Volume 3C: Heat Transfer 2013
DOI: 10.1115/gt2013-95108
|View full text |Cite
|
Sign up to set email alerts
|

Deposition With Hot Streaks in an Uncooled Turbine Vane Passage

Abstract: The effect of hot streaks on deposition in a high pressure turbine vane passage was studied both experimentally and computationally. Modifications to Ohio State’s Turbine Reaction Flow Rig allowed for the creation of simulated hot streaks in a four-vane annular cascade operating at temperatures up to 1093°C. Total temperature surveys were made at the inlet plane of the vane passage, showing the variation caused by cold dilution jets. Deposition was generated by introducing sub-bituminous ash particles with a m… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

1
30
1

Year Published

2015
2015
2022
2022

Publication Types

Select...
6
1

Relationship

0
7

Authors

Journals

citations
Cited by 19 publications
(32 citation statements)
references
References 0 publications
1
30
1
Order By: Relevance
“…For investigation of the particle deposition on NGV, RANS equations are mostly used to predict velocity, temperature fields, and film cooling effectiveness. A similar application has been reported by Casaday et al [38], Casaday et al [39], Barker et al [59] and Casaday et al [60]. In addition, the effect of particle deposition in a compressor stage was also investigated by using RANS [61].…”
Section: Solution To Turbulent Flowmentioning
confidence: 56%
See 1 more Smart Citation
“…For investigation of the particle deposition on NGV, RANS equations are mostly used to predict velocity, temperature fields, and film cooling effectiveness. A similar application has been reported by Casaday et al [38], Casaday et al [39], Barker et al [59] and Casaday et al [60]. In addition, the effect of particle deposition in a compressor stage was also investigated by using RANS [61].…”
Section: Solution To Turbulent Flowmentioning
confidence: 56%
“…For comparison of the capture efficiencies on the NGV, some results were obtained by considering various influence factors, such as particle material [24], particle size [25,29,34], gas temperature [25,34], blowing ratio [33], inlet turbulence level [37], deposition time [34], and hot streak [35,38,39]. Effects of the gas temperature and particle size are analyzed in fig.…”
Section: Testing Facility For Accelerated Depositionmentioning
confidence: 99%
“…Compared to experimental results in Webb et al [15], Barker et al [14] determined that their deposition model predicts initial deposition relatively well, but is not valid after large-scale deposits begin to form causing geometry changes. Casaday et al [16] used the deposition model to investigate the effects of a non-uniform inlet temperature profile on external deposition, and found by comparing to experiments that the model could be tuned to produce more representative results. Suman et al [17] performed a computational study of deposition in a full stage axial compressor using a multiple reference frame, frozen-rotor approach focused on small particulate that could evade an inlet filter.…”
Section: Figure 12: Damage To Turbine Blading Due To Deposition [4]mentioning
confidence: 99%
“…Because the experiment by Haldeman et al was performed at temperatures much lower than those typically found in gas turbines, a tuning process was performed in order to produce a fictitious particle type with constants, A, B, and C, that would deposit in the simulations. The critical viscosity model used was tuned using typical vane capture efficiencies from experiment for coal ash particles at an inlet temperature of approximately 1400 K [16]. By tuning the model parameters to the vane capture efficiencies from high temperature experiments, realistic vane capture efficiencies were achieved in the simulation at an inlet temperature of 443 K. This tuning was assumed to be appropriate for the blade surface as well due to the lack of experimental, rotating blade deposition data available.…”
Section: Model Tuning Processmentioning
confidence: 99%
See 1 more Smart Citation