Heterogeneous distribution of indoor environmental quality is known to have a great impact on human health, comfort, and productivity. A personalized work environment, that creates a localized and independent environment with capsules or partitions, is being developed worldwide to provide workers with a space that enables undisturbed concentration on studying and working. However, the minimized interior space of a personalized work environment can immediately cause adverse health impacts for occupant if the air quality and thermal environment in the personalized work environment is not controlled appropriately. Particularly, constant breathing can sharply increase the CO2 concentrations in an interior space with an insufficient ventilation rate. In order to design a healthy and comfortable indoor environment, especially in a personalized work environment, it is important to predict precisely and comprehensively the transient and heterogeneous structure of the indoor environment formed around a human body. With this background, we have developed an in silico human model that integrates a computational human model (virtual manikin combined with thermoregulation models) and respiratory model (virtual airway) for estimating indoor environmental quality, targeting the microclimate around a human body and breathing zone with high accuracy. In this study, we report the applicability of a comprehensive in silico human model to estimate the environmental quality in a personalized work environment. A coupled analysis of heat and contaminant transfer with computational fluid dynamics was conducted targeting the space around the in silico human model installed in a virtual personalized work environment. The informative data including human thermal comfort and breathing air quality were obtained, potentially forming the basis for the development of a digital twin of the personalized work environment and contributing to the design of a healthy, comfortable, and productive personalized work environment.
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