Impact of the Uncertainties of the RAOs of a Semisubmersible Platform on the Performance of a Motion-Based Wave Inference Method

2019 ◽  
Author(s):  
J. Mas-Soler ◽  
Pedro C. de Mello ◽  
Eduardo A. Tannuri ◽  
Alexandre N. Simos ◽  
A. Souto-Iglesias
Keyword(s):  
Bayesian Inference ◽  
Transfer Functions ◽  
Inference Model ◽  
Sea State ◽  
Statistical Errors ◽  
Regularization Parameters ◽  
Ill Posed ◽  
State Estimations ◽  
The Impact ◽  
Mathematical Process

Abstract Motion based wave inference allows the estimation of the directional sea spectrum from the measured motions of a vessel. Solving the resulting inverse problem is challenging as it is often ill-posed; as a matter of fact, statistical errors of the estimated platform response functions (RAOs) may lead to misleading estimations of the sea states as many noise values are severely amplified in the mathematical process. Hence, in order to obtain reliable estimations of the sea conditions some hypothesis must be included by means of regularization parameters. This work discusses how these errors affect the regularization parameters and the accuracy of the sea state estimations. For this purpose, a statistical quantification of the errors associated to the estimated transfer functions has been included in an expanded Bayesian inference approach. Then, the resulting statistical inference model has been verified by means of a comparison between the outputs of this approach and those obtained without considering the statistical errors in the Bayesian inference. The assessment of the impact on the accuracy of the estimations is based on the results of a dedicated model-scale experimental campaign, which includes more than 150 different test conditions.

scholarly journals Sea State Monitoring by Ship Motion Measurements Onboard a Research Ship in the Antarctic Waters

2021 ◽  
Vol 9 (1) ◽  
pp. 64
Author(s):  
Silvia Pennino ◽  
Antonio Angrisano ◽  
Vincenzo Della Corte ◽  
Giampaolo Ferraioli ◽  
Salvatore Gaglione ◽  
...  
Keyword(s):  
Transfer Functions ◽  
Wave Spectrum ◽  
Weather Forecast ◽  
Ship Motion ◽  
State Monitoring ◽  
Sea State ◽  
Peak Period ◽  
Research Ship ◽  
Antarctic Waters ◽  
The Antarctic

A parametric wave spectrum resembling procedure is applied to detect the sea state parameters, namely the wave peak period and significant wave height, based on the measurement and analysis of the heave and pitch motions of a vessel in a seaway, recorded by a smartphone located onboard the ship. The measurement system makes it possible to determine the heave and pitch acceleration spectra of the reference ship in the encounter frequency domain and, subsequently, the absolute sea spectra once the ship motion transfer functions are provided. The measurements have been carried out onboard the research ship “Laura Bassi”, during the oceanographic campaign in the Antarctic Ocean carried out in January and February 2020. The resembled sea spectra are compared with the weather forecast data, provided by the global-WAM (GWAM) model, in order to validate the sea spectrum resembling procedure.

Blind Identification of Impact Forces From Multiple Remote Vibratory Measurements

2000 ◽  
Author(s):  
J. Antunes ◽  
P. Izquierdo ◽  
M. Paulino
Keyword(s):  
Explicit Knowledge ◽  
Transfer Functions ◽  
Dynamical Behavior ◽  
Practical Interest ◽  
Operating Conditions ◽  
Blind Identification ◽  
Minimum Entropy ◽  
Response Measurement ◽  
Identification Techniques ◽  
The Impact

Abstract Structures and mechanical components are often subjected to impulsive forces. There is a need for identification techniques which enable monitoring of such loads under operating conditions. For safety reasons and convenience, force identification must often be based on response motions sensed at accessible locations, remote from the impact points. In our previous work we presented techniques for the experimental identification of both isolated impacts and complex rattling forces on a beam, generated at a single and also at several impacting supports. The system dynamical behavior was modeled using traveling flexural beam waves. Although successful, these techniques obviously assume a good understanding of the system dynamic parameters. This is not always the case, a fact that highlights the practical interest of blind identification techniques. This relatively recent field, connected with higher-order statistics, avoids any explicit knowledge of the system transfer functions or impulse responses. Our previous work, based on a single response measurement, is extended in this paper to include several simultaneous responses. We develop a multi-trace version of Wiggins minimum-entropy blind deconvolution algorithm. From numerical simulations and experiments, it is shown that the robustness to noise contamination is increased by using multiple response data. These results suggest that blind identification techniques will prove very useful in practical situations.

scholarly journals Impact of climate change on hydropower production in the Upper Danube

2021 ◽  
Author(s):  
Ignacio Martin Santos ◽  
Mathew Herrnegger ◽  
Hubert Holzmann
Keyword(s):  
Climate Change ◽  
Spatial Resolution ◽  
Transfer Functions ◽  
Danube River ◽  
Emission Scenarios ◽  
Hydropower Generation ◽  
Climate Change Projections ◽  
Impact Of Climate Change ◽  
The Future ◽  
The Impact

<p>In the last two decades, different climate downscaling initiatives provided climate scenarios for Europe. The most recent initiative, CORDEX, provides Regional Climate Model (RCM) data for Europe with a spatial resolution of 12.5 km, while the previous initiative, ENSEMBLES, had a spatial resolution of 25 km. They are based on different emission scenarios, Representative Concentration Pathways (RCPs) and Special Report on Emission Scenarios (SRES) respectively.</p><p>A study carried out by Stanzel et al. (2018) explored the hydrological impact and discharge projections for the Danube basin upstream of Vienna when using either CORDEX and ENSEMBLES data. This basin covers an area of 101.810<sup></sup>km<sup>2</sup> with a mean annual discharge of 1923 m<sup>3</sup>/s at the basin outlet. The basin is dominated by the Alps, large gradients and is characterized by high annual precipitations sums which provides valuable water resources available along the basin. Hydropower therefore plays an important role and accounts for more than half of the installed power generating capacity for this area. The estimation of hydropower generation under climate change is an important task for planning the future electricity supply, also considering the on-going EU efforts and the “Green Deal” initiative.</p><p>Taking as input the results from Stanzel et al. (2018), we use transfer functions derived from historical discharge and hydropower generation data, to estimate potential changes for the future. The impact of climate change projections of ENSEMBLE and CORDEX in respect to hydropower generation for each basin within the study area is determined. In addition, an assessment of the impact on basins dominated by runoff river plants versus basins dominated by storage plants is considered.</p><p>The good correlation between discharge and hydropower generation found in the historical data suggests that discharge projection characteristics directly affect the future expected hydropower generation. Large uncertainties exist and stem from the ensembles of climate runs, but also from the potential operation modes of the (storage) hydropower plants in the future.</p><p> </p><p> </p><p>References:</p><p>Stanzel, P., Kling, H., 2018. From ENSEMBLES to CORDEX: Evolving climate change projections for Upper Danube River flow. J. Hydrol. 563, 987–999. https://doi.org/10.1016/j.jhydrol.2018.06.057</p><p> </p>

scholarly journals Robust Bayesian Joint Inversion of Gravimetric and Muographic Data for the Density Imaging of the Puy de Dôme Volcano (France)

2021 ◽  
Vol 8 ◽  
Author(s):  
Anne Barnoud ◽  
Valérie Cayol ◽  
Peter G. Lelièvre ◽  
Angélie Portal ◽  
Philippe Labazuy ◽  
...  
Keyword(s):  
Joint Inversion ◽  
A Priori ◽  
Constant Density ◽  
Acquisition Duration ◽  
Regularization Parameters ◽  
Absolute Density ◽  
Gravimetric Data ◽  
Ill Posed ◽  
Leave One Out

Imaging the internal structure of volcanoes helps highlighting magma pathways and monitoring potential structural weaknesses. We jointly invert gravimetric and muographic data to determine the most precise image of the 3D density structure of the Puy de Dôme volcano (Chaîne des Puys, France) ever obtained. With rock thickness of up to 1,600 m along the muon lines of sight, it is, to our knowledge, the largest volcano ever imaged by combining muography and gravimetry. The inversion of gravimetric data is an ill-posed problem with a non-unique solution and a sensitivity rapidly decreasing with depth. Muography has the potential to constrain the absolute density of the studied structures but the use of the method is limited by the possible number of acquisition view points, by the long acquisition duration and by the noise contained in the data. To take advantage of both types of data in a joint inversion scheme, we develop a robust method adapted to the specificities of both the gravimetric and muographic data. Our method is based on a Bayesian formalism. It includes a smoothing relying on two regularization parameters (an a priori density standard deviation and an isotropic correlation length) which are automatically determined using a leave one out criterion. This smoothing overcomes artifacts linked to the data acquisition geometry of each dataset. A possible constant density offset between both datasets is also determined by least-squares. The potential of the method is shown using the Puy de Dôme volcano as case study as high quality gravimetric and muographic data are both available. Our results show that the dome is dry and permeable. Thanks to the muographic data, we better delineate a trachytic dense core surrounded by a less dense talus.

scholarly journals Range-relaxed criteria for choosing the Lagrange multipliers in nonstationary iterated Tikhonov method

2018 ◽  
Vol 40 (1) ◽  
pp. 606-627 ◽  
Author(s):  
R Boiger ◽  
A Leitão ◽  
B F Svaiter
Keyword(s):  
Lagrange Multipliers ◽  
Approximate Solutions ◽  
Identification Problem ◽  
Linear Operators ◽  
Type Method ◽  
Geometrical Properties ◽  
Regularization Parameters ◽  
New Strategy ◽  
Parameter Identification Problem ◽  
Ill Posed

Abstract In this article we propose a novel nonstationary iterated Tikhonov (NIT)-type method for obtaining stable approximate solutions to ill-posed operator equations modeled by linear operators acting between Hilbert spaces. Geometrical properties of the problem are used to derive a new strategy for choosing the sequence of regularization parameters (Lagrange multipliers) for the NIT iteration. Convergence analysis for this new method is provided. Numerical experiments are presented for two distinct applications: (I) a two-dimensional elliptic parameter identification problem (inverse potential problem); and (II) an image-deblurring problem. The results obtained validate the efficiency of our method compared with standard implementations of the NIT method (where a geometrical choice is typically used for the sequence of Lagrange multipliers).

scholarly journals Two-Parameter Regularization Method for a Nonlinear Backward Heat Problem with a Conformable Derivative

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-15
Author(s):  
Vu Ho ◽  
Donal O’Regan ◽  
Hoa Ngo Van
Keyword(s):  
Integral Equation ◽  
Regularization Method ◽  
Time Variable ◽  
Conformable Derivative ◽  
Heat Problem ◽  
Regularization Parameters ◽  
Ill Posed ◽  
Parameter Regularization ◽  
Two Parameter ◽  
Backward Heat Problem

In this paper, we consider the nonlinear inverse-time heat problem with a conformable derivative concerning the time variable. This problem is severely ill posed. A new method on the modified integral equation based on two regularization parameters is proposed to regularize this problem. Numerical results are presented to illustrate the efficiency of the proposed method.

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