Investigating the capabilities of CFD-based data-driven models for indoor environmental design and control

In this work, we study the accuracy of CFD-based data-driven models, which predict comfort-related 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. This work is supported by the Ministerio de Economía y Competitividad, Spain [ENE2017-88697-R]. N. Morozova is supported by the by the Ministerio de Economía y Competitividad, Spain [FPU16/06333 predoctoral contract]. Part of the calculations was performed on the MareNostrum 4 supercomputer at the Barcelona Supercomputing Center [RES project IM-2021-1-0015]. The authors thankfully acknowledge these institutions.