PyConTurb: an open-source constrained turbulence generator

Jennifer M. Rinker*

*Corresponding author for this work

Research output: Contribution to journalConference articleResearchpeer-review

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Abstract

This paper presents an open-source tool that can be used to simulate turbulence boxes constrained by measured data, which is useful for wind turbine model validation. The tool, called PyConTurb for “Python Constrained Turbulence”, uses a novel algorithm based on the Kaimal Spectrum with Exponential Coherence method, and the algorithm can efficiently generate turbulence boxes under a wide variety of measurement constraints. The theoretical background for the technique is presented along with a few notes on its implementation in Python. The utility of PyConTurb is demonstrated using real data measured using three-dimensional sonic anemometers at the Denmark Technical University Risø campus. The presented results demonstrate that PyConTurb can successfully generate turbulence boxes from real measured data, including recreating the desired spatial coherence relationships between the simulated and measured time series. PyConTurb is shown to be a promising tool for investigating new spatial coherence models and for future one-to-one wind turbine validation studies.
Original languageEnglish
Article number062032
Book seriesJournal of Physics: Conference Series
Volume1037
Issue number6
Number of pages9
ISSN1742-6596
DOIs
Publication statusPublished - 2018
EventThe Science of Making Torque from Wind 2018 - Politecnico di Milano (POLIMI), Milan, Italy
Duration: 20 Jun 201822 Jun 2018
Conference number: 7
http://www.torque2018.org/

Conference

ConferenceThe Science of Making Torque from Wind 2018
Number7
LocationPolitecnico di Milano (POLIMI)
CountryItaly
CityMilan
Period20/06/201822/06/2018
Internet address

Bibliographical note

Content from this work may be used under the terms of the Creative 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.

Cite this

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title = "PyConTurb: an open-source constrained turbulence generator",
abstract = "This paper presents an open-source tool that can be used to simulate turbulence boxes constrained by measured data, which is useful for wind turbine model validation. The tool, called PyConTurb for “Python Constrained Turbulence”, uses a novel algorithm based on the Kaimal Spectrum with Exponential Coherence method, and the algorithm can efficiently generate turbulence boxes under a wide variety of measurement constraints. The theoretical background for the technique is presented along with a few notes on its implementation in Python. The utility of PyConTurb is demonstrated using real data measured using three-dimensional sonic anemometers at the Denmark Technical University Ris{\o} campus. The presented results demonstrate that PyConTurb can successfully generate turbulence boxes from real measured data, including recreating the desired spatial coherence relationships between the simulated and measured time series. PyConTurb is shown to be a promising tool for investigating new spatial coherence models and for future one-to-one wind turbine validation studies.",
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PyConTurb: an open-source constrained turbulence generator. / Rinker, Jennifer M.

In: Journal of Physics: Conference Series, Vol. 1037, No. 6, 062032, 2018.

Research output: Contribution to journalConference articleResearchpeer-review

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AB - This paper presents an open-source tool that can be used to simulate turbulence boxes constrained by measured data, which is useful for wind turbine model validation. The tool, called PyConTurb for “Python Constrained Turbulence”, uses a novel algorithm based on the Kaimal Spectrum with Exponential Coherence method, and the algorithm can efficiently generate turbulence boxes under a wide variety of measurement constraints. The theoretical background for the technique is presented along with a few notes on its implementation in Python. The utility of PyConTurb is demonstrated using real data measured using three-dimensional sonic anemometers at the Denmark Technical University Risø campus. The presented results demonstrate that PyConTurb can successfully generate turbulence boxes from real measured data, including recreating the desired spatial coherence relationships between the simulated and measured time series. PyConTurb is shown to be a promising tool for investigating new spatial coherence models and for future one-to-one wind turbine validation studies.

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