• Journal of the Society of Naval Architects of Korea
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• v.55 no.6
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• pp.466-473
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• 2018

Most frequency domain-based approaches assume that structural response should be a Gaussian random process. But a lot of non-Gaussian processes caused by multi-excitation and non-linearity in structural responses or load itself are observed in many real engineering problems. In this study, the effect of non-Normality on fatigue damages are discussed through case study. The accuracy of four frequency domain methods for non-Gaussian processes are compared in the case study. Power-law and Hermite models which are derived for non-Gaussian narrow-banded process tend to estimate fatigue damages less accurate than time domain results in small kurtosis and in case of large kurtosis they give conservative results. Weibull model seems to give conservative results in all environmental conditions considered. Among the four methods, Benascuitti-Tovo model for non-Gaussian process gives the best results in case study. This study could serve as background material for understanding the effect of non-normality on fatigue damages.

• Wind and Structures
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• v.15 no.3
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• pp.223-245
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• 2012

Multivariate simulation is necessary for cases where non-Gaussian processes at spatially distributed locations are desired. A simulation algorithm to generate non-Gaussian wind pressure fields is proposed. Gaussian sample fields are generated based on the spectral representation method using wavelet transforms method and then mapped into non-Gaussian sample fields with the aid of a CDF mapping transformation technique. To illustrate the procedure, this approach is applied to experimental results obtained from wind tunnel tests on the domes. A multivariate Gaussian simulation technique is developed and then extended to multivariate non-Gaussian simulation using the CDF mapping technique. It is proposed to develop a new wavelet-based CDF mapping technique for simulation of multivariate non-Gaussian wind pressure process. The efficiency of the proposed methodology for the non-Gaussian nature of pressure fluctuations on separated flow regions of different rise-span ratios of domes is also discussed.

• Wind and Structures
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• v.35 no.6
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• pp.419-430
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• 2022

In wind-resistant designs, wind velocity is assumed to be a Gaussian process; however, local complex topography may result in strong non-Gaussian wind features. This study investigates the non-Gaussian wind features over complex terrain under atmospheric turbulent boundary layers by the large eddy simulation (LES) model, and the turbulent inlet of LES is generated by the consistent discretizing random flow generation (CDRFG) method. The performance of LES is validated by two different complex terrains in Changsha and Mianyang, China, and the results are compared with wind tunnel tests and onsite measurements, respectively. Furthermore, the non-Gaussian parameters, such as skewness, kurtosis, probability curves, and gust factors, are analyzed in-depth. The results show that the LES method is in good agreement with both mean and turbulent wind fields from wind tunnel tests and onsite measurements. Wind fields in complex terrain mostly exhibit a left-skewed Gaussian process, and it changes from a softening Gaussian process to a hardening Gaussian process as the height increases. A reduction in the gust factors of about 2.0%-15.0% can be found by taking into account the non-Gaussian features, except for a 4.4% increase near the ground in steep terrain. This study can provide a reference for the assessment of extreme wind loads on structures in complex terrain.

• Wind and Structures
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• v.36 no.1
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• pp.15-29
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• 2023

To better estimate the non-Gaussian extreme wind pressures for high-rise buildings, a data-driven revised Hermitetype peak factor estimation model is proposed in this papar. Subsequently, a comparative study on three types of methods, such as Hermite-type models, short-time estimate Gumbel method (STE), and new translated-peak-process method (TPP) is carried out. The investigations show that the proposed Hermite-type peak factor has better accuracy and applicability than the other Hermite-type models, and its absolute accuracy is slightly inferior to the STE and new TPP methods for non-Gaussian wind pressures by comparing with the observed values. Moreover, these methods generally overestimate the Gaussian wind pressures especially the STE.

• Wind and Structures
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• v.23 no.4
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• pp.275-300
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• 2016

Synchronous multi-pressure measurements were carried out with relatively long time duration for a double-layer reticulated shell roof model in the atmospheric boundary layer wind tunnel. Since the long roof is open at two ends for the storage of coal piles, three different testing cases were considered as the empty roof without coal piles (Case A), half coal piles inside (Case B) and full coal piles inside (Case C). Based on the wind tunnel test results, non-Gaussian time-dependent statistics of net wind pressure on the shell roof were quantified in terms of skewness and kurtosis. It was found that the direct statistical estimation of high-order moments and peak factors is quite sensitive to the duration of wind pressure time-history data. The maximum value of COVs (Coefficients of variations) of high-order moments is up to 1.05 for several measured pressure processes. The Mixture distribution models are proposed for better modeling the distribution of a parent pressure process. With the aid of mixture parent distribution models, the existing translated-peak-process (TPP) method has been revised and improved in the estimation of non-Gaussian peak factors. Finally, non-Gaussian peak factors of wind pressure, particularly for those observed hardening pressure process, were calculated by employing various state-of-the-art methods and compared to the direct statistical analysis of the measured long-duration wind pressure data. The estimated non-Gaussian peak factors for a hardening pressure process at the leading edge of the roof were varying from 3.6229, 3.3693 to 3.3416 corresponding to three different cases of A, B and C.

• Bulletin of the Korean Space Science Society
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• 2011.04a
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• pp.25.4-25.4
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• 2011

Various estimation methods based on Kalman filter have been applied to the real-time satellite orbit determination. The most popular method is the Extended Kalman Filter (EKF) and the Unscented Kalman Filter (UKF). The EKF is easy to implement and to use on orbit determination problem. However, the linearization process of the EKF can cause unstable solutions if the problem has the inaccurate reference orbit, sparse or insufficient observations. In this case, the UKF can be a good alternative because it does not contain linearization process. However, because both methods are based on Gaussian assumption, performance of estimation can become worse when the distribution of state parameters and process/measurement noise are non-Gaussian. In nonlinear/non-Gaussian problems the particle filter which is based on sequential Monte Carlo methods can guarantee more exact estimation results. This study develops and tests the particle filter for satellite orbit determination. The particle filter can be more effective methods for satellite orbit determination in nonlinear/non-Gaussian environment.

• Ocean Systems Engineering
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• v.4 no.4
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• pp.293-308
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• 2014

Unlike the traditional displacement type vessels, the high speed planing crafts are supported by the lift forces which are highly non-linear. This non-linear phenomenon causes their motions in an irregular seaway to be non-Gaussian. In general, it may not be possible to express the probability distribution of such processes by an analytical formula. Also the process might not be stationary or ergodic in which case the statistical behavior of the motion to be constantly changing with time. Therefore the extreme values of such a process can no longer be calculated using the analytical formulae applicable to Gaussian processes. Since closed form analytical solutions do not exist, recourse is taken to fitting a distribution to the data and estimating the statistical properties of the process from this fitted probability distribution. The peaks over threshold analysis and fitting of the Generalized Pareto Distribution are explored in this paper as an alternative to Weibull, Generalized Gamma and Rayleigh distributions in predicting the short term extreme value of a random process.

• Wind and Structures
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• v.18 no.6
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• pp.693-715
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• 2014

Stochastic processes are used to represent phenomena in many diverse fields. Numerical simulation method is widely applied for the solution to stochastic problems of complex structures when alternative analytical methods are not applicable. In some practical applications the stochastic processes show non-Gaussian properties. When the stochastic processes deviate significantly from Gaussian, techniques for their accurate simulation must be available. The various existing simulation methods of non-Gaussian stochastic processes generally can only simulate super-Gaussian stochastic processes with the high-peak characteristics. And these methodologies are usually complicated and time consuming, not sufficiently intuitive. By revealing the inherent coupling effect of the phase and amplitude part of discrete Fourier representation of random time series on the non-Gaussian features (such as skewness and kurtosis) through theoretical analysis and simulation experiments, this paper presents a novel approach for the simulation of non-Gaussian stochastic processes with the prescribed amplitude probability density function (PDF) and power spectral density (PSD) by amplitude modulation and phase reconstruction. As compared to previous spectral representation method using phase modulation to obtain a non-Gaussian amplitude distribution, this non-Gaussian phase reconstruction strategy is more straightforward and efficient, capable of simulating both super-Gaussian and sub-Gaussian stochastic processes. Another attractive feature of the method is that the whole process can be implemented efficiently using the Fast Fourier Transform. Cases studies demonstrate the efficiency and accuracy of the proposed algorithm.

• Environmental Engineering Research
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• v.11 no.2
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• pp.63-76
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• 2006

Multivariate analysis and batch monitoring on a pilot-scale sequencing batch reactor (SBR) are described for integrated wastewater treatment management system, where a batchwise multiway independent component analysis method (MICA) are used to extract meaningful hidden information from non-Gaussian wastewater treatment data. Three-way batch data of SBR are unfolded batch-wisely, and then a non-Gaussian multivariate monitoring method is used to capture the non-Gaussian characteristics of normal batches in biological wastewater treatment plant. It is successfully applied to an 80L SBR for biological wastewater treatment, which is characterized by a variety of error sources with non-Gaussian characteristics. The batchwise multivariate monitoring results of a pilot-scale SBR for integrated wastewater treatment management system showed more powerful monitoring performance on a WWTP application than the conventional method since it can extract non-Gaussian source signals which are independent and cross-correlation of variables.

• Structural Engineering and Mechanics
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• v.84 no.5
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• pp.651-664
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• 2022

A precise prediction of the ultimate bond strength between rebar and surrounding concrete plays a major role in structural design, as it effects the load-carrying capacity and serviceability of a member significantly. In the present study, Gaussian models are employed for modelling bond strength of ribbed steel bars embedded in concrete. Gaussian models offer a non-parametric method based on Bayesian framework which is powerful, versatile, robust and accurate. Five different Gaussian models are explored in this paper-Gaussian Process (GP), Variational Heteroscedastic Gaussian Process (VHGP), Warped Gaussian Process (WGP), Sparse Spectrum Gaussian Process (SSGP), and Twin Gaussian Process (TGP). The effectiveness of the models is also evaluated in comparison to the numerous design formulae provided by the codes. The predictions from the Gaussian models are found to be closer to the experiments than those predicted using the design equations provided in various codes. The sensitivity of the models to various parameters, input feature space and sampling is also presented. It is found that GP, VHGP and SSGP are effective in prediction of the bond strength. For large data set, GP, VHGP, WGP and TGP can be computationally expensive. In such cases, SSGP can be utilized.