The combined investigation of the flame dynamics of an industrial gas turbine combustor using high-speed imaging and an optically integrated data collection method

Ng, W B; Clough, E.; Syed, Khawar; Y, Zhang

doi:10.1088/0957-0233/15/11/016

Cited by 17 publications

(13 citation statements)

References 10 publications

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“…The digital flame color discrimination (DFCD) approach developed by Huang and Zhang , shows that the instantaneous flame combustion performance can be directly represented via flame color. The optical detection method and post-processing imaging provide a non-intrusive and alternative approach for monitoring the instantaneous soot formation behavior of liquid fuel combustion. ,,, …”

Section: Methodsmentioning

confidence: 99%

Effects of Alternative Fuel Properties on Particulate Matter Produced in a Gas Turbine Combustor

et al. 2018

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Diversified fuel supplies and stringent environmental pollution regulations in the aviation sector have promoted the development of the alternative fuels industry. The chemical and physical properties of some of these diverse fuel substitutes lie outside of historical experience. Therefore, their combustion behavior cannot be judged via research of petroleum-derived jet fuel. Particulate matter (PM) emissions are important for future alternative fuels, although extensive results in relations to combustors are not available in the literature. Hence, large-scale experimental testing is essential for improving our understanding of alternative fuel effects on combustion performance and environmental impact. The aim of this study is to evaluate the impact of fuel properties and composition on the PM emission characteristics and flame sooty tendency profile on a Rolls-Royce Tay gas turbine combustor. Extractive sampling and in situ measurement methods have been used in this study. A total of 16 types of alternative fuels have been tested under two different operating conditions. PM emissions were measured via a differential mobility spectrometer (DMS 500 fast particulate spectrometer), and the soot propensity profile was analyzed via an innovative visual method based on flame luminosity high-speed imaging. The results indicate that a higher aromatic can be found as the main factor for insufficient burning and greater soot formation. In addition, for fuel properties, the density and surface tension were supposed to be key factors for soot formation. For chemical compositions, fuels with higher cycloparaffin content have the potential to induce soot promotion. In contrast, a fuel with a high hydrogen content can perform in a much more environmentally friendly way. Furthermore, it was observed that the results of PM emission measured by DMS 500 and sooting tendency computed via an imaging method (in situ) correlated particularly well for all of the tested fuels and conditions in this study. The in situ soot emission monitoring method presented in this study can be used for detailed, instantaneous investigation of PM emissions within the combustor. Thus, this method can be considered an alternative evaluation method for measuring qualitative soot emissions.

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Section: Methodsmentioning

confidence: 99%

Effects of Alternative Fuel Properties on Particulate Matter Produced in a Gas Turbine Combustor

et al. 2018

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“…approach. An alternative non-intrusive approach can monitor the immediate soot formation behavior through imaging post-processing and optical detection method for liquid fuel combustion [36,37]. A well-known instrument, DMS 500, was used to quantify PM concentration over the full particle size spectrum and real-time size distribution information [38,39].…”

Section: Accepted Manuscript N O T C O P Y E D I T E Dmentioning

confidence: 99%

Impact of Aromatic Structures and Content in Formulated Fuel for Jet Engine Applications on Particulate Matter Emissions

Zheng

Singh

Cronly

et al. 2021

Journal of Energy Resources Technology

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Fuel formulation with particular selection of fuel components is a promising approach that offer the reduction of harmful emissions without altering the combustion system performance. Each fuel component has their own combustion characteristics and hence contribution in emissions. Aromatic is one of the main component of fossil based fuels and have a strong correlation with the formation of PM emissions. Besides aromatics presence in fuel is essential for compatibility of fuel with combustion system and maintaining the energy density of the fuel. In this regard, a Rolls Royce combustor rig was used to test 16 aromatics blended with jet fuels in three different proportions. Moreover, a novel approach of flame luminosity imaging is employed to measure the PM emissions through the soot propensity profile. The results show that PM emissions increase with the proportional increase of aromatics. The di- and cyclo-aromatics produced significantly higher PM emissions compared to alkyl-benzenes. 3-isopropylcumene has the tendency to lowest PM formation and thus is a consideration as a selection of aromatic type in future fuels for lower PM emissions. Furthermore, it was also observed that PM number concentration measured by extractive method with DMS500 instrument correlates well with imaging methods for all the tested fuels. The present study provides an information of particular selection of aromatic for future fuel development.

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“…A novelty detection method using SVMs [39] was able to achieve earlier identification of combustion instability, and greater distinction between stable and unstable classes than the conventional GMM method. The optical measurements methods described above have been used to study the flame dynamics of unstable combustion [40].…”

Section: Datasetsmentioning

confidence: 99%

Probabilistic Novelty Detection With Support Vector Machines

Clifton

Zhang

et al. 2014

IEEE Trans. Rel.

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Novelty detection, or one-class classification, is of particular use in the analysis of high-integrity systems, in which examples of failure are rare in comparison with the number of examples of stable behaviour, such that a conventional multi-class classification approach cannot be taken. Support Vector Machines (SVMs) are a popular means of performing novelty detection, and it is conventional practice to use a train-validate-test approach, often involving cross-validation, to train the one-class SVM, and then select appropriate values for its parameters. An alternative method, used with multi-class SVMs, is to calibrate the SVM output into conditional class probabilities. A probabilistic approach offers many advantages over the conventional method, including the facility to select automatically a probabilistic novelty threshold. The contributions of this paper are (i) the development of a probabilistic calibration technique for one-class SVMs, such that on-line novelty detection may be performed in a probabilistic manner; and (ii) the demonstration of the advantages of the proposed method (in comparison to the conventional one-class SVM methodology) using case studies, in which one-class probabilistic SVMs are used to perform condition monitoring of a high-integrity industrial combustion plant, and in detecting deterioration in patient physiological condition during patient vital-sign monitoring.

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The combined investigation of the flame dynamics of an industrial gas turbine combustor using high-speed imaging and an optically integrated data collection method

Cited by 17 publications

References 10 publications

Effects of Alternative Fuel Properties on Particulate Matter Produced in a Gas Turbine Combustor

Effects of Alternative Fuel Properties on Particulate Matter Produced in a Gas Turbine Combustor

Impact of Aromatic Structures and Content in Formulated Fuel for Jet Engine Applications on Particulate Matter Emissions

Probabilistic Novelty Detection With Support Vector Machines

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