Wave Runup on Cylinders Subject to Deep Water Random Waves

2004 ◽  
Author(s):  
Ann Kristin Indrebo ◽  
John M. Niedzwecki
Keyword(s):  
Analytical Model ◽  
Time Series Data ◽  
Weibull Model ◽  
Irregular Waves ◽  
Series Data ◽  
Random Waves ◽  
Wave Runup ◽  
Statistical Parameters ◽  
Zero Crossing ◽  
Two Parameter

The accurate prediction of wave runup on deepwater offshore platform columns is of great importance for design engineers. Although linear predictive models are commonly used in the design and analysis process, many of the important effects are of higher order, and thus can only be accounted for by complex nonlinear models that better reflect the physics of the problem. This study presents a two-parameter Weibull distribution function that utilizes empirical coefficients to model the surface wave runup. Laboratory measurements of irregular waves interacting with vertical platform cylinders were used to obtain the Weibull coefficients necessary for the analytical model. Six data sets with different configurations where the wave elevation was measured close to the test cylinders are analyzed. These data on wave runup in deepwater random waves were generated at similar water depths with identical significant wave heights and spectral peak periods. Statistical parameters, zero crossing analysis and spectral analysis were utilized to characterize and interpret the time series data. The analysis focused on interpreting the tails of the probability distributions by carefully fitting the analytical model to the measured model data. This study demonstrates that the two-parameter Weibull model can be used to accurately model the wave runup on platform cylinders for the range of experimental data investigated in this study.

Estimation of Most Probable Maximum From Short-Duration or Undersampled Time-Series Data

2015 ◽  
Author(s):  
Puneet Agarwal ◽  
William Walker ◽  
Kenneth Bhalla
Keyword(s):  
Time Series ◽  
Gaussian Process ◽  
Short Duration ◽  
Time Series Data ◽  
Extreme Value ◽  
Series Data ◽  
Sampled Data ◽  
Zero Crossing ◽  
Short Time ◽  
Undersampled Data

The most probable maximum (MPM) is the extreme value statistic commonly used in the offshore industry. The extreme value of vessel motions, structural response, and environment are often expressed using the MPM. For a Gaussian process, the MPM is a function of the root-mean square and the zero-crossing rate of the process. Accurate estimates of the MPM may be obtained in frequency domain from spectral moments of the known power spectral density. If the MPM is to be estimated from the time-series of a random process, either from measurements or from simulations, the time series data should be of long enough duration, sampled at an adequate rate, and have an ensemble of multiple realizations. This is not the case when measured data is recorded for an insufficient duration, or one wants to make decisions (requiring an estimate of the MPM) in real-time based on observing the data only for a short duration. Sometimes, the instrumentation system may not be properly designed to measure the dynamic vessel motions with a fine sampling rate, or it may be a legacy instrumentation system. The question then becomes whether the short-duration and/or the undersampled data is useful at all, or if some useful information (i.e., an estimate of MPM) can be extracted, and if yes, what is the accuracy and uncertainty of such estimates. In this paper, a procedure for estimation of the MPM from the short-time maxima, i.e., the maximum value from a time series of short duration (say, 10 or 30 minutes), is presented. For this purpose pitch data is simulated from the vessel RAOs (response amplitude operators). Factors to convert the short-time maxima to the MPM are computed for various non-exceedance levels. It is shown that the factors estimated from simulation can also be obtained from the theory of extremes of a Gaussian process. Afterwards, estimation of the MPM from the short-time maxima is explored for an undersampled process; however, undersampled data must not be used and only the adequately sampled data should be utilized. It is found that the undersampled data can be somewhat useful and factors to convert the short-time maxima to the MPM can be derived for an associated non-exceedance level. However, compared to the adequately sampled data, the factors for the undersampled data are less useful since they depend on more variables and have more uncertainty. While the vessel pitch data was the focus of this paper, the results and conclusions are valid for any adequately sampled narrow-banded Gaussian process.

scholarly journals Using artificial neural network (ANN) for prediction of sediment loads, application to the Mellah catchment, northeast Algeria

2017 ◽  
Vol 33 (1) ◽  
pp. 47-55 ◽  
Author(s):  
Housseyn Bouzeria ◽  
Abderrahmane N. Ghenim ◽  
Kamel Khanchoul
Keyword(s):  
Suspended Sediment ◽  
Time Series Data ◽  
Water Discharge ◽  
Absolute Error ◽  
Coefficient Of Determination ◽  
Series Data ◽  
Sediment Discharge ◽  
Statistical Parameters ◽  
Discharge Data ◽  
Sediment Loads

AbstractIn this study, we present the performances of the best training algorithm in Multilayer Perceptron (MLP) neural networks for prediction of suspended sediment discharges in Mellah catchment. Time series data of daily suspended sediment discharge and water discharge from the gauging station of Bouchegouf were used for training and testing the networks. A number of statistical parameters, i.e. root mean square error (RMSE), mean absolute error (MAE), coefficient of efficiency (CE) and coefficient of determination (R2) were used for performance evaluation of the model. The model produced satisfactory results and showed a very good agreement between the predicted and observed data. The results also showed that the performance of the MLP model was capable to capture the exact pattern of the sediment discharge data in the Mellah catchment.

scholarly journals Recent variations and trends in pan evaporation over India

MAUSAM ◽  
2021 ◽  
Vol 59 (3) ◽  
pp. 347-356
Author(s):  
I. J. VERMA ◽  
V. N. JADHAV
Keyword(s):  
Time Series Data ◽  
Linear Trend ◽  
Series Data ◽  
Pan Evaporation ◽  
Statistical Parameters ◽  
Class A ◽  
Linear Relationships ◽  
Analysis Technique ◽  
Annual Evaporation ◽  
The Mean

Thirty years pan evaporation time series data (1971-2000) recorded from US class-A evaporation pans for twenty well distributed locations in India, have been utilized in the present study. For all the locations, basic statistical parameters of annual evaporation [minimum, maximum, range, mean, standard deviation (S.D.) and coefficient of variation (C.V.)] have been computed. Annual, seasonal and monthly trends have been studied using linear trend analysis technique. Suitable graphs have been plotted to study the variations and changes in pan evaporation trends and to identify the specific periods as and when significant changes occur.   The mean annual pan evaporation was found to be lowest (1107 mm) at Buralikson and highest (3004 mm) at Rajkot. The highest C.V. of nearly 11% was observed at Rajamundry, Jodhpur, Buralikson and Nellore. The lowest C.V. of nearly 2% was observed at Ambikapur. Out of twenty locations, significant decreasing trend in annual pan evaporation was observed at fifteen locations and no significant trend at five locations. The annual dE/dt values varied from -6.27 (Canning) to -29.30 (Jodhpur) mm/year. The average annual dE/dt over India was found to be -14.90 mm/year. Linear relationship was obtained to quantitatively estimate annual dE/dt, at a given location, using pan evaporation range. On an average, over India, the contribution of seasonal dE/dt towards annual dE/dt (mm/year) is highest -5.63 (37.8 %) in Season-2 (March-April-May) and lowest -2.07 (13.9 %) in Season-1(January- February). On an average, over India, the contribution of monthly dE/dt towards annual dE/dt (mm/year) is highest - 2.08 (14.0 %) in May and lowest -0.77 (5.2 %) in August. Non linear relationships were obtained (a) to quantitatively estimate the average monthly dE/dt values over India, in any particular month (b) to quantitatively estimate the average cumulative dE/dt values over India (mm/year) upto any particular month and (c) to quantitatively estimate the contribution (per cent) towards average annual dE/dt over India, upto any particular month.

Intercomparison of Sea-State and Zero-Crossing Parameters From the WACSIS Field Experiment and Interpretation Using Video Evidence

2004 ◽  
Vol 126 (1) ◽  
pp. 35-42 ◽  
Author(s):  
Stephen F. Barstow, ◽  
Harald E. Krogstad ◽  
Lasse Lønseth ◽  
Jan Petter Mathisen ◽  
Gunnar Mørk ◽  
...  
Keyword(s):  
Time Series ◽  
Field Experiment ◽  
Time Series Data ◽  
Series Data ◽  
Sea State ◽  
Single Wave ◽  
Zero Crossing ◽  
Video Recordings ◽  
Wave Parameters ◽  
Wave Elevation

During the WACSIS field experiment, wave elevation time series data were collected over the period December 1997 to May 1998 on and near the Meetpost Nordwijk platform off the coast of the Netherlands from an EMI laser, a Saab radar, a Baylor Wave Staff, a Vlissingen step gauge, a Marex radar and a Directional Waverider. This paper reports and interprets, with the help of simultaneous dual video recordings of the ocean surface, an intercomparison of both single wave and sea state wave parameters.

The Statistical Boundary Polygon of a Two Parameter Stochastic Process

2006 ◽  
Author(s):  
Torfinn Ottesen ◽  
Jon A. Aarstein
Keyword(s):  
Convex Hull ◽  
Goodness Of Fit ◽  
Structural Integrity ◽  
Time Series Data ◽  
Extreme Values ◽  
Extreme Value Distribution ◽  
Series Data ◽  
Goodness Of Fit Test ◽  
Anderson Darling ◽  
Two Parameter

The simultaneous values of tension and bending are of primary interest when checking the structural integrity of dynamic risers and umbilicals. Considering extreme checks, it is generally sufficient to check the simultaneous values along the convex hull of the extreme contour. A method for obtaining a boundary polygon that approximates the statistical extreme convex hull from time series data is described. The procedure for obtaining the extreme values from the most suitable extreme value distribution as determined by the Anderson-Darling Goodness-of-Fit test is outlined.

scholarly journals Government expenditure and poverty reduction in the proliferation of new administrative areas of Central Kalimantan, Indonesia

2020 ◽  
Vol 3 (2) ◽  
pp. 145
Author(s):  
Andrie Elia ◽  
Yulianto Yulianto ◽  
Harin Tiawon ◽  
Sustiyah Sustiyah ◽  
Kusnida Indrajaya
Keyword(s):  
Economic Activity ◽  
Financial Management ◽  
Quantitative Research ◽  
Poverty Reduction ◽  
Time Series Data ◽  
Research Result ◽  
Government Expenditure ◽  
Series Data ◽  
Statistical Parameters ◽  
Central Kalimantan

The proliferation of new region in Indonesia is one of the most challenging issues related to regional autonomy, financial management and poverty reduction. The purpose of this study was to analyze the relationship between government expenditure and poverty linked to the regional economic activity and labor absorption. The study used a quantitative research by means of time series data collected from the new proliferation areas in Central Kalimantan, including Pulang Pisau, Katingan, East Barito, Seruyan, Gunung Mas, Murung Raya, Sukamara, and Lamandau.  The analysis method used the path analysis to estimate statistical parameters indicating relationship between variables. The research result shows that poverty significantly affects on government expenditure in the new eight regency in Central Kalimantan province. Poverty has also had an impact on government expenditure through the provision of employment and Gross Regional Domestic Product (GRDP).  The local government is expected to manage more effectively regional finances that focus on community economic activities.  The policy also opens  investment opportunity to increase economic activity and create jobs based on the prominent regional product such as agriculture, plantation and mining sectors. Investment can increase employment and indirectly reduce poverty.JEL Classification  H72; I38; J21

scholarly journals A study on recent changes in weekly evaporation at selected locations in India

MAUSAM ◽  
2021 ◽  
Vol 59 (2) ◽  
pp. 211-218
Author(s):  
I. J. VERMA ◽  
H. P. DAS ◽  
V. N. JADHAV
Keyword(s):  
Time Series ◽  
Coefficient Of Variation ◽  
Time Series Data ◽  
Linear Trend ◽  
Series Data ◽  
Evaporation Time ◽  
Statistical Parameters ◽  
Analysis Technique ◽  
Minimum Number ◽  
Almost All

Thirty years evaporation time series data (1971-2000) recorded from US class-A evaporation pans for ten well distributed locations in India, have been utilized in the present study. For these locations, basic statistical parameters of weekly evaporation [minimum, maximum, range, mean, standard deviation (S.D.) and coefficient of variation (C.V.)] have been computed. Variations in average weekly evaporation in different weeks and at different locations have been plotted and discussed. Changes in weekly evaporation have been studied using linear trend analysis technique on weekly evaporation time series data for standard meteorological weeks (1 to 52). Graphs have been plotted, for all ten different locations, to study week wise distribution of changes in weekly evaporation trends and to identify the specific periods when significant changes occur.   The highest average weekly evaporation of 107.5 mm has been observed at Jodhpur in standard week                    21(21 – 27 May). The lowest average weekly evaporation of 14.5 mm has been observed at Karimganj in standard week 3 (15 – 21 January). The peak in average weekly evaporation, at most of these locations is achieved around standard week   20 (14 – 20 May). The coefficient of variation (C.V.) at these locations varied between 18.7 and 51.8 percent. The highest C.V. of 51.8 % has been observed at Bikramganj, whereas the lowest C.V. of 18.7 % has been observed at Rajamundry. Out of 52 weeks, Pune and Rajamundry have shown significant decreasing trend in weekly evaporation in maximum number of weeks (37) and Bhubaneshwar has shown significant decreasing trend in weekly evaporation in minimum number of weeks (10). At six locations (Bikramganj, Hissar, Jodhpur, Pattambi, Pune and Rajamundry), the number of weeks showing significant decreasing trend in weekly evaporation have been found to be more than 23 weeks. At more than five locations significant decreasing trend in weekly evaporation occur, in almost all weeks, between standard weeks 1 to 19 (1 January - 13 May) and also between standard weeks 40 to 52 (1 October - 31 December). In almost all the locations, significant decreasing trend in weekly evaporation occur, in standard week numbers 1-2, 9-10, 13 and 15.

Run-up of narrow and wide-banded irregular waves on a beach

2020 ◽  
Author(s):  
Ahmed Abdalazeez ◽  
Denys Dutykh ◽  
Ira Didenkulova ◽  
Céline Labart
Keyword(s):  
Time Series ◽  
Gaussian Distribution ◽  
Irregular Waves ◽  
Statistical Characteristics ◽  
Higher Moments ◽  
Random Waves ◽  
Wave Runup ◽  
Mean Values ◽  
Wave Nonlinearity ◽  
The Right

<p>The runup of initial Gaussian narrow-banded and wide-banded wave fields and its statistical characteristics are investigated using direct numerical simulations, based on the nonlinear shallow water equations. The bathymetry consists of the section of a constant depth, which is matched with the beach of constant slope. To address different levels of nonlinearity, the time series with five different significant wave heights are considered. The total time of each such calculated time-series is 1000 hours.</p><p>It is shown for narrow-banded wave signal that runup oscillations are no more distributed by the Gaussian distribution. The distribution is shifted to the right towards larger positive values of wave runup. Its mean value increases with an increase in nonlinearity, which reflects the known phenomenon of wave set-up. The higher moments of runup oscillations, skewness and kurtosis are negative. The skewness is decreasing with an increase in wave nonlinearity, while kurtosis is negative and varies non-monotonically with an increase in wave nonlinearity. For Gaussian wide-banded signal, the runup oscillations also deviate from Gaussian distribution. The distribution is also shifted to the right towards larger positive values of wave runup. Its mean values increase with an increase in nonlinearity, while all other higher moments change non-monotonically.     </p><p>For the extreme wave runup heights, we conclude that the tail of the probability density function behaves like a conditional Weibull distribution if the incident random waves are represented by Gaussian narrow-banded or wide-banded spectrum. This distribution can be used for evaluation of wave inundation during extreme floods (rogue runups). </p>

scholarly journals BASIC STUDY ON A PSEUDO TREND PHENOMENON OF THE FEYNMAN-α ANALYSIS WITH BUNCHING METHOD

2021 ◽  
Vol 247 ◽  
pp. 09008
Author(s):  
Sin-ya Hohara ◽  
Atsushi Sakon ◽  
Tomohiro Endo ◽  
Tadafumi Sano ◽  
Kunihiro Nakajima ◽  
...  
Keyword(s):  
Time Series ◽  
Time Series Data ◽  
Noise Analysis ◽  
Statistical Characteristics ◽  
Series Data ◽  
Statistical Fluctuation ◽  
Multiple Use ◽  
Statistical Parameters ◽  
Basic Study ◽  
Trend Component

In these years, reactor noise analysis methods have been studied to apply for the Debris’ criticality management at the Fukushima Daiichi NPP, Japan. The Feynman-α analysis with bunching method is one of the candidate techniques, however the bunching method itself has never been validated in detail. This synthesis technique is useful to reduce a time required for the experiment, however it is known that a non-physical trend unrelated to the state of a nuclear reactor is generated by the multiple use of time series data, and this phenomenon (we call “pseudo trend phenomenon”) has never been systematically studied in detail. In this study, Poisson events, whose statistical characteristics were clarified, were employed to investigate the pseudo trend phenomenon of the bunching method. The time-sequence count data for various statistical parameters were generated by the Monte Carlo time series simulator. Comparing the two results obtained by applying the conventional bunching method and the moving-bunching method for the same Poisson event time series, and it was found that the same pseudo trend component appears in both results of the bunching method and the moving bunching method. In addition, it was also found that the fluctuation width of the pseudo trend component is smaller than the statistical fluctuation range.

Stabilization of Moored Vessels Using a Second-Order Volterra Filter and Feedforward Compensator

1991 ◽  
Vol 113 (2) ◽  
pp. 137-141 ◽  
Author(s):  
Y. S. Cho ◽  
S. B. Kim ◽  
E. J. Powers ◽  
R. W. Miksad ◽  
F. J. Fischer
Keyword(s):  
Time Series Data ◽  
Low Frequency ◽  
Digital Filtering ◽  
Recursive Least Squares ◽  
Series Data ◽  
Time Varying ◽  
Random Waves ◽  
Control Scheme ◽  
Modeling And Forecasting ◽  
Experimental Time Series

Modeling and forecasting of the sway of a moored vessel subjected to random waves were studied recently using quadratic digital filtering techniques. Moreover, it was observed that the future response of the vessel can be predicted with relatively high accuracy. This paper describes a control scheme, which utilizes a quadratic digital filter, to stabilize the low-frequency drift oscillation (LFDO) of moored vessels by a process, whose transfer function may be unknown or time-varying, and an appropriately chosen feedforward compensator. The feedforward compensator generates a control signal that counteracts the LFDO. The predictive filter coefficients are determined from experimental time series data of random sea excitation and the associated sway response of a scaled (1:48) moored barge. The parameters of the process are estimated using a recursive least squares (RLS) method. Simulation results show that the sway of the controlled vessel is reduced considerably, compared with that of a uncontrolled vessel.

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