Leading effect for wind turbine wake models

I. Neunaber; M. H\"olling; M . Obligado

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Leading effect for wind turbine wake models

I. Neunaber, M. Hölling, M . Obligado

Fluid Dynamics · Submitted 11 Sep 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim Testing different models from the literature for data sets from laboratory wind turbines and multi-megawatt turbines obtained by LiDAR, we find that all models perform significantly better when such a virtual origin is added.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 5,479 input tokens.

What the abstract itself states, as probabilities: claims first 0.08 · states limitation 0.14 · human participants 0.02 · controlled comparison 0.47 · randomized assignment 0.01 · states quantitative result 0.08 · compares with prior results 0.86 · mentions code or data availability 0.03.

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arXiv listing abstract v1. Results and the main claim are underlined; show every role.

As wind energy expands worldwide, the demand of reliable, fast, cost-efficient wind turbine wake models is growing. This is a significant challenge as wind turbines face various inflow conditions, that include turbulence, inhomogeneities/instationarities and upstream wakes. In consequence, an enormous number of engineering models, each one based on different physical concepts, has been proposed. The majority focuses on the far wake where the mean velocity recovers and turbulence decays after it built up. We argue that the most important, or the leading, parameter for wake modeling is the length scale of a virtual origin. Testing different models from the literature for data sets from laboratory wind turbines and multi-megawatt turbines obtained by LiDAR, we find that all models perform significantly better when such a virtual origin is added. Our results can therefore be used for a yet missing definition of a near wake zone.

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What the classifier reports for each sentence. The number is the probability it gave the label.
#	sentence	role	the authors' own work	states the main conclusion	strength
s0	As wind energy expands worldwide, the demand of reliable, fast, cost-efficient wind turbine wake models is growing.	background 1.00	0.04	0.02	asserted 1.00
s1	This is a significant challenge as wind turbines face various inflow conditions, that include turbulence, inhomogeneities/instationarities and upstream wakes.	background 0.99	0.05	0.02	asserted 1.00
s2	In consequence, an enormous number of engineering models, each one based on different physical concepts, has been proposed.	background 1.00	0.04	0.02	asserted 1.00
s3	The majority focuses on the far wake where the mean velocity recovers and turbulence decays after it built up.	background 0.94	0.05	0.02	asserted 1.00
s4	We argue that the most important, or the leading, parameter for wake modeling is the length scale of a virtual origin.	contribution 0.87	0.37	0.76	asserted 0.84
s5	Testing different models from the literature for data sets from laboratory wind turbines and multi-megawatt turbines obtained by LiDAR, we find that all models perform significantly better when such a virtual origin is added.	result 0.99	0.96	0.61	asserted 1.00
s6	Our results can therefore be used for a yet missing definition of a near wake zone.	interpretation 0.98	0.55	0.16	hedged possibility 0.58

What kind of paper the classifier reports this is: position or commentary 0.51 · evidence from analysis of existing data 0.79.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

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Original abstract

As wind energy expands worldwide, the demand of reliable, fast, cost-efficient wind turbine wake models is growing. This is a significant challenge as wind turbines face various inflow conditions, that include turbulence, inhomogeneities/instationarities and upstream wakes. In consequence, an enormous number of engineering models, each one based on different physical concepts, has been proposed. The majority focuses on the far wake where the mean velocity recovers and turbulence decays after it built up. We argue that the most important, or the leading, parameter for wake modeling is the length scale of a virtual origin. Testing different models from the literature for data sets from laboratory wind turbines and multi-megawatt turbines obtained by LiDAR, we find that all models perform significantly better when such a virtual origin is added. Our results can therefore be used for a yet missing definition of a near wake zone.

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Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

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