Isogeometric shape optimization of an acoustic horn using the teaching-learning-based optimization (TLBO) algorithm

Abstract Isogeometric shape optimization has been receiving great attention due to its advantages of exact geometry representation and smooth representation of the boundaries. In the present study, the optimum shape of a horn speaker was found to minimize the back reflection and improve impedance matching. The acoustic field was estimated by isogeometric analysis (IGA). Horn reflections are sensitive to the boundary shape and the smooth and non-faceted boundary representation in IGA makes it an attractive platform for shape optimization of acoustic devices. Teaching-Learning-Based Optimization (TLBO) algorithm was used to minimize, the reflection coefficient R, by changing the shape of the horn. The NURBS control points defining the design boundary of the horn were selected as the optimization design variables. Both single and multi-frequency optimization were performed. The objective function values found were considerably lower than those reported in the literature. The three-frequency shape optimization was performed successfully for which no results were reported in the literature due to the convergence issues. From the results, it was shown that the proposed method outperforms previous studies by finding the best shape for the horn that results in minimizing the reflection and produce improved reflection spectra.