Engineering · Machine learning
Predictive CFD with Neural Networks
A compact surrogate model for predicting indoor temperature from simulation data.
- Period
- 2022
- Status
- Course research project
- Perspectives
- Engineering · Research
30-second case brief
Question, responsibility, method, result.
- Question
- A compact surrogate model for predicting indoor temperature from simulation data.
- Responsibility
- Data preparation, ANN development, evaluation, and technical reporting within a three-person team
- Methods
- 20,000+ hourly observations · 13 inputs
- Result
- 20,000+ hourly records · 13 simulation inputs
Project scope and responsibility
- Context
- Drexel University
- Role
- Data preparation, ANN development, evaluation, and technical reporting within a three-person team
- Dataset
- 20,000+ hourly observations · 13 inputs
- Model
- Feed-forward ANN · ADAM optimization
- Team
- V. Edske · J. Moussa · R. Tajik
Overview
Detailed building simulations can be slow to rerun during iterative analysis. This project tested whether an artificial neural network could learn the relationship between 13 simulation inputs and indoor ambient temperature, creating a faster surrogate for a defined case and dataset.
Evaluation
More than 20,000 hourly records were separated into 70 percent training and 30 percent testing subsets. The selected configuration used sigmoid activation, ADAM optimization, a batch size of 36, and 100 epochs. Error definitions and validation scope remain visible alongside the result.
