Design of one-way acoustic energy flow devices using Bayesian network classifiers

Abstract

Linear acoustic metamaterials (AMM) displaying dynamic negative stiffness or density have been the subject of current research to design exotic devices such as acoustic super-lenses and cloaks. More recently, nonlinear AMM have been of interest as one means of creating non-reciprocal acoustic devices [Liang et al., Nat. Mater. 9 (2010)]. The efficiency of those types of non-reciprocal acoustic devices strongly depends on the material nonlinearity of one of its components and is therefore limited by existing fluids containing contrast agents. This work addresses this limitation by exploring the top-down design of a nonlinear AMM with non-resonant subwavelength structures that display non-monotonic pressure-volume strain response to generate an AMM with large parameters of nonlinearity. A simple one-way device consisting of a frequency-selective acoustic mirror and a nonlinear medium is described using a nonlinear multiscale model and a nonlinear propagation model that includes quadratic and cubic nonlinearity. Bayesian network classifiers (BNC) map regions of high performance at the device scale to each design level and intersect the high performance space across levels to identify multilevel solutions. Three design levels are considered: one-way performance, nonlinear acoustic propagation in the effective medium, and effective properties of the AMM based on subwavelength structure.