Exploring the Potency of Computational Fluid 

Dynamics for Holistic Reconstruction of flow 

behavior in the NIST Neutron Design

Evan Bures

August 3rd, 2023
• Evan Bures

• Senior at Texas A&M University

• Nuclear Engineering Major

• Worked in Thermal Hydraulics Verification and 

Validation Group for almost 2 years under Dr. Mark 

Kimber

• Close to 2 years of experience in Computational Fluid 

dynamics (CFD)

• Most work done with highly transient turbulent flows 

with heat transfer

About me

2
• NIST Neutron Source (NNS)

• Planned replacement for current reactor (NBSR)

• High power density reactor meant for neutron 

generation

• Flow modelling centered around:

• Inlet region (lower plenum)

• Curved fuel plates with curved fuel channels 

separating them (Active Height)

• Outlet region ( long stack)

NNS Reactor Design

3

[3]
Geometries of Interest 

Fuel Channels (Curved Rectangular Channels)

Single-channel Studies

Intermediate Mixing Plenum (3-channel mixing)

Parallel Mixing Studies

[1]
What is Computational Fluid Dynamics?

• We discretize fluid systems into multiple smaller elements known as cells to 

generate what is known as a “mesh”.

• Once we have our mesh we can attempt to model the Navier stokes equations in 

each individual cell to create one larger picture to represent the entire system

• In order to do this efficiently we employ multiple simplifications such as the 

Reynolds Averaged Navier Stokes (RANS) equations, Large Eddy decomposition 

equations (LES), or even in some cases attempting to directly solve the Navier 

Stokes equations in what's known as “Direct Numerical Simulation” (DNS)

• Furthermore, turbulent effects must be solved using a separate subset of models 

known as “turbulence models”.
Reynolds Averaged Navier Stokes (RANS)

• Rans Resolves turbulence by creating 

large, averaged, general approximations 

for turbulent effects in regions. 

• We can use RANS to model simple flows 

or flows that we want a general idea of 

how the flow will behave (no super 

specific values of velocity or pressure)

• RANS is fast!!! Work horse of CFD
Large Eddy Simulation (LES)

• Resolves turbulence at much smaller scales by seperating and directly 

solving large, more influential turbulence scales while modelling 

smaller scales.

• Much more accurate than RANS

• Significantly more computationally expensive

• Note: as models increase in order of fidelity, more refined meshes of 

higher cell counts are required so not only are your models becoming 

more expensive on just an equation basis, but your meshes are 

needing to increase in size too.
OpenFOAM (for CFD simulations)

• OpenFOAM is the most popular and commercially standard open 

source CFD software.

• Provides unequalled amounts of customization due to it being 

opensource

• Effectively a CFD sandbox.

[4]
ANSYS ICEM (for meshing)

• Commercial Meshing software

• Universal meshing software for all range of transport problems

• Entirely structured meshing (meshes that are hand crafted and not 

automatically generated)
Single Channel Studies
Models

Example of K-Omega-SST from OpenFOAM User Guide:

For the purpose of this work, we will focus around 3 models:

1. k-ε Realizable (2-equation model) ( A more complex K-epsilon that adds constraints to ensure predictions 

adhere to physics more closely)

2. Spalart Almaras (1-equation model)

3. k-ω SST (2 equation model)

[4]
Goals

• Are 2D simulations enough to approximate the channel flow?

• Comparing 1 channel models before scaling up to multi channel

• If the 2D models are in a comfortable range to find a tuning factor too, 

we can use 2D models for multi channel approach.

• A comparison of separate turbulence models is also done for similar 

reason.

• Experimental data from Ha et al. is used as a baseline for determining 

model accuracy
Design parameters

• FP channel design is used

• Height of 780 mm

• Width of 69.757 mm

• Inlet velocity of 12.78 m/s 

is prescribed at a fixed 

value across the whole 

length

• Pressure set to fixed value 

of 0 at the outlet

• Velocity is assumed to be 

0 at all walls

Outlet

Walls

Inlet
About y

+

• y

+ is a non-dimensional parameter we use in CFD to represent the ratio of 

inertial to viscous forces for a fluid near a wall boundary.

• We essentially use this term in grid dependence studies to help determine 

how much resolution is needed near the wall

• Think of it as just parameter of how many cells need to be near the wall to 

ensure we are modelling all the effects of that wall.

• So, if y

+ is close to 1 and there's not a lot of cells near the wall, that means 

the wall functions the model is using are pretty good at modelling the walls

• A high y+ that has a lot of cells near the wall means wall effects are quite 

strong and large amounts of cells will be needed near the wall to model the 

flow accurately

y

+ =

u

∗y

ν
2D Mesh design

• To the right is shown the full mesh and 

zoom in of 1 layer to show inflation layer.

• Due to the simplicity of the flow, we can 

employ wall functions and a very coarse 

gradient from the wall distance to the 

center of the channel.

• A comparison of multiple boundary layer 

thicknesses were tested and it was found 

to be negligible when y

+< 5.

• Mesh contains 752 elements

• y

+ was generally found to be around 2.
3D Mesh Design

• Same setup as 2D Mesh

• A high z direction partition is employed in 

order to catch the 3D effects of the model 

as much as possible.

• While a study was conducted to see if 

lower z partitions could capture the same 

amount of physics, ultimately- even 

though the results maintained low 

variance, the higher z was chosen due to 

the simple computational nature of the 

model.

• X and Y partitions are also greatly 

increased to maintain y

+ ~2 

• Total elements 99262
Turbulence Models

• 3 Turbulence models were tested.

• Spalart Almaras was chosen due to its computational efficiency and 

its ability to model confined flow.

• k-ε is chosen as a standard, generally works well for everything.

• k-ε Realizable model is used to analyze if wall effects are greatly 

varying the flow.
Points of interest

• For this study, the center of the 

channel along the y axis will be 

studied in order to validate with 

works of Ha et al. 

• A visualization of these points of 

interest are shown below from a 

visual from Ha et al. [1]
Grid Convergence Study

• 3 meshes are created for each 

mesh, each with a refinement 

factor of 1.25 the size of the coarser 

mesh

• A GCI study is conducted on the 

points of interest. This is done by 

taking 3 meshes and comparing 

them to each other at each cell 

center.

• Error bounds are found to be at a 

maximum of around 4% in Spalart

Almaras

Fs ≈ 1.25, r =

h2

h1

=

h3

h2

, p ≈

ln U3 − U2

U2 − U1

ln r

δnum ≡ GCI =

Fs

r

p − 1

U2 − U1

Numerical Uncertainty!
Comparison of models

• Data is taken from Ha et al. shown on the pink solid line

• 2D meshes are shown with dashed lines

• 3D meshes are shown with dotted lines

• It is found that for the central flow portion of the channel, 

the 2D model overapproximates by roughly 20% of the 

original velocity

• It is also found that velocity ramps slower in the 2D mesh 

coming off the walls, however this could be attributed to 

significantly larger inflation layers

• 3D data mostly agrees with the experimental data, slightly 

slower ramp with a larger approximation of central flow by 

about a max of 10%

• Furthermore, it is seen that K-epsilon-Resizable is marginally 

closer to experimental data than other turbulence models

• Since error is in range of 0-4% (most of the time less than 

1%), error bars were deemed not necessary to showcase the 

data
Conclusions so far

• Flow can be modeled by 2D if attempting to estimate center velocity 

however struggles around walls.

• K-Epsilon-resizable seems to best align with experimental data, only 

overestimating by 1-2% on center velocity.

• Very Coarse mesh sizes can be used to model channels if just 

attempting to find center velocity
Preliminary Parallel Mixing Studies

[2]
Goals

• Recall the geometry from before:

• Inside this geometry we have a 

mixing region that comes from 3 

parallel channels opening into an 

empty lower plenum.

• We are interested in determining 

how much mixing and if the flow will 

be fully developed by the time it 

reaches the active height.
Goals

• Fortunately for us, we have experimental data extracted by Weiss et al. , this 

allows us to validate multiple turbulence models to help find a group/set of 

models we can use to model the mixing region in the NNS.

• We are primarily looking to see just how much fidelity we will need to get an 

accurate representation of the mixing regions at the bottom of the core. (do we 

need 3d models? Wall resolved (explained later)? LES?)

[2]
Design Parameters

• Models remain 2d for now

• Case for preliminary study of an inlet 

velocity of 6.69 m/s is chosen

• Pressure set to fixed value of 0 at the 

outlet

• Velocity is assumed to be 0 at all walls

• ANSYS ICEM is used for Meshing

• OpenFOAM v2206 is used for simulation

• All preliminary models are tested at x/D = 

10.35

[2]
2D Mesh design

• For the purpose of the preliminary 

models, wall functions are being used 

but a close refinement is still kept near 

the walls to help determine the 

effectiveness of certain wall functions 

and varying y

+

• 8 meshes were tested for each 

turbulence model to determine grid 

dependence. 

• After study was conducted, 3 meshes of 

sizes (~8,000),(~20,000),(~40,000) cells 

were chosen for uncertainty analysis 

and validation (r = 1.5)

• y

+ floats around 1.1 with a minimum of 

1.02 and a maximum of 1.5
Grid Convergence Study

• At x/D = 10.35

• GCI points near walls reflective of a 

need for increased grid refinement 

for wall resolved models. (200% 

error at the highest case)

• Indicates a need for higher fidelity 

models
Comparison of models

• Data is taken at x/D = 10.35

• Mix of under and over 

approximation of experimental 

data

• Under approximating center 

channel

• Over approximating outer 

channels

• Failing to model extent of 

circulation in mixing regions

• Likely 3D fluctuations are 

necessary and potentially wall 

resolved.
Conclusions

• More complex models required, most likely starting with 3D wall 

resolved (y+ < 1 with no wall functions).

• Will likely need to work up to LES which will drastically increase 

computational cost

• 2D models and standard RANS deemed mostly ineffective
Citations

1. Ha, Taesung, and William J. Garland. "Hydraulic study of turbulent 

flow in MTR-type nuclear fuel assembly." Nuclear Engineering and 

Design 236.9 (2006): 975-984.

2. Shen, Joy, et al. "A Turbulence Model Sensitivity Analysis of 

Thermal-Hydraulic Properties on The Pre-Conceptual NIST Neutron 

Source Design." Proceedings of the International Conference on 

Nuclear Engineering. 2023.

3. Weiss, Abdullah G., et al. "Flow regime and Reynolds number 

variation effects on the mixing behavior of parallel 

flows." Experimental Thermal and Fluid Science 134 (2022): 

110619.

4. Weller, H. G., Tabor, G., Jasak, H., and Fureby, C., 1998, “A Tensorial 

Approach to Computational Continuum Mechanics Using Object-Oriented 

Techniques,” Comput. Phys., 12(6), pp. 620–631.
Acknowledgements

Mentors

• Abdullah Weiss

• Anil Gurgen

NCNR CORE Directors

• Julie Borchers

• Susana Marujo Teixeira

• Leland Harriger

Advisor & Texas A&M

• Dr. Mark Kimber
Questions?