CumInCAD is a Cumulative Index about publications in Computer Aided Architectural Design
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id acadia17_52
authors Ajlouni, Rima
year 2017
title Simulation of Sound Diffusion Patterns of Fractal-Based Surface Profiles
source ACADIA 2017: DISCIPLINES & DISRUPTION [Proceedings of the 37th Annual Conference of the Association for Computer Aided Design in Architecture (ACADIA) ISBN 978-0-692-96506-1] Cambridge, MA 2-4 November, 2017), pp. 52-61
summary Acoustical design is one of the most challenging aspects of architecture. A complex system of competing influences (e.g., space geometry, size, proportion, material properties, surface detail, etc.) contribute to shaping the quality of the auditory experience. In particular, architectural surfaces affect the way that sound reflections propagate through space. By diffusing the reflected sound energy, surface designs can promote a more homogeneous auditory atmosphere by mitigating sharp and focused reflections. One of the challenges with designing an effective diffuser is the need to respond to a wide band of sound wavelengths, which requires the surface profile to precisely encode a range of detail sizes, depths and angles. Most of the available sound diffusers are designed to respond to a narrow band of frequencies. In this context, fractal-based surface designs can provide a unique opportunity for mitigating such limitations. A key principle of fractal geometry is its multilevel hierarchical order, which enables the same pattern to occur at different scales. This characteristic makes it a potential candidate for diffusing a wider band of sound wavelengths. However, predicting the reflection patterns of complicated fractal-based surface designs can be challenging using available acoustical software. These tools are often costly, complicated and are not designed for predicting early sound propagation paths. This research argues that writing customized algorithms provides a valuable, free and efficient alternative for addressing targeted acoustical design problems. The paper presents a methodology for designing and testing a customized algorithm for predicting sound diffusion patterns of fractal-based surfaces. Both quantitative and qualitative approaches were used to develop the code and evaluate the results.
keywords design methods; information processing; simulation & optimization; data visualization
series ACADIA
email ajlouni@arch.utah.edu
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last changed 2017/10/17 09:12
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