The mechanisms responsible for ice propagation on surfaces undergoing dropwise condensation have been determined and characterized. Based on experimental data acquired non-invasively with high speed quantitative microscopy, the freezing process was determined to occur by two distinct mechanisms: inter-droplet and intradroplet ice crystal growth. The inter-droplet crystal growth mechanism was responsible for the propagation of the ice phase between droplets while the intra-droplet crystal growth mechanism was responsible for the propagation of ice within individual droplets. The larger scale manifestation of these two mechanisms cooperating in tandem was designated as the aggregate freezing process. The dynamics of the aggregate freezing process were characterized in terms of the substrate thermal di usivity, the substrate temperature, the free stream air humidity ratio, and the interfacial substrate properties of roughness and contact angle, which were combined into a single surface energy parameter. Results showed that for a given thermal di usivity, the aggregate freezing velocity increased asymptotically towards a constant value with decreasing surface temperature, increasing humidity, and decreasing surface energy. The inter-droplet freezing velocity was found to be independent of substrate temperature and only slightly dependent on humidity and surface energy. The intra-droplet freezing velocity was determined to be a strong function of substrate temperature, a weaker function of surface energy, and independent of humidity. From the data, a set of correlational models were developed to predict the three freezing velocities in terms of the independent variables. These models predicted the majority of the measured aggregate, inter- and intra-droplet freezing velocities to within 15%, 10%, and 35%, respectively. Basic thermodynamic analyses of the inter- and intra-droplet freezing mechanisms showed that the dynamics of these processes were consistent with the kinetics of crystal growth from the vapor and supercooled liquid phases, respectively. The aggregate freezing process was also analyzed in terms of its constituent mechanisms; those results suggested that the distribution of liquid condensate on the surface has the largest impact on the aggregate freezing dynamics.

Neutrino astronomy is expanding into the ultra-high energy (>1017eV) frontier with the use of in-ice detection of Askaryan radio emission from neutrino-induced particle showers. There are already pilot arrays for validating the technology and the next few years will see the planning and construction of IceCube-Gen2, an upgrade to the current neutrino telescope IceCube. This thesis aims to facilitate that planning by providing faster simulations using deep learning surrogate models. Faster simulations could enable proper optimisation of the antenna stations providing better sensitivity and reconstruction of neutrino properties. The surrogates are made for two parts of the end-to-end simulations: the signal generation and the signal propagation. These two steps are the most time-consuming parts of the simulations. The signal propagation is modelled with a standard fully connected neural network whereas for the signal generation a conditional Wasserstein generative adversarial network is used. There are multiple reasons for using these types of models. For both problems the neural networks provide the speed necessary as well as being differentiable -both important factors for optimisation. Generative adversarial networks are used in the signal generation because of the inherent stochasticity in the particle shower development that leads to the Askaryan radio signal. A more standard neural network is used for the signal propagation as it is a regression task. Promising results are obtained for both tasks. The signal propagation surrogate model can predict the parameters of interest at the desired accuracy, except for the travel time which needs further optimisation to reduce the uncertainty from 0.5 ns to 0.1 ns. The signal generation surrogate model predicts the Askaryan emission well for the limited parameter space of hadronic showers and within 5° of the Cherenkov cone. The two models provide a first step and a proof of concept. It is believed that the models can reach the required accuracies with more work.