Detecting the Onset of an Impending Thermoacoustic Instability Using Complex Networks

Murugesan, Meenatchidevi; Sujith, R. I.

doi:10.2514/1.b35914

Cited by 47 publications

(21 citation statements)

References 23 publications

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“…Okuno et al (2015) use com-plex networks to show that pseudo-periodicity and high-dimensionality exist in the dynamics of thermoacoustic instability, including the possible presence of a clear power-law distribution and small-world-like nature. Precursors for thermoacoustic instability (Murugesan & Sujith, 2016) can be devised through the statistical theory of complex networks (Lesne & Lagues, 2011). Finally, the most sensitive points in the network (Yu et al, 2009) might be identified, aiding effective passive control.…”

Section: Complex Network To Study Thermoacoustic Instabilitymentioning

confidence: 99%

Sensitivity and Nonlinearity of Thermoacoustic Oscillations

Juniper

Sujith

2018

Annu. Rev. Fluid Mech.

255

171

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Nine decades of rocket engine and gas turbine development have shown that thermoacoustic oscillations are difficult to predict, but can usually be eliminated with relatively small ad hoc design changes. These changes can, however, be ruinously expensive to devise. This review explains why linear and nonlinear thermoacoustic behaviour is so sensitive to parameters such as operating point, fuel composition, and injector geometry. It shows how non-periodic behaviour arises in experiments and simulations and discusses how fluctuations in thermoacoustic systems with turbulent reacting flow, which are usually filtered or averaged out as noise, can reveal useful information. Finally, it proposes tools to exploit this sensitivity in the future: adjoint-based sensitivity analysis to optimize passive control designs, and complex systems theory to warn of impending thermoacoustic oscillations and to identify the most sensitive elements of a thermoacoustic system.

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Section: Complex Network To Study Thermoacoustic Instabilitymentioning

confidence: 99%

Sensitivity and Nonlinearity of Thermoacoustic Oscillations

Juniper

Sujith

2018

Annu. Rev. Fluid Mech.

255

171

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“…Using recurrence plots and recurrence networks, Kasthuri et al (2020) studied the recurrence properties of the slow-fast dynamics in the heat release rate oscillations of a bluff body stabilized combustor and the acoustic pressure oscillations in a model liquid rocket combustor during the occurrence of thermoacoustic instability. Researchers have also been helped by network theory to devise precursors for an impending thermoacoustic instability (Murugesan & Sujith 2016;Murayama et al 2018;Kobayashi et al 2019).…”

Section: Introductionmentioning

confidence: 99%

Suppression of thermoacoustic instability by targeting the hubs of the turbulent networks in a bluff body stabilized combustor

et al. 2021

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“…The characteristic path length, known as the average shortest path length, is the average of the shortest path lengths between all pairs of nodes in the network [ 21 ]. They all reflect the connectivity of biological networks [ 19 , 22 , 23 ]. After deleting the six candidate targets, the above parameters were significantly decreased, and the entire network would be decomposed into small components.…”

Section: Resultsmentioning

confidence: 99%

Using Coexpression Protein Interaction Network Analysis to Identify Mechanisms of Danshensu Affecting Patients with Coronary Heart Disease

Huo

Wang

et al. 2017

IJMS

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Salvia miltiorrhiza, known as Danshen, has attracted worldwide interest for its substantial effects on coronary heart disease (CHD). Danshensu (DSS) is one of the main active ingredients of Danshen on CHD. Although it has been proven to have a good clinical effect on CHD, the action mechanisms remain elusive. In the current study, a coexpression network-based approach was used to illustrate the beneficial properties of DSS in the context of CHD. By integrating the gene expression profile data and protein-protein interactions (PPIs) data, two coexpression protein interaction networks (CePIN) in a CHD state (CHD CePIN) and a non-CHD state (non-CHD CePIN) were generated. Then, shared nodes and unique nodes in CHD CePIN were attained by conducting a comparison between CHD CePIN and non-CHD CePIN. By calculating the topological parameters of each shared node and unique node in the networks, and comparing the differentially expressed genes, target proteins involved in disease regulation were attained. Then, Gene Ontology (GO) enrichment was utilized to identify biological processes associated to target proteins. Consequently, it turned out that the treatment of CHD with DSS may be partly attributed to the regulation of immunization and blood circulation. Also, it indicated that sodium/hydrogen exchanger 3 (SLC9A3), Prostaglandin G/H synthase 2 (PTGS2), Oxidized low-density lipoprotein receptor 1 (OLR1), and fibrinogen gamma chain (FGG) may be potential therapeutic targets for CHD. In summary, this study provided a novel coexpression protein interaction network approach to provide an explanation of the mechanisms of DSS on CHD and identify key proteins which maybe the potential therapeutic targets for CHD.

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Detecting the Onset of an Impending Thermoacoustic Instability Using Complex Networks

Cited by 47 publications

References 23 publications

Sensitivity and Nonlinearity of Thermoacoustic Oscillations

Sensitivity and Nonlinearity of Thermoacoustic Oscillations

Suppression of thermoacoustic instability by targeting the hubs of the turbulent networks in a bluff body stabilized combustor

Using Coexpression Protein Interaction Network Analysis to Identify Mechanisms of Danshensu Affecting Patients with Coronary Heart Disease

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