ON THE MODELING ERRORS IN THE TIDAL POWER ASSESSMENT

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1 ON THE MODELING ERRORS IN THE TIDAL POWER ASSESSMENT JF Filipot, C Delafosse, T Marzin, S Baston November 5, 2014 JF Filipot, C Delafosse, T Marzin, S Baston Modeling errors November 5, / 12

2 Plan 1 Context 2 Illustration of the problem at stake 3 Models Models description Models source of uncertainties 4 Instruments used for model validation 5 Material of the study 6 Results Current velocities validation Tidal power validation 7 Conclusion and perspectives JF Filipot, C Delafosse, T Marzin, S Baston Modeling errors November 5, / 12

Context Why is the quantification of the uncertainty on the tidal resource important? Tidal project profitability is tight: marine operations in rough seas, etc.

3 Context Why is the quantification of the uncertainty on the tidal resource important? Tidal project profitability is tight: marine operations in rough seas, etc... Need to convince lenders that tidal farm projects are a sound investment. Our duty: provide the most accurate model results AND their uncertainties. Model accuracy assessed based on the current velocities, why not the power? JF Filipot, C Delafosse, T Marzin, S Baston Modeling errors November 5, / 12

Illustration of the problem at stake Validation of a model for tidal power assessment From Lalander et al. (2011). tidal power assessment of a Fjord in Norway.

4 Illustration of the problem at stake Validation of a model for tidal power assessment From Lalander et al. (2011). tidal power assessment of a Fjord in Norway. P = 1/2ρV 3 (W/m 2 ) Cubic relation: power comparison magnifies the discrepancies between model and observations. JF Filipot, C Delafosse, T Marzin, S Baston Modeling errors November 5, / 12

5 Models for tidal resource assessement Reynolds Averaged Navier Stokes Models Typical resolution 1km-100m. Difference between models: 2D/3D, numerical schemes, physics involved, (un)structured meshes, coupling with waves, atmosphere, etc... JF Filipot, C Delafosse, T Marzin, S Baston Modeling errors November 5, / 12

6 Model uncertainties Sources of uncertainties Spatial resolution (computational limits). Physics involved (e.g. wave-current interaction). Ability to represent flow variations over the water column. What about the effect of turbulence? Bathymetric data accuracy. JF Filipot, C Delafosse, T Marzin, S Baston Modeling errors November 5, / 12

How to validate the models? Comparison to observations Most widely used instruments: doppler based sensors (ADV and ADCP). Point measurements, no way to estimate the model performance over the domain.

7 How to validate the models? Comparison to observations Most widely used instruments: doppler based sensors (ADV and ADCP). Point measurements, no way to estimate the model performance over the domain. Not always deployed at the appropriate position. Field campaign duration must be long enough. Instruments are prone to measurements uncertainties that should be considered during the validation (work in progress). JF Filipot, C Delafosse, T Marzin, S Baston Modeling errors November 5, / 12

8 Material of the study 5 model-to-data comparisons Three studies taken from the litterature Tidal power assessment studies RANS models ADCP observations Our approach: model validation performed for the tidal power. JF Filipot, C Delafosse, T Marzin, S Baston Modeling errors November 5, / 12

Models scores for tidal current prediction Scores as a function of current magnitude. Errors range from 23 to 32 % Correlation is high: no phase issues. Bias is weak except at low velocities.

9 Models scores for tidal current prediction Scores as a function of current magnitude. Errors range from 23 to 32 % Correlation is high: no phase issues. Bias is weak except at low velocities. Models statistics generally improve with increasing current magnitude. Slack periods errors: model inaccuracy and/or instrumental noise. JF Filipot, C Delafosse, T Marzin, S Baston Modeling errors November 5, / 12

10 Models scores for tidal power prediction Scores as a function of power magnitude. P = 1 2 ρv 3, but NRMSE(P) 1 2ρNRMSE(V )3 (Same applies to NBIAS and CORR). Again, the error decreases with power magnitude NRMSE ranges between 44 and 73 % Much higher than for current velocities JF Filipot, C Delafosse, T Marzin, S Baston Modeling errors November 5, / 12

11 Conclusion and perspectives Conclusion Uncertainty on the tidal power resource is a key information. Models and instruments provide tidal power values. We must use them to estimate the resource uncertainty! Error in the power estimation significantly higher that for the velocity. Models are still fantastic tools that provide unique insights! Perspectives Measurement uncertainty must further be taken into account for model validation. Considering the barotropic current or power is not sufficient: need to validate model accuracy over the water column. Other sources of observation should be explored. => Work in progress. JF Filipot, C Delafosse, T Marzin, S Baston Modeling errors November 5, / 12

12 Perspectives Model validation with HF Radars HF radar prones to measurement noise that contaminante the model statistics (artificially increase the errors). It is possible to correct for that noise. JF Filipot, C Delafosse, T Marzin, S Baston Modeling errors November 5, / 12

Bottom friction estimation in the context of regional barotropic tide modelling

Bottom friction estimation in the context of regional barotropic tide modelling Martial Boutet1, Cyril Lathuilière1, Rémy Baraille2, Hong Son Hoang2, Yves Morel3 1 SHOM, Brest, France; 2SHOM, Toulouse,