Transforming Ground and Flight Testing through Digital Engineering

Kraft, Edward

doi:10.2514/6.2020-1840

Cited by 3 publications

(1 citation statement)

References 4 publications

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“…polars per programme) have been steadily increasing over the past 40 years. The computer has very clearly not replaced the high-speed wind tunnel in any form whatsoever, whilst the widely touted integrated use of computational and experimental fluid dynamics (EFD) [32–34] has in practice simply been a continuation of the complementary use of theoretical, numerical, and experimental methods that has always been routine in industrial testing.…”

Section: Status Of Industrial High-speed Facilitiesmentioning

confidence: 99%

Transonic industrial wind tunnel testing in the 2020s

Greenwell¹

2021

Aeronaut. j.

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Wind tunnels remain an essential element in the design and development of flight vehicles. However, graduates in aerospace engineering tend to have had little exposure to the demands of industrial experimental work, particularly at high speed, a situation exacerbated by a lack of up-to-date reference material. In an attempt to fill this gap, this paper presents an overview of the current and near-term status and usage of transonic industrial wind tunnels. The review is aimed at recent entrants to the field, with the aim of helping them make the step from research projects in small university facilities to commercial projects in large industrial facilities. In addition, a picture has emerged from the review that contradicts received wisdom that the wind tunnel is in decline. Globally, the industrial transonic wind tunnel is undergoing somewhat of a renaissance. Numbers are increasing, investment levels are rising, capabilities are being enhanced, and facilities are busy.

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Section: Status Of Industrial High-speed Facilitiesmentioning

confidence: 99%

Transonic industrial wind tunnel testing in the 2020s

Greenwell¹

2021

Aeronaut. j.

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Data Fusion of Wing Pressure Distributions Using Scalable Gaussian Processes

Anhichem,

Timme,

Castagna

et al. 2024

AIAA Journal

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Aircraft design requires a large volume of aerodynamic data to characterize various flight conditions throughout the aircraft’s flight envelope, and the data are typically obtained through wind tunnel testing and numerical analysis. Data acquisition can be costly and inevitably entails multiple sources of uncertainty. Data fusion techniques aim to bring together the strengths and mitigate the limitations of data from various information sources. A multifidelity data fusion framework employing Gaussian process regression is adopted herein and applied to the surface pressure data of a large aircraft wing model. The modeling approach is non-hierarchical in that there is no established hierarchy of accuracy among the information sources. Scalability issues arising from the large volume of data required for the study of pressure distributions are overcome by using an approximate Gaussian process regression based on stochastic variational inference, enabling the data fusion framework to be applied effectively to industry-relevant analysis and design challenges. The approach is demonstrated for a high-dimensional data set generated through wind tunnel testing in an industrial setting and numerical analysis.

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