High-order accurate direct numerical simulation of flow over a MTU-T161 low pressure turbine blade
File(s)
Published version
Author(s)
Type
Journal Article
Abstract
Reynolds Averaged Navier-Stokes (RANS) simulations and wind tunnel testing have become
the go-to tools for industrial design of Low-Pressure Turbine (LPT) blades. However, there
is also an emerging interest in use of scale-resolving simulations, including Direct Numerical
Simulations (DNS). These could generate insight and data to underpin development of improved
RANS models for LPT design. Additionally, they could underpin a virtual LPT wind tunnel
capability, that is cheaper, quicker, and more data-rich than experiments. The current study
applies PyFR, a Python based Computational Fluid Dynamics (CFD) solver, to fifth-order
accurate petascale DNS of compressible flow over a three-dimensional MTU-T161 LPT blade
with diverging end walls at a Reynolds number of 200, 000 on an unstructured mesh with over 11
billion degrees-of-freedom per equation. Various flow metrics, including isentropic Mach number
distribution at mid-span, surface shear, and wake pressure losses are compared with available
experimental data and found to be in agreement. Subsequently, a more detailed analysis of
various flow features is presented. These include the separation/transition processes on both
the suction and pressure sides of the blade, end-wall vortices, and wake evolution at various
span-wise locations. The results, which constitute one of the largest and highest-fidelity CFD
simulations ever conducted, demonstrate the potential of high-order accurate GPU-accelerated
CFD as a tool for delivering industrial DNS of LPT blades.
the go-to tools for industrial design of Low-Pressure Turbine (LPT) blades. However, there
is also an emerging interest in use of scale-resolving simulations, including Direct Numerical
Simulations (DNS). These could generate insight and data to underpin development of improved
RANS models for LPT design. Additionally, they could underpin a virtual LPT wind tunnel
capability, that is cheaper, quicker, and more data-rich than experiments. The current study
applies PyFR, a Python based Computational Fluid Dynamics (CFD) solver, to fifth-order
accurate petascale DNS of compressible flow over a three-dimensional MTU-T161 LPT blade
with diverging end walls at a Reynolds number of 200, 000 on an unstructured mesh with over 11
billion degrees-of-freedom per equation. Various flow metrics, including isentropic Mach number
distribution at mid-span, surface shear, and wake pressure losses are compared with available
experimental data and found to be in agreement. Subsequently, a more detailed analysis of
various flow features is presented. These include the separation/transition processes on both
the suction and pressure sides of the blade, end-wall vortices, and wake evolution at various
span-wise locations. The results, which constitute one of the largest and highest-fidelity CFD
simulations ever conducted, demonstrate the potential of high-order accurate GPU-accelerated
CFD as a tool for delivering industrial DNS of LPT blades.
Date Issued
2021-08-15
Date Acceptance
2021-04-23
Citation
Computers and Fluids, 2021, 226
ISSN
0045-7930
Publisher
Elsevier
Journal / Book Title
Computers and Fluids
Volume
226
Copyright Statement
©2021 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/ )
License URL
Sponsor
Engineering & Physical Science Research Council (EPSRC)
Engineering & Physical Science Research Council (EPSRC)
Commission of the European Communities
Grant Number
EP/K027379/1
EP/R030340/1
814837
Subjects
Science & Technology
Technology
Computer Science, Interdisciplinary Applications
Mechanics
Computer Science
Computational fluid dynamics
High-order methods
Direct numerical simulations
Low-Pressure turbines
LARGE-EDDY SIMULATION
COMPUTATION
Applied Mathematics
0102 Applied Mathematics
0913 Mechanical Engineering
0915 Interdisciplinary Engineering
Publication Status
Published
Article Number
ARTN 104989
Date Publish Online
2021-05-13