High-Repetition-Rate OH-PLIF and PIV Measurements in Bluff Body Stabilized Flames

Caswell, Andrew W.; Jiang, Naibo; Roy, Sukesh; Huelskamp, Bethany; Monfort, Jeffrey R.; Lynch, Amy; Belovich, Vincent; Gord, James R.

doi:10.2514/6.2014-0733

Cited by 3 publications

(4 citation statements)

References 6 publications

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“…Of course, coupling to the acoustic modes of the combustor rig results in accentuation of the symmetry in the wake. This effect has been observed in prior studies as well (Caswell et al, 2014;Kostka et al, 2012;Monfort, 2015).…”

Section: Behavior Across Equivalence Ratiossupporting

confidence: 82%

“…This is perhaps suggestive of the presence of larger oscillating patterns in cases close to the stoichiometric ratio. Such an increase in the spread of the shear layer flame structure has been reported elsewhere (Caswell et al, 2014).…”

Section: Behavior Across Equivalence Ratiossupporting

confidence: 81%

“…In the field of combustion, (Kostka et al, 2012) provided an analysis of the bluff body wake using high-speed chemiluminescence images. Since then, several papers can be found in the literature that involve the utilization of the proper orthogonal decomposition on high-speed flow and flame imagery (Blanchard et al, 2014;Caswell et al, 2014;Emerson, 2013;Monfort, 2015;Morales et al, 2021;Roy et al, 2015;Shaw et al, 2019;Wickersham et al, 2014;Yang et al, 2020).…”

Section: This Algorithm Is Packaged As Part Of Popular Computing Envi...mentioning

confidence: 99%

“…Thus, a lot of information contained in this chapter can be found in (Caswell et al, 2014;Monfort, 2015). This information is presented here for the sake of completeness.…”

Section: Introductionmentioning

confidence: 99%

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Spectral analysis of OH-PLIF snapshots of a bluff body wake

Subramani

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I would like to thank Dr. Terrence Meyer for supporting me throughout, especially at moments when I was calling it quits. I would also like to thank Dr. James Michael for agreeing to be my co-major professor and helping to guide my final MS thesis work. In addition, I would like to thank Dr. Rodney Fox for agreeing to be on my POSC committee and directing me towards the dynamic mode decomposition. This work would not be possible if not for the support from staff members of the graduate college. In particular, I would like to thank Samantha Hirschman, Natalie Robinson, and Lisa Elm. I also want to thank Dr. Pranav Shrotriya for agreeing to be on the committee if needed. I would like to thank Sukesh Roy and Andrew Caswell for the bluff body dataset and for technical guidance. And finally, I would like to thank Joel Buehler for taking care of the GPU computer.

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Section: Behavior Across Equivalence Ratiossupporting

confidence: 82%

Section: Behavior Across Equivalence Ratiossupporting

confidence: 81%

Section: This Algorithm Is Packaged As Part Of Popular Computing Envi...mentioning

confidence: 99%

“…Thus, a lot of information contained in this chapter can be found in (Caswell et al, 2014;Monfort, 2015). This information is presented here for the sake of completeness.…”

Section: Introductionmentioning

confidence: 99%

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Spectral analysis of OH-PLIF snapshots of a bluff body wake

Subramani

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Experimental Characterization of Decay Rates in Bluff-Body Stabilized Flames Using Sodium Injection

Monfort¹,

Caswell²,

Belovich³

et al. 2015

53rd AIAA Aerospace Sciences Meeting

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Robust Mode Analysis

Gunaratne

Roy

2021

Mathematics

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In this paper, we introduce a model-free algorithm, robust mode analysis (RMA), to extract primary constituents in a fluid or reacting flow directly from high-frequency, high-resolution experimental data. It is expected to be particularly useful in studying strongly driven flows, where nonlinearities can induce chaotic and irregular dynamics. The lack of precise governing equations and the absence of symmetries or other simplifying constraints in realistic configurations preclude the derivation of analytical solutions for these systems; the presence of flow structures over a wide range of scales handicaps finding their numerical solutions. Thus, the need for direct analysis of experimental data is reinforced. RMA is predicated on the assumption that primary flow constituents are common in multiple, nominally identical realizations of an experiment. Their search relies on the identification of common dynamic modes in the experiments, the commonality established via proximity of the eigenvalues and eigenfunctions. Robust flow constituents are then constructed by combining common dynamic modes that flow at the same rate. We illustrate RMA using reacting flows behind a symmetric bluff body. Two robust constituents, whose signatures resemble symmetric and von Karman vortex shedding, are identified. It is shown how RMA can be implemented via extended dynamic mode decomposition in flow configurations interrogated with a small number of time-series. This approach may prove useful in analyzing changes in flow patterns in engines and propulsion systems equipped with sturdy arrays of pressure transducers or thermocouples. Finally, an analysis of high Reynolds number jet flows suggests that tests of statistical characterizations in turbulent flows may best be done using non-robust components of the flow.

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High-Repetition-Rate OH-PLIF and PIV Measurements in Bluff Body Stabilized Flames

Cited by 3 publications

References 6 publications

Spectral analysis of OH-PLIF snapshots of a bluff body wake

Spectral analysis of OH-PLIF snapshots of a bluff body wake

Experimental Characterization of Decay Rates in Bluff-Body Stabilized Flames Using Sodium Injection

Robust Mode Analysis

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