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We present a simulation model that can be used to study the movement of an object in an acoustic levitator. The model uses the boundary element method (BEM) to compute the levitator's acoustic field, and the finite element method (FEM) to compute the movement of the levitating object. The model was built to act as a virtual tool for testing how objects move in acoustic pressure fields generated by phased array transducers (PATs). This was demonstrated by comparing object dynamics for different PAT optimization methods. We studied the stability of the levitation in fields created by two optimization methods. The fields were optimized to levitate an ellipsoid in the middle of our PAT geometry. By slightly displacing the levitating object from the intended levitation spot, we were able to show that the levitation became unstable and that the object would drop out from the trap. The results demonstrate that the model can be used to rapidly validate optimizers instead of having to run long experiments.

Controlled levitation has been limited to sub-wavelength samples with few exceptions of spheres and cubes. We demonstrate controlled airborne levitation of 2λ-sized complex-shaped objects (3D letters, X, U, T). The pressure field was tailored to a particular sample shape by arranging multiple small asymmetric acoustic traps. This allows stable locking of the object and control of its orientation and position in mid-air. The pressure field was produced with a 450-element phased array (f = 40kHz) featuring element-wise amplitude and phase control.

Localizing fouling in industrial pipes by AI-enhanced ultrasonic means shows promising results. However, the existing methods require large quantities of data measured from multiple locations and thus are either costly due to the number of transducers required or are hampered by readout uncertainties due to reattachment of the sensors. We present a wireless and affordable network of custom-built clamp-on transducers with drive electronics, in which transducers can be used as both transmitters and receivers. The network was used to generate and characterize propagating waves in pipes with and without fouling. The transducers and their driving electronics generate signals with sufficient signal-to-noise ratio in a repeatable manner and are sensitive enough to detect the signal change caused by fouling on a steel pipe.

Advances in phased array of transducers (PATs) allow for precise control of each element's phase and amplitude and can consist of hundreds of transducers. The widely adopted Gerchberg-Saxton type of algorithms do not provide sufficient accuracy for complex control tasks like formation of detailed holograms as they perform well only for a small number of control points with respect to the number of transducers. Here we present an efficient PATs amplitude and phase solver based on the iterative first-order gradient optimization of the user defined loss function (APGO). We demonstrate high resolution hologram reconstruction when the number of control points is 25 times the number of transducers.