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Energy consumption in buildings contribute to 41% of global carbon dioxide emissions through electricity and heat production, making the design of mechanical systems in buildings of paramount importance. Industry practice for design of mechanical systems is currently limited in the conceptual design phase, often leading to sub-optimal designs. By using Generative Design (GD), many design options can be created, optimized and evaluated, based on system energy consumption and life-cycle cost (LCC). By combining GD for Architecture with GD for HVAC, two areas of building design can be analyzed and optimized simultaneously, resulting in novel designs with improved energy performance. This paper presents GD for HVAC, a Matlab code developed to create improved zone level mechanical systems for improved energy efficiency. Through experiments, GD methodologies are explored and their applicability and effect on building HVAC design is evaluated.
While traditional programming practices have produced a wide range of relatively independent simulation methods, predictive models of extremely complex natural and artificial systems will require a more scalable, more collaborative approach to modeling. This project strives for software that will help researchers develop, debug, document, share, and integrate simulation code.
Project Discover is a workflow for generative design for architecture. It involves the integration of a rule-based geometric system, a series of measurable goals, and a system for automatically generating, evaluating, and evolving a very large number of design options. The result is a tool to explore a wide design space, and get closer and closer to achieving all of the goals simultaneously.