This document compares grid design and modeling capabilities between Delft3D-FLOW and the new flexible mesh module D-Flow FM. It finds that D-Flow FM requires fewer curvilinear cells than Delft3D-FLOW for the same accuracy due to its improved friction formulation. Case studies of the Wadden Sea and Venice Lagoon show that D-Flow FM runs over twice as fast as Delft3D-FLOW on the same grid and provides comparable accuracy, though higher resolution grids in D-Flow FM increase runtime significantly. The document concludes that D-Flow FM has accuracy advantages but care must be taken in grid design and numerical settings to optimize model efficiency.

1. Grid design in estuaries and lagoons using Delft3D Flexible Mesh
Bas van Maren, Arnold van Rooijen, Arthur van Dam, Giselle Lemos (Technital), Herman Kernkamp

2. 10 november 2014
Introduction

3. Introduction
Delft3D-FLOW *
D-Flow FM **
Morphodynamics
2015?
Sand-mud interaction
2016?
Vegetation
3D flow
Resolution
Numerical aspects: conveyance & definition of fluxes
Case studies: Wadden Sea & Venice Lagoon
* Delft3D-FLOW = hydrodynamic simulation engine of Delft3D 4
** D-Flow FM = hydrodynamic simulation engine of Delft3D Flexible Mesh

4. Model resolution
This presentation:
-Short introduction on computational methods in D-Flow FM related to model resolution (conveyance and 2nd order fluxes)
-Comparison of D-Flow FM – Delft3D-FLOW, for two lagoons:
-Wadden Sea
-Venice Lagoon

5. 5
Delft3D-FLOW: tile depths - uniform friction and depth per cell
D-Flow FM: bed levels at cell corners. 2D analytical conveyance - compute friction integral along entire cell’s edge, based on bathymetry at cell’s corner points.
Model resolution: conveyance

6. KfKI, Bremerhaven, 2 November 2011
6
Delft3D-FLOW, 3 cells
The computed discharge does not converge when increasing # cells, when using tile depths.
Correct discharge ≈ 497 m3/s

7. KfKI, Bremerhaven, 2 November 2011
7
The computed discharge does not converge when increasing # cells, when using tile depths.
Correct discharge ≈ 497 m3/s
Delft3D-FLOW, 48 cells

8. KfKI, Bremerhaven, 2 November 2011
8
The computed discharge now does converge when using 2D conveyance.
Correct discharge ≈ 497 m3/s
D-Flow FM, 48 cells

9. KfKI, Bremerhaven, 2 November 2011
9
The computed discharge now does converge when using 2D conveyance.
Correct discharge ≈ 497 m3/s
D-Flow FM, 3 cells
Less curvilinear cells needed in D-Flow FM compared to Delft3D-FLOW because of friction formulation

10. Model resolution: triangular or curvilinear
Less curvilinear cells needed in case of simple topographies
Channels in an D-Flow FM model are preferentially designed with a curvilinear grid
But also: larger cells  larger timestep possible

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Triangular grids lead to
cross-flow numerical diffusion
Model resolution: triangular or curvilinear
Channels in an D-Flow FM model are preferentially
designed with a curvilinear grid

12. Model resolution: conclusions
-Less curvilinear cells needed in D-Flow FM compared to Delft3D-FLOW because of the bed schematization (conveyance)
-Curvilinear cells are more efficient than triangular cells for simple geometry
-Less grid cells needed
-Larger grid cells  larger timestep possible
-Triangular grids lead to cross-flow numerical diffusion
Use curvilinear grids when possible and triangular grids when needed

13. Case study: the Wadden Sea
Curvilinear grid (Borsje et al. 2008)
Unstructured grid
Grid
Time step
Delft3D-FLOW
curvilinear
1 min
D-Flow FM: CL
curvilinear
1 min
D-Flow FM
unstructured
≈ 20 sec (CFL- condition based)

14. Case study: the Wadden Sea
10 november 2014
Triangular cells used as ‘glue’

15. Case study: the Wadden Sea
Delft3D-FLOW
D-Flow FM: CL
D-Flow FM
Data

16. Case study: the Wadden Sea
10 november 2014

17. Case study: the Wadden Sea
-Delft3D-FLOW model most accurate
-Related to numerical settings  optimization needed in the D-Flow FM model (and practical experience)
RMSE (cm)
Den Oever
Harlingen
Kornwerder zand
Delft3D-FLOW
9.9
6.8
8.2
D-Flow FM: CL
10.0
8.7
9.1
D-Flow FM
11.1
8.8
12.4

18. Case study: the Wadden Sea
-D-Flow FM is 2.5 times faster than Delft3D-FLOW for the curvilinear grid
-The new D-Flow FM model is much slower, because of much higher resolution
Model run
Wall clock time
# time steps x 1000
# grid cells
Delft3D-FLOW
143 m
176
20829
D-Flow FM: CL
63 m
187
20829
D-Flow FM
464 m
602
45134

19. Case study: the Venice Lagoon
-Venice lagoon model setup in Delft3D-FLOW and D-Flow FM (various configurations, see presentation Giselle Lemos)
-Continuous improvements in the past years

20. Case study: the Venice Lagoon
10 november 2014
VENICE LAGOON: SOUTHERN PART
3D-FLOW VENICE MODEL: SOUTHERN PART
D-FLOW VENICE MODEL: SOUTHERN PART
Triangular cells used as ‘glue’. Curvilinear cells when possible, triangular when needed

21. Case study: the Venice Lagoon – curvilinear grid
10 november 2014
D-Flow FM and Delft3D-FLOW give similar results on the same curvilinear grid, but D-Flow FM is 2 times faster

22. Case study: the Venice Lagoon – new grid
Fluxes
Water levels
New grid: D-Flow FM slightly better, but computationally more demanding

23. Conclusions
D-Flow FM is more accurate in complex topographies  less grid cells required
D-Flow FM is faster  combined with less grid cells the model should be much faster
Case studies: D-Flow FM is >2 times faster on same curvilinear grid and comparably accurate
Pitfall: increase the horizontal resolution (too much…) resulting in (much) slower models
Setting up an D-Flow FM grid takes time – think carefully before actual grid design
Need to improve hands-on experience for accurate numerical settings
Use curvilinear grids when possible and triangular grids when needed (‘glue’)