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**Harvard**

Johansson, C. (2018) *Evaluation of an in-house GPU based CFD solver*. Göteborg : Chalmers University of Technology (Examensarbete - Institutionen för mekanik och maritima vetenskaper, nr: 2018:12).

** BibTeX **

@mastersthesis{

Johansson2018,

author={Johansson, Christoffer},

title={Evaluation of an in-house GPU based CFD solver},

abstract={This thesis describes the evaluation of an in-house CFD solver at GKN aerospace.
The solver is called CUDA since it is GPU based, and CUDA cores are thereby used.
Legacy turbine rear structures, designs from previous projects, are used when
evaluating the solver.
The first part of this thesis compares two different versions of CUDA. A correction in
the definition of axial wall shear stress makes the new version more correct. By postprocessing
flow parameters directly from the solution the new version is 25% faster
than the previous version. The new version was officially released as a result of the
validations performed in this thesis.
The second part of this thesis evaluates the CUDA solver by comparing CFD results
with the commercial CFD solver Fluent. CUDA shows similar trends as fully resolved
k-ε realizable in Fluent. CUDA uses k-ε with realizability limiters as well, but for the
wall treatments wall-functions are used. One evaluated flow parameter is total
pressure loss. The pressure loss is presented in three different ways with respect to the
inlet swirl angle, using a so called loss bucket and also two factors with normalized
result. The two normalized factors are called off-design factor and loss difference. For
all these three ways of presenting the pressure loss CUDA and Fluent predicts similar
trends for all turbine rear structures. This correlation in pressure loss is predicted until
separation occurs. Separation occurs later for CUDA than Fluent. For CUDA the
point of separation is predicted to occur in average at 4 degrees more swirl than fully
resolved k-ε realizable and 11.5 degrees later than k-ω SST for the turbine rear
structures. The pressure loss is predicted to be lower in CUDA. CUDA predicts
between 1% and 13% lower pressure loss than fully resolved k-ε realizable in Fluent,
in average 6.75%.},

publisher={Institutionen för mekanik och maritima vetenskaper, Strömningslära, Chalmers tekniska högskola},

place={Göteborg},

year={2018},

series={Examensarbete - Institutionen för mekanik och maritima vetenskaper, no: 2018:12},

keywords={Aerodynamics, CFD solver, CUDA, GKN aerospace, GPU, in-house code, jet engine, turbine rear structure, pressure loss},

}

** RefWorks **

RT Generic

SR Electronic

ID 256206

A1 Johansson, Christoffer

T1 Evaluation of an in-house GPU based CFD solver

YR 2018

AB This thesis describes the evaluation of an in-house CFD solver at GKN aerospace.
The solver is called CUDA since it is GPU based, and CUDA cores are thereby used.
Legacy turbine rear structures, designs from previous projects, are used when
evaluating the solver.
The first part of this thesis compares two different versions of CUDA. A correction in
the definition of axial wall shear stress makes the new version more correct. By postprocessing
flow parameters directly from the solution the new version is 25% faster
than the previous version. The new version was officially released as a result of the
validations performed in this thesis.
The second part of this thesis evaluates the CUDA solver by comparing CFD results
with the commercial CFD solver Fluent. CUDA shows similar trends as fully resolved
k-ε realizable in Fluent. CUDA uses k-ε with realizability limiters as well, but for the
wall treatments wall-functions are used. One evaluated flow parameter is total
pressure loss. The pressure loss is presented in three different ways with respect to the
inlet swirl angle, using a so called loss bucket and also two factors with normalized
result. The two normalized factors are called off-design factor and loss difference. For
all these three ways of presenting the pressure loss CUDA and Fluent predicts similar
trends for all turbine rear structures. This correlation in pressure loss is predicted until
separation occurs. Separation occurs later for CUDA than Fluent. For CUDA the
point of separation is predicted to occur in average at 4 degrees more swirl than fully
resolved k-ε realizable and 11.5 degrees later than k-ω SST for the turbine rear
structures. The pressure loss is predicted to be lower in CUDA. CUDA predicts
between 1% and 13% lower pressure loss than fully resolved k-ε realizable in Fluent,
in average 6.75%.

PB Institutionen för mekanik och maritima vetenskaper, Strömningslära, Chalmers tekniska högskola,

T3 Examensarbete - Institutionen för mekanik och maritima vetenskaper, no: 2018:12

LA eng

LK http://publications.lib.chalmers.se/records/fulltext/256206/256206.pdf

OL 30