🌬️ Wind Turbine Blade Optimization

Machine Learning + Flask Web App for predicting and optimizing wind turbine blade performance.

🌐 Live Deployment

The full web application is deployed at:

👉 https://windturbinebladeoptimization.onrender.com

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🌬️ Overview

This project presents a complete machine-learning–driven pipeline for wind turbine blade structural behavior prediction and optimization.
It combines synthetic dataset generation, Random Forest regression modeling, interactive Flask-based prediction dashboard, and comprehensive visualization reports.

The goal is to help researchers, engineers, and digital twin developers simulate how turbine blade materials and geometry respond to load, pressure, vibration, and fatigue.

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🚀 Features

✔ End-to-end ML pipeline

• Synthetic dataset generation
• Data preprocessing
• Model training and evaluation
• Model serialization using joblib

✔ Flask web application

• Interactive input form
• Real-time predictions for:
  • Deformation
  • Stress
  • Strain
  • Factor of safety
  • Fatigue life
  • Damage index

✔ Scientific Visualization Suite

• Input distribution plots
• Correlation heatmaps
• Scatter relationships
• Model feature importance (Tree-based)
• Actual vs Predicted curves
• Residual analysis
• Engineering relationship plots

All charts saved under /reports/figures.

✔ Fully Modular Architecture

• src/windturbine/data → dataset generation
• src/windturbine/model → ML model training
• src/windturbine/app → web app
• src/windturbine/visualization → plotting suite
• docs/ → reports & documentation
• assets/ → screenshots, extracted images

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🧰 Tech Stack

Layer	Technology
Backend	Python 3, Flask
Machine Learning	Scikit-Learn, NumPy, Pandas
Visualization	Matplotlib, Seaborn
Deployment	Render.com
Packaging	joblib, modular Python architecture
Version Control	Git + GitHub

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📁 Project Structure

WindTurbineBladeOptimization/
│
├── assets/                     # screenshots & extracted PPT images
├── docs/
│   └── TECHNICAL_REPORT.md     # full engineering & ML report
│
├── reports/
│   └── figures/                # generated plots
│
├── src/
│   └── windturbine/
│       ├── data_generation.py
│       ├── train.py
│       ├── app.py
│       └── visualization.py
│
├── tests/                      # full CI test suite
│
├── synthetic_data/             # generated datasets
├── models/                     # saved ML models
├── README.md
└── LICENSE

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📸 Screenshots

🏠 Dashboard

🔢 Input Form

📊 Prediction Output

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📊 Example Generated Plots

Plots are auto-generated under /reports/figures.
Include:

Input distributions

Scatter relationships

Correlation heatmap

Feature importance

Actual vs Predicted

Residual histograms

🧪 Running Locally

1️⃣ Create & activate virtual env

python3 -m venv .venv
source .venv/bin/activate

2️⃣ Install dependencies

pip install -r requirements.txt

3️⃣ Generate synthetic dataset

python -m src.windturbine.data_generation --n 5000 --seed 42

4️⃣ Train the model

python -m src.windturbine.train

5️⃣ Run visualization suite

python -m src.windturbine.visualization

6️⃣ Start the web app

python -m src.windturbine.app

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🧪 Tests (CI-friendly)

pytest -q

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👤 Author

Dinesh Chandra — TheComputationalCore

• GitHub: https://github.com/TheComputationalCore
• YouTube: https://www.youtube.com/@TheComputationalCore
• Passion: AI × Simulation × Digital Twins × Computational Engineering

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📜 License

This project is open-source under the MIT License.

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⭐ Acknowledgements

This work is inspired by real-world engineering analysis, structural mechanics - "Analysis of wind turbine blade using ansys software" - my project during graduation.

If you like this project, consider ⭐ starring the repo!