Bayesian approach to decompression sickness model parameter estimation.

We examine both maximum likelihood and Bayesian approaches for estimating probabilistic decompression sickness model parameters. Maximum likelihood estimation treats parameters as fixed values and determines the best estimate through repeated trials, whereas the Bayesian approach treats parameters as random variables and determines the parameter probability distributions. We would ultimately like to know the probability that a parameter lies in a certain range rather than simply make statements about the repeatability of our estimator. Although both represent powerful methods of inference, for models with complex or multi-peaked likelihoods, maximum likelihood parameter estimates can prove more difficult to interpret than the estimates of the parameter distributions provided by the Bayesian approach. For models of decompression sickness, we show that while these two estimation methods are complementary, the credible intervals generated by the Bayesian approach are more naturally suited to quantifying uncertainty in the model parameters.

Comparison of real and idealized cetacean flippers.

When a phenomenon in nature is mimicked for practical applications, it is often done so in an idealized fashion, such as representing the shape found in nature with convenient, piece-wise smooth mathematical functions. The aim of idealization is to capture the advantageous features of the natural phenomenon without having to exactly replicate it, and it is often assumed that the idealization process does in fact capture the relevant geometry. We explored the consequences of the idealization process by creating exact scale models of cetacean flippers using CT scans, creating corresponding idealized versions and then determining the hydrodynamic characteristics of the models via water tunnel testing. We found that the majority of the idealized models did not exhibit fluid dynamic properties that were drastically different from those of the real models, although multiple consequences resulting from the idealization process were evident. Drag performance was significantly improved by idealization. Overall, idealization is an excellent way to capture the relevant effects of a phenomenon found in nature, which spares the researcher from having to painstakingly create exact models, although we have found that there are situations where idealization may have unintended consequences such as one model that exhibited a decrease in lift performance.