An aerodynamic variable-fidelity modelling framework for a low-observable UCAV

Stradtner, Mario; Liersch, Carsten M.; Bekemeyer, Philipp

doi:10.1016/j.ast.2020.106232

Cited by 12 publications

(7 citation statements)

References 21 publications

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“…This tandem approach generates complementary datasets in terms of fidelity and cost, typically consisting of extensive data points. Multi-fidelity GPs are often used for mixing CFD simulations and wind tunnel experiments [7,12,[35][36][37], but they cannot be applied to large databases. Hence, from a theoretical point of view, a potential future work is to extend the sparse approximations discussed in this paper to multi-fidelity GPs.…”

Section: Discussionmentioning

confidence: 99%

A Python Toolbox for Data-Driven Aerodynamic Modeling Using Sparse Gaussian Processes

Valayer,

Bartoli,

Castaño-Aguirre

et al. 2024

Aerospace

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In aerodynamics, characterizing the aerodynamic behavior of aircraft typically requires a large number of observation data points. Real experiments can generate thousands of data points with suitable accuracy, but they are time-consuming and resource-intensive. Consequently, conducting real experiments at new input configurations might be impractical. To address this challenge, data-driven surrogate models have emerged as a cost-effective and time-efficient alternative. They provide simplified mathematical representations that approximate the output of interest. Models based on Gaussian Processes (GPs) have gained popularity in aerodynamics due to their ability to provide accurate predictions and quantify uncertainty while maintaining tractable execution times. To handle large datasets, sparse approximations of GPs have been further investigated to reduce the computational complexity of exact inference. In this paper, we revisit and adapt two classic sparse methods for GPs to address the specific requirements frequently encountered in aerodynamic applications. We compare different strategies for choosing the inducing inputs, which significantly impact the complexity reduction. We formally integrate our implementations into the open-source Python toolbox SMT, enabling the use of sparse methods across the GP regression pipeline. We demonstrate the performance of our Sparse GP (SGP) developments in a comprehensive 1D analytic example as well as in a real wind tunnel application with thousands of training data points.

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Section: Discussionmentioning

confidence: 99%

A Python Toolbox for Data-Driven Aerodynamic Modeling Using Sparse Gaussian Processes

Valayer,

Bartoli,

Castaño-Aguirre

et al. 2024

Aerospace

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“…More details on that approach are given by Mancini et al [2]. The resulting conceptual design is then enhanced with a more detailed aerodynamic shape [3] in a manual step by the DLR Institute of Aerodynamics and Flow Technology. The resulting geometry of the FFD is shown in Fig.…”

Section: The Dlr Future Fighter Demonstratormentioning

confidence: 99%

“…In a first step, three different CFD solutions are compared. The Tau Euler and the SU2 Euler solutions are prepared using the mesh described in Section 2.4 while the Tau RANS solutions [3] are provided by the DLR Institute of Aerodynamics and Flow Technology, for which the authors are very thankful. Fig.…”

Section: Comparison Of Panel Aerodynamics With Cfdmentioning

confidence: 99%

Parametric aeroelastic modeling, maneuver loads analysis using CFD methods and structural design of a fighter aircraft

Voß

Klimmek

2023

Aerospace Science and Technology

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“…For examples, Krishen [54] expected that antenna technologies would incorporate advanced microstrips and superconductive materials and devices to acquire data (such as ranges, velocities, postures, surface roughness and dielectric properties) actively or passively in a near or far space. Stradtner et al [182] discussed the need of reliable and efficient tools to collect and process big aerodynamic data to control unmanned combat aerial vehicles. The information was integrated to predict non-linear aerodynamic phenomena.…”

Section: A Space Sensingmentioning

confidence: 99%

“…With the advance of observation technology, the astronomical survey data has been big and scaled up to petabytelevel. Stradtner et al [182] gave an quantified example of big aerodynamic data in a high-dimensional design space; the design space consisted of many control variables such as Mach number, altitude, angle of attack, angle of sideslip, turn rates, and several control surfaces), and around 200,000 entries were recorded for each configuration in continuous morphing. Laufer et al [195] introduced that an excessive increase of the satellite data at a distributed active archive center posed a big challenge to archive data, and process and utilize data efficiently; since the raw data from satellites required a large amount of computing and processing before the data became understandable to scientists, government officials and other end-users.…”

Section: Big Space Data Analysismentioning

confidence: 99%

The State of the Art of Information Integration in Space Applications

Yung

et al. 2022

IEEE Access

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This paper aims to present a comprehensive survey on information integration (II) in space informatics. With an ever-increasing scale and dynamics of complex space systems, II has become essential in dealing with the complexity, changes, dynamics, and uncertainties of space systems. The applications of space II (SII) require addressing some distinctive functional requirements (FRs) of heterogeneity, networking, communication, security, latency, and resilience; while limited works are available to examine recent advances of SII thoroughly. This survey helps to gain the understanding of the state of the art of SII in sense that (1) technical drivers for SII are discussed and classified; (2) existing works in space system development are analyzed in terms of their contributions to space economy, divisions, activities, and missions; (3) enabling space information technologies are explored at aspects of sensing, communication, networking, data analysis, and system integration; (4) the importance of first-time right (FTR) for implementation of a space system is emphasized, the limitations of digital twin (DT-I) as technological enablers are discussed, and a concept digital-triad (DT-II) is introduced as an information platform to overcome these limitations with a list of fundamental design principles; (5) the research challenges and opportunities are discussed to promote SII and advance space informatics in future.INDEX TERMS Information integration (II), space informatics, digital triad (DT-II), internet of digital-triad things (IoDTT), first-time right (FTR), space information integration (SII). FIGURE 2. Technical drivers of II applications.

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An aerodynamic variable-fidelity modelling framework for a low-observable UCAV

Cited by 12 publications

References 21 publications

A Python Toolbox for Data-Driven Aerodynamic Modeling Using Sparse Gaussian Processes

A Python Toolbox for Data-Driven Aerodynamic Modeling Using Sparse Gaussian Processes

Parametric aeroelastic modeling, maneuver loads analysis using CFD methods and structural design of a fighter aircraft

The State of the Art of Information Integration in Space Applications

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