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Schlieren imaging allows visualising local density modulations in optically transparent media, which enables to analyse ultrasonic wave propagation. In typical measurement setups, spatial filtering is applied. Commonly, phase information of the ultrasonic wave is lost, which e.g. prevents accurate tomographic reconstruction. In this work two methods are presented to preserve phase information. The first method relies on the combination of two different spatial filters, while the second method does not require any spatial filtering. The fractional Fourier transform is applied to explain and simulate the effects occurring if the position of one of the optical lenses in a schlieren measurement setup is varied. Exemplary images of ultrasonic waves are shown to demonstrate the application of both methods.

Transmission measurements can be used to determine the frequency-dependent material parameters of polymers for simulative purposes. To achieve this goal, a hollow cylindrical sample is placed between two ultrasonic transducers. Hereafter, the material parameters are determined by ultrasound excitation in the MHz range and an inverse approach. However, the coupling conditions between the transducers and the sample is prone to uncertainties. Therefore, modeling of the setup for the inverse approach proves to be difficult. To reduce the uncertainties, this paper presents a measurement setup using a pulse-echo method in order to obtain frequency-dependent material parameters.

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Most measurement methods based on ultrasound, such as sound velocity, absorption or flow measurement systems, require that the acoustic wave propagation is linear. In many cases, linear wave propagation is assumed due to small signal amplitudes or verified, for example, by analysing the received signal spectra for the generation of harmonic frequency components. In this contribution, we present an approach to quantify occurrence of non-linear effects of acoustic wave propagation in ultrasonic measurement systems based on the evaluation of the acoustic Reynolds number. One parameter required for the determination of the acoustic Reynolds number is the particle velocity of the acoustic wave, which is not trivially obtained in most measurement systems. We thus present a model-based approach to estimate the particle velocity of an acoustic wave by identifying a Mason model from electrical impedance measurements of a given transducer. The Mason model is then used to determine the transducer's velocity output for a given electrical signal, allowing for an evaluation of the acoustic Reynolds number for different target media.