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Wind turbine gearbox reliability verification by multivariate Gaidai reliability method
... Amplitudes ≈100 MPa were noted in both hogging and sagging situations during transatlantic voyages. On both the sagging and hogging sides, there was recorded a noticeable relative rise in the amplitudes at both low and high VM stress levels, [112][113][114][115][116][117][118][119][120]. Table 2 lists the primary benefits of the presented Gaidai hypersurface reliability methodology. ...
This case study presents state‐of‐the‐art, multimodal structural reliability and risk evaluation methodology, particularly suitable for naval architecture, transportation and marine engineering applications.
Existing reliability methods do not easily tackle systems with a number of critical components higher than 2, while the advocated multimodal reliability and risk evaluation methodology has no limitations on the system's number of dimensions, parts or components. The 4400 TEU container vessel's onboard measured deck panel stresses raw data, collected during numerous vessel's trans‐Atlantic crossings, was analysed. Risk of ship hull and panel structural damage caused by excessive whipping (slamming and springing) wave loads, representing types of highly nonlinear wave‐induced vibrations, are among primary safety concerns for the contemporary marine transportation industry. It is often challenging to accurately forecast excessive vessel's deck panel hot‐spot stresses, possessing complex nonlinear, nonstationary properties. The proposed multimodal hypersurface reliability method fully accounts for a large number of structural components, as well as dynamic nonlinearities. Lab testing may often be disputed, as obtained measurements will depend on biased incident wave properties and model scales. As a result, the onboard dataset, obtained from a particular cargo ship, operating in the North Atlantic provides especially valuable insights into an overall dynamic vessel hull system's durability and reliability.
This investigation aimed at providing generic state‐of‐the‐art reliability methodology, enabling accurate extraction of pertinent information about vessel hull system's dynamics, e.g., deck panel hot‐spot stresses, derived from the onboard sensor‐recorded time histories. Utilising proposed hypersurface reliability methodology, structural failure, hazard or damage risks may be effectively yet accurately forecasted, based on spatially distributed vessel deck panel stresses. The presented multimodal state‐of‐the‐art reliability methodology may be particularly suitable for the evaluation of structural hazards of large dynamic systems, having virtually unlimited numbers of principal/key components. The presented study made use of the full scale onboard measured dataset, kindly provided by Det Norske Veritas, Oslo, Norway (DNV), which is commercially valuable on its own.
... This research will also provide novel ideas for installing wind turbine manufacturers to save maintenance times and energy costs. The Weibull approach modelled with the 2-dimensional prototype is analysed with the help of multibody dynamics software for identifying the various failures occurring in the gearboxes and most of the failures occur at the high-speed stage due to the improper alignments of bearings connected to the generator [22]. Most wind turbine gearboxes fail due to the phenomenon of pitting, occurred due to the fatigue loads or shock loads on the exterior surface of the gears causing heavy maintenance costs, This failure is avoided by the improved stiffness material of the gearbox tooths to absorb time-dependent frequency signals for avoiding these critical substantial failures [23]. ...
The key thrust of this research work is to in-situ Analysis of the drop in Thermal Performance of wind turbine gearbox systems and global challenges and sustainable ideas detection of several failures that occur in the wind turbine gearbox with the help of the latest emerging sensors, and the source of the slave gearbox is a significant input for the master gearbox setup running at 1100 to 1150 rpm at generator side from Jan 2020 to Dec 2020. From the Experimental results, it is clearly understood that the majority of failures occurred due to irregular velocities ranging from 9 to 10 m s⁻¹ at average velocities, and the kinematic viscosities of the oil exceed 400 μ resulting in heavy vibrations resulting in higher maintenance costs. The Master gearbox gives more Predominant results than the Slave gearbox; oil sump temperatures exceeding 85 °C result in poor active power generation falling below 100 Kw, and slave Gearbox oil sump temperatures are achieved at 100 °C; the Maximum temperature attained from the IMS Drive end-stage rate of 85 °C to 90 °C at lower speeds for master gearbox; Whereas slave gearbox temperatures are attained at 110 °C. The average temperature attained from the HSS stage of the Gearbox coupled with the generator is 90 °C, causing heavy vibration frequencies ranging from decibels causing tooth pitting failures for the master gearbox and for the slave gearbox. Average temperatures were attained by 120 °C.
... It can accommodate cases where the risks of damage, failure, or hazards to individual critical components (or dimensions) of the container vessel system do not necessarily indicate an imminent breakdown of the entire system. Instead, such risks may represent a series of interdependent occurrences among the local maxima of key 1D components within the vessel dynamic system, [75][76][77][78][79][80][81]. ...
This case study introduces an innovative multivariate methodology for assessing the lifetime of marine engineering systems, specifically in cargo vessel transportation. The analysis focused on stress data collected onboard a 4400 TEU container vessel during multiple trans‐Atlantic voyages. One of the major challenges in marine cargo transport lies in mitigating the risk of container loss due to excessive whipping loads. Accurate prediction of extreme stress levels on vessel deck panels remains difficult, primarily because of the nonlinear and non‐stationary nature of wave and ship motion interactions. Higher‐order dynamic effects, such as second‐ and third‐order responses, often become significant when ships operate under adverse environmental conditions, amplifying nonlinear influences. Laboratory simulations, constrained by wave characteristics and scale similarity issues, may not always provide reliable results. Consequently, data collected from vessels navigating extreme weather conditions serves as a critical resource for comprehensive container ship risk assessment. The primary goal of this study was to validate and demonstrate the effectiveness of a novel multivariate risk evaluation approach, leveraging onboard measurements of dynamic areal pressure on cargo ship deck panels as the core dataset. The Gaidai methodology for multivariate risk evaluation proved to be a robust tool for assessing failure, hazard, and damage risks in complex, nonlinear vessel deck panel and ship hull stress systems.
... Due to the failure, hazard, and damage events related to the cargo vessel dynamic system becoming nearly independent when reaching high/extreme failure/ hazard/risk levels, target LTD will follow Poisson's PDFf Poisson (x) = e − P x P ∕x! , with P ≈ + ( ) T being Poisson parameter. Poisson's process is the main focus of this work, however, proposed methodology may have broader implications if the damage/failure/hazard/risk of individual cargo vessel system's critical components/dimensions does not portend an impending breakdown of the entire cargo vessel's system, but rather merely a succession of key/critical cargo vessel system's critical components local maxima's inter-dependent occurrences, [66][67][68][69][70][71][72]. ...
This study presents a novel lifetime assessment technique that may be used in cargo vessel transportation marine engineering applications. onboard measured a 4400 TEU container vessel ship panel stress data was analyzed, the data was measured during numerous trans-Atlantic crossings. The risk of container loss caused by excessive whipping loads is one of the key issues with cargo ship transportation. It is challenging to predict with accuracy excessive vessel deck panel stresses due to the complex nonlinear and nonstationary properties of wave and ship motions. 2nd and higher order vessel motion effects are typically observed when cargo ship is sailing in a severe, stormy environment, and the influence of nonlinearity grows noticeably. Depending on the wave flow characteristics and similarity ratios employed, laboratory testing may also be in dispute. Because of this, information acquired from ships operating in extreme weather conditions offers unique insight into container ship risks evaluation, as a whole. This study highlights novel multidimensional reliability approach, based on inherent qualities of the multivariate raw underlying dataset itself.
The main objective of the current study had been to benchmark the novel Gaidai multivariate risk assessment approach, using onboard measured cargo ship deck panel areal pressure dynamic system as underlying dataset. Gaidai multivariate risks evaluation methodology enabled efficient assessment of failure, hazard or damage risks for a variety of the non-linear multivariate vessel deck panel and ship hull stress systems.
... The on-board full-scale measurement datasets may not often be made available for public research presentations due to confidentiality concerns. The main goal of the current study was to support development of state-of-the-art safety guidelines for contemporary marine and naval industry, encouraging safer design and economically viable operations of cargo ships, [92][93][94][95][96]. The joint quasi-stationarity assumption of the system has been the main source of constraint for this inquiry. ...
Intercontinental transportation relies heavily on medium-to-large cargo vessels, constituting an essential component of the global trade. Thus, it is of paramount importance for trading companies, engineers, designers to advance innovative, economically viable logistical and structural schemes, that are safe and reliable. Full-scale onboard recorded data, when available, serves as valuable diagnostic tool that is hard to overestimate. An operating medium-size cargo ship TEU2800 had been selected for the current investigation. TEU2800 hull dynamics is by excessive deck panel strains, occurring during ship's intercontinental sailings through potentially adverse weather conditions. Inherent risks of damaging vessel hull and losing containers, caused by excessive motions/loads, being one of the primary concerns for the cargo transportation industry. Primary novelty of this investigation is twofold: for the first, a unique onboard measured representative dataset had been analyzed; for the second, novel multimodal reliability methodology had been employed to analyzed raw measured onboard data.
Presented study advocates the state-of-the-art Gaidai multimodal spatiotemporal risks assessment methodology, enabling conservative structural hazard/damage/failure risk forecasting for nonstationary, nonlinear, multimodal cargo vessel hull dynamics, under accumulated fatigue damage. Note that the advocated novel multimodal risks evaluation methodology being of generic nature and may be straightforwardly applied to a wide range of contemporary complex naval, offshore, marine systems, hence not being limited to cargo vessels only.
... Proper settings of epidemiological alarm/risk limits (risk/failure/hazard/damage limits) for each nation/country are briefly discussed. [94][95][96][97][98][99][100][101][102] 5 | CONCLUSIONS The current study examined recorded HIV deathrates from all world countries/nations, constituting an example of a 195-dimensional dynamic biosystem, observed within three recent decades 1990-2020. A novel bio-risk assessment methodology has been applied to HIV annual death numbers, constituting multidimensional biosystems in real-time. ...
Objectives
HIV is a contagious disease with reportedly high transmissibility, being spread worldwide, with certain mortality, allegedly presenting a burden to public health worldwide. The main objective of this study was to determine excessive HIV death risks at any time within any region or country of interest.
Study design
Current study presents a novel multivariate public health system bio‐risk assessment approach that is particularly applicable to environmental multi‐regional, biological, and public health systems, being observed over a representative period of time, yielding reliable long‐term HIV deathrate assessment. Hence, the development of a new bio‐statistical approach, that is, population‐based, multicenter, and medical survey‐based. The expansion of extreme value statistics from the univariate to the bivariate situation meets with numerous challenges. Firstly, the univariate extreme value types theorem cannot be directly extended to the bivariate (2D) case, ‐ not to mention challenges with system dimensionality higher than 2D.
Methods
Existing bio‐statistical methods that process spatiotemporal clinical observations of multinational bio‐processes often do not have the advantage of efficiently dealing with high regional dimensionalities and complex nonlinear inter‐correlations between different national raw datasets. Hence, this study advocates the direct application of the novel bio‐statistical Gaidai method to a raw unfiltered clinical data set.
Results
This investigation described the successful application of a novel bio‐risk assessment approach, yielding reliable long‐term HIV mortality risk assessments.
Conclusions
The suggested risk assessment methodology may be utilized in various public bio and public health clinical applications based on available raw patient survey datasets.
... The methodology described can be used for nonstationary naval systems with distinctive underlying trends. The underlying trend, e.g., global warming cyclic pattern, may be subtracted to bring raw (unfiltered) measurements to the uniformly quasistationary condition, [76][77][78][79][80]. ...
The current study outlines a novel multimodal structural reliability methodology, suitable for naval dynamic systems, that are either numerically simulated, or directly physically measured. Cross-correlations between the system's principal/key dimensions/components, along with the high dimensionality of complex naval dynamic systems, are not easily addressed by existing reliability methods, that are mostly univariate or bivariate. The objective of this study had been to apply a novel reliability/hazard assessment methodology to UIKKU chemical tanker onboard dynamic measurements to demonstrate the efficiency of the proposed multimodal structural reliability methodology. Current investigation utilized onboard measured ice loadings, exerted on the UIKKU tanker hull and propulsion devices during its Arctic voyage. Onboard dynamic data recordings then were interpreted to gain knowledge about dynamic load levels. The hull of UIKKU had been instrumented so that ice-induced loads/stresses on vessel shell plating had been monitored at various key locations, both in longitudinal as well as in vertical directions, along the vessel’s hull. Longitudinal bending stresses at several locations on the vessel hull and vessel vertical accelerations had been measured to analyze global vessel behaviour when colliding e.g., moderately heavy ice ridges. In addition to UIKKU onboard measurements onboard, Russian partners had instrumented icebreaker vessel Capitan Dranitsyn to real-time measure strains/stresses within ice belt grillage, located at the vessel’s bow. Icebreaker vessel Capitan Dranitsyn acted as escort icebreaker during the whole Arctic voyage. Obtained results may be utilized to harmonize IACS Polar ship rules. In the current study, the onboard measuring system along with measured results, during the whole voyage have been briefly represented.
Nowadays renewable, sustainable green energy generation gaining momentum, as environmental concerns, e.g., climate change making fossil fuel usage less attractive. Resultingly, offshore wave and wind power are gaining popularity, steadily replacing hydrocarbon energy sources. Floating offshore wind turbines (FOWT), being pivotal for contemporary offshore green wind energy generation.
Accurate structural lifespan prognostics is necessary for safe and resilient technological design, operational safety and economic viability. Non-stationary, multi-modal dynamic environmental wave-wind loads result in accumulated fatigue damage, as well as excessive structural deformations. Presented case study introduces generic, robust multi-modal structural reliability evaluation methodology, based on accurate numerical modelling of in-situ environmental hydro- and aero-dynamic stressors, acting on operating FOWT. Coupled aero-hydro-servo-elastic nonlinear software package OpenFAST was employed for numerical Monte Carlo Simulations (MCS). Investigated 5 MW FOWT is designed to withstand nonlinear, nonstationary, periodically adverse ambient environmental conditions throughout its complete designed service-life. This case study outlines state-of-the-art multi-modal hypersurface risk evaluation and lifetime assessment methodology.
The primary novelty and practical advantage of the proposed multi-modal Gaidai hypersurface structural risk evaluation approach lie within its robust capacity to evaluate structural damage (hazard/ failure) risks for complex dynamic structural systems, with no limitation on the structural Number of Degrees Of Freedom (NDOF), i.e., the number of inter-correlated system dimensions/components.
Planet pin position error is a critical geometric quality characteristic that significantly influences key attributes of planetary systems, including power density, load sharing, and operational reliability. Its tolerance parameters are among the key design factors determining the fatigue reliability of planetary systems in large wind turbines. In order to analyze the mechanism of these errors on the fatigue reliability of wind turbine planetary systems, a fatigue reliability evaluation model is established based on an extension of the computational logic of the full probability formula. It considers the failure correlation among the gear teeth and the temporal sequencing of the gear teeth meshing. Starting from balancing the contradiction between evaluation accuracy and evaluation costs, a hybrid finite element simulation including planet pin position errors and planet carrier flexibility, and an accelerated lifetime test for tooth probabilistic lifetime transformation are employed to respectively provide load and strength input variables for the reliability model. In particular, the Monte Carlo method is incorporated in the simulation and analysis process to take into account the randomness of planet pin position errors and the coupling influence mechanism between the error individuals. Finally, a mapping relationship from the planet pin position tolerance to the fatigue reliability of the planetary system is established. Depending on the specific reliability requirements, the upper limit of the planet pin positional tolerance zone can be determined at an early stage of the design in order to achieve as much as possible a balance between the service reliability of the planetary system and the manufacturing economy.
It is important to quantify multimodal uncertainties, associated with the monitoring and the management of natural resources e.g., ocean wave and wind energy. Current case the study offers a state-of-the-art methodology for multidimensional environmental/structural systems damage risk and natural hazard prognostics. Proposed reliability approach has been specifically designed for the analysis of quasi-stationary, multi-dimensional engineering systems (both environmental and structural), that were either been simulated numerically over a representative period, or were physically monitored. Presented case study shows that relatively accurate forecasts of the system’s hazard or failure probability/risk are attainable even given a limited underlying dataset. Due to nonstationary and nonlinear correlations between system’s essential elements (or dimensions), high dimensional environmental/structural systems are not easily treated by to existing reliability and risk assessment techniques. Risk assessment being important design issue for marine, naval and offshore structures, operating in particular ocean regions of interest, occasionally encountering adverse weather conditions. Advocated multimodal risk evaluation methodology makes it possible to forecast natural hazards for nonlinear high-dimensional dynamic environmental and structural systems robustly and effectively. Ability to assess risks for spatiotemporal environmental systems, possessing number of interconnected components higher than two, i.e., beyond bivariate systems, being the primary advantage of the advocated novel Gaidai hazard/risk evaluation methodology. Artificial intelligence pattern recognition features of the underlying windspeed dataset are briefly discussed.
Purpose
As a clean and renewable energy source, wind energy will become one of the main sources of new energy supply in the future. Relying on the stable and strong wind resources at sea, wind energy has great potential to become the primary energy. As a critical part of the wind turbine, the gearbox of a wind turbine often bears a large external load. Especially at sea, due to the effects of ocean corrosion, waves and wind, the burden of the wind turbine gearbox is greater, which brings great challenges to its reliability analysis. This study aims to systematically review the reliability research in wind turbine gearboxes and guide future research directions and challenges.
Design/methodology/approach
This study systematically reviews some design requirements and reliability analysis methods for wind turbine gearboxes. Then, it summarizes previous studies on wind load uncertainty modeling methods, including the processing of wind measurement data and the summary of three different classifications of random wind speed prediction models. Finally, existing reliability analysis studies on two major parts of the gearbox are described and summarized.
Findings
First, the basic knowledge of wind turbine gearboxes and their reliability analysis is introduced. The requirements and reliability analysis methods of wind turbine gearboxes are explained. Then, the processing methods of wind measurement data and three different random wind speed prediction models are described in detail. Furthermore, existing reliability analysis studies on two common parts of wind turbine gearboxes, gears and bearings, are summarized and classified, including a summary of bearing failure modes. Finally, three possible future research directions for wind turbine gearbox reliability analysis are discussed, namely, reliability research under the influence of multiple factors on gears, damage indicators of bearing failure modes and quantitative evaluation criteria for the overall dynamic characteristics of offshore wind turbine gearboxes and a summary is also given.
Originality/value
This paper aims to systematically introduce the relevant contents of wind turbine gearboxes and their reliability analysis. The contents of wind speed data processing, predictive modeling and reliability analysis of major components are also comprehensively reviewed, including the classification and principle introduction of these contents.
Current study presents a state-of-the-art structural spatiotemporal multimodal risk assessment methodology, that is particularly well-suited for multimodal structural dynamics, either recorded physically or numerically simulated over a representative timelapse. Offshore Jacket-type offshore platform, operating in Bohai Bay waters had been selected for the method’s verification. This investigation had shown that, in the presence of in-situ environmental stressors, it is possible to appropriately estimate dynamic structural system’s failure and damage risks. High dimensionality of engineering dynamic structural systems, alomg with nonlinear nonstationary inter-correlations between critical structural components, often present challenges for contemporary reliability methods, mostly limited to univariate and bivariate systems. Operating Jacket platform, subjected to in situ wave loads, had been chosen for this case study to benchmark advocated risk evaluation methodology. Number of hotspot stresses had been selected to represent multivariate structural system. Advocated multimodal method had been proven to be suitable for robust assessment of operational failure/damage risks, as well as for accurate structural life projection. Novel non-parametric deconvolution extrapolation scheme had been employed, providing enhanced numerical stability. Given increased safety concerns within offshore, naval, marine engineering, advocated multimodal reliability methodology may be utilized for safer and economically more viable structural design.
Marine risers and umbilicals being utilized extensively within the offshore industrial sector, mainly for oil transport from drilling location at seabed to the mother vessel/platform; however, they are often exposed to excessive dynamic environmental and operational loads. Excessive loading on marine risers may increase both fatigue damage and instantaneous structural collapse damage, creating additional operational risks. For operating offshore structures employing marine risers, design requires an accurate prediction of the riser dynamics, under the influence of in-situ ocean/sea currents and consequent hydrodynamic VIV (i.e., Vortex-Induced Vibrations) loads. In the current study, an experimental riser dynamics dataset had been utilized, representing marine riser's hydrodynamics under realistic currents load pattern.
Current study proposes a novel robust multimodal structural reliability methodology, based on the raw measured dataset. The bivariate modified Weibull-type method had been briefly introduced. It had been concluded that the bivariate modified Weibull-type method is capable of considering realistic environmental inputs and delivering robust and accurate predictions.
Multivariate state-of-the-art Gaidai reliability methodology had been utilized to cross-validate bivariate predictions, it had been shown that multivariate approach offers more accurate design values predictions than bivariate one. The adopted multimodal reliability methodology may be utilized within contemporary marine riser design phase to optimize riser/umbilical structural characteristics, thereby limiting potential structural and environmental damages. Regarding the potential future applications of marine risers and their reliability analysis, one may mention deep-sea mining.
The current study advances research on the consequences of global climate change by utilizing the novel Gaidai multivariate risks evaluation methodology to conduct spatiotemporal analysis of areal windspeeds. Multidimensional structural and environmental dynamic systems that have been either physically observed or numerically simulated over a representative time-lapse are particularly suitable for the Gaidai risks evaluation methodology. Current research also presents a novel non-parametric deconvolution extrapolation method. As this study has shown, given in situ environmental input, it is possible to accurately predict environmental system hazard risks, based even on a limited underlying dataset. Furthermore, because of their complex nonlinear cross-correlations between various environmental system-critical dimensions or components and large dimensionality, environmental dynamic systems are difficult to handle using traditional methods for evaluating risks. In the North Pacific, close to the Hawaiian Islands, NOAA buoys gathered raw in situ wind speed data, which has been utilized in the current study. Areal ocean wind speeds constitute quite a complex environmental dynamic system that is challenging to analyze because of its nonlinear, multidimensional, cross-correlated nature. Global warming had impacts on ocean windspeeds in the recent decade. Developing novel state-of-the-art environmental system risk evaluation methods is a principal component of modern offshore structural analysis in light of adverse weather. The advocated novel risk/hazard assessment approach may be used for resilient island cities design, especially those that are near ocean shore and hence exposed to extreme weather.
Current study presents state-of-the-art approach for evaluating spatiotemporal multivariate environmental risks, especially suitable for complex environmental systems that have been either numerically simulated or physically observed. Advocated methodology provides accurate hazard risk forecasts, based on real-time in situ environmental data. Design of offshore structures requires multivariate risk assessment – offshore and naval operations require both short- and long-term risk and reliability analyses. Contemporary risk evaluation techniques often struggle with raw timeseries multivariate data due to intrinsic multidimensionality and nonlinear interconnections among critical system’s dimensions/components. In the current study effectiveness of the Gaidai multivariate risk evaluation method is illustrated by utilizing significant wave heights dataset, measured within two offshore zones: Heidrun and Troll Norwegian oil fields. Analyzing offshore waves being particularly challenging due to their complexity, high nonlinearity, multidimensionality, yet dynamic inter-correlations. Global warming being among several significant factors, affecting ocean and sea wave heights. For naval, offshore and marine structures operating in harsh weather conditions, robust multivariate environmental risk evaluation methods being crucial for both design and safe operations. Current study aims to validate and benchmark state-of-the-art methodology, enabling extraction. Advocated approach allows for efficient yet accurate assessment of global damages, failures or hazard risks for a wide range of complex nonlinear multivariate environmental and energy systems. Primary advantage of presented multivariate reliability methodology lies within its ability to treat complex systems with practically unlimited number of dimensions, while existing reliability methods being mostly limited to univariate and bivariate analyses.
Renewable clean energy in some cases may be viewed as an alternative to limited fossil resources. Offshore FWTs (Floating Wind Turbines) are among the most attractive green alternatives. However, FWTs, in particular their essential components may sustain structural damages from cyclic loads brought on by torque, bending, longitudinal loadings, as well as twisting moments. Multibody simulation tool SIMPACK was utilized to assess structural bending moments and internal forces occurring within the FWT drivetrain during its field operation. The novel risk and damage evaluation method advocated in the current study is intended to serve contemporary FWT design, enabling accurate assessments of structural lifespan distribution, given in-situ environmental/field conditions. The approach described in the current study may be utilized to analyze complex multi-dimensional sustainable energy systems, subjected to excessive stressors during their intended service life. Contemporary risk evaluation approaches, dealing with complex energy systems are not always well-suited for handling dynamic system's high dimensionality, aggravated by nonlinear cross-correlations between structural components, subjected to dynamic nonlinear nonstationary loadings. The current study advocates a novel general-purpose lifetime assessment methodology, having a wide area of potential engineering and design applications, not limited to offshore wind/wave renewable energy systems. Key advantages of the advocated methodology lie within its robust ability to assess damage risks of complex energy and environmental systems, with a virtually unlimited number of system components (dimensions), along with the further potential to incorporate nonlinear cross-correlations between system components in real-time. Note that to the author's knowledge, there are no comparable risk evaluation methods, that can deal with the system's high-dimensionality, utilizing raw/unfiltered simulated/measured datasets, beyond one or two-dimensional dynamic systems – except for computationally expensive direct MC (i.e., Monte Carlo) simulations.
Floating Production Storage and Offloading (FPSO) unit being an offshore vessel, storing and producing crude oil, prior to crude oil being transported by accompanying shuttle tanker. Critical mooring/hawser strains during offloading operation have to be accurately predicted, in order to maintain operational safety and reliability. During certain types of offloading, excessive hawser tensions may occur, causing operational risks. Current study examines FPSO vessel’s dynamic reactions to hydrodynamic wave-induced loads, given realistic in situ environmental conditions, utilizing the AQWA software package. Current study advocates novel multi-dimensional spatiotemporal risks assessment approach, that is particularly well suited for large dataset analysis, based on numerical simulations (or measurements). Advocated multivariate reliability methodology may be useful for a variety of marine and offshore systems that must endure severe environmental stressors during their intended operational lifespan. Methodology, presented in this study provides advanced capability to efficiently, yet accurately evaluate dynamic system failure, hazard and damage risks, given representative dynamic record of multidimensional system’s inter-correlated critical components. Gaidai risk assessment method being novel dynamic multidimensional system’s lifetime assessment methodology. In order to validate and benchmark Gaidai risk assessment method, in this study it was applied to FPSO and potentially LNG (i.e., Liquid Natural Gas) vessels dynamics. Major advantage of the advocated approach is that there are no existing alternative risk assessment methods, able to tackle unlimited number of system’s dimensions. Accurate multi-dimensional risk assessment had been carried out, based on numerically simulated data, partially verified by available laboratory experiments. Confidence intervals had been given for predicted dynamic high-dimensional system risk levels.
As the global agenda turns more towards the so-called challenge of climate change and lowering carbon emissions, research into green, renewable energy sources becoming nowadays more and more popular. Offshore wind power, produced by FOWTs (i.e., Floating Offshore Wind Turbines), is one such substitute. It is a significant industrial part of the contemporary offshore wind energy industry and produces clean, renewable electricity. Accurate operational lifetime assessment for FOWTs is an important technical safety issue, as environmental in situ loads can lead to fatigue damage as well as extreme structural dynamics, which can cause structural damage. In this study, in situ environmental hydro and aerodynamic environmental loads, that act on FOWT, given actual local sea conditions have been numerically assessed, using the FAST coupled nonlinear aero-hydro-servo-elastic software package. FAST combines aerodynamics and hydrodynamics models for FOWTs, control and electrical system dynamics models, along with structural dynamics models, enabling coupled nonlinear MC simulation in the real time. The FAST software tool enables analysis of a range of FOWT configurations, including 2- or 3-bladed horizontal-axis rotor, pitch and stall regulation, rigid and teetering hub, upwind and downwind rotors. FAST relies on advanced engineering models—derived from the fundamental laws, however with appropriate assumptions and simplifications, supplemented where applicable with experimental data. Recently developed Gaidai reliability lifetime assessment method, being well suitable for risks evaluation of a variety of sustainable energy systems, experiencing nonlinear, potentially extreme in situ environmental loads, throughout their designed service life. The main advantage of the advocated Gaidai risks evaluation methodology being its ability to tackle simultaneously a large number of dynamic systems' degrees of freedom, corresponding to the system's critical components.
Due to climate change, commercial vessels may pass now through Arctic pack ice during summer, when ice beginning to melt. While Arctic ice is melting, there are floating broken-ice pieces, impeding navigation. Complex process of vessels and ice interaction includes analysis of areal stochastic ice loadings, acting on the vessel’s hull. Accurate statistical extrapolation methods need to be utilized, to accurately assess critical bow stresses, for the sake of safe Arctic ship design. Numerical analysis has been done in 2 steps. First, oil-tanker bow areal stress distribution has been simulated, using software ANSYS/LS-DYNA. Second, extreme bow pressures are predicted to assess return levels related to long return periods, using the novel reliability approach. This study is focused on oil-tanker bow stress distribution, taking into consideration in situ Arctic Ocean ice-thickness distribution. Vessel’s route being typically chosen to take advantage of summer thinning ice. In terms of ice-thickness statistics in the Arctic region, the onboard dataset being obviously route biased, but it is accurate in terms of the ice-thickness data, specific to the vessel’s route. This study proposed an accurate yet practical methodology for calculating high bow stresses for oil-tankers, voyaging along certain Arctic routes. Primary goal of this study was to validate novel methodology, making it possible to extract pertinent information regarding vessel hull areal pressure system’s extreme dynamics, from either numerically or experimentally recorded time-histories. Methodology presented in this study provides capability to efficiently, yet accurately predict failure or damage risks for a wide range of nonlinear multidimensional vessel hull pressure systems.
Wind turbines are designed to withstand extreme wind- and wave-induced loads, hence a reliability study is vital. This study presents a bivariate reliability approach, suitable for accurate assessment of critical forces and moments, occurring within the wind turbine’s critical mechanical parts, such as the drivetrain. A ecently developed bivariate modified Weibull method has been utilized in this study. Multivariate statistical analysis is more appropriate than a univariate one, as it accounts for cross-correlations between different system components. This study employed a bivariate modified Weibull method to estimate extreme operational loads acting on a 10-mega watt (MW) semi-submersible type floating wind turbine (FWT). Longitudinal, bending, twisting, and cyclic loads being among typical load types that FWTs and associated parts are susceptible to. Furthermore, environmental loads acting on an operating FWT being impacted by incoming wind’s stochastic behavior in terms of wind speed, direction, shear, vorticity, necessitates accurate nonlinear extreme load analysis for FWT critical parts such as the drivetrain. Appropriate numerical methods were used in this study to model dynamic, structural, aerodynamic, and control aspects of the FWT system. Bending moments acting on the FWT drivetrain have been obtained from SIMPACK (Multibody Simulation Method), given realistic in-situ environmental conditions. For a 5-year return period of interest, a bivariate modified Weibull method offered robust assessment of FWT’s coupled drivetrain’s bending moments.
This study validates a novel structural reliability method, particularly suitable for high‐dimensional green energy harvesting device dynamic systems, versus a well‐established bivariate statistical method, known to accurately predict two‐dimensional system extreme response contours. Classic reliability methods dealing with time series do not always have an advantage of dealing easily with dynamic system high dimensionality, along with complex cross‐correlations among different system components. Energy harvesters constitute an important part of modern offshore green energy engineering; hence, proper experimental study along with safety and reliability analysis are of practical design and engineering importance. To study the performance of galloping energy harvesters, a series of laboratory wind tunnel tests have been conducted, selecting different wind speeds. This study illustrates the usage of the advocated novel reliability method, by analyzing bivariate statistics of experimental galloping energy harvester's dynamics. The bivariate statistics was extracted from available experimental results, more specifically for the device's voltage‐force dataset. Advantage of the proposed methodology being that relatively short experimental data record may still yield meaningful design results, provided proper statistical methods have been applied. Safety and reliability are important engineering concerns for all kinds of green energy devices. In the case of measured device's structural response, an accurate prediction of system failure or damage probability is possible, as illustrated in this study. Distinctive advantage of advocated novel semi‐analytical reliability methodology being the fact that it can tackle dynamic systems with practically unlimited number of dimensions (or components), along with complex nonlinear cross‐correlations between different system key components.
Modern cargo vessel transport constitutes an important part of global economy; hence it is of paramount importance to develop novel, more efficient reliability methods for cargo ships, especially if onboard recorded data is available. Classic reliability methods, dealing with timeseries, do not have the advantage of dealing efficiently with system high dimensionality and cross-correlation between different dimensions. This study validates novel structural reliability method suitable for multi-dimensional structural systems versus a well-established bivariate statistical method. An example of this reliability study was a chosen container ship subjected to large deck panel stresses during sailing. Risk of losing containers, due to extreme motions is the primary concern for ship cargo transport. Due to non-stationarity and complicated nonlinearities of both waves and ship motions, it is challenging to model such a phenomenon. In the case of extreme motions, the role of nonlinearities dramatically increases, activating effects of second and higher order. Moreover, laboratory tests may also be questioned. Therefore, data measured on actual ships during their voyages in harsh weather provides a unique insight into statistics of ship motions. This study aimed at benchmarking and validation of the state-of-the-art method, which enables extraction of the necessary information about the extreme system dynamics from onboard measured time histories. The method proposed in this study opens up broad possibilities of predicting simply, yet efficiently potential failure or structural damage risks for the nonlinear multi-dimensional cargo vessel dynamic systems as a whole. Note that advocated novel reliability method can be used for a wide range of complex engineering systems, thus not limited to cargo ship only.
Modern offshore and onshore green energy engineering includes energy harvesting—as a result, extensive experimental investigations, as well as safety and reliability analysis are crucial for design and engineering. For this study, several wind-tunnel experiments under realistic in situ wind speed conditions have been conducted to examine the performance of galloping energy harvester. Next, a novel structural reliability approach is presented here that is especially well suited for multi-dimensional energy harvesting systems that have been either numerically simulated or analog observed during the representative time lapse, yielding an ergodic system time record. As demonstrated in this study, the advocated methodology may be used for risk assessment of dynamic system structural damage or failure. Furthermore, traditional reliability methodologies dealing with time series do not easily cope with the system’s high dimensionality, along with nonlinear cross-correlations between the system’s components. This study’s objective was to assess state-of-the-art reliability method, allowing efficient extraction of relevant statistical information, even from a limited underlying dataset. The methodology described in this study aims to assist designers when assessing nonlinear multidimensional dynamic energy harvesting system’s failure and hazard risks.
Fatigue damage prediction is essential for safety of contemporary offshore energy industrial projects, like offshore wind turbines, that are to be designed for sufficiently long operational period of time, with minimal operational disruptions. Offshore structures being designed to withstand environmental loadings due to winds and waves. Due to accumulated fatigue damage, offshore wind floating turbines may develop material cracks in their critical locations sooner than expected. Dataset needed for an accurate assessment of fatigue damage may be produced by either extensive numerical modeling, or direct measurements. However, in reality, temporal length of the underlying dataset being typically too short to provide an accurate calculation of direct fatigue damage and fatigue life. Hence, the objective of this work is to contribute to the development of novel fatigue assessment methods, making better use of limited underlying dataset. In this study, in-situ environmental conditions were incorporated to assess offshore FWT tower base stresses; then structural cumulative fatigue damage has been assessed. Novel deconvolution extrapolation method has been introduced in this study, and it was shown to be able to accurately predict long-term fatigue damage. The latter technique was validated, using artificially reduced dataset, and resulted in fatigue damage that was shown to be close to the damage, calculated from the full original underlying dataset. Recommended method has been shown to utilize available dataset much more efficiently, compared to direct fatigue estimation. Accurate fatigue assessment of offshore wind turbine structural characteristics is essential for structural reliability, design, and operational safety.
As marine renewable energy technologies developing, there is a growing need for energy transportation systems. During offshore operations, deep sea risers can be subjected to excessive environmental loadings, causing operational risks. In this study, hydrodynamic loads, caused by in situ sea currents, acting on a riser under real-world sea conditions were modelled and examined, with experimental data being used as a calibration tool. Major safety problems for various offshore energy systems being an accurate assessment of excessive riser external loads, under influence of local sea currents, and hence resulting vortex induced vibrations (VIV). The method outlined in this study may be applied to complex sustainable energy systems, that are exposed to environmental loads, throughout the whole period of their intended service life. Approach advocated in this study offers practical way to estimate failure risks for nonlinear multidimensional dynamic offshore riser systems in an easy yet accurate manner. With regard to defense technology, risers and umbilicals play an important role for modern submarine operations.
One of the most important instruments used in engineering is the statistical prediction of extreme values. Engineering design should envisage structural disaster resilience, in particular to withstand harsh environmental conditions during system operations. For instance, extreme value analysis can forecast the extreme values of the environment's wind and waves as well as engineering responses and moments. Although a variety of statistical techniques are employed to forecast extreme values, it is imperative to improve statistical techniques in order to enable improved forecasting. The innovative deconvolution strategy put forth in this study is one such way. Data on measured wind speeds were utilized to benchmark and evaluate the approach. Additionally, this method is employed to foretell the extreme values of a unique subsea shuttle tanker (SST), a cutting-edge undersea freight tanker that transports CO2 to marginal fields. The novel deconvolution method’s results are validated against the modified Weibull extrapolation method. Time-domain-stimulated 2D planar Simulink model was used to generate representative underlying dynamic system dataset. The proposed methodology provides an accurate response to extreme value prediction utilizing all available data efficiently, which enables modelling and design optimizations of the SST. The proposed deconvolution method’s overall performance indicated that the extreme response prediction results of the dynamic vessel motion numerical simulations are robust and accurate. Significance and importance of this study lie within addressing state-of-art carbon capture and storage subsea system, in particular its safety and reliability design aspects.
Wind turbines and their associated parts are subjected to cyclical loads, such as bending, torque, longitudinal stresses, and twisting moments. The novel spatiotemporal reliability technique described in this research is especially useful for high-dimensional structural systems that are either measured or numerically simulated during representative observational time span. As this study demonstrates, it is possible to predict risks of dynamic system failure or damage given the in situ environmental load pattern. As an engineering example for this reliability, the authors have chosen 10-MW floating wind turbines and their dynamic responses, under environmental loadings, caused by wind and waves. The aim of this study was to benchmark a state-of-the-art approach suitable for the reliable study of offshore wind turbines. Existing reliability methods do not easily cope with dynamic system high dimensionality. The advocated reliability technique enables accurate and efficient assessment of dynamic system failure probability, accounting for system nonlinearities and high dimensionality as well as cross-correlations between different system components.
In contrast to well-known bivariate statistical approach, which is known to properly forecast extreme response levels for two-dimensional systems, the research validates innovative structural reliability method, which is particularly appropriate for multi-dimensional structural responses. The disadvantage of dealing with large system dimensionality and cross-correlation across multiple dimensions is not a benefit of traditional dependability approaches that deal with time series. Since offshore constructions are built to handle extremely high wind and wave loads, understanding these severe stresses is essential, e.g. wind turbines should be built and operated with the least amount of inconvenience. In the first scenario, the blade root flapwise bending moment is examined, whereas in the second, the tower bottom fore-aft bending moment is examined. The FAST simulation program was utilized to generate the empirical bending moments for this investigation with the load instances activated at under-rated, rated, and above-rated speeds. The novel reliability approach, in contrast to conventional reliability methods, does not call for the study of a multi-dimensional reliability function in the case of numerical simulation. As demonstrated in this work, it is now possible to assess multi-degree-of-freedom nonlinear system failure probability, in the case when only limited system measurements are available.
Background
To estimate cardiovascular and cancer death rates by regions and time periods.
Design
Novel statistical methods were used to analyze clinical surveillance data.
Methods
A multicenter, population‐based medical survey was performed. Annual recorded deaths from cardiovascular diseases were analyzed for all 195 countries of the world. It is challenging to model such data; few mathematical models can be applied because cardiovascular disease and cancer data are generally not normally distributed.
Results
A novel approach to assessing the biosystem reliability is introduced and has been found to be particularly suitable for analyzing multiregion environmental and healthcare systems. While traditional methods for analyzing temporal observations of multiregion processes do not deal with dimensionality efficiently, our methodology has been shown to be able to cope with this challenge.
Conclusions
Our novel methodology can be applied to public health and clinical survey data.
This study proposes an innovative method for predicting extreme values in offshore engineering. This includes and is not limited to environmental loads due to offshore wind and waves and related structural reliability issues. Traditional extreme value predictions are frequently constructed using certain statistical distribution functional classes. The proposed method differs from this as it does not assume any extrapolation-specific functional class and is based on the data set's intrinsic qualities. To demonstrate the method's effectiveness, two wind speed data sets were analysed and the forecast accuracy of the suggested technique has been compared to the Naess-Gaidai extrapolation method. The original batch of data consisted of simulated wind speeds. The second data related to wind speed was recorded at an offshore Norwegian meteorological station.
Safety and reliability are essential engineering concerns for energy-harvesting installations. In the case of the piezoelectric galloping energy harvester, there is a risk that excessive wake galloping may lead to instability, overload, and thus damage. With this in mind, this paper studies bivariate statistics of the extreme, experimental galloping energy harvester dynamic response under realistic environmental conditions. The bivariate statistics were extracted from experimental wind tunnel results, specifically for the voltage-force data set. Authors advocate a novel general-purpose reliability approach that may be applied to a wide range of dynamic systems, including micro-machines. Both experimental and numerically simulated dynamic responses can be used as input for the suggested structural reliability analysis. The statistical analysis proposed in this study may be used at the design stage, supplying proper characteristic values and safeguarding the dynamic system from overload, thus extending the machine’s lifetime. This work introduces a novel bivariate technique for reliability analysis instead of the more general univariate design approaches.
Two novel methods are being outlined that, when combined, can be used for spatiotemporal analysis of wind speeds and wave heights, thus contributing to global climate studies. First, the authors provide a unique reliability approach that is especially suited for multi-dimensional structural and environmental dynamic system responses that have been numerically simulated or observed over a substantial time range, yielding representative ergodic time series. Next, this work introduces a novel deconvolution extrapolation technique applicable to a wide range of environmental and engineering applications. Classical reliability approaches cannot cope with dynamic systems with high dimensionality and responses with complicated cross-correlation. The combined study of wind speed and wave height is notoriously difficult, since they comprise a very complex, multi-dimensional, non-linear environmental system. Additionally, global warming is a significant element influencing ocean waves throughout the years. Furthermore, the environmental system reliability method is crucial for structures working in any particular region of interest and facing actual and often harsh weather conditions. This research demonstrates the effectiveness of our approach by applying it to the concurrent prediction of wind speeds and wave heights from NOAA buoys in the North Pacific. This study aims to evaluate the state-of-the-art approach that extracts essential information about the extreme responses from observed time histories.
This paper demonstrates the validity of the Naess–Gadai method for extrapolating extreme value statistics of second-order Volterra series processes through application on a representative model of a deep water small size tension leg platform (TLP), with specific focus on wave sum frequency effects affecting restrained modes: heave, roll and pitch. The wave loading was estimated from a second order diffraction code WAMIT, and the stochastic TLP structural response in a random sea state was calculated exactly using Volterra series representation of the TLP corner vertical displacement, chosen as a response process. Although the wave loading was assumed to be a second order (non-linear) process, the dynamic system was modelled as a linear damped mass-spring system. Next, the mean up-crossing rate based extrapolation method (Naess–Gaidai method) was applied to calculate response levels at low probability levels. Since exact solution was available via Volterra series representation, both predictions were compared in this study, namely the exact Volterra and the approximate one. The latter gave a consistent way to estimate efficiency and accuracy of Naess–Gaidai extrapolation method. Therefore the main goal of this study was to validate Naess–Gaidai extrapolation method by available analytical-based exact solution. Moreover, this paper highlights limitations of mean up-crossing rate based extrapolation methods for the case of narrow band effects, such as clustering, typically included in the springing type of response.
The paper investigates a nonlinear vibration mitigation strategy of a variable length pendulum subjected to a harmonic external excitation. A nonlinear absorber in a form of a tri-pendulum system is used to reduce the response of the primary pendulum. Thus, the paper investigates a non-stationary problem of nonlinear vibration mitigation of the primary pendulum using another nonlinear passive pendulum absorber. Due to genuine interest in capturing the nonlinear dynamic interaction, the paper numerically studies the performance of the primary mass and absorber, first, by constructing 2D maps in the unrestrained parametric space, which demonstrate the qualitative behavior of the system. Then, the surrogate optimization technique is used to tune the absorber’s parameters within a given bounded set of parameters’ values. The optimization is conducted based on a priori known reeling speed or acceleration/deceleration of the primary pendulum, thereby completely removing the need for acquiring a current system states essential for active feedback control. The obtained numerical results validate the proposed strategy and demonstrate high performance of the nonlinear passive absorber when it is properly tuned.
This article provides two unique methodologies that may be coupled to study the dependability of multidimensional nonlinear dynamic systems. First, the structural reliability approach is well suited for multidimensional environmental and structural reactions and is either measured or numerically simulated over sufficient time, yielding lengthy ergodic time series. Second, a unique approach to predicting extreme values has technical and environmental implications. In the event of measurable environmental loads, it is also feasible to calculate the probability of system failure, as shown in this research. In addition, traditional probability approaches for time series cannot cope effectively with the system's high dimensionality and cross-correlation across dimensions. It is common knowledge that wind speeds represent a complex, nonlinear, multidimensional, and cross-correlated dynamic environmental system that is always difficult to analyze. Additionally, global warming is a significant element influencing ocean waves throughout time. This section aims to demonstrate the efficacy of the previously mentioned technique by applying a novel method to the Norwegian offshore data set for the greatest daily wind cast speeds in the vicinity of the Landvik wind station. This study aims to evaluate the state-of-the-art approach for extracting essential information about the extreme reaction from observed time histories. The approach provided in this research enables the simple and efficient prediction of failure probability for the whole nonlinear multidimensional dynamic system.
Extreme value predictions typically originate from certain functional classes of statistical distributions to fit the data and are subsequently extrapolated. This paper describes an alternative method for extrapolation that is based on the intrinsic properties of the data set itself and that does not pre-assume any extrapolation functional class. The proposed novel extrapolation method can be utilized in engineering design. To illustrate this, this study uses two examples to showcase the advantages of the proposed method. The first example used synthetic data from a non-linear Duffing oscillator to illustrate the new method. The second example was an actual container ship sailing between Europe and America and experiencing large deck panel stresses in severe weather. In this example, actual onboard measured data were used in the present study. This example represents a real and physical case that is challenging to model due to the non-stationary and highly non-linear natures of the wave-ship load responses. This is especially so in the case of extreme responses, where the roles of second and higher-order responses tend to be more prominent and have higher contributions. The prediction accuracy of the proposed method was also validated versus the Naess–Gaidai extrapolation method. Finally, this study discusses new methods for generic smoothing of distribution tail irregularities due to underlying scarcity in the data set.
The paper describes a novel structural reliability method, particularly suitable for multi-dimensional environmental systems, either measured or numerically simulated over a sufficient period, resulting in sufficiently long ergodic time series. This study illustrates the efficiency of the proposed methodology by applying it to predict extreme wind speeds of a group of selected measured sites in Southern Norway in the region near the Landvik wind station. It is well known that wind speeds at different locations are highly non-linear, multi-dimensional and cross-correlated dynamic environmental responses, which can be challenging to analyse accurately. Unlike other environmental reliability methods, the new method does not require restarting the simulation each time the system fails, e.g., in the case of numerical simulation. In the case of measured environmental system response, an accurate prediction of system failure probability is also possible, as illustrated in this study. Moreover, in contrast to classical reliability methods, the proposed method can handle systems with high dimensionality and cross-correlation between the different dimensions.
Floating offshore wind turbines (FOWT) generate green renewable energy and are a vital part of the modern offshore wind energy industry. Robust predicting extreme offshore loads during FOWT operations is an important safety concern. Excessive structural bending moments may occur during certain sea conditions, posing an operational risk of structural damage. This paper uses the FAST code to analyze offshore wind turbine structural loads due to environmental loads acting on a specific FOWT under actual local environmental conditions. The work proposes a unique Gaidai-Fu-Xing structural reliability approach that is probably best suited for multi-dimensional structural responses that have been simulated or measured over a long period to produce relatively large ergodic time series. In the context of numerical simulation, unlike existing reliability approaches, the novel methodology does not need to re-start simulation again each time the system fails. As shown in this work, an accurate forecast of the probability of system failure can be made using measured structural response. Furthermore, traditional reliability techniques cannot effectively deal with large dimensionality systems and cross-correction across multiple dimensions. The paper aims to establish a state-of-the-art method for extracting essential information concerning extreme responses of the FOWT through simulated time-history data. Three key components of structural loads are analyzed, including the blade-root out-of-plane bending moment, tower fore-aft bending moment, and mooring line tension. The approach suggested in this study allows predicting failure probability efficiently for a non-linear multi-dimensional dynamic system as a whole.
The design of a medium‐speed drivetrain for the Technical University of Denmark (DTU) 10‐MW reference offshore wind turbine is presented. A four‐point support drivetrain layout that is equipped with a gearbox with two planetary stages and one parallel stage is proposed. Then, the drivetrain components are designed based on design loads and criteria that are recommended in relevant international standards. Finally, an optimized drivetrain model is obtained via an iterative design process that minimizes the weight and volume. A high‐fidelity numerical model is established via the multibody system approach. Then, the developed drivetrain model is compared with the simplified model that was proposed by DTU, and the two models agree well. In addition, a drivetrain resonance evaluation is conducted based on the Campbell diagrams and the modal energy distribution. Detailed parameters for the drivetrain design and dynamic modelling are provided to support the reproduction of the drivetrain model. A decoupled approach, which consists of global aero‐hydro‐servo‐elastic analysis and local drivetrain analysis, is used to determine the drivetrain dynamic response. The 20‐year fatigue damages of gears and bearings are calculated based on the stress or load duration distributions, the Palmgren‐Miner linear accumulative damage hypothesis, and long‐term environmental condition distributions. Then, an inspection priority map is established based on the failure ranking of the drivetrain components, which supports drivetrain inspection and maintenance assessment and further model optimization. The detailed modelling of the baseline drivetrain model provides a basis for benchmark studies and support for future research on multimegawatt offshore wind turbines.
A model for Quick Load Analysis of Floating wind turbines (QuLAF) is presented and validated here. The model is a linear, frequency-domain, efficient tool with four planar degrees of freedom: floater surge, heave, pitch and first tower modal deflection. The model relies on state-of-the-art tools from which hydrodynamic, aerodynamic and mooring loads are extracted and cascaded into QuLAF. Hydrodynamic and aerodynamic loads are pre-computed in WAMIT and FAST, respectively, while the mooring system is linearized around the equilibrium position for each wind speed using MoorDyn. An approximate approach to viscous hydrodynamic damping is developed, and the aerodynamic damping is extracted from decay tests specific for each degree of freedom. Without any calibration, the model predicts the motions of the system in stochastic wind and waves with good accuracy when compared to FAST. The damage-equivalent bending moment at the tower base is estimated with errors between 0.2 % and 11.3 % for all the load cases considered. The largest errors are associated with the most severe wave climates for wave-only conditions and with turbine operation around rated wind speed for combined wind and waves. The computational speed of the model is between 1300 and 2700 times faster than real time.
This paper illustrates the mechatronic design of the wind tunnel scale model of the DTU 10MW reference wind turbine, for the LIFES50+ H2020 European project. This model was designed with the final goal of controlling the angle of attack of each blade by means of miniaturized servomotors, for implementing advanced individual pitch control (IPC) laws on a Floating Offshore Wind Turbine (FOWT) 1/75 scale model. Many design constraints were to be respected: among others, the rotor-nacelle overall mass due to aero-elastic scaling, the limited space of the nacelle, where to put three miniaturized servomotors and the main shaft one, with their own inverters/controllers, the slip rings for electrical rotary contacts, the highest stiffness as possible for the nacelle support and the blade-rotor connections, for ensuring the proper kinematic constraint, considering the first flapwise blade natural frequency, the performance of the servomotors to guarantee the wide frequency band due to frequency scale factors, etc. The design and technical solutions are herein presented and discussed, along with an overview of the building and verification process. Also a discussion about the goals achieved and constraints respected for the rigid wind turbine scale model (LIFES50+ deliverable D.3.1) and the further possible improvements for the IPC-aero-elastic scale model, which is being finalized at the time of this paper.
Current study presents state of the art approach to risk and reliability assessment for multivariate nonlinear dynamic systems. Specifically, a novel hypersurface Gaidai risk evaluation methodology has been presented for evaluating offshore structural risks. The advocated approach is particularly suitable for offshore engineering multidimensional dynamic systems, possessing a large number of critical components, that have either been physically observed/measured, or numerically modelled over a representative timelapse. Advocated Gaidai hypersurface structural risks evaluation methodology is applicable for a wide range of engineering and industrial systems. This study demonstrates that given in situ environmental conditions, it is well possible to assess accurately the risks of system failures, damages or natural hazards, caused by excessive structural dynamics. Multivariate dynamic systems, possessing nonlinear cross-correlations between critical system’s components often present design challenges, when utilizing classic structural risks evaluation approaches, as those are mostly only univariate or bivariate. Dynamic offshore Jacket hot spot stresses have been utilized as an example in this reliability investigation. Modelling system's excessive dynamics being often challenging because of the non-stationarity of the system and the intricacy of fluid-structural dynamic interactions, arising from in situ wave-induced loads, influencing Jacket’s structural dynamics. Nonlinearities greatly affect structural dynamics, e.g., 2nd, 3rd, and higher order effects within fluid-structural interactions. The methodology presented in this work offers a straightforward, effective, yet precise means of assessing the risks of failure or hazard for multivariate, non-stationary, non-linear dynamic offshore systems. For bivariate cases, a verification note has been added
Floating production storage and offloading unit (FPSO) is an offshore vessel, producing, storing natural gas or crude oil, prior to oil shuttle tanker transport. The equivalent of natural gas is known as floating liquefied natural gas (FLNG). Robust prediction of the extreme mooring hawser tensions, during FPSO operations, is an important design and engineering reliability and safety concern. Excessive mooring hawser tensions may occur during certain types of offloading operations, posing potential operational risks. In this study, ANSYS-AQWA-software package has been used to model vessel dynamics, subjected to hydrodynamic wave loads, acting on FPSO or liquefied natural gas (LNG) vessel, under actual in situ environmental conditions. Experimental validation of the numerical results has been briefly discussed as well.
This study presents novel multi-dimensional reliability method, based on Monte Carlo simulations (or alternatively on measurements). Proposed methodology provides accurate failure or damage risks assessment, utilizing available underlying dataset efficiently. Described approach may be well utilized at the vessel design stage, while selecting optimal vessel’s parameters, minimizing potential FPSO mooring hawser tensions. The aim of this study was to benchmark state of the art Gaidai reliability method, proposed recently; this novel methodology opens up the possibility to predict simply and efficiently failure or damage risks for non-linear multi-dimensional dynamic offshore energy system as a whole.
Key advantage of the suggested methodology is its multi-dimensionality (with unlimited number of system dimensions/components/processes, all having different physical dimensions), while classic reliability methods typically are not covering dimensions higher than two.
Energy harvesting is a component of contemporary offshore and onshore green energy engineering. Rigorous experimental studies, as well as safety and reliability research, being essential for modern green energy design and engineering. In order to evaluate dynamic performance of galloping energy harvesters, this study utilized extensive wind-tunnel tests, performed under realistic in situ windspeed conditions. State of art Gaidai structural reliability approach has been presented, that is particularly well suitable for non-stationary imperfect or damaged multi-dimensional energy harvesting systems. This approach utilizes analog observations made during representative timelapse, producing quasi-ergodic system dynamic record. As shown in the current study, the recommended technique may be utilized to evaluate the risk of damage or failure in dynamic systems. Additionally, high-dimensionality, deterioration, and nonlinear cross-correlations between dynamic system's key components are challenging to handle for standard reliability approaches, dealing with nonstationary, multidimensional systems. The goal of this study was to benchmark novel Gaidai multivariate reliability approach that allows for effective processing of pertinent statistical data even from limited, multivariate non-stationary underlying dataset. Gaidai multivariate reliability approach attempts to assist designers in evaluating risks of failure and hazards for nonlinear multidimensional dynamic energy harvesting systems, when initial manufacturing imperfections being present.
Two new techniques for risk and reliability analysis of multidimensional nonlinear dynamic systems are presented in this study, combined into one novel Gaidai reliability method. First, a novel reliability method for assessing offshore structural risk and reliability is proposed. This method is particularly helpful for offshore engineering multidimensional dynamic structures with numerous components that are either physically measured over a meaningful time period, or numerically modelled. Second, novel deconvolution technique for extreme values prediction is described. This technique is ideally suited for use in a variety of engineering applications. This work shows that under actual climatic circumstances, it is also possible to appropriately estimate the risks of system failures and dangers brought on by severe structural responses. Managing multi-dimensional dynamic systems and their cross-correlation across many system components is not necessarily a practical advantage for traditional reliability methodologies dealing with system time series. This reliability study utilized dynamic offshore Jacket stresses as an example. Due to system non-stationarity and complexity of fluid-structural interactions resulting from wave stresses acting on Jacket support structure, modelling such events is highly difficult. Extreme motions significantly increase the contribution of nonlinearities, resulting in impacts of second, third, and higher order fluid-structural interaction. Methods put forward in this study provides a simple, efficient, and accurate way to evaluate failure/hazard risks for multi-dimensional nonlinear dynamic systems.
This study presents novel reliability-based technique, being especially useful for lifetime assessment of multidimensional
dynamic systems, consisting of cross-correlated components, that have been either measured, or numerically simulated over representative time span. This study has potential applications within wide range of marine structural systems design, where reliability analysis is required. As demonstrated in this study, it is
possible to accurately forecast risks of a system failure due to excessive structural responses. Furthermore, time series-based traditional reliability techniques do not always manage system’s high-dimensionality, along with complex cross-correlations between different system components. When sailing in harsh weather, cargo ships are exposed to structural damage risks, this study used operating cargo vessel as an underlying example. Another serious safety concern for modern ship trans-Atlantic navigation is the potential loss of cargo, due to the ship’s violent movements. Simulating such a situation is quite challenging, due to waves and ship motions being both non-stationary and highly nYar. Validity of laboratory tests may also be questioned, hence, on-board data, gathered from actual cargo ships during their trans-Atlantic voyages, under realistic storms gives a unique research opportunity. Novel Monte Carlo based multi-dimensional reliability method, based on-board measurements is presented in
this study. Advocated methodology can be applied to a large variety of complex sustainable marine systems, that are designed to endure environmental loadings for the entire length of their planned service life. This study’s approach offers the opportunity to robustly and accurately evaluate operational risks of an entire nonlinear multidimensional dynamic system.
This study analyses dynamic influence of stochastic vibro-impact ship behaviour on the ship’s launch and recovery capability. To deliver cargo and people to the Arctic regions, ships must withstand harsh environmental conditions and interact with large floating ice pieces. This interaction may result in impact-type loading of a ship hull by ice, preventing planned navigation and even causing to abort of some routine launch and recovery operations of delivering cargo or other equipment. The major safety concern is the risk of collision between the payload and the mother ship hull. The ship-based crane, which served for conducting launch and recovery operations, was assumed to be rigid, mimicking the ship dynamics, whereas the payload is modelled as a single-degree-of-freedom pendulum. This study advocates practical engineering approach, applicable to various scenarios with vessels operating in relevant in situ environmental sea and ice conditions. The proposed study intends to contribute to improving launch and recovery operational reliability, as well as motion control, especially in Arctic aquatic regions. When mentioning Arctic and defence technologies, launch and recovery systems have significant relevance for unmanned vehicles onboard ships.
Floating Production Storage and Offloading Unit (FPSO) is designed to produce, store and transport hydrocarbon products. FPSO's hawsers may be exposed to both extreme and fatigue loads during operations. Hence prediction of their fatigue life is important for operational safety. During some unloading operations, consistent hawser tensions could develop as a result of internal friction in nylon ropes, casing wear and accumulated fatigue damage. Methodology, suggested in this study, may be effectively employed at the vessel design phase, when optimizing vessel parameters, reducing potential FPSO hawser tension fatigue damage. This study aims to contribute to development of novel fatigue assessment approaches, in order to use limited available datasets more effectively. Stresses occurring within FPSO hawsers have been modelled, using actual in situ environmental conditions. Simulated continuous stress time series were used as input for the rainflow counting analysis; the cumulative fatigue damage was then evaluated. Note on experimental validation has been provided.
Gulf of Eilat is rich with energy resources, however any industrial natural resource development requires additional safety, as local eco‐system has to be preserved. In contrast to bivariate reliability approaches, known to their accurate predictions of extreme response and load levels for two‐dimensional dynamic systems, this study suggests and validates novel structural reliability method, which is being appropriate method for high‐dimensional dynamic systems. Conventional reliability methods do not have an advantage of dealing easily with high‐dimensional nonlinear dynamic systems, especially with non‐linear cross‐correlations between different system components. Advocated approach does not have limitations on the system's number of degrees of freedom, and it can accurately assess dynamic system's failure risks. Main purpose of this study was to benchmark state‐of‐the‐art reliability methodology, while utilizing available dataset efficiently. Note that advocated approach is not limited to offshore engineering example, studied here, and it has wide range of potential engineering and design applications.
Novel coronavirus disease is spread worldwide with considerable morbidity and mortality and presents an enormous burden on worldwide public health. Due to the non-stationarity and complicated nature of novel coronavirus waves, it is challenging to model such a phenomenon. Few mathematical models can be used because novel coronavirus data are generally not normally distributed. This paper describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of novel coronavirus infection rate. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality and cross-correlation between infection rate and mortality. To determine extreme novel coronavirus death rate probability at any time in any region of interest. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality and cross-correlation between different regional observations. Apply modern novel statistical methods directly to raw clinical data. Multicenter, population-based, medical survey data based bio statistical approach. Due to the non-stationarity and complicated nature of novel coronavirus, it is challenging to model such a phenomenon. Few mathematical models can be used because novel coronavirus data are generally not normally distributed. This paper describes a novel bio-system reliability approach, particularly suitable for multi-country environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of extreme novel coronavirus death rate probability. The suggested methodology can be used in various public health applications, based on their clinical survey data.
The research examines the motion response and hydrodynamic wave loads of a deep-water Tension Leg Platform (TLP), emphasising the impacts of the wave sum frequency on the restrained modes of heave, roll, and pitch. The stochastic TLP structural reaction in a random sea state was precisely computed using a Volterra series representation of the TLP corner vertical displacement, which was selected as a response process. The wave loading was evaluated using the second-order diffraction code WAMIT and applied to a linear damped mass-spring model representing the dynamic system. Then, platform displacement response at the design low probability level has been determined using a novel deconvolution approach. Since the Volterra series represented the analytical solution, the exact Volterra and the approximated predictions have been compared in this study. The latter provided an accurate way to validate the effectiveness and precision of the proposed novel deconvolution method. Compared to existing engineering techniques, the most attractive advantage of the proposed deconvolution method is that it does not rely on any pre-assumed asymptotic probability distribution class. The latter may be an attractive point for practical engineering design. Thus the primary objective of this work was to validate a novel deconvolution approach using exact quasi-analytical solutions. This work also highlights the limitations of mean up-crossing rate-based extrapolation methodologies for the situation of narrowband effects, including clustering, which are often included in the springing type of response.
Cardiovascular diseases (CVD) are heart and blood vessels diseases with considerable morbidity and mortality and presenting worldwide public health burden, moreover CVDs are the leading cause of death globally. This paper describes a novel bio-system reliability approach, particularly suitable for multi-regional environmental and health systems, observed over a sufficient period of time, resulting in a reliable long-term forecast of cardiovascular diseases mortality probability. Objective has been to determine extreme cardiovascular diseases death rate probability at any time in any region of interest. Traditional statistical methods dealing with temporal observations of multi-regional processes do not have the advantage of dealing efficiently with extensive regional dimensionality and cross-correlation between different regional observations. Design of this analysis was based on applying novel statistical methods directly to a raw clinical data, with subsequent data analysis using multicenter, population-based, medical survey data based bio-statistical approach. For this study, cardiovascular diseases annual numbers of recorded deaths in all 195 world countries were chosen. The suggested methodology can be used in various public health applications, based on their clinical survey data.
The paper validates novel structural reliability Gaidai-Fu-Xing (GFX) method, particularly suitable for multi-dimensional structural responses, versus well established bivariate statistical method, that is known to accurately predict two-dimensional system extreme response levels. Classic reliability methods, dealing with time series do not have an advantage of dealing easily with system high dimensionality and cross-correlation between different dimensions. An operating Jacket located in the Bohai bay was taken as an example to demonstrate the proposed methodology. Novel state of art bimodal extrapolation technique was applied to predict system reliability with five years return period, which is of practical importance for design of fixed offshore structures.
Unlike other reliability methods the new method does not require to re-start simulation each time system fails, in case of numerical simulation. In case of measured structural response, an accurate prediction of system failure probability is also possible as illustrated in this study.
Jacket offshore platform subjected to large environmental wave loads, thus structural stresses in different structural critical locations were chosen as an example for this reliability study. The method proposed in this paper opens up the possibility to predict simply and efficiently failure probability for nonlinear multi-dimensional dynamic system as a whole.
The paper describes a novel structural reliability method, particularly suitable for multi-dimensional structural responses, either measured or numerically simulated over sufficient period of time, resulting in sufficiently long ergodic time series. Unlike other reliability methods the new method does not require to re-start simulation each time system fails, in case of numerical simulation. In case of measured structural response, an accurate prediction of system failure probability is also possible as illustrated in this study. Moreover, classic reliability methods, dealing with time series do not have an advantage of dealing easily with system high dimensionality and cross-correlation between different dimensions.
As an example for this reliability study was chosen container ship subjected to large deck panel stresses and extreme roll angles occurring during sailing in harsh weather. Risk of losing containers due to extreme motions is primary concern for ship transport. Due to non-stationarity and complicated nonlinearities of both waves and ship motions, it is a considerable challenge to model such a phenomenon. In case of extreme motions, the role of nonlinearities dramatically increases, activating effects of second and higher order. Moreover, laboratory tests may also be questioned because of the scaling and the sea state choice. Therefore, data measured on actual ships during their voyages in harsh weather provides a unique insight into statistics of ship motions.
The aim of this work is to benchmark state of art method, which makes it possible to extract the necessary information about the extreme response from onboard measured time histories. The method proposed in this paper opens up the possibility to predict simply and efficiently failure probability for nonlinear multi-dimensional dynamic system as a whole.
A subsea shuttle tanker (SST) is a pioneering underwater submarine specifically designed to economically transport CO2 to smaller hydrocarbon fields. During loading and unloading of the CO2, the SST hovers over the well and experiences extreme heave motions resulting in extreme hydrostatic loading. Sustaining the hydrostatic loading is indispensable as it forms the dominating load and is a driving factor determining the SST hull's collapse design. Furthermore, the SST's extreme surge motion regulates the length of the flowline to evade snap loads. This paper focuses on the problem of determining these extreme positional responses at one location when the aft thruster fails during offloading using data with a shorter time record from another location at the SST. This scenario could be of practical engineering importance when either one of the sensors is malfunctioning or another similar vessel is being designed for the same environmental condition. A 2D planar Simulink model is used to generate the empirical data required for the study.
Offshore engineering often requires estimation of extreme wind speeds and wave heights at offshore in-situ locations. This paper presents practical approach to study extreme wind speed and wave height statistics, based on available in-situ hourly wind speed and wave height maxima. The wind and wave data, studied in this paper, was obtained from numerical hind cast model, locally applied to the offshore the area that covers SEM-REV (European wind and wave energy research site) location, for the time period 2001–2010 years.
To obtain accurate local wind and wave dataset, accurate wave and wind measurements from in-situ monitoring tools (meteorological stations and buoys) as well as remote satellite sensing data (ENVISAT and TOPEX satellite records) were plugged into atmospheric wind and wave model.
The novel bivariate correction technique based on the Average Conditional Exceedance Rate (ACER) method has been presented in brief detail. The bivariate correction method produced quite accurate extreme value predictions, efficiently utilizing available wind speeds and wave heights data set.
In some practical situations it would be useful to improve accuracy of some statistical predictions, using information supplied by another synchronous highly correlated random process that has been measured for a longer time than the process of interest. In this paper the novel technique of improving correlated extreme wind speed and wave height predictions has been presented.
This paper deals with statistical and modeling uncertainty on the estimation of long-term extrapolated extreme responses in a monopile offshore wind turbine. The statistical uncertainty is addressed by studying the effect of simulation length. Modeling uncertainty is explored by evaluating the effects of considering a rigid and flexible foundation. The soil's flexibility is taking into account by considering the improved apparent fixity method. To identify the most relevant environmental conditions, the modified environmental contour method is used.
The analysis focuses on the fore-aft shear force (FASF) and the fore-aft bending moment (FABM) at the mudline. The results show that using a simulation length of 10-min, does not provide sufficient accuracy. It was found that for the FASF, simulation lengths of at least 30-min are required to achieve an accuracy of about +/-5%. For the FABM, it was found that both the extrapolations made with 20-min and 30-min simulations achieved similar levels of accuracy of about 20%. Meanwhile, the results obtained from 10-min simulations reached deviations of about 40%.
Finally, from the comparison made between a rigid and flexible foundation, it was found that the extrapolated responses exhibit maximum deviations up to around 5% and 10% for the FASF and the FABM, respectively. Also, for the FABM, it was observed that the consideration of a flexible foundation causes the critical wind speed to shift from 16.5 m/s (rigid) to 18 m/s (flexible).
According to design standards, offshore wind turbines need to withstand environmental loads with a return period of 50 years. This work compares the extreme response along the 50-year environmental contour with the true 50-year wind turbine response. It
was found that the environmental contour method that is currently described in the IEC design standard for offshore wind turbines can strongly under-predict the 50-year return value of response variables whose annual maxima typically occur during power production. The bias in the contour-based estimate of the 50-year response can be
attributed to three sources: (1) the method used to construct the contour; (2) neglecting serial correlation in environmental conditions; and (3) neglecting the short-term variability in the response. In our analysis the 50-year maximum mudline overturning moment was underestimated by 4-8% by the contour-based approach that is currently recommended, whereas the bending moment at 10 m water depth was underestimated by 25-28%. This underestimation was mainly due to ignoring the short-term variability in the response. The bias associated with contour construction, an effect much discussed in recent publications, was of much smaller magnitude.
Extreme value statistics are one way to determine the maximum design loads for systems in extreme conditions, such as operational loads experienced by ships. Accurate predictions typically require large sample sizes, which are not always possible to obtain. Conversely, small sample sizes lead to more variation in the predictions. Increasing the sample size improves the variance to a desired range. The proposed method aimed to estimate a minimum sample size for an extreme value process by specifying and obtaining an acceptable variance. Minimum sample sizes for extreme value statistics depend on the distribution's behavior, so the method proposed here was designed for use before and during measurements. To test the proposed method, the response of a cantilever fin with a varying angle of attack was measured. The proposed method was able to estimate minimum sample sizes for several distributions. Accuracy was demonstrated by randomly drawing measured and simulated samples.
The characteristic values of the extreme environmental load effects should correspond to a specified annual probability of exceedance. These load effects can be calculated using short-term or long-term methods. The full long-term method is considered the most accurate approach, but it requires tremendous computational effort for complicated structures, especially when nonlinearities must be considered. In a case study of the dynamic behavior of a three-span suspension bridge with two floating pylons, these nonlinearities are found to have a significant effect on the extreme values of some of the load effects. It is thus recommended to determine these responses in the time domain. However, time-domain simulations can be very time consuming even by using simplified approaches such as the environmental contour method (ECM) and the inverse first-order reliability method (IFORM). Therefore, this paper introduces a computationally efficient approach utilizing the ECM and the IFORM to determine long-term extreme values based on responses from combined frequency- and time-domain simulations.
The International Electrotechnical Commission (IEC) design standard (IEC 61400-3) for offshore wind turbines includes a design load case which considers loads on the turbine during extreme conditions, when the wind turbine is not operating and the blades are feathered. The recommendation of this design standard is to simulate 6 one-hour periods, each with wind and wave fields modeled as random processes, and to calculate design loads as the mean of the maximum from the six simulations. Previous studies have investigated this recommendation for fixed bottom offshore wind turbines and raised concerns about the stability of the estimate of the design load calculated using these guidelines. The research presented in this paper calculates statistics of extreme design loads as a function of the number of one-hour simula¬tions considered for three offshore wind turbine support structures: a fixed-bottom turbine supported by a monopile and two floating turbines, a spar buoy and a semi-submersible. The study considers one extreme design load, the moment at the tower base, one set of 50-year metocean conditions representive of the Northeast U.S. Atlantic coast, and five combinations of wind and wave models, including linear and nonlinear representations of irregular waves. The data generated from this study are used to assess the stability of the estimate of the extreme load for various numbers of one-hour simulations for each turbine type. The study shows that, for the considered metocean conditions and models, the monopile exhibits the least stability in the estimate of the extreme load and therefore requires more one-hour simulations to have comparable stability as the two floating support structures. Explanations for this result are provided along with a probabilistic formulation for estimating variability of design loads more efficiently, using fewer one-hour simulations.