Minimizing errors in interpolated discrete stochastic wind fields

作者：Fluck Manuel; Crawford Curran

来源：Journal of Wind Engineering and Industrial Aerodynamics, 2016, 152: 15-22.

DOI：10.1016/j.jweia.2016.02.007

• Unsteady processes
• Interpolation
• Uncorrelated data
• Statistical increment
• Discrete data
• Turbulent wind
• Wind statistics
• Wind turbine

For many unsteady processes (e.g. turbulent wind, electricity demand, traffic, financial markets, space physics, etc.) data is only available at discrete points, be it due to data storage or data gathering limitations. However, derived forms of that data are often used in further studies where the discretization may be different from the discretization of the original data. This paper addresses the question of how to obtain values between discrete data points, for example, when sampling turbulent wind. Linear interpolation is often the standard answer. Yet, it is shown that this is a poor choice for unsteady processes where the sample step size is significantly larger than the fluctuation scale. An alternative employing probability density functions of data increments is suggested. While this new method does not require much more effort than linear interpolation, it yields significantly more accurate results. Unsteady wind is used to exemplify this: turbulent wind speeds on a (rotating) wind turbine blade are synthesized from a coarse data grid via the introduced method of velocity increments. Thus the superiority of the presented approach over linear interpolation is demonstrated - with important implications for blade load and power output computations.

• 出版日期2016-5