| Nanoparticle | BaseFluid | Temperature (°C) | Particle Size (nm) | TC (W/mK) |
|---|---|---|---|---|
| Al₂O₃ | Water | 25 | 30 | 0.67 |
| CuO | EG | 40 | 50 | 0.82 |
The Unified Thermal Conductivity Dataset of Nanofluids is a machine-learning-ready dataset containing 987 experimental records collected and consolidated from multiple peer-reviewed nanofluid studies.
This dataset was developed to support research in:
- Machine Learning for Material Informatics
- Thermal Conductivity Prediction
- Data-Driven Thermal Engineering
- Scientific Computing
- Feature Engineering for Engineering Applications
The dataset combines experimental measurements and engineered features to facilitate predictive modeling and analysis of nanofluid thermal conductivity.
| Attribute | Value |
|---|---|
| Total Records | 987 |
| Features | 12 |
| Domain | Thermal Engineering / Material Informatics |
| Data Source | Peer-Reviewed Research Studies |
| Task Type | Regression |
| Format | CSV |
| Feature | Description |
|---|---|
| Nanoparticle | Type of nanoparticle used |
| BaseFluid | Base fluid used in the nanofluid |
| Temperature (°C) | Experimental temperature |
| Particle Size (nm) | Average nanoparticle size |
| Particle Volume Fraction (%) | Concentration of nanoparticles |
| TC (W/mK) | Thermal conductivity of nanofluid (Target Variable) |
| Thermal Conductivity of Particle (kp) | Thermal conductivity of nanoparticle |
| Thermal Conductivity of Liquid (km) | Thermal conductivity of base fluid |
| kp/km ratio | Ratio of particle to fluid conductivity |
| Temp_Volume | Temperature-volume interaction feature |
| Inverse_Particle_Size | Reciprocal particle size feature |
| Brownian_Feature | Engineered feature representing Brownian motion effects |
This dataset can be used for:
- Thermal Conductivity Prediction
- Machine Learning Regression Models
- Feature Importance Analysis
- Material Informatics Research
- Engineering Data Analytics
- Scientific Machine Learning
- Comparative Model Evaluation
Researchers and students can use this dataset with:
- Linear Regression
- Random Forest Regressor
- XGBoost
- Gradient Boosting
- Support Vector Regression (SVR)
- Artificial Neural Networks
- Ensemble Learning Methods
To improve machine learning performance, several domain-informed features were engineered:
- kp/km Ratio to represent relative conductivity effects
- Temperature-Volume Interaction to capture coupled thermal behavior
- Inverse Particle Size to model nanoscale size-dependent effects
- Brownian Feature to represent particle motion influence on heat transfer
These features were designed using engineering knowledge from nanofluid heat-transfer literature.
Nanofluids have gained significant attention due to their enhanced thermal properties and applications in heat transfer systems.
The objective of this dataset is to provide a standardized and machine-learning-ready benchmark for researchers interested in applying artificial intelligence and data-driven techniques to thermal conductivity prediction.
If you use this dataset in academic work, research projects, or publications, please cite this repository and acknowledge the original experimental studies from which the data was compiled.
Soumy Mittal
B.Tech Artificial Intelligence & Data Science Amrita Vishwa Vidyapeetham
- Machine Learning
- Material Informatics
- Scientific Computing
- Computational Modelling
- Data-Driven Engineering
- Intelligent Systems
This dataset is released under the MIT License.