The active cloak comprises a discrete set of multipole sources that destructively interfere with an incident time harmonic scalar wave to produce zero total field over a finite spatial region. For a given number of sources and their positions in two dimensions it is shown that the multipole amplitudes can be expressed as infinite sums of the coefficients of the incident wave decomposed into regular Bessel functions. The field generated by the active sources vanishes in the infinite region exterior to a set of circles defined by the relative positions of the sources. The results provide a direct solution to the inverse problem of determining the source amplitudes. They also define a broad class of non-radiating discrete sources.
An active elastodynamic cloak destructively interferes with an incident time
harmonic in-plane (coupled compressional/shear) elastic wave to produce zero
total elastic field over a finite spatial region. A method is described which
explicitly predicts the source amplitudes of the active field. For a given
number of sources and their positions in two dimensions it is shown that the
multipole amplitudes can be expressed as infinite sums of the coefficients of
the incident wave decomposed into regular Bessel functions. Importantly, the
active field generated by the sources vanishes in the far-field. In practice
the infinite summations are clearly required to be truncated and the accuracy
of cloaking is studied when the truncation parameter is modified
We derive a formula for the gradients of the total scattering cross-section (TSCS) with respect to positions of a set of cylindrical scatterers. The analytic form enhances modeling capability when combined with optimization algorithms and parallel computing. As application of the method, we consider a gradient-based minimization of TSCS for a set of cylindrical obstacles by incrementally repositioning them so that they eventually act as an effective cloaking device. The gradient-based optimization algorithm reduces the TSCS by evaluating its derivative with respect to the cylinder positions and then perturbatively optimizing the position of each cylinder in the cloaking device while taking into account acoustic multiple scattering between the cylinders. The method is illustrated for clusters of hard cylinders and sets of elastic thin shells in water.
This paper presents a semi-analytical method of suppressing acoustic scattering using reinforcement learning (RL) algorithms. We give a RL agent control over design parameters of a planar configuration of cylindrical scatterers in water. These design parameters control the position and radius of the scatterers. As these cylinders encounter an incident acoustic wave, the scattering pattern is described by a function called total scattering cross section (TSCS). Through evaluating the gradients of TSCS and other information about the state of the configuration, the RL agent perturbatively adjusts design parameters, considering multiple scattering between the scatterers. As each adjustment is made, the RL agent receives a reward negatively proportional to the root mean square of the TSCS across a range of wavenumbers. Through maximizing its reward per episode, the agent discovers designs with low scattering. Specifically, the double deep Q-learning network and the deep deterministic policy gradient algorithms are employed in our models. Designs discovered by the RL algorithms performed well when compared to a state-of-the-art optimization algorithm using fmincon.
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