Towards an innovative mechanistic model to predict the detachment process of a deposited layer

Friso, Riccardo; Suman, Alessio; Vulpio, Alessandro; Zanini, Nicola; Casari, Nicola; Pinelli, Michele

doi:10.1016/j.ijheatmasstransfer.2022.123525

Cited by 3 publications

(1 citation statement)

References 35 publications

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“…The coarse powder (ARD M) does not determine relevant deposits concerning the smaller powder. In addition, the rotor surface is subjected to the centrifugal force that could be responsible for the detachment process of the deposits over the compressor operation [27].…”

Section: Resultsmentioning

confidence: 99%

Compressor fouling detection by image analysis

Suman

Zanini

Pinelli

2023

J. Phys.: Conf. Ser.

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Gas turbine fouling is commonly known as responsible for performance degradation in terms of compression ratio and efficiency. The adhesion of micro-sized particles (soil, soot, salt, etc.) caused the modification of the blade shapes and the surface roughness. Both of these two effects determine the modification of the compressor performance over the unit operation. Due to the lack of capability to forecast the fouling intensity, it could be useful to estimate the fouling intensity during the machine overhaul, collecting strategical data by which a specific characterization of a given machine in a given operating site can be done. The present paper proposes and validates a helpful methodology for estimating the deposit intensity by an image analysis procedure. An image-detection technique has been carried out before and after the contamination process, and, using a subtraction process, a quantitative analysis of the fouled regions can be developed. The results show that, with a careful light and camera setup, the intensity of the deposits can be estimated with an acceptable tolerance band, which allows the possibility of collecting quantitative data on compressor deposits during overhaul operations. This generates a valuable starting point for predicting the overtime degradation of the unit and/or estimating the filtration section efficiency.

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Section: Resultsmentioning

confidence: 99%

Compressor fouling detection by image analysis

Suman

Zanini

Pinelli

2023

J. Phys.: Conf. Ser.

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High-temperature deposition model for molten particle prediction based on droplet-surface interaction criteria

Yang,

Wang,

Chen

et al. 2024

International Communications in Heat and Mass Transfer

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Experimental Analysis of the Time-Wise Compressor Fouling Phenomenon

Suman,

Zanini,

Pinelli

2024

Journal of Turbomachinery

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Compressor fouling is still an unforeseen phenomenon. Gas turbines for heavy-duty and propulsion applications are subject to performance degradation due to fouling, erosion, and corrosion mechanisms. Unlike the last two, the fouling phenomenon is recoverable depending on weather, operating conditions, and operator actions. It is generated by the adhesion of micro-sized particles on the relevant surfaces, generating blade shape and surface roughness variations. Fouling could affect the machine performance differently. The interaction between machine characteristics, airborne contaminants, and environmental conditions determines fouling prediction challenges. An experimental campaign has been carried out in the present analysis to study the over-time modification of compressor performance and mass deposits on the blade and vane surfaces. The axial unit has been operated to control the performance and deposition process by keeping the contamination and the relative humidity constant over time. The test matrix has been based on two relative humidity values and four exposure time intervals. The results have shown the cation of the competitive phenomena as adhesion and detachment, generating a non-linear trend of the deposited mass-time relation depending on impact characteristics and humidity values. Compressor performance follows similar behavior and shows a non-linear trend instead of constant contamination. Data interpretation and generalization are reported to increase the possibility of better predicting the effects of particle adhesion on gas turbine performance degradation.

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Towards an innovative mechanistic model to predict the detachment process of a deposited layer

Cited by 3 publications

References 35 publications

Compressor fouling detection by image analysis

Compressor fouling detection by image analysis

High-temperature deposition model for molten particle prediction based on droplet-surface interaction criteria

Experimental Analysis of the Time-Wise Compressor Fouling Phenomenon

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