RANS Capabilities for Transonic Axial Compressor: A Perspective From GPPS Computational Fluid Dynamics WorkshopSource: Journal of Turbomachinery:;2024:;volume( 147 ):;issue: 002::page 21007-1DOI: 10.1115/1.4066431Publisher: The American Society of Mechanical Engineers (ASME)
Abstract: Reynolds-averaged Navier–Stokes (RANS) simulations currently serve as the prevailing industrial method for simulating axial compressor flows, and this status is expected to persist in the foreseeable future. To evaluate the capabilities of contemporary RANS solvers for compressors, this article presents a statistical analysis of RANS simulation results submitted to the first and the second Global Power and Propulsion Society (GPPS) computational fluid dynamics (CFD) workshops, where blind tests on the TUDa-GLR-OpenStage transonic axial compressor were performed. The workshops were held online in December 2021 and in a hybrid format at Chania, Greece, in September 2022, which are the first primary turbomachinery CFD workshops following the 1994 International Gas Turbine Institute (IGTI) CFD blind test event on NASA Rotor 37. A total of 35 submissions were received from 12 distinct RANS solvers, contributed by 14 participants affiliated with 11 organizations across 5 countries. Participants include academic researchers, engineers from the turbomachinery industry, and developers of commercial CFD solvers. First, the grid convergence behavior exhibited by various solvers employing different turbulence models is examined. Afterward, the prediction accuracy of the ensemble of the simulation results is evaluated, and the representative simulation results are compared and analyzed in detail. The key factors that improve the prediction accuracy are identified. These results foster improved usage and further development of turbomachinery RANS solvers.
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contributor author | He, Xiao | |
contributor author | Klausmann, Fabian | |
date accessioned | 2025-04-21T10:00:49Z | |
date available | 2025-04-21T10:00:49Z | |
date copyright | 10/8/2024 12:00:00 AM | |
date issued | 2024 | |
identifier issn | 0889-504X | |
identifier other | turbo_147_2_021007.pdf | |
identifier uri | http://yetl.yabesh.ir/yetl1/handle/yetl/4305312 | |
description abstract | Reynolds-averaged Navier–Stokes (RANS) simulations currently serve as the prevailing industrial method for simulating axial compressor flows, and this status is expected to persist in the foreseeable future. To evaluate the capabilities of contemporary RANS solvers for compressors, this article presents a statistical analysis of RANS simulation results submitted to the first and the second Global Power and Propulsion Society (GPPS) computational fluid dynamics (CFD) workshops, where blind tests on the TUDa-GLR-OpenStage transonic axial compressor were performed. The workshops were held online in December 2021 and in a hybrid format at Chania, Greece, in September 2022, which are the first primary turbomachinery CFD workshops following the 1994 International Gas Turbine Institute (IGTI) CFD blind test event on NASA Rotor 37. A total of 35 submissions were received from 12 distinct RANS solvers, contributed by 14 participants affiliated with 11 organizations across 5 countries. Participants include academic researchers, engineers from the turbomachinery industry, and developers of commercial CFD solvers. First, the grid convergence behavior exhibited by various solvers employing different turbulence models is examined. Afterward, the prediction accuracy of the ensemble of the simulation results is evaluated, and the representative simulation results are compared and analyzed in detail. The key factors that improve the prediction accuracy are identified. These results foster improved usage and further development of turbomachinery RANS solvers. | |
publisher | The American Society of Mechanical Engineers (ASME) | |
title | RANS Capabilities for Transonic Axial Compressor: A Perspective From GPPS Computational Fluid Dynamics Workshop | |
type | Journal Paper | |
journal volume | 147 | |
journal issue | 2 | |
journal title | Journal of Turbomachinery | |
identifier doi | 10.1115/1.4066431 | |
journal fristpage | 21007-1 | |
journal lastpage | 21007-13 | |
page | 13 | |
tree | Journal of Turbomachinery:;2024:;volume( 147 ):;issue: 002 | |
contenttype | Fulltext |