Study on Nonlinear Stochastic Process of Deck Slamming on Floating Offshore Platform

In this paper, a semi-analytic method is introduced to predict the deck-slamming probability and corresponding loads. This method is based on a nonlinear statistical approach that takes into account the linear and second-order components of the relative wave elevation up to the second order. The linear and second-order wave elevation is assumed to be a two-term Volterra series. The joint probability density function of the relative wave elevation and velocity are formulated using the Hermite-moment method, and the probability distributions of the wave crest and relative wave velocity are calculated. These probability distributions are verified using the data sampled from the linear and second-order relative wave elevation. Based on this formulation, the probabilities of deck slamming and slamming-induced loads are estimated. This method is applied to a tension leg platform (TLP) model, and the effects of the second-order component of the relative wave elevation on the deck slamming are investigated.

Plane Partitions in Batch Track-Track Associations

The most difficult multiple target tracking problem includes multiple sensors with different viewing angles, measurement geometries, fields of view, accuracies, resolutions and scan rates. Such variations in sensor output characteristics as well as channel delays, countermeasures, inherent target features and maneuvers have solidified the consensus that an effective fusion system must handle several levels of “tracklets” from distributed sources in order to produce the desired long tracks as described in Waltz and Llinas (1990). In view of the increased attention given to hypersonics as well as the increased need for low-level signal processing, the computational complexity of track association is a vital factor in determining an autonomous vehicles’ ability to complete its objectives quickly. We are given a set of tracklets where the particular methods used to make the detections are taken for granted. Following joint probability density association filters, we assume short tracklets are completed (i.e, detections are correctly correlated with state estimates) and take a computational geometric approach to associating tracklets. If N is the number of short term tracklets, this method fuses them in O(N2). Using covariance as a distance, this report suggests the applicability of a class of sweep-line algorithms developed in computational geometry in data fusion.

Fitting Precipitation Particle Size–Velocity Data to Mixed Joint Probability Density Function with an Expectation Maximization Algorithm

This paper proposes an estimation method of joint size and terminal velocity distribution on the basis of sampling data of precipitation particles containing multiple types. Assuming that the velocity follows the normal distribution and the size follows the gamma distribution, the method searches a locally maximum logarithmic likelihood within a realistic parameter range using the expectation–maximization algorithm. Several test populations were prepared with a realistic number of elements, and then the method was evaluated by retrieving the populations from their sample. The results showed that the original parameters were successfully estimated in most cases of the test population containing some of liquids, graupels, and rimed and unrimed aggregates. The original number of elements was also estimated with an adjustment of the number of elements in a manner such that each of their minority fractions exceeded a threshold. Applied to the two-dimensional disdrometer observation data, the method was helpful to discard frequently observed erroneous data with unrealistically large fall velocity.

Modeling of Future Extreme Storm Surges at the NW Mediterranean Coast (Spain)

Storm surges are one of the main drivers for extreme flooding at the coastal areas. Such events can be characterized with the maximum level in an extreme storm surge event (surge peak), as well as the duration of the event. Surge projections come from a barotropic model for the 1950–2100 period, under a severe climate change scenario (RCP 8.5) at the northeastern Spanish coast. The relationship of extreme storm surges to three large-scale climate patterns was assessed: North Atlantic Oscillation ( N A O ), East Atlantic Pattern ( E A W R ), and Scandinavian Pattern ( S C ). The statistical model was built using two different strategies. In Strategy #1, the joint probability density was characterized by a moving-average series of stationary Archimedean copula, whereas in Strategy #2, the joint probability density was characterized by a non-stationary probit copula. The parameters of the marginal distribution and the copula were defined with generalized additive models. The analysis showed that the mean values of surge peak and event duration were constant and were independent of the proposed climate patterns. However, the values of N A O and S C influenced the threshold and the storminess of extreme events. According to Strategy #1, the variance of the surge peak and event duration increased with a fast shift of negative S C and a positive N A O , respectively. Alternatively, Strategy #2 showed that the variance of the surge peak increased with a positive E A W R . Both strategies coincided in that the joint dependence of the maximum surge level and the duration of extreme surges ranged from low to medium degree. Its mean value was stationary, and its variability was linked to the geographical location. Finally, Strategy #2 helped determine that this dependence increased with negative N A O .