Multiparameter spectral analysis for aeroelastic instability problems

This paper presents a novel application of multiparameter spectral theory to the study of structural stability, with particular emphasis on aeroelastic flutter. Methods of multiparameter analysis allow the development of new solution algorithms for aeroelastic flutter problems; most significantly, a direct solver for polynomial problems of arbitrary order and size, something which has not before been achieved. Two major variants of this direct solver are presented, and their computational characteristics are compared. Both are effective for smaller problems arising in reduced-order modelling and preliminary design optimization. Extensions and improvements to this new conceptual framework and solution method are then discussed.Comment: 20 pages, 8 figure

Underlying physics of thermal actuation in composite MEMS

Integrated micro- and nano-electromechanical (N/MEMS) sensor and actuator technology has become increasingly important to any applications with parallel processes, which clearly provide advantages in fields such as e.g. high-speed imaging and precision metrology of large substrates. Although micro-fabrication processes for integrated technology are well-established, there remain several fundamental research questions regarding optimized design parameters for an improved performance of sensors and actuators. In this work we investigate the underlying physics of a thermal actuator of a composite MEMS structure for a selected range of design parameters such as e.g. layer thicknesses, number of layers, as well as material properties. We derive and present a one-dimensional heat conduction model of an M-layered composite slab and investigate the heat transfer across three layers using Green’s function. The work, although entirely theoretical here, finds direct meaning and implementation in our ongoing collaborative work on MEMS arrays for Atomic Force Microscopy (AFM)

Enabling adaptive and enhanced acoustic sensing using nonlinear dynamics

Transmission of real-time data is strongly increasing due to remote processing of sensor data, among other things. A route to meet this demand is adaptive sensing, in which sensors acquire only relevant information using pre-processing at sensor level. We present here adaptive acoustic sensors based on mechanical oscillators with integrated sensing and actuation. Their dynamics are shifted into a nonlinear regime using feedback or coupling. This enhances dynamic range, frequency resolution and signal-to-noise ratio. Combining tunable sensing properties with sound analysis could enable acquiring of only relevant information rather than extracting this from irrelevant data by post-processing

Computer Vision Estimation of Physical Parameters and Its Application to Power Requirements of Natural and Artificial Swimmers

A useful measure of efficiency of transport in aquatic animals and autonomous underwater vehicles is cost of transport. Often, cost of transport data on specific animals or platforms is not readily available or does not fit specific use cases, but images are readily available. In this work, we present a methodology to synthesize such data without the need for a specimen or laboratory tests. We propose a computer vision in a methodology called Ika-Fit to determine important physical characteristics, such as surface area, slenderness ratio, and mass, that are used for a cost of transport model. The Ika-Fit method provides a good estimation of parameters when compared to biological data and robotic platforms. These parameters are estimated for existing engineered systems, and the model is compared to published data; the model is found to demonstrate higher accuracy using fewer parameters in estimating cost of transport over existing methods