Mechanisms of heat and mass transfer to and from single drops freely-suspended in an air stream

A specially-designed vertical wind tunnel was used to freely suspend individual liquid drops of 5 mm initial diameter to investigate drop dynamics, terminal velocity and heat and mass transfer rates. Droplets of distilled, de-ionised water, n-propanol, iso-butanol, monoethanolamine and heptane were studied over a temperature range of 50oC to 82oC. The effects of substances that may provide drop surface rigidity (e.g. surface active agents, binders and polymers) on mass transfer rates were investigated by doping distilled de-ionised water drops with sodium di-octyl sulfo-succinate surfactant. Mass transfer rates decreased with reduced drop oscillation as a result of surfactant addition, confirming the importance of droplet surface instability. Rigid naphthalene spheres and drops which formed a skin were also studied; the results confirmed the reduced transfer rates in the absence of drop fluidity. Following consideration of fundamental drop dynamics in air and experimental results from this study, a novel dimensionless group, the Oteng-Attakora, (OT), number was included in the mass transfer equation to account for droplet surface behaviour and for prediction of heat and mass transfer rates from single drops which exhibit surface instability at Re>=500. The OT number and the modified mass transfer equation are respectively: OT=(ava2/d).de1.5(d/) Sh = 2 + 0.02OT0.15Re0.88Sc0.33 Under all conditions drop terminal velocity increased linearly with the square root of drop diameter and the drag coefficient was 1. The data were correlated with a modified equation by Finlay as follows: CD=0.237.((Re/P0.13)1.55(1/We.P0.13) The relevance of the new model to practical evaporative spray processes is discussed.

A pragmatic Bayesian approach to wind field retrieval

The ERS-1 Satellite was launched in July 1991 by the European Space Agency into a polar orbit at about 800 km, carrying a C-band scatterometer. A scatterometer measures the amount of backscatter microwave radiation reflected by small ripples on the ocean surface induced by sea-surface winds, and so provides instantaneous snap-shots of wind flow over large areas of the ocean surface, known as wind fields. Inherent in the physics of the observation process is an ambiguity in wind direction; the scatterometer cannot distinguish if the wind is blowing toward or away from the sensor device. This ambiguity implies that there is a one-to-many mapping between scatterometer data and wind direction. Current operational methods for wind field retrieval are based on the retrieval of wind vectors from satellite scatterometer data, followed by a disambiguation and filtering process that is reliant on numerical weather prediction models. The wind vectors are retrieved by the local inversion of a forward model, mapping scatterometer observations to wind vectors, and minimising a cost function in scatterometer measurement space. This thesis applies a pragmatic Bayesian solution to the problem. The likelihood is a combination of conditional probability distributions for the local wind vectors given the scatterometer data. The prior distribution is a vector Gaussian process that provides the geophysical consistency for the wind field. The wind vectors are retrieved directly from the scatterometer data by using mixture density networks, a principled method to model multi-modal conditional probability density functions. The complexity of the mapping and the structure of the conditional probability density function are investigated. A hybrid mixture density network, that incorporates the knowledge that the conditional probability distribution of the observation process is predominantly bi-modal, is developed. The optimal model, which generalises across a swathe of scatterometer readings, is better on key performance measures than the current operational model. Wind field retrieval is approached from three perspectives. The first is a non-autonomous method that confirms the validity of the model by retrieving the correct wind field 99% of the time from a test set of 575 wind fields. The second technique takes the maximum a posteriori probability wind field retrieved from the posterior distribution as the prediction. For the third technique, Markov Chain Monte Carlo (MCMC) techniques were employed to estimate the mass associated with significant modes of the posterior distribution, and make predictions based on the mode with the greatest mass associated with it. General methods for sampling from multi-modal distributions were benchmarked against a specific MCMC transition kernel designed for this problem. It was shown that the general methods were unsuitable for this application due to computational expense. On a test set of 100 wind fields the MAP estimate correctly retrieved 72 wind fields, whilst the sampling method correctly retrieved 73 wind fields.

Vibration analysis of submerged rectangular microplates with distributed mass loading

This paper investigates the vibration characteristics of the coupling system of a microscale fluid-loaded rectangular isotropic plate attached to a uniformly distributed mass. Previous literature has, respectively, studied the changes in the plate vibration induced by an acoustic field or by the attached mass loading. This paper investigates the issue of involving these two types of loading simultaneously. Based on Lamb's assumption of the fluid-loaded structure and the Rayleigh–Ritz energy method, this paper presents an analytical solution for the natural frequencies and mode shapes of the coupling system. Numerical results for microplates with different types of boundary conditions have also been obtained and compared with experimental and numerical results from previous literature. The theoretical model and novel analytical solution are of particular interest in the design of microplate-based biosensing devices.