Toward an efficient data sampling methodology for micro-siting CFD simulations regarding thermal stratification

Introduction

In the context of micro-siting using CFD method, applicants used to serve the mean wind speed profiles as boundary for the evaluation in each directional sector. This methodology tends to lose the stratified characteristics and leads to inconsistent representation of velocity prediction due to the general averaging of rapidly changed thermal effects from e.g. day to night periods. This issue motivates to categorize the stratified conditions in more specific manners, especially also under the concerns of computational expands regarding CFD calculations for improving the accuracy of site-assessment regarding wind speed, as well as AEP.
Statistical analysis of measurement data can be represented in the wind rose and Weibull distribution. Referring to the CFD simulation setups, discretizing Weibull curve into different bins used to be taken if the computational power is sufficient. This methodology is not capable to obtain the thermal characteristics, though the distribution of Monin-Obukhov Length could have completely different form.

Approach

In order to concern the stratified characteristics, we classified the wind data by mean of frictional velocity (uStar) and Monin-Obukhov length (MOL) in every wind sector. The two-dimensional diagram demonstrates the probability of each wind pattern incorporated with the thermal conditions. The boundary condition are then determined by selecting the predominant uStar—MOL region. For this, even though the arrangement of simulation groups is individual for each site, the analyzing method can save certain amount of computational resources.

Main body of abstract

For the demonstration of this methodology, a wind farm in eastern China is taken as validation case. The wind farm consists of 25 turbines and one met. mast in the complex terrain. By utilizing the extended k-Epsilon turbulence model regarding buoyant force conditions. Three simulations are carried out for one directional sector. Comparing to the above-mentioned method discretizing of Weibull distribution, results of objective method show the a better agreement of wind speed from met. mast to the SCADA data.

Conclusion

This method denotes, some certain wind patterns can be accompanied with certain stratified conditions. By focusing on these coincided regions, CFD calculations can provide more realistic statistics. Simulation utilizing CFD method can be more efficient in the the micro-siting context.


Learning objectives
- CFD regarding thermal stratification
- Wind data analysis