PPolySim OS
Use case · powered by Wind Turbine Yield

Wind Turbine Yield for a power-factor fix

Simulate a power-factor fix live in your browser. This runs the real Wind Turbine Yield solver — adjust the inputs, watch it respond instantly, and export the result. No install, no account.

1

Airfoil Polar

open full solver →
Airfoil Lift & DragLive

Controls

Presets

Lift rises almost linearly with angle of attack — until the airflow separates and the wing stalls, losing lift sharply. Drag has a fixed part plus induced drag that grows with lift squared and shrinks with aspect ratio. The lift-to-drag ratio, peaking at a modest angle, is the single number that governs a wing's efficiency.

▶ Run in Python

Data Inspector

Lift coeff. CL0.53
Drag coeff. CD0.033
Lift/Drag16.0
Statusattached

Governing equation

Reading this result: Pre-stall: lift rises almost linearly with angle of attack at 6°, well below the ~15° stall angle. Adding camber would lift the entire curve higher.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

Wind Turbine PowerLive

Controls

Presets

Wind power scales with the cube of wind speed and the square of rotor diameter — which is why turbines keep getting bigger and why a windy site is worth so much more. No turbine can extract more than 59.3% of the wind's energy (the Betz limit); real machines reach about 45%. Below cut-in and above cut-out speeds they produce nothing.

▶ Run in Python

Data Inspector

Power output1.64 MW
Rated power2.83 MW
Swept area6362 m²
% of Betz limit71%

Governing equation

Reading this result: In the ramp region power climbs with the cube of wind speed, so even a small gust adds a large jump in output.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

Levelized Cost of EnergyLive

Controls

Presets

LCOE spreads a power project's lifetime cost over every megawatt-hour it produces, letting wildly different technologies be compared fairly. It rewards high capacity factors and cheap capital, and punishes idle plants. The capital-recovery factor discounts future costs to today. It is the number that has made solar and wind the cheapest new power in most of the world.

▶ Run in Python

Data Inspector

LCOE$52/MWh
Per kWh5.2¢
Capacity factor25%

Governing equation

Reading this result: At a 25% capacity factor the plant sits idle most of the time, so its capital is spread over few MWh — that thin denominator is what pushes LCOE up to $52/MWh.

Runs locally in your browser — free forever. Scale to the cloud when reality gets heavy.

or unlock everything with Pro →

About this simulation

The full Wind Turbine Yield tool models a power-factor fix with the same numerics engineers and scientists use — running entirely client-side. Change any parameter and the result updates in real time, so you can build intuition, check a design, or teach the concept without spreadsheets or installs.

More you can do with Wind Turbine Yield

Other ways to simulate a power-factor fix

Frequently asked questions

How do I simulate a power-factor fix?
Open this page and use the live Wind Turbine Yield tool below — set your inputs and the simulation runs instantly in your browser using real numerics. No install, no account needed.
Is it free?
Yes. The simulation runs free in your browser. A one-time unlock or a Pro plan adds advanced parameters, saved presets, data import, and clean exports.
Can I use my own numbers?
Absolutely — every input is adjustable, and with data import you can drive a power-factor fix from your own measurements.