Month: September 2020

A transfer learning metamodel using deep neural networks applied to natural convection flows in enclosures

Sep. 2020
Artificial neural networks
deep learning
engineering
heat transfer
machine learning
metamodeling
natural convection
transfer learning

We develop a framework for transfer learning with deep neural networks for natural convection. We consider the benchmark problem of an air-filled enclosure and train our network to predict the heat transfer rate for different Rayleigh inputs. We then transfer the learning to consider enclosures filled with an arbitrary fluid.

A multi-grid simulation framework for metamodeling by artificial neural networks

Sep. 2020
Artificial neural networks
engineering
heat transfer
machine learning
metamodeling
natural convection
transfer learning

The numerical simulations using coarse grid systems may provide precise results for a limited range of input parameters. As such, we utilize a multi-grid dataset for training an ANN in order to reduce simulation times. We denoised the dataset, and retrained the ANN based on abnormalities observed in training losses.