Multi-Fidelity Aerodynamic Dataset Generation of a Fighter Aircraft with a Deep Neural-Genetic Network

Millidere, Murat; Gomec, Fazil S.; Kurt, Huseyin B.; Akgül, Ferhat

doi:10.2514/6.2021-3007

Cited by 3 publications

(2 citation statements)

References 11 publications

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“…ANNs mimic the operations of an interconnected arbitrary number of nodes by traversing a number of levels with the input characteristics [86]. The basic components of an ANN's structure can be considered to consist of inputs, weights, a transfer function, an activation function and bias [87]. The inputs refer to the measurements of the features that will be modified.…”

Section: Neural Networkmentioning

confidence: 99%

A State-of-the-Art Review of Non-Destructive Testing Image Fusion and Critical Insights on the Inspection of Aerospace Composites towards Sustainable Maintenance Repair Operations

2023

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Non-destructive testing (NDT) of aerospace structures has gained significant interest, given its non-destructive and economic inspection nature enabling future sustainable aerospace maintenance repair operations (MROs). NDT has been applied to many different domains, and there is a number of such methods having their individual sensor technology characteristics, working principles, pros and cons. Increasingly, NDT approaches have been investigated alongside the use of data fusion with the aim of combining sensing information for improved inspection performance and more informative structural health condition outcomes for the relevant structure. Within this context, image fusion has been a particular focus. This review paper aims to provide a comprehensive survey of the recent progress and development trends in NDT-based image fusion. A particular aspect included in this work is providing critical insights on the reliable inspection of aerospace composites, given the weight-saving potential and superior mechanical properties of composites for use in aerospace structures and support for airworthiness. As the integration of NDT approaches for composite materials is rather limited in the current literature, some examples from non-composite materials are also presented as a means of providing insights into the fusion potential.

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Section: Neural Networkmentioning

confidence: 99%

A State-of-the-Art Review of Non-Destructive Testing Image Fusion and Critical Insights on the Inspection of Aerospace Composites towards Sustainable Maintenance Repair Operations

2023

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“…In all optimization and training processes, 80% of the data is randomly selected for training the signal classifier, and the remaining 20% is allocated for validation. It has been seen that the training data is highly variable when used for testing [28], [29]. Therefore, these data sets were collected separately.…”

Section: B Model Optimization and Trainingmentioning

confidence: 99%

Performance Enhancement of Low-Cost INS/GNSS Navigation System Operating in Urban Environments

Ozdemir

Işık

Geragersian

et al. 2023

AIAA SCITECH 2023 Forum

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As a result of the increasing usage of UAVs (Unmanned Air Vehicles) in urban environments for UAM (Urban Air Mobility) applications, the preciseness and reliability of PNT (Positioning, Navigation and Timing) systems have critical importance for mission safety and success. With its high accuracy and global coverage, GNSS (Global Navigation Satellite System) is the primary PNT source for UAM applications. However, GNSS is highly vulnerable to Non-Line-of-Sight (NLoS) blockages and multipath (MP) reflections, which are quite common, especially in urban areas. This study proposes a machine learning-based NLoS/MP detection and exclusion algorithm using GNSS observables to enhance position estimations at the receiver level. By using the ensemble machine learning algorithm with the proposed method, overall 93.2% NLoS/MP detection accuracy was obtained, and 29.8% accuracy enhancement was achieved by excluding these detected signals.1 Graduate student, School of Aerospace, Transport and Manufacturing (SATM) 2 PhD candidate, School of Aerospace, Transport and Manufacturing (SATM) 3 Senior Lecturer, Centre for Autonomous and Cyberphysical Systems

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Aircraft Closed-Loop Dynamic System Identification in the Entire Flight Envelope Range Based on Deep Learning

Wang,

Li,

et al. 2024

J. Aerosp. Eng.

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Multi-Fidelity Aerodynamic Dataset Generation of a Fighter Aircraft with a Deep Neural-Genetic Network

Cited by 3 publications

References 11 publications

A State-of-the-Art Review of Non-Destructive Testing Image Fusion and Critical Insights on the Inspection of Aerospace Composites towards Sustainable Maintenance Repair Operations

A State-of-the-Art Review of Non-Destructive Testing Image Fusion and Critical Insights on the Inspection of Aerospace Composites towards Sustainable Maintenance Repair Operations

Performance Enhancement of Low-Cost INS/GNSS Navigation System Operating in Urban Environments

Aircraft Closed-Loop Dynamic System Identification in the Entire Flight Envelope Range Based on Deep Learning

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