Efficient Algorithm for Discretization of Metocean Data Into Clusters of Arbitrary Size and Dimension

2017 ◽  
Author(s):  
Samuel Kanner ◽  
Alexia Aubault ◽  
Antoine Peiffer ◽  
Bingbin Yu
Keyword(s):  
Environmental Conditions ◽  
Two Dimensions ◽  
Offshore Wind Turbine ◽  
Specific Cell ◽  
Test Cases ◽  
Large Set ◽  
Data Set ◽  
Arbitrary Size ◽  
Tolerance Level ◽  
Population Threshold

In order to run a fatigue analysis on a floating structure, it is common practice among ocean engineers to rely upon a large set of test cases, each with a unique set of environmental conditions. For a specific test site, the issue remains of how to obtain a limited set of environmental conditions for these test cases, sometimes known as bins, which can accurately recreate the conditions. When considering a floating offshore wind turbine, it is necessary to obtain a timeseries of not only the wave conditions, but also the wind conditions (and perhaps current, if possible). Thus, it is common to have greater than 5 dimensions in the time-series (e.g., significant wave height, wave period, wave direction, wind speed, wind direction, etc). The creation of bins in two dimensions is quite easily solved by creating an arbitrary grid and taking the mean of all the observations which fall in a specific cell. In higher dimensions, an N-dimensional cell is not easily visualized and so the resulting set of bins cannot easily be graphically represented. In this paper, an efficient, iterative algorithm is developed to convert N-dimensional metocean data into a set of discrete bins of arbitrary size. The algorithm works by setting a tolerance level on the number of observations that must be included in a cell in order to create a bin. If the population threshold is not met, the observations remain unbinned and another iteration is required. Generally, the population threshold can be a function of iteration number so that all observations will be binned. The algorithm can properly take into account extreme data by setting a tolerance level on the N-dimensional distance by which an observation can be included in a certain bin. A quality measure, q, is created to measure the level of representation of the original data by a set of bins, independent of the number of bins. Depending on the tolerance levels, the algorithm can be completed in seconds on a normal laptop for the available data set of 20 years with a 3-hour sampling rate. The observations and bins from a case study are shown as an example of how the bins can be created and visualized.

Maximum Dissimilarity-Based Algorithm for Discretization of Metocean Data Into Clusters of Arbitrary Size and Dimension

2018 ◽  
Author(s):  
Samuel Kanner ◽  
Alexia Aubault ◽  
Antoine Peiffer ◽  
Bingbin Yu
Keyword(s):  
Fatigue Life ◽  
Environmental Conditions ◽  
Clustering Algorithm ◽  
Two Dimensions ◽  
Specific Cell ◽  
Large Set ◽  
Data Set ◽  
Final Number ◽  
Previous Algorithm ◽  
The Creation

In order to accurately estimate the fatigue life a floating structure, it is necessary to have a large set of discrete environmental conditions. If the damage to a structure largely stems from wave-induced forces, then the creation of a set of environmental conditions or ‘bins’ is trivial. However, when considering a floating platform supporting a wind turbine, it is necessary to consider not only the wave conditions, but also the wind conditions (and perhaps current, if possible). Thus, it is common to have greater than 5 dimensions in the timeseries (e.g., significant wave height, wave period, wave direction, wind speed, wind direction, etc). The creation of bins in two dimensions is quite easily solved by creating an arbitrary grid and taking the mean of all the observations which fall in a specific cell. In higher dimensions, a p-dimensional cell is not easily visualized and so the resulting set of bins cannot easily be graphically represented. In this paper, an iterative algorithm is developed to convert N observations, each with p-dimensions, into a set with M discrete bins, where M << N. The algorithm presented borrows heavily from the maximum dissimilarity algorithm used in a wide array of fields. The benefit of using this algorithm is that there is no ‘bias’ introduced by an initial grid from the user. That is, given a desired final number of clusters and a certain distance tolerance, a unique set of cluster exists for a given data set. Inherently, the algorithm selects a diverse array of observations, usually including extreme events or outliers, which may have undue impact on the fatigue life of a structure. Although the algorithm is computationally expensive O(N2M), reductions in computational cost are possible. Most importantly, the algorithm can be written in such a way that memory constraints are not an issue even for N = O(105). The clustering algorithm is described in both graphical and logical terms. A case study is presented, using publicly available data from the Netherlands Enterprise Agency. The data is visualized in two dimensions with the final number of bins equaling approximately 50, 100, 200, 500, 1000, and 2000 bins. These bins are compared with a previous algorithm from these authors. Various measures are presented to assess the fidelity of a set of bins with respect to the initial observations. Each set of bins are analyzed and it is clear the MDA-based algorithm outperforms the previous algorithm.

scholarly journals Validation of Improved Sampling Concepts for Offshore Wind Turbine Fatigue Design

Energies ◽  
2019 ◽  
Vol 12 (4) ◽  
pp. 603 ◽  
Author(s):  
Clemens Hübler ◽  
Wout Weijtjens ◽  
Cristian Gebhardt ◽  
Raimund Rolfes ◽  
Christof Devriendt
Keyword(s):  
Environmental Conditions ◽  
Wind Farm ◽  
Measurement Data ◽  
Monte Carlo Sampling ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Sampling Effort ◽  
Data Set ◽  
Ongoing Research ◽  
Fatigue Design

Fatigue damage is a design-driving phenomenon for substructures of offshore wind turbines. However, fatigue design based on numerical simulations is quite uncertain. One main reason for this uncertainty is scattering offshore conditions combined with a limited number of simulations (samples). According to current standards, environmental conditions are sampled using a deterministic grid of the most important environmental conditions (e.g., wind speed and direction, significant wave height, and wave period). Recently, there has been some effort to reduce the inherent uncertainty of damage calculations due to limited data by applying other sampling concepts. Still, the investigation of this uncertainty and of methods to reduce it is a subject of ongoing research. In this work, two improved sampling concepts—previously proposed by the authors and reducing the uncertainty due to limited sampling—are validated. The use of strain measurement data enables a realistic estimate of the inherent uncertainty due to limited samples, as numerical effects, etc., are excluded. Furthermore, an extensive data set of three years of data of two turbines of the Belgian wind farm Northwind is available. It is demonstrated that two previously developed sampling methods are generally valid. For a broad range of model types (i.e., input dimensions as well as degrees of non-linearity), they outperform standard sampling concepts such as deterministic grid sampling or Monte Carlo sampling. Hence, they can reduce the uncertainty while keeping the sampling effort constant, or vice versa.

Molecular Views of Ice-Embedded Lumbricus Terrestris Erythrocruorin Obtained By Invariant Classification

1990 ◽  
Vol 48 (1) ◽  
pp. 450-451
Author(s):  
Michael schatz ◽  
Joachim Jäger ◽  
Marin van Heel
Keyword(s):  
Image Interpretation ◽  
Lumbricus Terrestris ◽  
Reference Image ◽  
Large Set ◽  
Multivariate Statistical ◽  
Data Set ◽  
Low Contrast ◽  
Negative Stain ◽  
Reference Images ◽  
Statistical Resolution

Lumbricus terrestris erythrocruorin is a giant oxygen-transporting macromolecule in the blood of the common earth worm (worm "hemoglobin"). In our current study, we use specimens (kindly provided by Drs W.E. Royer and W.A. Hendrickson) embedded in vitreous ice (1) to avoid artefacts encountered with the negative stain preparation technigue used in previous studies (2-4).Although the molecular structure is well preserved in vitreous ice, the low contrast and high noise level in the micrographs represent a serious problem in image interpretation. Moreover, the molecules can exhibit many different orientations relative to the object plane of the microscope in this type of preparation. Existing techniques of analysis requiring alignment of the molecular views relative to one or more reference images often thus yield unsatisfactory results.We use a new method in which first rotation-, translation- and mirror invariant functions (5) are derived from the large set of input images, which functions are subsequently classified automatically using multivariate statistical techniques (6). The different molecular views in the data set can therewith be found unbiasedly (5). Within each class, all images are aligned relative to that member of the class which contributes least to the classes′ internal variance (6). This reference image is thus the most typical member of the class. Finally the aligned images from each class are averaged resulting in molecular views with enhanced statistical resolution.

scholarly journals Unsupervised Damage Detection for Offshore Jacket Wind Turbine Foundations Based on an Autoencoder Neural Network

Sensors ◽  
2021 ◽  
Vol 21 (10) ◽  
pp. 3333
Author(s):  
Maria del Cisne Feijóo ◽  
Yovana Zambrano ◽  
Yolanda Vidal ◽  
Christian Tutivén
Keyword(s):  
Neural Network ◽  
Wind Turbine ◽  
Environmental Conditions ◽  
Wind Farms ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Small Subset ◽  
Scaled Model ◽  
Vibration Data ◽  
Large Water

Structural health monitoring for offshore wind turbine foundations is paramount to the further development of offshore fixed wind farms. At present time there are a limited number of foundation designs, the jacket type being the preferred one in large water depths. In this work, a jacket-type foundation damage diagnosis strategy is stated. Normally, most or all the available data are of regular operation, thus methods that focus on the data leading to failures end up using only a small subset of the available data. Furthermore, when there is no historical precedent of a type of fault, those methods cannot be used. In addition, offshore wind turbines work under a wide variety of environmental conditions and regions of operation involving unknown input excitation given by the wind and waves. Taking into account the aforementioned difficulties, the stated strategy in this work is based on an autoencoder neural network model and its contribution is two-fold: (i) the proposed strategy is based only on healthy data, and (ii) it works under different operating and environmental conditions based only on the output vibration data gathered by accelerometer sensors. The proposed strategy has been tested through experimental laboratory tests on a scaled model.

Evaluating the Coupledness of the Aerodynamics and Hydrodynamics on the Estimation of Fatigue Damage Equivalent Load for a Floating Offshore Wind Platform

2018 ◽  
Author(s):  
Samuel Kanner ◽  
Bingbin Yu
Keyword(s):  
Fatigue Life ◽  
Fatigue Damage ◽  
Environmental Conditions ◽  
Offshore Wind ◽  
Coupled Analysis ◽  
Offshore Wind Turbine ◽  
Wave Conditions ◽  
Equivalent Load ◽  
The Waves

In this research, the estimation of the fatigue life of a semi-submersible floating offshore wind platform is considered. In order to accurately estimate the fatigue life of a platform, coupled aerodynamic-hydrodynamic simulations are performed to obtain dynamic stress values. The simulations are performed at a multitude of representative environmental states, or “bins,” which can mimic the conditions the structure may endure at a given site, per ABS Floating Offshore Wind Turbine Installation guidelines. To accurately represent the variety of wind and wave conditions, the number of environmental states can be of the order of 103. Unlike other offshore structures, both the wind and wave conditions must be accounted for, which are generally considered independent parameters, drastically increasing the number of states. The stress timeseries from these simulations can be used to estimate the damage at a particular location on the structure by using commonly accepted methods, such as the rainflow counting algorithm. The damage due to either the winds or the waves can be estimated by using a frequency decomposition of the stress timeseries. In this paper, a similar decoupled approach is used to attempt to recover the damages induced from these coupled simulations. Although it is well-known that a coupled, aero-hydro analysis is necessary in order to accurately simulate the nonlinear rigid-body motions of the platform, it is less clear if the same statement could be made about the fatigue properties of the platform. In one approach, the fatigue damage equivalent load is calculated independently from both scatter diagrams of the waves and a rose diagram of the wind. De-coupled simulations are performed to estimate the response at an all-encompassing range of environmental conditions. A database of responses based on these environmental conditions is constructed. The likelihood of occurrence at a case-study site is used to compare the damage equivalent from the coupled simulations. The OC5 platform in the Borssele wind farm zone is used as a case-study and the damage equivalent load from the de-coupled methods are compared to those from the coupled analysis in order to assess these methodologies.

scholarly journals Separating damage from environmental effects affecting civil structures for near real-time damage detection

2017 ◽  
Vol 17 (4) ◽  
pp. 850-868 ◽  
Author(s):  
William Soo Lon Wah ◽  
Yung-Tsang Chen ◽  
Gethin Wyn Roberts ◽  
Ahmed Elamin
Keyword(s):  
Damage Detection ◽  
Real Time ◽  
Environmental Effects ◽  
Environmental Conditions ◽  
Principal Component ◽  
Detection Methods ◽  
Real Time Monitoring ◽  
Civil Structures ◽  
Data Set ◽  
Wide Range

Analyzing changes in vibration properties (e.g. natural frequencies) of structures as a result of damage has been heavily used by researchers for damage detection of civil structures. These changes, however, are not only caused by damage of the structural components, but they are also affected by the varying environmental conditions the structures are faced with, such as the temperature change, which limits the use of most damage detection methods presented in the literature that did not account for these effects. In this article, a damage detection method capable of distinguishing between the effects of damage and of the changing environmental conditions affecting damage sensitivity features is proposed. This method eliminates the need to form the baseline of the undamaged structure using damage sensitivity features obtained from a wide range of environmental conditions, as conventionally has been done, and utilizes features from two extreme and opposite environmental conditions as baselines. To allow near real-time monitoring, subsequent measurements are added one at a time to the baseline to create new data sets. Principal component analysis is then introduced for processing each data set so that patterns can be extracted and damage can be distinguished from environmental effects. The proposed method is tested using a two-dimensional truss structure and validated using measurements from the Z24 Bridge which was monitored for nearly a year, with damage scenarios applied to it near the end of the monitoring period. The results demonstrate the robustness of the proposed method for damage detection under changing environmental conditions. The method also works despite the nonlinear effects produced by environmental conditions on damage sensitivity features. Moreover, since each measurement is allowed to be analyzed one at a time, near real-time monitoring is possible. Damage progression can also be given from the method which makes it advantageous for damage evolution monitoring.

A revision of Hofstede’s model of national culture: old evidence and new data from 56 countries

2018 ◽  
Vol 25 (2) ◽  
pp. 231-256 ◽  
Author(s):  
Michael Minkov
Keyword(s):  
Cultural Differences ◽  
National Culture ◽  
Well Being ◽  
Two Dimensions ◽  
Internal Reliability ◽  
Data Set ◽  
Content Type ◽  
Gender Egalitarianism ◽  
Fifth Dimension ◽  
National Differences

PurposeHofstede’s model of national culture has enjoyed enormous popularity but rests partly on faith. It has never been fully replicated and its predictive properties have been challenged. The purpose of this paper is to provide a test of the model’s coherence and utility.Design/methodology/approachAnalyses of secondary data, including the World Values Survey, and a new survey across 56 countries represented by nearly 53,000 probabilistically selected respondents.FindingsImproved operationalizations of individualism-collectivism (IDV-COLL) suggest it is a robust dimension of national culture. A modern IDV-COLL index supersedes Hofstede’s 50 year-old original one. Power distance (PD) seems to be a logical facet of IDV-COLL, rather than an independent dimension. Uncertainty avoidance (UA) lacks internal reliability. Approval of restrictive societal rules and laws is a facet of COLL and is not associated with national anxiety or neuroticism. UA is not a predictor of any of its presumed main correlates: importance of job security, preference for a safe job, trust, racism and xenophobia, subjective well-being, innovation, and economic freedom. The dimension of masculinity-femininity (MAS-FEM) lacks coherence. MAS and FEM job goals and broader values are correlated positively, not negatively, and are not related to the MAS-FEM index. MAS-FEM is not a predictor of any of its presumed main correlates: achievement and competition orientation, help and compassion, preference for a workplace with likeable people, work orientation, religiousness, gender egalitarianism, foreign aid. After a radical reconceptualization and a new operationalization, the so-called “fifth dimension” (CWD or long-term orientation) becomes more coherent and useful. The new version, called flexibility-monumentalism (FLX-MON), explains the cultural differences between East Asian Confucian societies at one extreme and Latin America plus Africa at the other, and is the best predictor of national differences in educational achievement.Research limitations/implicationsDifferences between subsidiaries of a multinational company, such as IBM around 1970, are not necessarily a good source of knowledge about broad cultural differences. A model of national culture must be validated across a large number of countries from all continents and its predictions should withstand various plausible controls. Much of Hofstede’s model (UA, MAS-FEM) fails this test while the remaining part (IDV-COLL, PD, LTO) needs a serious revision.Practical implicationsConsultancies and business schools still teach Hofstede’s model uncritically. They need to be aware of its deficiencies.Originality/valueAs UA and MAS-FEM are apparently misleading artifacts of Hofstede’s IBM data set, a thorough revision of Hofstede’s model is proposed, reducing it to two dimensions: IDV-COLL and FLX-MON.

Dynamic Analysis of a Floating Offshore Wind Turbine Under Extreme Environmental Conditions

2012 ◽  
Author(s):  
Tomoaki Utsunomiya ◽  
Shigeo Yoshida ◽  
Hiroshi Ookubo ◽  
Iku Sato ◽  
Shigesuke Ishida
Keyword(s):  
Dynamic Analysis ◽  
Wind Turbine ◽  
Environmental Conditions ◽  
Aerodynamic Force ◽  
Experimental Results ◽  
Offshore Wind ◽  
Offshore Wind Turbine ◽  
Analysis Tool ◽  
Simulation Code ◽  
Floating Offshore Wind Turbine

This paper is concerned with the development of a Floating Offshore Wind Turbine (FOWT) utilizing spar-type floating foundation. In order to design such a structure, it is essential to evaluate the dynamic response under extreme environmental conditions. In this study, therefore, a dynamic analysis tool has been developed. The dynamic analysis tool consists of a multi-body dynamics solver (MSC.Adams), aerodynamic force evaluation library (NREL/AeroDyn), hydrodynamic force evaluation library (In-house program named SparDyn), and mooring force evaluation library (In-house program named Moorsys). In this paper, some details of the developed dynamic analysis tool are given. In order to validate the program, comparison with the experimental results, where the wind, current and wave are applied simultaneously, has been made. The comparison shows that satisfactory agreements between the simulation and the experimental results are obtained. However, when VIM (Vortex Induced Motion) occurs, the current loads and cross flow responses (sway and roll) are underestimated by the simulation since the simulation code does not account for the effect of VIM.

scholarly journals Calibration of a Multiphase Model Based on a Comprehensive Data Set for a Normal Strength Concrete

Materials ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 791 ◽  
Author(s):  
Peter Gamnitzer ◽  
Martin Drexel ◽  
Andreas Brugger ◽  
Günter Hofstetter
Keyword(s):  
Water Content ◽  
Accurate Determination ◽  
Autogenous Shrinkage ◽  
Shrinkage Strain ◽  
Desorption Isotherm ◽  
Multiphase Model ◽  
Large Set ◽  
Creep Tests ◽  
Data Set ◽  
Water Desorption

Hygro-thermo-chemo-mechanical modelling of time-dependent concrete behavior requires the accurate determination of a large set of parameters. In this paper, the parameters of a multiphase model are calibrated based on a comprehensive set of experiments for a particular concrete of grade C30/37. The experiments include a calorimetry test, tests for age-dependent mechanical properties, tests for determining the water desorption isotherm, shrinkage tests, and compressive creep tests. The latter two were performed on sealed and unsealed specimens with accompanying mass water content measurements. The multiphase model is based on an effective stress formulation. It features a porosity-dependent desorption isotherm, taking into account the time-dependency of the desorption properties. The multiphase model is shown to yield excellent results for the evolutions of the mechanical parameters. The evolution of the autogenous shrinkage strain and evolutions of the creep compliances for loading at concrete ages of 2 days, 7 days, and 28 days are well predicted together with the respective mass water content evolution. This also holds for the evolution of the drying shrinkage strain, at least for moderate drying up to one year. However, it will be demonstrated that for longer drying times further conceptual thoughts concerning the coupled representation of shrinkage and creep are required.

scholarly journals A Feed-Forward Subnetwork Emerging from Integrated TOR- and cAMP/PKA-Signaling Architecture Reinforces Magnaporthe oryzae Appressorium Morphogenesis

2019 ◽  
Vol 32 (5) ◽  
pp. 593-607 ◽  
Author(s):  
Guangchao Sun ◽  
Xiaobo Qi ◽  
Richard A. Wilson
Keyword(s):  
Cell Cycle ◽  
Adenylate Cyclase ◽  
Magnaporthe Oryzae ◽  
Environmental Conditions ◽  
Germ Tube ◽  
Nutrient Status ◽  
Specific Cell ◽  
Appressorium Formation ◽  
G2 Arrest ◽  
Feed Forward

Appressoria are important mediators of plant–microbe interactions. In the devastating rice blast pathogen Magnaporthe oryzae, appressorial morphogenesis from germ tube tips requires activated cAMP/PKA signaling and inactivated TOR signaling (TORoff). TORoff temporarily arrests G2 at a metabolic checkpoint during the single round of mitosis that occurs following germination. G2 arrest induces autophagy and appressorium formation concomitantly, allowing reprogression of the cell cycle to G1/G0 quiescence and a single appressorial nucleus. Inappropriate TOR activation abrogates G2 arrest and inhibits cAMP/PKA signaling downstream of cPKA. This results in multiple rounds of germ tube mitosis and the loss of autophagy and appressoria formation. How cAMP/PKA signaling connects to cell cycle progression and autophagy is not known. To address this, we interrogated TOR and cAMP/PKA pathways using signaling mutants, different surface properties, and specific cell cycle inhibitors and discovered a feed-forward subnetwork arising from TOR- and cAMP/PKA-signaling integration. This adenylate cyclase-cAMP-TOR-adenylate cyclase subnetwork reinforces cAMP/PKA-dependent appressorium formation under favorable environmental conditions. Under unfavorable conditions, the subnetwork collapses, resulting in reversible cell cycle-mediated germ tube growth regardless of external nutrient status. Collectively, this work provides new molecular insights on germ tube morphogenetic decision-making in response to static and dynamic environmental conditions.

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