Space debris interaction across a two-dimensional oblique shock wave

Kovács, Dániel G.; Grossir, Guillaume; Dimitriadis, Grigorios; Chazot, Olivier

doi:10.1007/s00348-023-03686-9

Cited by 5 publications

(1 citation statement)

References 26 publications

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“…The utilization of segmentation and edge or object detection techniques is crucial to derive important flow physical quantities from output data obtained through imaging methods. Examples of features that may be sought include, the coordinates of a dye plume (Yadav 2018), oil or smoke patterns (Arivoli 2023), the position of a shock wave (Kováics et al 2023), vortical structures (Lindner et al 2020) or particle clusters (Metzger et al 2022), species concentrations and iso-surfaces (Zheng et al 2022), interfaces and zones (Reuther and Kähler 2018). The methodologies applied in this context span a range of experimental techniques (Tropea et al 2007), including chemiluminescence (CL) (Guethe et al 2012), laser induced fluorescence (LIF) (Eghtesad et al 2024) and phosphorescence (LIP) (Charogiannis 2013), filtered Rayleigh scattering (FRS) (Doll et al 2023), thermography (Astarita et al 2006), schlieren imaging and shadowgraphy (Settles and Hargather 2017), high-speed photography (Versluis 2013), dye injection (Di et al 2022), smoke visualization (Willmott et al 1997), oil film measurements (Cai et al 2022) as well as experiments involving shear-or temperature/pressuresensitive liquid crystals (SLC, TLC) (Ireland and Jones Jul 2000) and paints (TSP, PSP) (Gregory et al 2008).…”

Section: Introductionmentioning

confidence: 99%

Deep learning-based image segmentation for instantaneous flame front extraction

Strässle,

Faldella,

Doll

2024

Exp Fluids

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This paper delves into the methodology employed in examining lean premixed turbulent flame fronts extracted from Planar Laser Induced Fluorescence (PLIF) images at elevated pressures. In such flow regimes, the PLIF signal suffers from significant collisional quenching, typically resulting in image data with low signal-to-noise ratio (SNR). This poses severe difficulties for conventional flame front extraction algorithms based on intensity gradients and requires intense user intervention to yield acceptable results. In this work, we propose Convolutional Neural Network (CNN)-based Deep Learning (DL) models as an alternative to problem specific conventional methods. The pretrained DL models were fine-tuned, employing data augmentation, on a small annotated dataset including a variety of conditions between SNR $$\approx$$ ≈ 1.6 to 2.6 and subsequently evaluated. All DL models significantly outperformed the best-scoring conventional implementation both quantitatively and visually, while having similar inference times. IoU-scores and Recall values were found to be up to a factor $$\approx$$ ≈ 1.2 and $$\approx$$ ≈ 2.5 higher, respectively, with $$\approx$$ ≈ 1.15 times improved Precision. Small-scale structures were captured much better with fewer erroneous predictions, becoming particularly pronounced for the lower SNR data investigated. Moreover, by applying artificially modeled noise, it was shown that the range of image conditions in terms of SNR that can be reliably processed extends well beyond the images included in the training data, and satisfactory segmentation performances were found for SNR as low as $$\approx$$ ≈ 1.1. The presented DL-based flame front detection algorithm marks a methodology with significantly increased detection performance, while a similar computational effort for inference is achieved and the need for user-based parameter tuning is eliminated. It enables a very accurate extraction of instantaneous flame fronts in large image datasets where supervised processing is infeasible, unlocking unprecedented possibilities for the study of flame dynamics and instability mechanisms at industry-relevant conditions.

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Section: Introductionmentioning

confidence: 99%

Deep learning-based image segmentation for instantaneous flame front extraction

Strässle,

Faldella,

Doll

2024

Exp Fluids

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Aerodynamic interactions of blunt bodies free-flying in hypersonic flow

Seltner,

Willems,

Gülhan

2024

Exp Fluids

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This paper takes a new look at how the aerodynamic interactions of multiple bodies in high-speed flow affect their motion behaviors. The influence of the body shape and orientation on aerodynamic and stability behavior in the case of shock–shock and wake–shock interactions is the focus of this publication. Experiments were performed in the hypersonic wind tunnel H2K at the German Aerospace Center (DLR) in Cologne. Free-flight tests with tandem arrangements of spheres and cubes were performed with a synchronized dropping of both objects at various initial conditions of relative streamwise and vertical distance as well as pitch angle. A high-speed stereo-tracking captured the model motions during free-flight, and high-speed schlieren videography provided documentation of the flow topology. Based on the measured 6-degrees-of-freedom (6DoF) motion data, aerodynamic coefficients were determined. As a result, the final lateral velocity of trailing cubes is found to be many times greater than that of spheres regarding shock-wave surfing. For rotating cubes, the results showed that stable shock-wave surfing can become possible over an increasingly wide range of initial positions. This study has identified that the trailing drag coefficient of two axially aligned objects varies strongly with their relative streamwise distance. Furthermore, it was shown that the wake is a region of stability for downstream objects. Graphical abstract

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Modeling the Interaction of Proximal Fragments for Destructive Atmospheric Entry Analysis

Morgado,

Fossati,

Kovács

2024

AIAA Journal

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The destructive atmospheric entry process is distinguished by the formation of several fragments due to the severe aerothermal loads encountered during the descent. The proximity of debris leads to shock wave interactions and collisions between bodies, influencing the overall dynamics of the fragments and affecting the prediction of demise and ground impact location. To account for these effects, the use of computational fluid dynamics is introduced to resolve the shock interaction patterns by employing a quasi-steady approach, and the use of a simple rigid-body collision model is introduced to represent the dynamics of fragments in the cloud of debris. The quasi-steady approach is assessed and validated by comparative analysis with a few studies in the literature. One of these examples is the rebuilding of the VKI’s experiment of a free-flying ring crossing a shock wave generated by the presence of a stationary cylinder. The impact of explicitly modeling collision dynamics in the trajectory of fragments and ground spread distance is validated by considering a set of relevant test cases from the literature and tested them for destructive atmospheric reentry of the Attitude Vernier Upper Module, the launcher VEGA upper stage.

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Space debris interaction across a two-dimensional oblique shock wave

Cited by 5 publications

References 26 publications

Deep learning-based image segmentation for instantaneous flame front extraction

Deep learning-based image segmentation for instantaneous flame front extraction

Aerodynamic interactions of blunt bodies free-flying in hypersonic flow

Modeling the Interaction of Proximal Fragments for Destructive Atmospheric Entry Analysis

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