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

Processes ◽  
2021 ◽  
Vol 9 (2) ◽  
pp. 231
Author(s):  
Hyun-Seung Nam ◽  
Yonghwan Kim
Keyword(s):  
Statistical Approach ◽  
Volterra Series ◽  
Probability Distributions ◽  
Joint Probability ◽  
Second Order ◽  
Wave Crest ◽  
Order Component ◽  
Wave Elevation ◽  
Order Components ◽  
Joint Probability Density

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.

Probability Distributions of Vertical Bending Moments on a FPSO in Abnormal Wave Seastates

2007 ◽  
Author(s):  
Nuno Fonseca ◽  
Ricardo Pascoal ◽  
Carlos Guedes Soares
Keyword(s):  
Probability Distributions ◽  
The Other ◽  
Wave Crest ◽  
Large Set ◽  
Bending Moments ◽  
Wave Groups ◽  
Wave Elevation ◽  
Other Hand

A method to calculate the responses of a FPSO in deterministic wave traces with abnormal waves, has been applied to calculate the motions and global structural loads induced by a large set of abnormal waves that were measured in different places. The present paper is focused on the probability distributions of the motions and global structural loads induced by the seastates which included the abovementioned abnormal waves. The objective is to compare the distributions of the wave elevation with the abnormal wave crest and trough, and to compare the distributions of the platform responses with the responses induced by the abnormal waves. In this way it is possible to conclude if the abnormal waves induce abnormal responses of the platform, or if on the other hand there are wave groups with waves smaller than the abnormal wave that induce larger responses.

Processing of Color- and Noncolor-Coded Signals in the Gourami Retina. II. Amacrine Cells

1997 ◽  
Vol 78 (4) ◽  
pp. 2018-2033 ◽  
Author(s):  
Hiroko M. Sakai ◽  
Hildred Machuca ◽  
Ken-Ichi Naka
Keyword(s):  
White Noise ◽  
Amacrine Cell ◽  
Amacrine Cells ◽  
Second Order ◽  
Order Component ◽  
Response Dynamics ◽  
Dendritic Field ◽  
State Response ◽  
Order Components ◽  
Nonlinear Components

Sakai, Hiroko M., Hildred Machuca, and Ken-Ichi Naka. Processing of color- and noncolor-coded signals in the gourami retina. II. Amacrine cells. J. Neurophysiol. 78: 2018–2033, 1997. The same set of stimuli and analytic methods that was used to study the dynamics of horizontal cells ( Sakai et al. 1997a ) was applied to a study of the response dynamics and signal processing in amacrine cells in the retina of the kissing gourami, Helostoma rudolfi. The retina contains two major classes of amacrine cells that could be identified from their morphology: C and N amacrine cells. C amacrine cells had a two-layered dendritic field, whereas N cells had a monolayered dendritic field. Both types of amacrine cell were tracer-coupled but coupling was more extensive in the N amacrine cells. Responses from C amacrine cells lacked a DC component and had a small linear component that was <10% in terms of mean square error (MSE); the second-order component often accounted for >50% of the modulation response. The C amacrine cells did not show any characteristic color coding under any stimulus condition. Most responses of N cells to a pulsatile stimulus consisted of a series of depolarizing transient potentials and steady illumination did not generate any DC potential in these cells. The response to a white-noise modulated input was composed of well-defined first- and second-order components and, possibly, higher-order components. The response evoked by a red or green white-noise–modulated stimulus given alone was not color coded. Modulated red illumination in the presence of a green illumination elicited a color-coded response from >70% of N amacrine cells. Color information was carried not only by the polarity but also by the dynamics of the first-order component. No convincing evidence was obtained to indicate that the second-order component might be involved in color processing. Some N amacrine cells produced a well-defined (second-order) interaction kernel to show that the temporal sequence of red and green stimuli was a parameter to be considered. In a complex cell such as an amacrine cell, responses evoked by a pulsatile stimulus given in darkness and by modulation of a mean luminance could be very different in terms of their characteristics. It was not always possible to predict the response evoked by one stimulus from observing the cell's response to another stimulus. This is because, in N cells, a flash-evoked (nonsteady state) response is composed largely of nonlinear components whereas a modulation (steady state) response is composed of linear as well as nonlinear components.

Joint Distributions of Successive Wave Crest Heights and Successive Wave Trough Depths for Second-Order Nonlinear Waves

2002 ◽  
Vol 46 (03) ◽  
pp. 175-185
Author(s):  
Hanne T. Wist ◽  
Dag Myrhaug ◽  
Håvard Rue
Keyword(s):  
Nonlinear Waves ◽  
Probability Distributions ◽  
Field Measurements ◽  
Second Order ◽  
Wave Crest ◽  
Wave Statistics ◽  
Joint Distributions ◽  
Wave Trough ◽  
Successive Wave ◽  
Central North Sea

Joint distributions of successive wave crest heights and successive wave trough depths for nonlinear waves are presented. Two different approaches are used in order to derive the probability distributions. The first method includes the effect of second-order Stokes-type nonlinearity on successive wave statistics in finite water depth, and the second method is a parametric model only for crest heights based on second-order simulations. The theoretical distributions are compared with observed wave data obtained from field measurements in the central North Sea.

Inability of Humans to Discriminate between Visual Textures That Agree in Second-Order Statistics—Revisited

Perception ◽  
1973 ◽  
Vol 2 (4) ◽  
pp. 391-405 ◽  
Author(s):  
B Julesz ◽  
E N Gilbert ◽  
L A Shepp ◽  
H L Frisch
Keyword(s):  
Order Statistics ◽  
Probability Distributions ◽  
Joint Probability ◽  
Second Order ◽  
Two Dimensional ◽  
Third Order ◽  
Texture Discrimination ◽  
Second Order Statistics ◽  
Joint Probability Distributions ◽  
The Individual

In an earlier study by Julesz (1962) pairs of random textures were generated side-by-side using a Markov process with different third-order joint-probability distributions but identical first- and second-order distributions. Such texture pairs could not be discriminated from each other by the human visual system without scrutiny. Unfortunately, Markov processes are inherently one-dimensional while the general processes underlying visual texture discrimination are two-dimensional. Here three new methods are introduced that generate two-dimensional non-Markovian textures with different third-order but identical first- and second-order statistics. All three methods generate texture pairs that cannot be discriminated from each other. The lack of texture discrimination is the more astonishing since the individual elements that form the texture pair are clearly perceived as being very different. However, a counterexample was found that yields discrimination although the texture pair has approximately identical second-order statistics. This case can be explained by assuming that early feature extractors do some preprocessing. These new demonstrations give support to a model of texture discrimination in which the stimulus is first analyzed by local feature extractors that can detect only simple features such as dots and edges of given sizes and orientations. Then the outputs of these simple extractors are evaluated by a global processor that can compute only second- or first-order statistics (that is can compare at most two such outputs).

scholarly journals A new approach to mapping landslide hazards: a probabilistic integration of empirical and physically based models in the North Cascades of Washington, USA

2019 ◽  
Vol 19 (11) ◽  
pp. 2477-2495
Author(s):  
Ronda Strauch ◽  
Erkan Istanbulluoglu ◽  
Jon Riedel
Keyword(s):  
Statistical Approach ◽  
Joint Probability ◽  
North Cascades ◽  
Physically Based Model ◽  
New Approach ◽  
The North ◽  
Debris Avalanches ◽  
Landslide Hazards ◽  
Infinite Slope Stability Model ◽  
Physically Based

Abstract. We developed a new approach for mapping landslide hazards by combining probabilities of landslide impacts derived from a data-driven statistical approach and a physically based model of shallow landsliding. Our statistical approach integrates the influence of seven site attributes (SAs) on observed landslides using a frequency ratio (FR) method. Influential attributes and resulting susceptibility maps depend on the observations of landslides considered: all types of landslides, debris avalanches only, or source areas of debris avalanches. These observational datasets reflect the detection of different landslide processes or components, which relate to different landslide-inducing factors. For each landslide dataset, a stability index (SI) is calculated as a multiplicative result of the frequency ratios for all attributes and is mapped across our study domain in the North Cascades National Park Complex (NOCA), Washington, USA. A continuous function is developed to relate local SI values to landslide probability based on a ratio of landslide and non-landslide grid cells. The empirical model probability derived from the debris avalanche source area dataset is combined probabilistically with a previously developed physically based probabilistic model. A two-dimensional binning method employs empirical and physically based probabilities as indices and calculates a joint probability of landsliding at the intersections of probability bins. A ratio of the joint probability and the physically based model bin probability is used as a weight to adjust the original physically based probability at each grid cell given empirical evidence. The resulting integrated probability of landslide initiation hazard includes mechanisms not captured by the infinite-slope stability model alone. Improvements in distinguishing potentially unstable areas with the proposed integrated model are statistically quantified. We provide multiple landslide hazard maps that land managers can use for planning and decision-making, as well as for educating the public about hazards from landslides in this remote high-relief terrain.

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