🌪️ Curled Wake Model

A fast, modular, and extensible Python solver for wind farm flows using the Curled Wake Model (CWM), based on the method introduced by Martínez-Tossas et al. (2021). This tool enables rapid simulation and analysis of wind turbine wake interactions in atmospheric boundary layer (ABL) flows.

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📘 Reference

The curled wake model was first introduced in:

Martínez-Tossas, L. A., Annoni, J., Fleming, P. A., and Churchfield, M. J. (2019).
The aerodynamics of the curled wake: a simplified model in view of flow control.
Wind Energy Science, 2019.
https://doi.org/10.5194/wes-4-127-2019

This latest implementation follows the methodology in:

Martínez-Tossas, L. A., King, J., Quon, E., Bay, C. J., Mudafort, R., Hamilton, N., Howland, M. F., and Fleming, P. A. (2021).
The curled wake model: a three-dimensional and extremely fast steady-state wake solver for wind plant flows.
Wind Energy Science, 2021.
https://doi.org/10.5194/wes-6-555-2021

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🚀 Features

• ✅ Steady-state and unsteady wake solvers using Runge-Kutta integration
• ✅ Support for yawed turbines and multiple wake interaction models
• ✅ Modular turbulence model options (standard, kl, etc.)
• ✅ Flexible boundary layer initialization (power-law, log-law, TI-derived)
• ✅ Wake veer and dynamic inflow support
• ✅ Time-resolved output for comparison with SCADA or LES
• ✅ Plotting tools and error analysis against SCADA data

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🧠 Structure

curled_wake_model/
├── wind_farm.py                  # Main driver and solver orchestrator
├── wind_farm_plot.py               # Visualization methods (attached as mixins)
├── wind_farm_solvers/
│   ├── solver_steady.py            # Steady-state RK solver
│   ├── solver_time.py              # Full unsteady RK solver
│   └── solver_quasisteady.py       # Quasi-steady solver (run multiple steady-state solves)
├── turbine_model.py                # Turbine class including actuator and wake properties
├── examples/                       # Example wind farm layouts and runs
├── requirements.txt                # Dependencies
└── README.md

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📦 Installation

Install dependencies via conda and pip:

# 1. Create a conda environment with Python >= 3.12
conda create -n cwm_env python=3.12

# 2. Activate the environment
conda activate cwm_env

# 3. Clone the rk4_solvers branch of the repository
git clone https://github.com/tonyinme/curled_wake_model.git
cd curled_wake_model

# 4. Install dependencies
pip install -r requirements.txt

# 5. Install the package in development mode
pip install -e .

If using Windows this can be done using Powershell

:: 1. Make sure Python 3.12 or newer is installed
::    You can check with:
python --version

:: 2. (Optional) Create a virtual environment
python -m venv cwm_env

:: 3. Activate the virtual environment
cwm_env\Scripts\activate

:: 4. Clone the repository
git clone https://github.com/tonyinme/curled_wake_model.git
cd curled_wake_model

:: 5. Install dependencies
pip install -r requirements.txt

:: 6. Install the package in development mode
pip install -e .

You may also want to install:

• matplotlib
• scipy
• pandas
• numba
• ffmpeg (optional, for animation exports)

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🌀 Quick Start

cd examples
python example_1_turbulence_models.py

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📊 Output

• Wake field components: U, V, W, and deficits uw, vw, ww
• Time-resolved wake snapshots: wf.u_time, wf.time_video
• Turbine-specific diagnostics: power, Ct, Cp, time series

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📜 License

This project is licensed under the GNU General Public License v2.0.
See the LICENSE file for details.

© 2025 Luis A. Martínez Tossas

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🤝 Acknowledgments

This model is based on the Curled Wake Model developed at the National Renewable Energy Laboratory.

See the original papers:

https://wes.copernicus.org/articles/6/555/2021/