Methodology for evaluating hot gas path defects in an exhaust jet

Adamczuk, Rafael R.; Luehrmann, Jan; Seume, Joerg R.

doi:10.1016/j.ast.2014.08.011

Cited by 2 publications

(4 citation statements)

References 20 publications

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“…In this work, both experimental BOS measurements and synthetic measurements based on numerical simulations are considered. For the numerical simulations, synthetic BOS measurements are performed with the methodology presented by Adamczuk et al (2014). This methodology uses numerical simulations of the exhaust jet to calculate the deflections that would be measured with BOS for a given tomographic set-up.…”

Section: Background-oriented Schlieren Methodsmentioning

confidence: 99%

Automated detection of hot-gas path defects by Support Vector Machine based analysis of exhaust density fields

Oettinger

Wein

Mimic

et al. 2021

J. Glob. Power Propuls. Soc.

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Defects in the hot-gas path of aero engines have been shown to leave typical signatures in the density distribution of the exhaust jet. These signatures are superposed when several defects are present. For improved maintenance and monitoring applications, it is important to not only detect that there are defects present but to also identify the individual classes of defects. This diagnostic approach benefits both, the analysis of prototype or acceptance test and the preparation of Maintenance, Repair, and Overhaul. Recent advances in the analysis of tomographic Background-Oriented Schlieren (BOS) data have enabled the technique to be automated such that typical defects in the hot-gas path of gas turbines can be detected and distinguished automatically. This automation is achieved by using Support Vector Machine (SVM) algorithms. Choosing suitable identification parameters is critical and can enable SVM algorithms to distinguish between different defect types. The results show that the SVM can be trained such that almost no defects are missed and that false attributions of defect classes can be minimized.

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Section: Background-oriented Schlieren Methodsmentioning

confidence: 99%

Automated detection of hot-gas path defects by Support Vector Machine based analysis of exhaust density fields

Oettinger

Wein

Mimic

et al. 2021

J. Glob. Power Propuls. Soc.

View full text Add to dashboard Cite

show abstract

“…The inlet conditions included six areas with different temperatures, to simulate malfunctions in individual burners in the annular combustion chamber of the engine. A subsequent study in [30] investigated whether these non-uniform inlet temper atures result in irregularities in the exhaust jet which are detectable with tomographic BOS. The exhaust jet was simulated with CFD to get realistic density gradients between the jet and the surrounding air.…”

Section: Comparison Of Fbp and Artmentioning

confidence: 99%

“…To facilitate this, the results in the last plane of the LPT from [29] were used as boundary conditions for the simulation of the exhaust jet. The density distribution 30 mm downstream of the LPT was used to perform synthetic BOS measurements with the method presented in [30], and investigate whether the irregularities in the exhaust jet may be obtained with BOS or not. The density distribution is illustrated in figure 7(a).…”

Section: Comparison Of Fbp and Artmentioning

confidence: 99%

“…This complex density distribution is used to test the combination of FBP and ART. Synthetic BOS measurements are performed using the method presented in [30] with eight cameras on a grid of 199 199 × cells for the FBP and the ART algorithm. The grid size is chosen to match the resolution of the hardware used for the BOS measurements performed above.…”

Section: Comparison Of Fbp and Artmentioning

confidence: 99%

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Combining ART and FBP for improved fidelity of tomographic BOS

Hartmann

Seume

2016

Meas. Sci. Technol.

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Engine component defects along the hot-gas path (HGP) of jet engines influence the density distribution of the flow, and thus result in characteristic patterns in the exhaust jet. These characteristic patterns can be reconstructed with the optical background-oriented schlieren (BOS) method in a tomographic set-up, which in turn allows the identification of defects inside the engine through an exhaust jet analysis.The quality of the tomographic reconstruction strongly influences how easily defects can be detected inside the jet engine. In particular, the presence of high gradients in the reconstruction area has a strong impact on the reconstruction quality. An algebraic reconstruction algorithm (ART) is implemented and compared to a filtered-back projection (FBP) algorithm in terms of the capability of performing high-gradient tomographic BOS reconstructions. A combination of both algorithms is presented which significantly improves the reconstruction quality of high-gradient tomographic BOS in terms of artifact reduction. The combination of both algorithms is applied to both synthetic and real measurement data in this paper, in order to show possible applications and the achievable improvement of high-gradient tomographic BOS reconstructions.

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Methodology for evaluating hot gas path defects in an exhaust jet

Cited by 2 publications

References 20 publications

Automated detection of hot-gas path defects by Support Vector Machine based analysis of exhaust density fields

Automated detection of hot-gas path defects by Support Vector Machine based analysis of exhaust density fields

Combining ART and FBP for improved fidelity of tomographic BOS

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