Paper Conference

Proceedings of Building Simulation 2021: 17th Conference of IBPSA


Data-driven prediction of flow parameters in a ventilated cavity using high-fidelity CFD simulations

Nina Morozova, Fransesc Xavier Trias Miquel, Roser Capdevila Paramio, Asensio Oliva Llena
?Heat and Mass Transfer Technological Center (CTTC), Universitat Polit├Ęcnica de Catalunya (UPC), Spain

Abstract: In this study, we develop a machine-learning-based data-driven model, which predicts comfort-related flow parameters in a ventilated room. The model is based on the results of high-fidelity computational fluid dynamics (CFD) simulations with different geometrical configurations and boundary conditions. The developed model could be used as a cheaper alternative to CFD for applications where rapid predictions of complex flow configurations are required, such as model predictive control. Even though the developed model provides acceptable accuracy for most of the tested configurations, more input data is required to improve the model performance.
Keywords: Computational Fluid Dynamics, Large Eddy Simulation, Data Driven Models, Artificial Neural Network, Mixed Convection
Pages: 2147 - 2154