In this work, we study the accuracy of CFD-based data-driven models, which predict comfortrelated flow parameters in a ventilated cavity with a heated floor. We compare the computational cost and accuracy of three different models, namely artificial neural network, support vector regression, and gradient boosting regression. The tested scenarios include short and long cavities with different inlet velocities. Among the studied frameworks, the artificial neural network provides the most accurate predictions for most of the tested flow configurations. However, test configurations with jet separation and a secondary vortex are more difficult to predict correctly; thus more high-fidelity data is required in order to construct a more robust and reliable model.

Full document

The PDF file did not load properly or your web browser does not support viewing PDF files. Download directly to your device: Download PDF document
Back to Top

Document information

Published on 11/03/21
Submitted on 11/03/21

Volume 600 - Fluid Dynamics and Transport Phenomena, 2021
DOI: 10.23967/wccm-eccomas.2020.181
Licence: CC BY-NC-SA license

Document Score


Views 29
Recommendations 0

Share this document

claim authorship

Are you one of the authors of this document?