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This paper presents an assessment and comparison of the effects of static and kinetic external shading elements on the dynamic measurement of daylighting. For this purpose, we used a method and parametric tool developed previously for the design and analysis of external shading elements in buildings. The proposed approach was used to compare static and dynamic movement scenarios for achieving optimal internal adjusted useful daylight illuminances (AUDI). The current paper presents the results of a methodical analysis, which compared various types of louvers in static and dynamic operation scenarios for a typical office in a Mediterranean climate. The results show that dynamically adjusted louvers perform notably better than fixed or seasonally adjusted modes of operation. The results show that dynamic operation scenarios can increase the AUDI by up to 51%. The results also show that in some conditions the existing rules of thumb fail to predict the correct design approach to louver geometry and that the use of rules of thumb in architectural daylight design needs to be revaluated.

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In recent years, sustainable design methods have become a major concern within the building industry. There is also a growing awareness of the impact urban morphologies have on the overall energy and fuel consumption of a city. This paper investigates digital form-finding methods for generating an urban tissue to suit climatic conditions. In this research, a cascading series of genetic algorithms at multiple scales is coupled with environmental evaluation methods as fitness criteria. The methods devised in this paper integrate evaluation tools written with an object-oriented scripting language together with the Galapagos genetic solver in the Rhino/Grasshopper/Python platform. It is shown that the developed methods can be used to create large-scale urban layouts with improved street-level climate conditions as well as aggregations of buildings that function together to improve environmental and architectural parameters. The methodology developed in this paper is tested on a site with an area of approximately 1 km 2 in Brooklyn, New York, chosen because its climate features a large yearly variation in temperature and wind regime. The existing surrounding urban fabric, along with the local climatic conditions, is taken as the initial input in order to develop algorithmic processes with sensitivity to the site context.