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New tools for applying force to animals, tissues, and cells are critically needed in order to advance the field of mechanobiology, as few existing tools enable simultaneous imaging of tissue and cell deformation as well as cellular activity in live animals. Here, we introduce a novel microfluidic device that enables high-resolution optical imaging of cellular deformations and activity while applying precise mechanical stimuli to the surface of the worms cuticle with a pneumatic pressure reservoir. To evaluate device performance, we compared analytical and numerical simulations conducted during the design process to empirical measurements made with fabricated devices. Leveraging the well-characterized touch receptor neurons (TRNs) with an optogenetic calcium indicator as a model mechanoreceptor neuron, we established that individual neurons can be stimulated and that the device can effectively deliver steps as well as more complex stimulus patterns. This microfluidic device is therefore a valuable platform for investigating the mechanobiology of living animals and their mechanosensitive neurons.

This work is concerned with a structural health monitoring campaign of a 52-m wind turbine blade. Multiple artificial damages are introduced in the blade sequentially, and fatigue testing is conducted with each damage in sequence. Progressive fatigue-driven damage propagation is achieved, enabling investigations concerning detection of initiation and propagation of damage in the blade. Using distributed accelerometers, operational modal analysis is performed to extract the lower-order natural vibration modes of the blade, which are shown to not be sensitive to small damages in the blade. To enable monitoring of small damages, an active vibration monitoring system is used, comprised of an electrodynamic vibration shaker and distributed accelerometers. From the accelerometer data, frequency domain methods are used to extract features. Using the extracted features, outlier detection is performed to investigate changes in the measurements resulting from the introduced damages. Capabilities of using features based on the active vibration data for detection of initiation and progression of damage in a wind turbine blade during fatigue testing are investigated, showing good correlation between the observed damage progression and the calculated changes in the damage index. [ABSTRACT FROM AUTHOR]

Ultrasonic guided waves offer a convenient and practical approach to structural health monitoring and non-destructive evaluation. A key property of guided waves is the fully defined relationship between central frequency and propagation characteristics (phase velocity, group velocity and wavenumber)—which is described using dispersion curves. For many guided wave-based strategies, accurate dispersion curve information is invaluable, such as group velocity for localisation. From experimental observations of dispersion curves, a system identification procedure can be used to determine the governing material properties. As well as returning an estimated value, it is useful to determine the distribution of these properties based on measured data. A method of simulating samples from these distributions is to use the iterative Markov-Chain Monte Carlo (MCMC) procedure, which allows for freedom in the shape of the posterior. In this work, a scanning-laser Doppler vibrometer is used to record the propagation of Lamb waves in a unidirectional-glass-fibre composite plate, and dispersion curve data for various propagation angles are extracted. Using these measured dispersion curve data, the MCMC sampling procedure is performed to provide a Bayesian approach to determining the dispersion curve information for an arbitrary plate. The distribution of the material properties at each angle is discussed, including the inferred confidence in the predicted parameters. The percentage errors of the estimated values for the parameters were 10–15 points larger when using the most likely estimates, as opposed to calculating from the posterior distributions, highlighting the advantages of using a probabilistic approach. [ABSTRACT FROM AUTHOR]