World population is expected to become predominantly urban and grow by 2/3 in the next few decades. High-rises will play an increasingly important role in helping address population growth and the resulting sprawl. However, most existing high-rises have a poor life-cycle performance. In a 50-year life-cycle of a high-rise, energy costs contribute 34% of the total cost (Yeang, 1996). Close to 50% of energy use in high-rises comes from artificial illumination (Alread, Leslie, 2007).The cause lies in the current conceptual design methods used by the Architecture, Engineering, and Construction (AEC) industry. Early design decisions that determine the building’s future energy performance are generally taken by architects who develop and analyze very few design options that respond mostly to architectural criteria (Gane & Haymaker, 2009). As a result, the design post rationalization currently performed by engineers leads to solutions with mediocre daylighting, and excessive thermal loads and energy demands. Better design methods are required to increase the energy efficiency of future high-rises. A mere 10% improvement in the energy efficiency per household of the 4 billion additional people would lead to annual savings of 10.1 million Btu x 1.4 billion households!
This research proposes a methodology called Design Scenarios to help a multidisciplinary conceptual design team (a) explicitly define context specific requirements that determine the building’s energy performance; (b) formally transform these requirements into design spaces described in terms of geometric and material parameters; (c) use parametric modeling to translate identified parameters into multiple design options; (d) use Process Integration and Design Optimization (PIDO) to automate the generation and analysis of design solutions; (e) run sensitivity analysis and visually report the design options' performance back to the designers and stakeholders. We will develop a Design Scenarios web based tool, for which we’ll synthesize concepts from Requirements Engineering to help design teams define and manage building design and energy performance criteria in terms of formally structured goals and constraints, Process Modeling to help represent and measure multidisciplinary requirements-driven processes, Parametric Modeling to help build and manage requirements-driven parametric models, Model-based Analysis to help understand building performance, and Decision Analysis to help formally identify and communicate to project stakeholders design options with improved energy performance.
This work leverages and synthesizes ongoing Precourt research in process integration and optimization and sensitivity analysis. We propose to test the methodology in practice on several high-rise projects in consultation with Skidmore, Owings and Merrill – a leading international AE practice.
Project Abstract: Improving Energy Efficiency of High-Rises through Requirements Driven Parametric Modeling (0.6MB PDF)
John Haymaker (PI), Vladimir Bazjanac (PI), Victor Gane (RA)
Final Report: Improving Energy Efficiency of High-Rises through Requirements Driven Parametric Modeling (0.9MB PDF)
Martin Fischer (PI), John Haymaker (PI), Victor Gane (RA)