Data-based analysis of multimodal partial cavity shedding dynamics

Barwey, Shivam; Ganesh, Harish; Hassanaly, Malik; Raman, Venkat; Ceccio, Steven L.

doi:10.1007/s00348-020-2940-x

Cited by 20 publications

(18 citation statements)

References 30 publications

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“…The clustering process is done in an unsupervised manner using K-means clustering [46][47][48], which has been successfully applied in a variety of fluid applications [49][50][51][52]. The K-means algorithm takes as an input a number of clusters and subsequently groups the realizations (or snapshots) into these many clusters.…”

Section: K-means Clusteringmentioning

confidence: 99%

“…In the following results, the distance chosen for the clustering is the L 2 norm computed in physical space. While this choice has been sufficient in other applications [49][50][51], it is not necessarily ideal. Other distance measures based on image recognition concepts were investigated [58], though these led to the same conclusions as the ones presented in Sec.…”

Section: The Number Of Clusters and The Normmentioning

confidence: 99%

“…There exist indicators that evaluate how suited a candidate number of clusters is to a particular dataset. However, these indicators should not be considered as a systematic way of choosing the number of clusters [50], but rather as guidelines that should be refined by the user. Here, two different indicators (silhouette score [53] and X-means [54][55][56]) were used to determine the optimal cluster number.…”

Section: The Number Of Clusters and The Normmentioning

confidence: 99%

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Data-driven analysis of relight variability of jet fuels induced by turbulence

Hassanaly

Tang

Barwey

et al. 2021

Combustion and Flame

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For safety purposes, reliable reignition of aircraft engines in the event of flame blow-out is a critical requirement. Typically, an external ignition source in the form of a spark is used to achieve a stable flame in the combustor. However, such forced turbulent ignition may not always successfully relight the combustor, mainly because the state of the combustor cannot be precisely determined. Uncertainty in the turbulent flow inside the combustor, inflow conditions, and spark discharge characteristics can lead to variability in sparking outcomes even for nominally identical operating conditions. Prior studies have shown that of all the uncertain parameters, turbulence is often dominant and can drastically alter ignition behavior. For instance, even when different fuels have similar ignition delay times, their ignition behavior in practical systems can be completely different. In practical operating conditions, it is challenging to understand why ignition fails and how much variation in outcomes can be expected. The focus of this work is to understand relight variability induced by turbulence for two different aircraft fuels, namely Jet-A and a variant named C1. A detailed, previously developed simulation approach is used to generate a large number of successful and failed ignition events. Using this data, the cause of misfire is evaluated based on a discriminant analysis that delineates the difference between turbulent initial conditions that lead to ignition or failure. From the discriminant analysis, a compressed sensing algorithm is then applied to help pinpoint the locations of relevant turbulent features. Findings from the discriminant analysis are confirmed with the time history of near kernel properties. Next, a clustering strategy is used to identify ignition and misfire modes. With this approach, it was determined that the cause of ignition failure is different for the two fuels. While it was found that Jet-A is influenced by fuel entrainment, C1 was found to be more sensitive to small scale turbulence features. A larger variability is found in the ignition modes of C1, which can be subject to extreme events induced by kernel breakdown.

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Section: K-means Clusteringmentioning

confidence: 99%

Section: The Number Of Clusters and The Normmentioning

confidence: 99%

Section: The Number Of Clusters and The Normmentioning

confidence: 99%

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Data-driven analysis of relight variability of jet fuels induced by turbulence

Hassanaly

Tang

Barwey

et al. 2021

Combustion and Flame

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“…The latter emerges owing to coherent vortex rollup extending significantly within the flow core and exhibits an elongated or horse-shoe type shape. The overall topology and dynamics of partial and vortical cavities have been visualised with reference to a range of benchmark geometrical layouts such as hydrofoils, wedges and forward facing steps [16][17][18][19][20][21][22], converging diverging or throttle nozzles [23] and bluff bodies or obstacles [24][25][26][27][28].…”

Section: Introductionmentioning

confidence: 99%

X-ray phase contrast and absorption imaging for the quantification of transient cavitation in high-speed nozzle flows

et al. 2021

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High-flux synchrotron radiation has been employed in a time-resolved manner to characterise the distinct topology features and dynamics of different cavitation regimes arising in a throttle orifice with an abrupt flow-entry contraction. Radiographs obtained though both X-ray phasecontrast and absorption imaging have been captured at 67,890 frames per second. The flow lied in the turbulent regime (Re=35,500), while moderate (CN=2.0) to well-established (CN=6.0) cavitation conditions were examined encompassing the cloud and vortical cavitation regimes with pertinent transient features, such as cloud-cavity shedding. X-ray Phase-Contrast Imaging (XPCI), exploiting the shift in the X-ray wave phase during interactions with matter, offers sharp-refractive index gradients in the interface region. Hence, it is suitable for capturing fine morphological fluctuations of transient cavitation structures. Nevertheless, the technique cannot provide information on the quantity of vapour within the orifice. Such data have been obtained utilising absorption imaging, where beam attenuation is not associated with scattering and refraction events, and hence can be explicitly correlated with the projected vapour thickness in line-of-sight measurements. A combination of the two methods is proposed, as it has been found is capable of quantifying the vapour content arising in the complex nozzle flow, while also faithfully illustrating the dynamics of the highly-transient cavitation features.

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“…This technique enables the efficient elimination of redundant data points from quiescent or otherwise unimportant regions when the phenomenon of interest is intermittent, effectively attempting an approximate phase-space sampling like the one proposed in the present work. However, IS via clustering is sensitive to the choice of the number of clusters, and there is no systematic way of deciding on an appropriate number of clusters [6,41,42]. Furthermore, in practice, clustering tends to underrepresent rare data points (see Appendix B).…”

Section: Introductionmentioning

confidence: 99%

Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows

Hassanaly¹,

Perry²,

Müeller³

et al. 2021

Preprint

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Figure 1: Graphical illustration of the data selection method. Starting from a large dataset (top left scatter plot), a reduced uniform-in-phase-space dataset is constructed (right scatter plot). The reduction method relies on the probability map which is iteratively computed (Step 1 and Step 1 correction). An acceptance probability constructed with the probability map is used to accept or reject each data point (Step 2).

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Data-based analysis of multimodal partial cavity shedding dynamics

Cited by 20 publications

References 30 publications

Data-driven analysis of relight variability of jet fuels induced by turbulence

Data-driven analysis of relight variability of jet fuels induced by turbulence

X-ray phase contrast and absorption imaging for the quantification of transient cavitation in high-speed nozzle flows

Uniform-in-Phase-Space Data Selection with Iterative Normalizing Flows

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