Input Parameters
Generate Visualization
Visualization Results
About This Project
Fuel Cell Performance Prediction
This tool provides predictions for PEM fuel cell performance metrics based on physical parameters.
The predictions are generated using two neural network models:
- DelpNN: Predicts pressure drop across the fuel cell
- TstaNN: Predicts stack temperature
Users can input six key parameters to generate predictions or create visualizations to understand how changes in these parameters affect fuel cell performance.
Input Parameters:
Parameter | Description | Range | Unit |
---|---|---|---|
HCC | Height of Cathode Channel | 1.0 - 2.0 | mm |
WCC | Width of Cathode Channel | 0.5 - 1.5 | mm |
LCC | Length of Cathode Channel | 30 - 90 | mm |
Tamb | Ambient Temperature | -20 - 40 | °C |
Uin | Airflow Velocity | 1 - 10 | ms⁻¹ |
Q | Heat Generation | 1272 - 5040 | Wm⁻² |