Default configuration. Each solver runs with its own legacy settings. Differences between the solvers therefore mix model differences with configuration differences such as how the rotor is sampled and which wind speed scales the wake deficit.

Solver settings in this view

Setting	FLORIS	FOXES	PyWake
Deficit reference wind speed	Freestream (sosfs)	Local (ws_quadratic)	Local effective (use_effective_ws=True)
Rotor grid	TurbineGrid, 3 x 3 square	grid16, 4 x 4 cell centers	GridRotorAvg, 4 points
Power / thrust averaging	power cubic-mean, thrust cubic-mean	linear	linear

turbopark — four turbines

velocitySoftware: FLORIS, FOXES, PyWake · Rotor diameter 240 m · Wind speed 9.8 m/s

View the one turbine scenario for this wake model →

Model parameters and implementation notes

The windIO inputs and their software-specific mappings are listed below. Matching literature-model names do not imply identical solver, rotor-averaging, superposition, turbulence, induction, or yaw treatments.

windIO case inputs

velocity: turboparkA = 0.04

FLORIS

velocity: turboparkgaussA = 0.04

Uses sum-of-squares freestream deficit superposition (sosfs), the TurbineGrid, 3 x 3 square rotor grid with power cubic-mean, thrust cubic-mean averaging, and Crespo-Hernandez wake-added turbulence. Secondary steering, yaw-added recovery, transverse velocities, and active wake mixing are disabled.

FOXES

velocity: TurbOParkWakeka = 0.04, induction = Betz

Uses Betz induction, quadratic wind-speed superposition with a local (ws_quadratic) deficit reference, grid16, 4 x 4 cell centers rotor sampling, rotor-point partial wakes, and the rotor_wd wake frame. No separate wake-added turbulence model is configured.

PyWake

velocity: TurboGaussianDeficitA = 0.04

Uses PropagateDownwind with squared-sum superposition, GridRotorAvg, 4 points rotor averaging, local effective (use_effective_ws=true) deficit reference, and ct2a_mom1d induction. No separate wake-added turbulence model is configured.

Uses the lower-level TurboGaussianDeficit class rather than PyWake's Nygaard_2022 literature preset.

Layout & sample locations

Turbine positions (bold, rotated by yaw angle where applicable) and the cross-section sample planes (dashed) used by the plots below.

Streamwise velocity

Cross-sections

Horizontal contour

X-Y plane at hub height.

Vertical contour

X-Z plane at Y=0 (lateral centerline).

Cross-section contour

Y-Z plane at X=20D.

Effect of aligning the configuration

Streamwise RMSE for each software pair in both configurations of this case. Thedefault andaligned views differ only in the solver settings listed at the top of the page.

Software pair	Default RMSE (m/s)	Aligned RMSE (m/s)
FLORIS vs FOXES	1.2855	0.2345
FLORIS vs PyWake	1.5561	0.4674
FOXES vs PyWake	0.3293	0.3914

Comparison metrics

Pairwise error between software for each plotted quantity. Click a column to sort.

Software pair	Target	RMSE (m/s)	Max abs error (m/s)	Mean abs error (m/s)	NRMSE

How to interpret this comparison

Along the hub-height streamwise line the closest pair is FOXES vs PyWake (0.33 m/s RMSE, 4.0% NRMSE) and the furthest is FLORIS vs PyWake, at 1.56 m/s RMSE (16.7% NRMSE). For scale, the free-stream wind speed is 9.8 m/s.
For FLORIS vs PyWake, cross-section RMSE is 0.000 m/s at 4D past first turbine, 1.10 m/s at 1D past last turbine, 0.82 m/s at 5D past last turbine, 0.67 m/s at 10D past last turbine. NRMSE is normalized by the range of the first-listed software's own values in that plot, so it can rise with distance even as RMSE falls, because the wake profile becomes shallower.
The horizontal contour shows how a small RMSE can hide a large maximum error: FLORIS vs PyWake has an RMSE of 0.69 m/s but a maximum of 8.69 m/s. Those maxima come from a small fraction of grid points, found at the rotor plane (X = 0) or at the sharp edge of a wake, where the packages disagree about whether a point is inside the wake. Use RMSE for overall agreement and the maximum to locate where to look.
TurbOPark depends on the local turbulence intensity. FLORIS adds wake turbulence with Crespo-Hernández while FOXES and PyWake configure none.
FLORIS separates from both FOXES and PyWake, which stay close to each other. Wake-added turbulence only matters when wakes overlap, so it is a leading candidate; this page does not isolate it.
A value of 0.0000 means the two packages agree at the precision shown in the table.
These metrics measure how consistently the packages implement the same inputs. They are not errors against measurements, so close agreement does not show that a wake model is accurate, and differences should be read together with the implementation notes above.