VALIDATION OF COMPUTATIONAL FLUID DYNAMICS METHODOLOGY FOR WIND TURBINE

1. VALIDATION OF COMPUTATIONAL FLUID DYNAMICS METHODOLOGY FOR WIND TURBINE José Palma, Fernando Castro, Carlos Santos, Álvaro Rodrigues and José Matos

2. Questions in this session • How can the determination of extreme wind speeds be validated ? • What is the quality of the additional information simulated by CFD models ? • How good is the resource assessment for the site in extreme regions ? Examples of additional useful information. Detailed mapping of: • All 3 components of velocity • Turbulence intensity • Shear Factor

3. Contents • The current situation of CFD (Computational Fluid Dynamics) • Validation and Verification (V&V) in CFD • Methodology • Example – case study • Conclusions

4. Computational Fluid Dynamics (CFD) • Increasingly importance of CFD in all areas of science and engineering. • Aeronautics and automotive engineering are on the forefront and have triggered the establishment of guidelines and standards on the use of CFD in all stages of design and product development. • Wind energy engineering can benefit of developments in CFD and computational power to an extent that has not happened in the past. Reasons for the current situation: • A long and well-established practice based on linear flow models (WAsP, MSH3D), which is difficult to beat, particularly in wind energy resource evaluation. • Uncertainties and difficulties associated with the use of CFD techniques, some of them typical of wind energy engineering. • It is not clear how and when CFD can be used within the wind energy engineering.

5. CFD in general Scientific publications and engineering societies have been fighting this trend, by requiring uncertainty analysis of all computer results and writing guidelines and standards on the use of CFD techniques. • AIAA American Institute of Aeronautics and Astronautics Guide for the Verification and Validation of Computational Fluid Dynamics (AIAA G-077-1998) • ASME American Society of Mechanical Engineers Request for an ASME Standard on Verification and Validation in Computational Solid Mechanics (July 2000) • ASCI Accelerated Strategic Computing Initiative of the US Department of Energy’s (DOE’s) • DMSS Defence Modelling and Simulation Office of the US Department of Defence (DoD) • Commercial interests have raised the expectations of the users to unrealistic levels, harming the credibility and further use of CFD techniques. • Expertise needed for proper use of CFD codes. The importance of this expertise has been hidden by user–friendly interfaces, mouse–driven menus and high-quality graphic packages. The user tends to think that things are easy and the results are right, even when they are not.

6. Verification and Validation (V&V) • Validation is crucial, if one has to rely and make decisions based on computational results. • Computer results do not replace experimental field data. • Field data is needed at all stages of computer simulations, from setting the boundary conditions to validation of the computational results. Verification and Validation (V&V) emerged has two major concepts in building our confidence on any computer code. Verification is the assessment of the accuracy of the solution of a computational model by comparison with known solutions. Verification  mathematical issue Validation is the assessment of the accuracy of a computational simulation by comparison with real field data. Validation  real world (physical issue)

7. Steps in a CFD application in wind energy • Choice and extent of region covered by the computer simulation. • Choice of wind conditions at the edges of the integration domain. • Choice of computer code • Mathematical model to the fluid flow equations • Physical modelling (turbulence, stratification) • System of partial differential equations: mass and momentum conservation plus turbulence model equations • Steady or time-dependent formulation • Numerical techniques (discretisation techniques, set the maximum accuracy achievable) • Running • Validating • Reporting • Steps are not sequential: trial and error procedure. • Initial choices (1 and 2) may proof wrong, assessment of the boundary condition implies some degree of validation.

8. Sources of uncertainty in CFD applications in wind energy • Not strictly related to the wind energy engineering • Discretization error is known at a point, not over the whole domain • Error propagation within the calculation • Numerical stability and convergence of the equation set • Turbulence modelling, etc. • Strictly related to the wind energy engineering • Terrain digital representation Anything better than the digitised maps (10 to 50 m resolution) is questionable. Terrain meshing techniques must be clearly stated, anything above linear interpolation is bound to introduce details that are not real. • Wind (boundary) conditions at the domain boundaries Assumptions are needed on: • ground characterisation (roughness) • ground effects on turbulence phenomena • inlet conditions, velocity and turbulence profiles as a function of distance a.g.l One single wind direction and velocity per computer simulation Question: How many wind directions and speeds are needed to characterise the site ?

9. Wind conditions - setting and validation VALIDATION AND UNCERTAINTY There are no methods for quantifying uncertainty in CFD calculations. However, we can always: quantify the agreement between computational results and field data. assess the influence of parameter choice and computational conditions (sensitivity tests) WIND CONDITION (velocity direction and magnitude, and turbulence) • Settings Wind conditions at the boundaries are such that the wind conditions by the computer code at a selected location in the field (mast A) are the “same” as measured - boundary condition tuning. • Validation and uncertainty appraisal Question: what are the wind conditions at mast B for given wind conditions at mast A ? END RESULT / CONCLUSION Agreement at one point (mast B) under well-known conditions allows us, at least, to expect the same level of agreement at other points under identical conditions. • Any wind condition at that site, using the same code under identical conditions. • Any computation based on that code, with different wind conditions and computational meshes

10. Validation: mean horizontal velocity • Mast 1 as a reference mast • Validation at all remaining mass for all 12 directions • Differences (uncertainty ?) in average below 10% • Critical or 1 critical direction, 180 degree winds • Why is it so? Is this important, i.e. how frequent  wind rose

11. Flow pattern (180 deg winds)

12. Validation - Flow angle, turbulence and shear factor • Access to many otherwise unknown quantities • Increased knowledge of the wind flow • Increased confidence in wind turbine layout • Hopefully, increased park efficiency, i.e. lower failures

13. Results

14. Results Even when we are mainly concerned with point-to-point correlations, 2D plots covering the whole area of interest must be shown at different heights above the ground level The reader may want to perform further analysis, which can increase his/her own confidence on the computational results.

15. Methodology • Select the domain size and spatial discretization. • Select the boundary conditions (wind direction and speed) based on real measurements. • Perform preliminary calculations and adjust the boundary conditions in such a way that the measurements at one mast can be replicated. • Assess the results sensitivity (uncertainty appraisal) to numerical, computational and model parameters, boundary conditions, etc. • Provide evidence of these tests. • Analyse the results, including detailed reporting on all parameters and conditions under which the calculations were performed.

16. Conclusions • Complex flow models, namely CFD, can contribute to the wind energy engineering practice, if carefully used and preceded by proper validation. • CFD models are particularly useful by uncovering flow details and intricacies not available via more conventional techniques, including experimental techniques. • Use and confidence in the use of CFD, uncertainty determination, can be made only on a case-by-case basis. • Uncertainty to be found by comparison with field data. • Simpler linear models must not be discarded, even in complex terrain applications, since they provide a basis for results comparison.