Development of a stochastic computational fluid dynamics approach for offshore wind farms

M. Richmond; A. Kolios; V. S. Pillai; T. Nishino; L. Wang

doi:10.1088/1742-6596/1037/7/072034

Development of a stochastic computational fluid dynamics approach for offshore wind farms

M. Richmond, A. Kolios, V. S. Pillai, T. Nishino, L. Wang

Cranfield University

Research output: Contribution to journal › Conference article › peer-review

3 Citations (Scopus)

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Abstract

In this paper, a method for stochastic analysis of an offshore wind farm using computational fluid dynamics (CFD) is proposed. An existing offshore wind farm is modelled using a steady-state CFD solver at several deterministic input ranges and an approximation model is trained on the CFD results. The approximation model is then used in a Monte-Carlo analysis to build joint probability distributions for values of interest within the wind farm. The results are compared with real measurements obtained from the existing wind farm to quantify the accuracy of the predictions. It is shown that this method works well for the relatively simple problem considered in this study and has potential to be used in more complex situations where an existing analytical method is either insufficient or unable to make a good prediction.

Original language	English
Article number	072034
Number of pages	10
Journal	Journal of Physics: Conference Series
Volume	1037
DOIs	https://doi.org/10.1088/1742-6596/1037/7/072034
Publication status	Published - 19 Jun 2018
Event	7th Science of Making Torque from Wind, TORQUE 2018 - Milan, Italy Duration: 20 Jun 2018 → 22 Jun 2018

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Content from this work may be used under the terms of theCreative Commons Attribution 3.0 licence. Any further distribution
of this work must maintain attribution to the author(s) and the title of the work, journal citation and DOI.

ASJC Scopus subject areas

Physics and Astronomy(all)

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Cite this

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abstract = "In this paper, a method for stochastic analysis of an offshore wind farm using computational fluid dynamics (CFD) is proposed. An existing offshore wind farm is modelled using a steady-state CFD solver at several deterministic input ranges and an approximation model is trained on the CFD results. The approximation model is then used in a Monte-Carlo analysis to build joint probability distributions for values of interest within the wind farm. The results are compared with real measurements obtained from the existing wind farm to quantify the accuracy of the predictions. It is shown that this method works well for the relatively simple problem considered in this study and has potential to be used in more complex situations where an existing analytical method is either insufficient or unable to make a good prediction.",

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AU - Kolios, A.

AU - Pillai, V. S.

AU - Nishino, T.

AU - Wang, L.

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N2 - In this paper, a method for stochastic analysis of an offshore wind farm using computational fluid dynamics (CFD) is proposed. An existing offshore wind farm is modelled using a steady-state CFD solver at several deterministic input ranges and an approximation model is trained on the CFD results. The approximation model is then used in a Monte-Carlo analysis to build joint probability distributions for values of interest within the wind farm. The results are compared with real measurements obtained from the existing wind farm to quantify the accuracy of the predictions. It is shown that this method works well for the relatively simple problem considered in this study and has potential to be used in more complex situations where an existing analytical method is either insufficient or unable to make a good prediction.

AB - In this paper, a method for stochastic analysis of an offshore wind farm using computational fluid dynamics (CFD) is proposed. An existing offshore wind farm is modelled using a steady-state CFD solver at several deterministic input ranges and an approximation model is trained on the CFD results. The approximation model is then used in a Monte-Carlo analysis to build joint probability distributions for values of interest within the wind farm. The results are compared with real measurements obtained from the existing wind farm to quantify the accuracy of the predictions. It is shown that this method works well for the relatively simple problem considered in this study and has potential to be used in more complex situations where an existing analytical method is either insufficient or unable to make a good prediction.

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Development of a stochastic computational fluid dynamics approach for offshore wind farms

Abstract

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