High‐throughput computation and the applicability of Monte Carlo integration in fatigue load estimation of floating offshore wind turbines. Issue 5 (24th July 2015)

• Record Type: Journal Article
• Authors: Graf, Peter A.; Stewart, Gordon; Lackner, Matthew; Dykes, Katherine; Veers, Paul
• Is Part Of: Wind energy. Volume 19:Issue 5(2016)
• Page Start: 861
• Page End: 872
• Publication Date: 2015-07-24
• Subjects: FAST -- high throughput -- openMDAO -- turbine fatigue -- Monte Carlo integration; Wind power -- Periodicals; 621.312136
• DOI: 10.1002/we.1870
• Languages: English
• ISSNs: 1095-4244

Abstract

Abstract: Long‐term fatigue loads for floating offshore wind turbines are hard to estimate because they require the evaluation of the integral of a highly nonlinear function over a wide variety of wind and wave conditions. Current design standards involve scanning over a uniform rectangular grid of metocean inputs (e.g., wind speed and direction and wave height and period), which becomes intractable in high dimensions as the number of required evaluations grows exponentially with dimension. Monte Carlo integration offers a potentially efficient alternative because it has theoretical convergence proportional to the inverse of the square root of the number of samples, which is independent of dimension. In this paper, we first report on the integration of the aeroelastic code FAST into NREL's systems engineering tool, WISDEM, and the development of a high‐throughput pipeline capable of sampling from arbitrary distributions, running FAST on a large scale, and postprocessing the results into estimates of fatigue loads. Second, we use this tool to run a variety of studies aimed at comparing grid‐based and Monte Carlo‐based approaches with calculating long‐term fatigue loads. We observe that for more than a few dimensions, the Monte Carlo approach can represent a large improvement in computational efficiency, but that as nonlinearity increases, the effectiveness of Monte Carlo is correspondingly reduced. The present work sets the stage for future research focusing on using advanced statistical methods for analysis of wind turbine fatigue as well as extreme loads. Copyright © 2015 John Wiley & Sons, Ltd.