Prediction of Response of a SCR Using the Multi-Gaussian Maximum Entropy Method

2015 ◽  
Author(s):  
Umberto Alibrandi ◽  
Chenyin Ma ◽  
Ying Min Low ◽  
Chan Ghee Koh
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Production Systems ◽  
Computational Cost ◽  
Maximum Entropy Principle ◽  
Entropy Method ◽  
Wave Forces ◽  
Reliability Design ◽  
Steel Catenary Riser ◽  
Entropy Principle

The dynamic analysis of deepwater floating production systems has many complexities, such as the dynamic coupling between the vessel and the lines, the coupling between the first-order and second-order wave forces, several sources of nonlinearities. These complexities can be captured by fully coupled time domain analyses. However they require an enormous computational cost, especially for the evaluation of tails of the distributions of the extreme responses, which are of great interest for practical reliability design purposes. In this paper the non-Gaussian probability density functions of the responses are evaluated through a novel moment-based approach, based on the Maximum Entropy principle, called Multi-Gaussian Maximum Entropy Method (MGMEM). The application to a Steel Catenary Riser (SCR) shows the accuracy and effectiveness of the presented procedure.

Natural partitioning of orientation elements and determination of the ODF from individual orientations according to the maximum-entropy principle

1999 ◽  
Vol 32 (3) ◽  
pp. 404-408
Author(s):  
Y. D. Wang ◽  
A. Vadon ◽  
J. Bessières ◽  
J. J. Heizmann
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Maximum Entropy Principle ◽  
Orientation Distribution ◽  
Entropy Method ◽  
Euler Space ◽  
Entropy Principle ◽  
Orientation Density ◽  
Preliminary Orientation

The orientation distribution function (ODF) of a polycrystalline material is usually constructed from individual orientations by the harmonic method on the assumption of a certain function distribution in the Euler space around each orientation. In the present paper, a new method is developed to determine the ODF from individual orientations. A natural partitioning of the orientation elements in the Euler space around some clustered orientations is proposed. Thus, the preliminary values of orientation density in the elements are directly estimated by the volumes of the orientation elements and the number of grains (or measured points) in each orientation element. Then, the texture vector is further refined using the maximum-entropy method with the preliminary orientation densities as constraints. The validity of this method is exemplified by the texture analysis of a cubic material from individual orientations modelled by Gaussian distribution.

Seismic Reliability Analysis of Hydraulic Aqueduct with Maximum Entropy Method

2011 ◽  
Vol 282-283 ◽  
pp. 120-123
Author(s):  
Yu Jun Li ◽  
Jian Guo Xu
Keyword(s):  
Maximum Entropy ◽  
Seismic Waves ◽  
Maximum Entropy Principle ◽  
Water Structure ◽  
Internal Force ◽  
Entropy Method ◽  
Thin Wall ◽  
Seismic Reliability ◽  
Rubber Bearing ◽  
Entropy Principle

With the thin-wall dynamic analytical model of aqueduct for fluid-structure coupling, the seismic reliability of aqueduct is studied. In this model the influence of the bend-torsion coupling, water-structure coupling and nonlinear rubber bearing isolation are all taken into consideration. Based on the type of site soil, the artificial seismic waves are created. In accordance with the maximum entropy principle and the measured data of structural internal force under earthquake, the reliability distribution function and the aqueduct reliability are calculated.

A Piecewise Linear Maximum Entropy Method for Invariant Measures of Random Maps with Position-Dependent Probabilities

2018 ◽  
Vol 28 (12) ◽  
pp. 1850154 ◽  
Author(s):  
Congming Jin ◽  
Tulsi Upadhyay ◽  
Jiu Ding
Keyword(s):  
Maximum Entropy ◽  
Invariant Measures ◽  
Piecewise Linear ◽  
Maximum Entropy Principle ◽  
Entropy Method ◽  
Absolutely Continuous ◽  
Random Maps ◽  
Moment Functions ◽  
Entropy Principle ◽  
Absolutely Continuous Invariant

We present a numerical method for the approximation of absolutely continuous invariant measures of one-dimensional random maps, based on the maximum entropy principle and piecewise linear moment functions. Numerical results are also presented to show the convergence of the algorithm.

Uncertainty Quantification With Maximum Entropy Method for Fatigue Life Estimation

2020 ◽  
Author(s):  
S. M. Nielsen ◽  
H. A. Hougaard ◽  
O. Balling
Keyword(s):  
Fatigue Life ◽  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Probability Distributions ◽  
Computational Cost ◽  
Entropy Method ◽  
Element Analysis ◽  
Fatigue Life Estimation ◽  
Order Of Magnitude ◽  
General Statistical

Abstract Use of high-fidelity fatigue models that incorporate not only material uncertainty but also part variability and operational uncertainties can improve the accuracy of predictive maintenance and thus decrease operational cost. However, due to the large number of computationally expensive cost function evaluations necessary, little work has been done to explore this field. In this research, the expected life probability distributions with low computational cost is estimated through a general statistical framework that applies Maximum Entropy Method (MEM), fractional statistical moments and Multiplicative Dimensional Reduction (M-DRM). The framework is tested on advanced models of a 6204 SKF ball bearing. The influence of critical part tolerances and load conditions on fatigue life with a probability density function with only 80 function evaluations is quantified in both a finite element analysis and a non-linear analytical model. The number of function evaluations is one order of magnitude lower than necessary for a comparable accuracy achieved by Monte Carlo simulation.

Reliability Design of Car Suspension Coil Spring Based on Maximum Entropy Method

2011 ◽  
Vol 697-698 ◽  
pp. 736-739 ◽  
Author(s):  
Chun Zhe Liu
Keyword(s):  
Maximum Entropy ◽  
Design Theory ◽  
Maximum Entropy Method ◽  
State Equation ◽  
Entropy Method ◽  
Arbitrary Distribution ◽  
Reliability Design ◽  
Car Suspension ◽  
Distribution Parameters ◽  
Automobile Components

A practical and effective numerical method of reliability design with arbitrary distribution parameters is proposed in the paper. And techniques from the maximum entropy method, the reliability design theory and probabilistic engineering design are employed to present an approach for the reliability design with arbitrary distribution parameters. Therefore maximizing the entropy of the state equation distribution should lead to the least biased estimate of its form, which helps us analyze the reliability design of automobile components from an elementarily.

Enhanced Information Recovery in 2D On-and Off-Resonance Nutation NQR using the Maximum Entropy Method

1996 ◽  
Vol 51 (5-6) ◽  
pp. 337-347 ◽  
Author(s):  
Mariusz Maćkowiak ◽  
Piotr Kątowski
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Theoretical Description ◽  
Entropy Method ◽  
Zero Field ◽  
Resonance Effects ◽  
Information Recovery ◽  
Truncation Errors ◽  
Enhanced Resolution ◽  
2D Data

Abstract Two-dimensional zero-field nutation NQR spectroscopy has been used to determine the full quadrupolar tensor of spin - 3/2 nuclei in serveral molecular crystals containing the 3 5 Cl and 7 5 As nuclei. The problems of reconstructing 2D-nutation NQR spectra using conventional methods and the advantages of using implementation of the maximum entropy method (MEM) are analyzed. It is shown that the replacement of conventional Fourier transform by an alternative data processing by MEM in 2D NQR spectroscopy leads to sensitivity improvement, reduction of instrumental artefacts and truncation errors, shortened data acquisition times and suppression of noise, while at the same time increasing the resolution. The effects of off-resonance irradiation in nutation experiments are demonstrated both experimentally and theoretically. It is shown that off-resonance nutation spectroscopy is a useful extension of the conventional on-resonance experiments, thus facilitating the determination of asymmetry parameters in multiple spectrum. The theoretical description of the off-resonance effects in 2D nutation NQR spectroscopy is given, and general exact formulas for the asymmetry parameter are obtained. In off-resonance conditions, the resolution of the nutation NQR spectrum decreases with the spectrometer offset. However, an enhanced resolution can be achieved by using the maximum entropy method in 2D-data reconstruction.

Estimating the distribution of primary reflection coefficients

Geophysics ◽  
2003 ◽  
Vol 68 (4) ◽  
pp. 1417-1422 ◽  
Author(s):  
Danilo R. Velis
Keyword(s):  
Maximum Entropy ◽  
Maximum Entropy Method ◽  
Entropy Method ◽  
Reflection Coefficients ◽  
Log Data ◽  
Neuquen Basin ◽  
Neuquén Basin ◽  
Skewness And Kurtosis

The distribution of primary reflection coefficients can be estimated by means of the maximum entropy method, giving rise to smooth nonparametric functions which are consistent with the data. Instead of using classical moments (e.g. skewness and kurtosis) to constraint the maximization, nonconventional sample statistics help to improve the quality of the estimates. Results using real log data from various wells located in the Neuquen Basin (Argentina) show the effectiveness of the method to estimate both robust and consistent distributions that may be used to simulate realistic sequences.

An entropy concentration theorem: applications in artificial intelligence and descriptive statistics

1990 ◽  
Vol 27 (2) ◽  
pp. 303-313 ◽  
Author(s):  
Claudine Robert
Keyword(s):  
Artificial Intelligence ◽  
Maximum Entropy ◽  
Large Deviation ◽  
Maximum Entropy Principle ◽  
Statistical Modelling ◽  
Linear Constraints ◽  
Descriptive Statistics ◽  
Entropy Principle ◽  
Maximum Entropy Distribution ◽  
Mathematical Justification

The maximum entropy principle is used to model uncertainty by a maximum entropy distribution, subject to some appropriate linear constraints. We give an entropy concentration theorem (whose demonstration is based on large deviation techniques) which is a mathematical justification of this statistical modelling principle. Then we indicate how it can be used in artificial intelligence, and how relevant prior knowledge is provided by some classical descriptive statistical methods. It appears furthermore that the maximum entropy principle yields to a natural binding between descriptive methods and some statistical structures.

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