Assessment of outdoor thermal comfort and its relation to urban geometry

Abstract

Microclimate conditions in urban open spaces are directly linked to the configuration of street axes and building heights and their attributes. Within street canyons, public places, and open spaces, the local microclimate depends directly on the physical properties of the surrounding surfaces and objects, producing well-known effects that can decrease or increase thermal loads. All of these phenomena can greatly influence the comfort of a city and the thermal comfort of pedestrians. Thermal comfort is an indicator that cannot be easily converted into physical parameters. However, it may be defined more qualitatively as the range of climatic conditions in which most people feel comfortable. One well-recognized thermal comfort index used to measure comfort levels inside a space is the predicted mean vote (PMV). Fanger’s PMV index has been widely used in the last ten years. It is based on six factors: air temperature, air speed, humidity, mean radiant temperature, metabolic rate, and clothing levels. The comfort equation establishes relationships among the abovementioned environmental variables, clothing type, and metabolic rate. The authors present results of PMV simulations using a multi-objective optimization tool (i.e., modeFrontier). ModeFRONTIER is an integration platform used to optimize and arrange PMV algorithms linked to urban geometry parameters (e.g., the height-to-width (H/W) ratio of urban streets). The optimization process employs given constraints, custom procedural algorithms, and genetic algorithms to examine a wide urban space and identify interesting relationships among the variables considered. Urban geometry, meteorological data, and latent influences are examined and negotiated quantitatively to improve outdoor thermal comfort.