DES of a Slingsby Firefly Aircraft: Unsteady Flow Feature Extraction Using POD and HODMD

Corrochano, Adrián; Neves, Ana F.; Khanal, Bidur; Clainche, Soledad Le; Lawson, Nicholas J.

doi:10.1061/(asce)as.1943-5525.0001457

Cited by 10 publications

(3 citation statements)

References 45 publications

(40 reference statements)

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“…[4]. As an illustration, modeling of flow fields surrounding variously shaped airfoils [5] or newly morphed geometry [6] have to be filled the design space in order to find the optimization results. That is to say, obtaining enough information to predict a design requirements and result is a barrier in many optimizations [7].…”

Section: Introductionmentioning

confidence: 99%

“…Currently, machine learning and deep learning techniques are highly regarded in academia and industry with widespread success. Big Data is related to AI and ML, a word used to describe the massive volume of data that either originates from CFD models and experimental observations in aerospace engineering, or is constantly flooded into the aviation industry [5,18]. Recently, the "Big Model" of fluid dynamics jointly released by Northwestern Polytechnical University and Huawei is a hydrodynamic model based on deep machine learning of previous simulation results from neural DOI: 10.54254/2977-3903/6/2024060 networks, which greatly reduces the computational cost of numerical simulation of high-speed fluids.…”

Section: Introductionmentioning

confidence: 99%

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A review: Applications of machine learning and deep learning in aerospace engineering and aero-engine engineering

Wang,

2024

AEI

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The domain of aeronautical engineering and aero-engine engineering has witnessed considerable interest in the application of machine learning (ML) and deep learning (DL) techniques, revolutionizing various aspects of the field. This review provides a comprehensive review of the application of ML and DL in aerospace engineering and aero-engine engineering, focusing on aircraft aerodynamics, CFD, aircraft design and aeroacoustics and for aero-engine engineering focusing on health state evaluation, component optimization, blade defect detection and combustion. The review highlights the advantages and challenges of ML methods, presenting key concepts and strategies for ML. Furthermore, in terms of technical applications, DL has the potential to be on par with ML, despite being a branch of ML. The review further emphasizes the pressing requirement for a comprehensive examination of DL techniques concerning data-driven challenges within the realms of aerospace engineering and aero-engine engineering. It introduces representative DL methods and presents their mathematical definitions and illustrative applications.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A review: Applications of machine learning and deep learning in aerospace engineering and aero-engine engineering

Wang,

2024

AEI

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“…It can be used for dimensionality reduction or to study flow patterns in combustion, related to flow instabilities [19,20,21,22]. More recently, Higher Order Dynamic Mode Decomposition (HODMD) [23] was found to be more robust for the analysis of complex flows [24,25,26,27]. This algorithm was also validated for the analysis of reacting flows [28].…”

Section: Introductionmentioning

confidence: 99%

A predictive physics-aware hybrid reduced order model for reacting flows

Corrochano¹,

Freitas²,

Parente³

et al. 2023

Preprint

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In this work, a new hybrid predictive Reduced Order Model (ROM) is proposed to solve reacting flow problems. This algorithm is based on a dimensionality reduction using Proper Orthogonal Decomposition (POD) combined with deep learning architectures. The number of degrees of freedom is reduced from thousands of temporal points to a few POD modes with their corresponding temporal coefficients. Two different deep learning architectures have been tested to predict the temporal coefficients, based on recursive (RNN) and convolutional (CNN) neural networks. From each architecture, different models have been created to understand the behavior of each parameter of the neural network. Results show that these architectures are able to predict the temporal coefficients of the POD modes, as well as the whole snapshots. The RNN shows lower prediction error for all the variables analyzed. The model was also found capable of predicting more complex simulations showing transfer learning capabilities.

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Improving aircraft performance using machine learning: A review

Clainche¹,

Ferrer²,

Gibson³

et al. 2023

Aerospace Science and Technology

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DES of a Slingsby Firefly Aircraft: Unsteady Flow Feature Extraction Using POD and HODMD

Abstract: This document is the author's post-print version, incorporating any revisions agreed during the peer-review process. Some differences between the published version and this version may remain and you are advised to consult the published version if you wish to cite from it.

Cited by 10 publications

References 45 publications

A review: Applications of machine learning and deep learning in aerospace engineering and aero-engine engineering

A review: Applications of machine learning and deep learning in aerospace engineering and aero-engine engineering

A predictive physics-aware hybrid reduced order model for reacting flows

Improving aircraft performance using machine learning: A review

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