CFD modeling

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M.Sc. Aleksandra Ziemińska-Stolarska
Supervisor: Prof. Jerzy Skrzypski
Lodz University of Technology, Poland
Faculty of Process and Environmental Engineering

Plan of presentation
1. Aim of the thesis
2. Study area – Sulejow Reservoir
3. 3D CFD model of flow hydrodynamic in the Sulejow Reservoir
4. Verification of CFD model
5. Analysis of water quality in the Sulejow Resrevoir (WASP)
6. Conclusions
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Eutrophication
Eutrophication (Greek: eutrophia-healthy, adequate nutrition,
development) resulted from the river phosphorus and nitrogen supply,
effects with a disturbance of the ecological balance of the ecosystem and
occurrence of blue-green algae blooms during summer.

Eutrophication
Seasonal transience of microcystin May-September 2008 5
Concentration of microcystin μg/dm3
Source: www.geoportal.gov.pl

Aim of the study
 Application of coupled CFD and WASP models allows to obtain a full picture
of the ecological status of the reservoir and will enable the identification of
areas with the highest accumulation biogenic components and thus areas
particularly vulnerable to the formation of cyanobacterial blooms
 Develop three dimensional model of flow hydrodynamic in the Sulejow
Reservoir using CFD technique.
 Perform calculations of water quality in the Sulejow Reservoir with the use
of the WASP (Water Analysis Simulation Program) program for which
hydrodynamic data were supplied by my own CFD model. It allows to
obtain an realistic image of the distribution of temperatures, flow
velocities and concentrations of main substances responsible for the
eutrophication process.
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AApppplliiccaattiioonn aarreeaa AAssssoocciiaatteedd ssttuuddiieess
Flow-field prediction
Fang and Rodi (2003),
Fangkai et al. (2007),
Zinke et al. (2010),
Wang et al. (2010),
Khosronejad (2010),
Analysis of particulate behaviour Stovin and Saul (1996;2000),
Adamson et al. (2003),
Bridgeman et al. (2009)
Prediction of water surface profiles
Ta and Brignal (1998),
Kouyi et al.(2005),
Lau et al. (2007),
Anderson et al. (2013)
Residence time distribution (RTD)
Faram et al. (2004),
Kennedy et al. (2006),
Lau et al. (2007)
Sediment transport pattern
Faram and Harwood (2003),
Dargahi (2004),
Gupta et al. (2005),
Stovin et al. (2005),
Townsend (2007)
State of the art

State of the art
Dargahi (2004) Sanjiv K. Sinha (1998) Zinke et al. (2010)
8
Andersson (2013)

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Models Model Version Description
Streeter-Phelps
models
S-P model
Thomas BOD-DO
O`Connor BOD-DO
Dobbins-Camp
BOD-DO
1D steady-state models focus on oxygen balance and
one-order decay of BOD.
QUAL
QUAL I
QUAL II
QUAL 2E
QUAL2E UNCAS
QUAL2K
1D river and stream water quality models suitable
for dendritic river and non-point source pollution
including steady-state or dynamic models.
WASP
(Water Analysis
Symulation
Programme)
WASP 7.1
Dynamic compartment-modeling program for
aquatic systems, including water column and the
underlying benthos. Allows to investigate 1, 2, and
3D systems, and a variety of pollutant types. Can be
linked with hydrodynamic and sediment transport
models that can provide flows, depths velocities,
temperature, salinity and sediment fluxes.
BASINS (Better
Assessment Science
Integrating point &
Non-point Sources)
BASIN 1
BASIN 2
BASIN 3
BASIN 4
GIS tool for watershed analysis and monitoring.
Multipurpose environmental analysis systems,
which integrate point and non-point pollution
suitable for water quality analysis at watershed
scale.
State of the art

State of the art
GEMSS
(Generalized
Environmental
Modeling System
for Surface
waters)
GEMSS
3D hydrodynamic and transport models embedded in a
geographic information and environmental data system
(GIS). Compute time-varying velocities, water surface
elevations, and water quality constituent concentrations
in rivers, lakes, reservoirs, estuaries, and coastal
waterbodies.
QUASAR QUASAR 1D dynamic model suitable for dissolved oxygen simulation
in large rivers.
MIKE
Mike 11
Mike 21
Mike 31
1,2,3D models simulate flow and water level, water quality
and sediment transport in rivers, flood plains, irrigation
canals, reservoirs and other inland water bodies.
EFDC EFDC
Hydrodynamic model used to simulate aquatic systems in 1,
2,3D. Solves 3D, vertically hydrostatic, free surface, turbulent
averaged equations of motion for a variable-density fluid.
Dynamically-coupled transport equations for turbulent
kinetic energy, turbulent length scale, salinity and
temperature are also solved.
CE-QUAL-W2 CE-QUAL-W2
2D hydrodynamic and water quality model, assumes lateral
homogeneity. Best suited for long and narrow water bodies
exhibiting longitudinal and vertical water quality gradients.
Can be applied to rivers, lakes, reservoirs, estuaries.
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Models Model Version Description

Phytoplankton kinetics
k4j p1j p1j s4j j S = (G - D - k ) P
Where:
Sk4j = reaction term, mg carbon/L day
Pj = phytoplankton population, mg carbon/L
Gp1j = growth rate constant, day-1
Dp1j = death plus respiration rate constant, day-1
ks4j = settling rate constant, day-1
j = segment number, unitless
ù
ö
o
11
P1j 1c RTj RIj RNj G = k ×G ×G ×G
æ
DIP
G Min DIN
RN ,
é
æ
G e f exp I
o
exp( exp
Ij I
 GRTj= the temperature adjustment factor, dimensionless
 GRIj= the light limitation factor as a function of I, f, D, and Ke, dimensionless
 GRNj= the nutrient limitation factor as a function of dissolved inorganic phosphorus and
nitrogen (DIP and DIN), dimensionless:
 T= ambient water temperature, °C
 I= incident solar radiation, ly/day
 f= fraction day that is daylight, unitless
 D= depth of the water column or model segment, m
 Ke= total light extinction coefficient, m-1
 Io= the average incident light intensity during daylight hours just below the surface,
assumed to average 0,9 I/f, ly/day
 Is= the saturating light intensity of phytoplankton, ly/day
20
RTj 1 G = QT -
c
ö
÷ ÷ø
ç çè
+ +
=
K DIP
K DIN
nM mP
úû
êë
÷ ÷ø
ç çè
- -
þ ý ü
î í ì
- - ×
×
=
s
e
s
e
K D I
I
K D

Study Area - Sulejow Reservoir
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Table 1. Parameters of the
Sulejow Reservoir.
Lodz Name Value
Total length 17,1 km
Maximum width 2,1 km
Average width 1500 m
Average depth 3,3 m
Maximum depth 15 m
Shoreline length 58 km
Surface area 22 km2
Usable capacity 61 x 106 m3
Maximum capacity 75 x 106 km3
Retention time ~30 days
Fig.1. Location of the Sulejow Reservoir.

Study Area - Sulejow Reservoir
Table 2. Characteristic of main rivers supplying
the Sulejow Reservoir.
Characteristic Pilica Luciaza
Catchment area A [km2] 3919 766
River lenght L [km] 160 49
A/L ratio 25 16
Mean discharge [m3/s] 22,8 2,48
Nutrient Load
Sulejow (Pilica River)
•43,3 TP/year (2005-2009 IMGW, WIOŚ)
•986 TN/year (2005-2009 IMGW, WIOŚ)
Kludzice (Luciaza River)
•8,68 TP/year (2005-2009 IMGW, WIOŚ)
•215 TN/year (2005-2009 IMGW, WIOŚ)
Fig.2 Pilica catchment Source: Corine Land Cover 2006 13

3D CFD MODEL OF
HYDRODYNAMIC IN THE
SULEJOW RESERVOIR
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3D CFD modeling
Geometry modeling & Grid generation
Fig.5. 36 cross section profiles of the Sulejow Reservoir.
Source: Regional Board of Water Management, Warsaw, (2008)

3D CFD modeling
Computational mesh
16
air
Fig. 7 Fragment of the structural mesh
with the boundary layer.
Boundary layer
First raw – 0,01
Growth factor -1,01

3D CFD modeling
Parameters, Boundary & Initial Conditions
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 Boundary conditions: two inlets (Pilica and Luciaza rivers), one outlet
(dam).
 Pressure value was equal to the atmospheric, which enable to simulate
the flow as the open channel.
 Bottom and sides were treated as a wall. At the walls including the
reservoir base, the no-slip conditions were applied. The bottom and sides
were assigned a 0,02 m roughness height.
 At the water table the moving wall function was used.
 Simulated inflow boundaries were specified with mass flow rate, normal
to the boundary.
 The k-ω SST turbulence model was applied to the calculations.
Table 2. Solution conditions and methods for the Sulejow Reservoir simulation.
Model
Space Three dimensional
Time Steady
Turbulence k-ɷ SST
Discretization method
Pressure Standard
Pressure-velocity coupling scheme SIMPLEC
Momentum Second order Upwind
Turbulence energy kinetic First order Upwind
Turbulence dissipation rate First order Upwind

Results of CFD calculations
Simulation results under steady-state conditions were first reviewed
to understand the general flow behavior indicated by the model.
A B
Fig. 9. Velocity field (m/s) in the Sulejow Reservoir in A) July B) December. 19

3D CFD modeling
Two-phase flow model
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Two-phase flow model:
Lenght – 80 m
Width – 3m
Wind speed – 2m/s
Wind direction - southeast

Results of CFD calculations
No wind conditions Wind
Wind direction (SE)
~2 m/s
Fig. 9. Velocity field (m/s) in the Sulejow Reservoir in October. 21

Acoustic Doppler current profilers (ADCPs) are highly efficient and reliable
instruments for flow measurements in rivers and open-channel environments.
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Fig.8. Acoustic Doppler
current profilers ADCP
(StreamPro)
3D CFD modeling
Model verification

3D CFD modeling
Model verification
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Moving boat ADCP measurements provided spatially overall picture of flow
1
conditions in the Sulejow Reservoir.
2
3 4

3D CFD modeling
Model verification
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1 2
3 4

25
3D CFD modeling
Model verification

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Inlets
SSQ [m3*s-1]
March July December
Pilica
River 31,74 9,76 17,55
Luciaza
River 1,64 1,91 2,55

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Inlets
SSQ [m3*s-1]
March July December
Pilica
River 31,74 9,76 17,55
Luciaza
River 1,64 1,91 2,55

WATER ANALYSIS
SIMULATION PROGRAME
(WASP)
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Analysis of water quality in the Sulejow
Resrevoir
Water Analysis Symulation Programme (WASP)
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Table 6. Parameters of the segments.

Nutrient cycling in WASP
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PPhhyyttooppllaannkkttoonn
Respiration
NNHH33
PPeerriipphhyyttoonn
DDeettrriittuuss
DDiiss..
OOrrgg.. PP
DDiiss..
CCBBOODD OOrrgg.. NN 11
CCBBOODD22
CCBBOODD33
DDOO atmosphere
PPOO44
SSSSiinnoorrgg
Settling
Photosynthesis
Nitrification
NNOO33
Denitrification
Adsorption
Oxidation
Mineralization
Reaeration
NN22
CC PP NN
Death&Gazing

CChhlloorroopphhyyllll „„aa””
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Case study
37
Load reduction

SSuummmmaarryy
 A 3D single-phase CFD model of flow hydrodynamic in the Sulejow Reservoir with
accurate depiction of basin bathymetry was developed and verified.
 The results generated by the model indicate that the flow field in the Sulejow
Reservoir is transient in nature, containing turbulent structures and swirl flow.
Analysis of the flow velocities show that main path of flow is approximately along
the bad of the Pilica River.
 The WASP eutrophication model was applied to simulate the complex nutrient
transport and cycling in the Sulejow Reservoir.
 Proper correlation between the measured and calculated values ware obtained,
which is a result of application a realistic hydrodynamics in the lake, determined
from the CFD calculations in the WASP analysis.
 Analysis of the results shown correlation between hydrodynamics and
concentrations of selected nutrients in the reservoir.
 The resulting model is accurate, robust and the methodology develop in the frame of
this work can be applied to all types of storage reservoir configurations,
characteristics, and hydraulic conditions.
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THANK YOU FOR YOUR
ATTENTION
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CFD modeling

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