Inverse design of a thermally comfortable indoor environment with a coupled CFD and multi-segment human thermoregulation model

A thermally comfortable indoor environment is critical for ensuring the health and productivity of the occupants. To design a thermally comfortable environment, CFD-based methods assume the occupants' surface temperature to be fixed values for simplicity and use PMV to estimate thermal comfort level. The constant surface temperature assumption would lead to inaccurate prediction of the indoor environment and the use of PMV would lead to a waste of the rich spatial information calculated by CFD. Therefore, this study developed and validated a coupled CFD and multi-node human thermoregulation model (HTM). The CFD and HTM synchronize data during the simulation and the occupant skin temperature could be updated. The final skin temperature could be used to quantify the thermal comfort level. The accuracy of the coupled model in predicting the skin temperature was validated by experimental data from literature. The coupled model was further integrated with genetic algorithm for inverse design. The inverse design of thermal environment in an office with two occupants and displacement ventilation was used for demonstration. With the CFD-HTM model, genetic algorithm was able to identify an optimal condition that leads to the least deviation of skin temperature of local body parts from the neutral values. The developed CFD-HTM coupling scheme can be used to effectively design indoor environment with improved thermal comfort.

Automated summary

A thermally comfortable indoor environment is crucial for the health and productivity of occupants. Current CFD-based methods for designing such an environment assume fixed surface temperatures and use PMV to estimate thermal comfort, which can lead to inaccuracies and wasted spatial information. This study developed a coupled CFD and HTM model that synchronizes data and updates occupant skin temperature, accurately quantifying thermal comfort. The model was validated using experimental data and integrated with genetic algorithm for inverse design, demonstrating its effectiveness in improving thermal comfort in indoor environments.