Aligned configuration. The solver settings that can be matched across the three packages are matched: the wake deficit is scaled by the freestream speed, rotor averaging is linear, and FLORIS and PyWake sample the rotor with a nine-point cubature. The remaining differences are listed below.

Solver settings in this view

Setting	FLORIS	FOXES	PyWake
Deficit reference wind speed	Freestream (sosfs)	Freestream (ws_quadratic_amb_target)	Freestream (use_effective_ws=False)
Rotor grid	TurbineCubatureGrid, 9 points	grid16, 4 x 4 cell centers	CGIRotorAvg, 9 points
Power / thrust averaging	power linear-cubature, thrust linear-cubature	linear	linear

Differences that remain in this view:

FOXES: FOXES keeps its grid16 rotor model; the requested cubature with 9 points rotor grid is not applied.

← All comparisons

turbopark — one turbine

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

View the four turbines 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 TurbineCubatureGrid, 9 points rotor grid with power linear-cubature, thrust linear-cubature 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 freestream (ws_quadratic_amb_target) 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.

FOXES keeps its grid16 rotor model; the requested cubature with 9 points rotor grid is not applied.

PyWake

velocity: TurboGaussianDeficitA = 0.04

Uses PropagateDownwind with squared-sum superposition, CGIRotorAvg, 9 points rotor averaging, freestream (use_effective_ws=false) 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=5D.

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	0.0000	0.0000
FLORIS vs PyWake	0.0003	0.0003
FOXES vs PyWake	0.0003	0.0003

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 every software pair agrees to within 0.005 m/s RMSE (largest: FLORIS vs PyWake, 0.000 m/s). For this configuration the streamwise profiles are equivalent at plotting precision.
The horizontal contour shows how a small RMSE can hide a large maximum error: FLORIS vs PyWake has an RMSE of 0.19 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.
With a single turbine all three agree along the profiles, so the TurbOPark deficit is consistent for one wake.
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.