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In this study, a ribbed surface in an interior channel of a stationary turbine blade is benchmarked computationally and validated using experimental data. In order to investigate the CFD conjugate heat transfer and duplicate a model from a published article, STAR-CCM+ was used to find the turbulence model that best matched the published experimental values. An internal experimental rig was confirmed by comparing convective heat transfer coefficients and pressure profiles using those computational parameters and the CFD results. When compared to a smooth channel, this cooling technique improves turbulent mixing by detaching and reattaching the boundary layer, increasing heat transfer. The general objective is to assess a practical cooling technique while taking into account the flow physics, efficient heat transfer rates, and channel pressure drop reduction. The V2f turbulence model produced the closest matches to the experimental results, however EBk- turbulence model was employed for initial testing because to its instability at high Reynolds number. Shorter reattachment lengths and higher Nusselt number values between the ribs were the results for EBk-turbulence. The reported results' 6.8% and 6.66% respective levels of uncertainty are met by both the heat transport and friction components. Benchmark computational results will support the experimental setup's validity for further optimization and testing of various rib arrangement configurations.
Keywords:
CFD, STAR-CCM+, Nusselt number, EBk-Turbulence
Cite Article:
"Analytical Investigation of Ribbed Channels for Gas Turbine", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2455-2631, Vol.7, Issue 8, page no.1680 - 1696, August-2022, Available :http://www.ijrti.org/papers/IJRTI2208266.pdf
Downloads:
000205230
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator