20 juni 2014

3D lagoon modeling using D-Flow FM:

Baía de São Marcos

Lucas Silveira, Jonas Oliveira, Alex Falkenberg, Luana

Taiani, Leticia Nascimento, Joao Dobrochinski

(Arnold van Rooijen)

20 juni 2014

Rio de Janeiro

Florianópolis

Study location:

Baía de São Marcos

Porto Alegre (NL-AUS)

Salvador (NL-SP) Baía de São Marcos

20 juni 2014

Content

• Introduction

• Delft3D modeling

• D-Flow FM curvilinear

• D-Flow FM unstructured

Introduction

• Baía de São Marcos

(Maranhão, NE Brazil)

• Mearim River discharge up

to 2000 m3/s

• Tidal range

• 4-5 m (ocean)

• Up to 7m within bay

• Tidal bore phenomenon

(Pororoca)

• Navigation channel all the

way into the river

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Terminal Portuario do

Mearim

Introduction

Measurements in 2009

• 2 tidal gauges (Aug-Sep)

• 8 ADCP (Mar 10 – May 28)

Bathymetric surveys:

2007 and 2009

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Delft3D – model setup

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Delft3D –model setup

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• Boundary conditions

• Water level (North)

• Total discharge (South)

• No wind forcing

• Calibration

• C = 65 and 105 m0.5/s

Delft3D – model results

R2 = 0.95

RMSE = 0.40 m

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R2 = 0.96

RMSE = 0.42 m

Delft3D – model results

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P2A

P1A

P1B

D-Flow FM curvilinear

• Converted Delft3D model using dflowfmConverter.m (Open Earth

Tools)

• Same grid,

• Same boundary conditions,

• Same model settings,

• Fixed computational timestep,

• 2DH vs. 3D model

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D-Flow FM curvilinear

• Water levels 2DH model

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D-Flow FM curvilinear

• Water levels 2DH model

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D-Flow FM curvilinear

• Velocities 2DH model

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D-Flow FM curvilinear

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Delft3D D-Flow FM Delft3D D-Flow FM

water level

Taua2 0.952 0.951 0.402 0.408

Perizes2 0.959 0.956 0.423 0.44

Mean 0.956 0.954 0.413 m 0.424 m

velocity

Canal2A 0.921 0.922 0.322 0.345

P1A 0.966 0.967 0.345 0.322

P1B 0.914 0.95 0.304 0.242

P1C 0.967 0.979 0.25 0.221

P2A 0.921 0.913 0.294 0.306

P2B 0.966 0.955 0.331 0.35

P2C 0.977 0.974 0.292 0.318

P3 0.954 0.964 0.238 0.227

Mean 0.948 0.952 0.306 m/s 0.3 m/s

RMSER2

2DH model

D-Flow FM curvilinear

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Delft3D D-Flow FM Delft3D D-Flow FM

water level

Taua2 0.953 0.938 0.395 0.46

Perizes2 0.96 0.947 0.423 0.466

Mean 0.956 0.943 0.409 m 0.463 m

velocity

Canal2A 0.876 0.922 0.538 0.328

P1A 0.91 0.965 0.32 0.284

P1B 0.697 0.923 0.607 0.293

P1C 0.809 0.967 0.634 0.249

P2A 0.85 0.919 0.369 0.283

P2B 0.865 0.953 0.531 0.335

P2C 0.845 0.961 0.662 0.324

P3 0.89 0.956 0.411 0.221

Mean 0.843 0.946 0.509 m/s 0.29 m/s

R2 RMSE

3D model

D-Flow FM unstructured

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• Setting up a new grid using the full flexible

mesh functionality:

• Using DeltaShell user interface

• ‘Curvilinear grids where possible, triangles

etc. where needed’

• Same forcing, settings etc.

D-Flow FM unstructured

Preliminary results

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Fixed timestep

(30 s)

Auto timestep

D-Flow FM unstructured

Preliminary results

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D-Flow FM unstructured

Preliminary results

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D-Flow FM unstructured

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D-Flow FM unstructured

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Delft3D D-Flow FM Delft3D D-Flow FM

water level

Taua2 0.952 0.963 0.402 0.44

Perizes2 0.959 0.942 0.423 0.542

Mean 0.956 0.952 0.413 m 0.491 m

velocity

Canal2A 0.921 0.92 0.322 0.399

P1A 0.966 0.913 0.345 0.566

P1B 0.914 0.971 0.304 0.215

P1C 0.967 0.979 0.25 0.201

P2A 0.921 0.796 0.294 0.553

P2B 0.966 x 0.331 x

P2C 0.977 x 0.292 x

P3 0.954 0.955 0.238 0.553

Mean 0.948 0.922 0.306 m/s 0.376 m/s

R2 RMSE

Model efficiency

Processor: Intel(R) Core™i7-3930K CPE @ 3.20GHz 3.20 GHz

Memory (RAM): 16,0 GB

System Type: 64-bit Operating System

Number of Threads: 12

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Delft3D D-Flow Curvilinear D-Flow Unstructured

2D 258 240 202

2D (auto ts) x 330 321

3D 2248 903 x

3D (auto ts) x 3308 x

• D-Flow FM faster than Delft3D, especially in 3D

• Probably (a.o.) due to OpenMP multicore

Conclusions

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• Delft3D model of Baía de São Marcos was converted to a D-Flow

FM model

• Curvilinear (Delft3D grid)

• Unstructured grid (from scratch)

• Results Delft3D vs. D-Flow FM curvilinear very similar

• DFM runs a little faster for this setup

• D-Flow FM unstructured model seems to perform similar in

accuracy, however there was a numerical issue halfway the

simulation

Next steps

• Watch the World Cup

• Look into numerical instability in unstructured model (adjust grid)

• Look into 3D model performance

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Extra slides

Root Mean Square Error (RMSE) (Equation 1):

𝑅𝑀𝑆𝐸 = (𝑦𝑠𝑖𝑚𝑢𝑙𝑎𝑡𝑒𝑑 − 𝑦𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑)

2

𝑛

RMSE-observations Standard deviation Ratio (RSR) (Equation 2) :

𝑅𝑆𝑅 = 𝑅𝑀𝑆𝐸

𝑆𝑇𝐷𝐸𝑉𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑=

(𝑦𝑠𝑖𝑚𝑢𝑙𝑎𝑡𝑒𝑑 − 𝑦𝑚𝑒𝑎𝑠𝑢𝑟𝑒𝑑)2

(𝑦𝑠𝑖𝑚𝑢𝑙𝑎𝑡𝑒𝑑 −𝑚𝑒𝑎𝑛)2

Determination Coefficient (R²) (Equation 3):

𝑅² = ( (𝑥1 − 𝑥𝑚)(𝑦1 − 𝑦𝑚)

𝑛 − 1

(𝑥1 − 𝑥𝑚)²𝑛 − 1

. (𝑦1 − 𝑦𝑚)²

𝑛 − 1

)2

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20 juni 2014