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

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

Research output: Contribution to journalArticle

1 Citation (Scopus)

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.

LanguageEnglish
Article number072034
Number of pages10
JournalJournal of Physics: Conference Series
Volume1037
Issue number7
DOIs
Publication statusPublished - 19 Jun 2018

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computational fluid dynamics
predictions
approximation

Keywords

  • wind farm
  • offshore
  • stochastic analysis
  • computational fluid dynamics

Cite this

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title = "Development of a stochastic computational fluid dynamics approach for offshore wind farms",
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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Development of a stochastic computational fluid dynamics approach for offshore wind farms. / Richmond, M; Kolios, A; Pillai, V S; Nishino, T; Wang, L.

In: Journal of Physics: Conference Series, Vol. 1037, No. 7, 072034, 19.06.2018.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Development of a stochastic computational fluid dynamics approach for offshore wind farms

AU - Richmond, M

AU - Kolios, A

AU - Pillai, V S

AU - Nishino, T

AU - Wang, L

PY - 2018/6/19

Y1 - 2018/6/19

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.

KW - wind farm

KW - offshore

KW - stochastic analysis

KW - computational fluid dynamics

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