Effect of pulsation on the near flow field of a submerged water jet

Yadav, Harekrishna; Agrawal, Amit

doi:10.1007/s12046-018-0814-1

Cited by 9 publications

(2 citation statements)

References 19 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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“…Yadav and Agrawal [15] investigated the effect of pulsation frequency on the near-field characteristics of a submerged water jet using the technique of dye visualization. e results showed that the initiation and growth of vortices in the shear layer depends on the pulse frequency.…”

Section: Introductionmentioning

confidence: 99%

Investigation of Vortex Rings for Free Jet and Synthetic Jet at Various Reynolds Numbers and Strouhal Numbers

Chang

Zheng

et al. 2020

Mathematical Problems in Engineering

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The behaviors of vortex rings for free jet and synthetic jet with various Reynolds numbers and Strouhal numbers are numerically studied by the k-ε model. The positions of the leading vortex of free jet are investigated under the three different conditions, and the effect of Strouhal number on the vortex ring is analyzed in detail. The results show that different Reynolds numbers lead to different positions for the shedding of the vortex rings. During the movement of the vortex rings, the symmetry of the vortex ring in synthetic jet retains in good state, and the vortex rings do not break up with well-arranged distributions. The distance between two adjacent vortex rings near the synthetic jet exit has the close link between the Strouhal number yet is independent of the Reynolds numbers.

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