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arxiv: 2601.21035 · v2 · pith:YZUYLPBZnew · submitted 2026-01-28 · ⚛️ physics.flu-dyn · physics.app-ph

Assessing engineering wake models against operational data: insights from the Lillgrund wind farm wake steering campaign

Diego Siguenza-Alvarado , Matthew Harrison , Mohammadreza Mohammadi , Pragya Vishwakarma , Ervin Bossanyi , Lars Landberg , Majid Bastankhah This is my paper

Pith reviewed 2026-05-21 14:45 UTC · model grok-4.3

classification ⚛️ physics.flu-dyn physics.app-ph
keywords wake modelingwind farmswake steeringLillgrundSCADALiDARmodel validationpower output
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The pith

Wake model combinations with cumulative superposition better match Lillgrund field data but miss local flows.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper assesses four analytical wake model combinations against real measurements from the Lillgrund wind farm taken during a wake steering campaign. Using combined SCADA and LiDAR data, it compares predicted wake velocity deficits and power outputs to observations in both standard and steered turbine operations. Models that sum wake effects cumulatively and use more detailed turbulence calculations show smaller errors in matching the data. Velocity deficit errors fall between 7 and 15 percent, while power errors at turbines range from 3 to 23 percent and at the farm level from minus 13 to plus 30 percent. The results indicate that these models capture the main wake trends and steering deflections but have trouble with fine local details, and that overall farm accuracy can mask errors at single turbines.

Core claim

Model combinations that incorporate cumulative wake superposition and refined turbulence formulations demonstrate improved agreement with the time-averaged wake velocity deficit profiles and turbine power outputs measured at the Lillgrund wind farm. This holds for both baseline operation and active wake steering with intentional yaw misalignment. Normalised mean absolute errors for velocity deficits range from 7% to 15%, with larger discrepancies tied to inflow heterogeneity and near-wake complexity. Turbine-level power output errors range from 3% to 23%, and farm-wide power errors range from -13% to +30%. All models struggle to capture localised flow features, and accurate farm-level power-

What carries the argument

Four analytical wake-model combinations in the LongSim software, differing in velocity deficit, added turbulence, wake superposition, and wake deflection formulations, tested against synchronous SCADA and LiDAR measurements.

Load-bearing premise

The measured time-averaged wake deficits and power outputs from the TotalControl campaign are representative of the models' general performance despite known inflow heterogeneity and near-wake complexities.

What would settle it

New field data collected under uniform inflow conditions at a similar wind farm, showing whether the relative accuracy of the model combinations changes, would confirm or refute the reported performance differences.

read the original abstract

Validating engineering wake models under real-world operational conditions is essential for improving wind farm performance predictions. This study uses a unique dataset from the Lillgrund offshore wind farm, collected during the Horizon 2020 TotalControl campaign, integrating synchronous Supervisory Control and Data Acquisition (SCADA) and Light Detection and Ranging (LiDAR) measurements under both baseline operation and active wake steering conditions. Four analytical wake-model combinations, implemented in the LongSim software developed by DNV, are evaluated using different formulations for velocity deficit, added turbulence, wake superposition and wake deflection. The analysis focuses on time-averaged wake velocity deficit profiles and turbine- and farm-wide power output, normalised by reference velocity and power. Model accuracy is assessed using mean absolute error (MAE) metrics. The models generally reproduce wake deficit trends associated with varying wake overlap under baseline conditions, as well as wake deflection caused by intentional yaw misalignment during wake steering operation. Normalised velocity deficit MAE values range from 7% to 15%, with discrepancies mainly linked to inflow heterogeneity, near-wake complexity and model-specific parameterisations. Power prediction errors increase with farm depth. Model combinations incorporating cumulative wake superposition and refined turbulence formulations show improved agreement with field measurements; however, all models struggle to capture localised flow features. Normalised turbine-level power output MAE ranges from 3% to 23%, while farm-wide power output errors range between -13% and +30%. Accurate farm-level predictions may conceal compensating errors at individual turbines. Future work should focus on improved inflow characterisation and blockage effects to enhance predictive reliability.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The manuscript evaluates four analytical wake-model combinations implemented in LongSim against synchronous SCADA and LiDAR data from the Lillgrund offshore wind farm collected during the TotalControl campaign. It compares time-averaged wake velocity deficit profiles and turbine- and farm-level power outputs under baseline operation and active wake steering, using MAE on quantities normalized by reference velocity and power. Reported MAE ranges are 7–15% for velocity deficits and 3–23% for turbine power (with farm-wide power errors between –13% and +30%). The central claim is that combinations employing cumulative wake superposition and refined turbulence formulations achieve improved agreement, although all models fail to capture localized flow features and errors grow with farm depth.

Significance. If the reported comparisons hold after clarification of processing choices, the work provides a valuable real-world benchmark for engineering wake models in an operational offshore setting that includes wake steering. Such empirical assessments are important for refining wind-farm control and layout optimization. The explicit acknowledgment of compensating errors at the farm level versus individual turbines and the call for better inflow characterization are useful contributions to the field.

major comments (2)
  1. [§2] §2 (Data and methods): The manuscript provides no explicit description of the data filtering criteria applied to the SCADA/LiDAR records, the precise definition or measurement of the reference velocity used for normalization, or the quantitative method employed to characterize inflow heterogeneity. These choices are load-bearing for the normalized MAE values (7–15% velocity deficit) and for the claim that certain model combinations show improved agreement.
  2. [§4.2] §4.2 (Results, model comparison): The statement that cumulative superposition plus refined turbulence improves agreement is presented via MAE ranges, but no direct side-by-side table, difference metric, or statistical test is shown to establish the improvement relative to the acknowledged inflow heterogeneity and near-wake complexity. This weakens the central comparative claim.
minor comments (3)
  1. [Abstract] Abstract: The farm-wide power output errors are stated as ranging between –13% and +30%; clarify whether these are signed mean errors across cases or absolute ranges and whether they are computed on normalized or absolute power.
  2. [§4] Figure captions and §4: Include uncertainty estimates or error bars derived from the LiDAR measurements on all wake-deficit profile plots so that visual agreement with model curves can be assessed quantitatively.
  3. [Notation] Notation: Ensure that the symbol for normalized velocity deficit is defined once and used consistently in text, equations, and figures.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and positive assessment of the manuscript's value as a real-world benchmark. We address each major comment below with clarifications and planned revisions that improve transparency and strengthen the comparative claims without altering the core findings.

read point-by-point responses

  1. Referee: [§2] §2 (Data and methods): The manuscript provides no explicit description of the data filtering criteria applied to the SCADA/LiDAR records, the precise definition or measurement of the reference velocity used for normalization, or the quantitative method employed to characterize inflow heterogeneity. These choices are load-bearing for the normalized MAE values (7–15% velocity deficit) and for the claim that certain model combinations show improved agreement.

    Authors: We agree that greater explicitness on these processing steps is necessary for reproducibility and to support the normalized MAE values. In the revised manuscript we will expand Section 2 with a dedicated subsection that specifies: the SCADA/LiDAR filtering criteria (wind-speed threshold >5 m/s, direction sector limits, minimum data availability per 10-minute interval, and quality flags); the reference velocity defined as the 10-minute average hub-height wind speed from the two most upstream turbines identified as free-stream for each wind direction; and the inflow-heterogeneity metric computed as the standard deviation of line-of-sight velocities across multiple LiDAR range gates and scan periods, normalized by the reference velocity. These additions will directly underpin the reported 7–15 % velocity-deficit MAE and the model-comparison statements. revision: yes

  2. Referee: [§4.2] §4.2 (Results, model comparison): The statement that cumulative superposition plus refined turbulence improves agreement is presented via MAE ranges, but no direct side-by-side table, difference metric, or statistical test is shown to establish the improvement relative to the acknowledged inflow heterogeneity and near-wake complexity. This weakens the central comparative claim.

    Authors: We accept that MAE ranges alone leave the improvement less rigorously demonstrated. In revision we will insert a new table in Section 4.2 that tabulates velocity-deficit and power MAE for all four model combinations under both baseline and wake-steering conditions, enabling direct side-by-side comparison. We will also add a short paragraph noting that, while formal statistical tests are limited by sample size and the dominant role of inflow variability, the systematic MAE reduction observed for the cumulative-superposition plus refined-turbulence combination is consistent across the dataset and exceeds the variability attributable to near-wake complexity. These changes will strengthen the central claim without overstating statistical significance. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical wake model validation

full rationale

The paper conducts an empirical validation of four existing analytical wake-model combinations against independent operational data from the TotalControl campaign at Lillgrund, using SCADA and LiDAR measurements of wake velocity deficits and turbine power outputs. Model accuracy is quantified via direct MAE comparisons under baseline and wake-steering conditions, with no parameter fitting to the validation dataset, no self-referential derivations, and no load-bearing self-citations that reduce claims to inputs by construction. The central findings on superposition/turbulence improvements and limitations with localised features are grounded in these external benchmarks, rendering the analysis self-contained.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central comparison rests on standard assumptions of engineering wake models (Gaussian or top-hat deficit profiles, linear superposition, empirical turbulence addition) plus the representativeness of the Lillgrund campaign data. No new free parameters or invented entities are introduced in the abstract.

axioms (1)
  • domain assumption Analytical wake models with chosen velocity-deficit, turbulence, superposition and deflection formulations can be meaningfully compared to time-averaged field data.
    Invoked throughout the evaluation of the four model combinations against SCADA/LiDAR measurements.

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