practical system identification methodology based on the iterative equation error method to determine the nonlinear aerodynamic and engine thrust models in the absence of engine manufacturer data is developed. The estimated values obtained using the method are compared with the actual parameter values. A mathematical engine model that can be used to estimate the thrust force for any flight condition is also developed. The findings demonstrate that the proposed method yields accurate results. The developed methodology is well-suited for the identification of isolated aerodynamic drag and lift coefficients and the thrust model.

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