Enhancing acoustical design using parametric optimization of surface geometries

Abstract

Raytracing is commonly used to design surface geometries. To optimize this process, we propose combining a parametric design approach with an evolutionary solver. These optimization principles are widely implemented across engineering fields and could be exploited for acoustical design. Evolutionary optimization is an iterative tool to increase the “fitness”—or optimality—of a predefined target value, in this case reflecting and non-reflecting geometries given any acoustical design brief. The iterative nature of an evolutionary solver—which evaluates each generation in terms of its proximity to the target value—enables acousticians/designers to continually assess results at different stages of the optimization process. The proposed algorithms optimize parameters such as size, shape, number or orientation of surfaces by defining raytracing target values (e.g., number of rays reaching a receiver after n-reflections, density of rays in a diffuse field). We implement this approach using standard software: the Galapagos solver within the Grasshopper framework, a parametric design tool in the Rhino 3D environment. The limitless design options offered by the parametric approach are thus made available for acoustical optimization. This method can be used on all scales of geometrical acoustics, from alignment of absorber panels to geometries of enclosed spaces, such as concert halls.