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id acadia12_87
authors Menicovich, David ; Gallardo, Daniele ; Bevilaqua, Riccardo ; Vollen, Jason
year 2012
title Generation and Integration of an Aerodynamic Performance Data Base Within the Concept Design Phase of Tall Buildings
doi https://doi.org/10.52842/conf.acadia.2012.087
source ACADIA 12: Synthetic Digital Ecologies [Proceedings of the 32nd Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-1-62407-267-3] San Francisco 18-21 October, 2012), pp. 87-96
summary Despite the fact that tall buildings are the most wind affected architectural typology, testing for aerodynamic performance is conducted during the later design phases well after the overall geometry has been developed. In this context, aerodynamic performance studies are limited to evaluating an existing design rather than a systematic performance study of design options driving form generation. Beyond constrains of time and cost of wind tunnel testing, which is still more reliable than Computational Fluid Dynamics (CFD) simulations for wind conditions around buildings, aerodynamic performance criteria lack an immediate interface with parametric design tools. This study details a framework for empirical data collection through wind tunnel testing of building mechatronic models and the expansion of the collected dataset by determining a mathematical interpolating model using an Artificial Neural Network (ANN) algorithm developing an Aerodynamic Performance Data Base (APDB). Frederick Keisler called the interacting of forces CO-REALITY, which he defined as The Science of Relationships. In the same article Keisler proclaims that the Form Follows Function is an outmoded understanding that design must demonstrate continuous variability in response to interactions of competing forces. This topographic space is both constant and fleeting where form is developed through the broadcasting of conflict and divergence as a system seeks balance and where one state of matter is passing by another; a decidedly fluid system. However, in spite of the fact that most of our environment consists of fluids or fluid reactions, instantaneous and geologic, natural and engineered, we have restricted ourselves to approaching the design of buildings and their interactions with the environment through solids, their properties and geometry; flow is considered well after the concept design stage and as validation of form. The research described herein explores alternative relations between the object and the flows around it as an iterative process, moving away from the traditional approach of Form Follows Function to Form Follows Flow.
keywords Tall Buildings , Mechatronics , Artificial Neural Network , Aerodynamic Performance Data Base
series ACADIA
type normal paper
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