Extreme Loads Research Articles

Synchronous high-speed measurements of a flexible structure under wind gust load

Simultaneously measuring the fluid flow around a flexible structure and the resulting deformations during short-term yet highly dynamic flow events is the focus of this fluid–structure interaction (FSI) study. These scenarios occur when a wind gust impacts a flexible structure, leading to extreme loads and significant deflections. To mimic such gusts, a specifically designed wind gust generator is used within a wind tunnel featuring an open test section. A high-speed particle-image velocimetry system records the flow field, while the digital-image correlation technique captures the structural deformations. That allows us to perform synchronized coupled fluid–structure measurements for a T-structure under wind gust load. The time-resolved measurements are repeated up to 104 times, allowing for phase-averaging of both the flow and the structural data, and to examine the convergence of the statistics. A comprehensive analysis of the instantaneous and phase-averaged data reveals that the flow field in the vicinity of the structure undergoes noticeable changes during the gust impact. The recirculation region behind the T-structures perceptibly increases when the gust hits the structure. A maximum deformation of about 10% of its height is observed during the highly dynamic gust event. Given (1) the availability of synchronously recorded data for both the fluid flow and the structure deformation, (2) the simplicity of the structure's geometry, and (3) the moderate Reynolds number of about 4×104, this case also serves as a well-suited benchmark test case for evaluating simulation methodologies for strongly coupled, highly dynamic FSI problems.

Pre-Construction predictions of the Loads from large Ice Ridges Interacting with the Confederation Bridge Piers

During the design phase of the Confederation Bridge in Canada, the Canadian government Public Works and Government Services Canada asked the National Research Council (NRC) of Canada to provide information on ice loads from large ridges using a wide range of predictive technologies. The NRC put together a team that looked at loads from several sources including analytical models, physical model tests, finite element models, discrete particle models, and full-scale data. The ice loading scenario was an extreme first-year ridge loading one of the bridge piers. A large number of analytical models were used and the load components were separated into those from the consolidated layer, the sail, and the keel. An upper bound prediction from this approach gave a value of 16 MN on a pier, but the assumptions that were used to arrive at this value did not match observed behavior in the physical and numerical studies of the program. Physical model tests indicated that the loads could be 10.5 MN with a load of 7.3 MN from the keel and 3.2 MN from the consolidated layer and sail. A finite element analysis indicted a range of predicted values of 10 MN to 12 MN depending upon the assumptions used. A discrete particle analysis predicted load values from 2.2 MN to 9.5 MN depending upon the assumptions used in describing the stiffness of the ridge. A review of full-scale measurements on lighthouses and ships suggested that the loads could range from 7.3 MN to 10.4 MN. These predicted values compare to the highest load measured on the Confederation Bridge over a twenty-year span of just over 8 MN. This paper outlines the approaches used for this prediction study and their resulting predictions. It shows the value of using multiple approaches for load predictions for offshore structures in ice-covered waters.

A comprehensive analysis of gas film pressure distribution in gas foil thrust bearings with PVDF sensor array

This research introduces an innovative approach for evaluating gas film pressure distribution in gas foil thrust bearings (GFTBs) utilizing polyvinylidene fluoride (PVDF) sensor arrays. Chosen for their exceptional sensitivity and rapid response, these sensors facilitate precise pressure analysis across various operational scenarios. The study investigates the pressure of integral and circumferentially separated GFTBs through experiments at 12 krpm to 16 krpm and loads of 60N to 120 N. The findings, derived from detailed PVDF sensor data and interpolation analysis, indicate that bearings with circumferential separation achieve more uniform pressure distributions. Experiments under extreme load conditions demonstrate that the separated type has a higher load-bearing capacity. This presents a novel PVDF-based measurement technique and advances the understanding of GFTB dynamics by correlating pressure distribution with operational parameters, thereby offering potential improvements in bearing design and performance.

A framework for implementing structural integrity management of an aging fixed offshore platform using wave modeling for risk-based underwater inspection provision

Offshore platforms have become lucrative in the oil and gas industries due to their capabilities, which are exposed to harsh environmental hazards that affect the Structural Integrity.The scope of this study represents a risk assessment strategy for evaluating the structural integrity and proposed a framework for the Risk-Based Underwater Inspection to implement Structural Integrity Management for a fixed offshore jacket platform.The jacket platform is adopted for the Non-linear Quasi-Static Analysis (Push-over) using relevant code criteria. Regarding the required site-specific meteorological and oceanographic parameters, wave is simulated using WAVEWATCH III, third-generation version 4.18, considered input data and uncertain parameters.The Long-Term Load Distribution parameters are localized to determine the Probability of Failure due to the extreme wave loading action, and the platform structural damage rate is obtained by referring to the latest available underwater inspection carried out in the understudied oilfield. Moreover, the platform's structural failure may lead to safety, environmental, and economic impacts, which are the essential factors for evaluating the Consequence of Failure.Finally, the risk level is estimated by ranking the Probability and Consequences of Failure in the risk matrix, and resulting from the risk analysis, the framework for the Risk-Based Underwater Inspection is carried out.

Numerical Investigation of Solitary Wave Attenuation by a Vertical Plate-Type Flexible Breakwater Constructed Using Hyperelastic Neo-Hookean Material

This study conducted numerical investigations on solitary wave attenuation by a vertical plate-type flexible breakwater constructed using hyperelastic neo-Hookean material. The wave attenuation performance and elastic behaviors of the flexible breakwater were discussed systematically by considering the effects of three prominent factors: mass coefficient, stiffness coefficient, and Poisson’s ratio. It is indicated that more compressible and flexible materials are beneficial for enhancing efficiency in mitigating solitary wave energy and protecting the structure from damage. In addition, the performance of the hyperelastic neo-Hookean material model was compared with that of a linear elastic isotropic material model coupled with linear and nonlinear geometry analysis (LGEOM and NLGEOM) by evaluating several key targets: wave reflection coefficient, transmission coefficient, horizontal tip displacement, and wave load. Our findings revealed that the hyperelastic neo-Hookean material model showed almost the same predictions as the linear elastic isotropic material model with NLGEOM, but significantly diverged from that with LGEOM. The linear elastic isotropic material model with LGEOM cannot capture the nonlinear variations in structural geometry and stress–strain relationship, resulting in the underestimation and overestimation of horizontal tip displacement under moderate and extreme wave loads, respectively. Moreover, it underestimates the damage inflicted by solitary waves due to inaccurately predicted wave reflection and transmission.

Site selection for offshore renewable energy platforms: A multi-criteria decision-making approach

This paper presents a novel methodology for site selection of Offshore Renewable Energy (ORE) systems, addressing the growing global energy demand and the need for sustainable solutions to climate change. Focusing on the wave energy, a relatively untapped renewable energy source, this research focuses on optimizing the Wave Energy Converter (WEC) deployment amidst the uncertainties of the offshore environment. The study involves a comprehensive evaluation of potential sites, considering key factors like power generation capacity, mooring system fatigue life, and tether response to extreme loads. Initial wave data analysis for various locations is followed by numerical simulations of a point absorber WEC under different environmental conditions. A Bayesian Network (BN) model is then employed to integrate uncertainties into a multi-criteria decision-making (MCDM), enhancing the robustness of site selection. This approach facilitates the calculation of utility values for various sites, leading to the identification of the optimal decision alternative based on maximum expected utility. This work provides a detailed framework for renewable energy stakeholders, helping in the assessment of both profitability and survivability of WECs in chosen locations. It significantly contributes to minimizing economic and performance risks associated with ORE system installations, promoting efficient and sustainable energy production from ocean resources.

A semi-empirical model for time-domain tower Vortex induced Vibration load simulations of wind turbines

A new method for simulating aeroelastic resonances between vortex shedding and structural eigenfrequencies of a wind turbine tower is presented in this publication. This aero-elastic effect of Vortex induced Vibrations (ViV) has the potential to disrupt installation processes and cause fatigue and extreme loads on the tower. Despite this issue, there are no established simulation methods available in literature, able and sufficiently efficient to predict the ViV in turbulent and site specific design load conditions. This state-of-the-art is extended by introducing an efficient semi-empirical, time-domain simulation method, which is capable of analyzing the tower ViV excitations and aeroelastic responses in wind turbine load simulations of the isolated tower or the full turbine for fatigue and extreme loads. The method is based on an adaptive harmonic equation, which is automatically tuned to the instantaneous tower motions.

Resilience of coastal bridges under extreme wave-induced loads

Records of wave-induced damage on coastal bridges during natural hazards have been well documented over the past two decades. It is of utmost importance to decipher the loading mechanism and enhance the resilience of coastal bridges during extreme wave-inducing events. Quantification of vulnerability of these structures is an essential step in designing a resilient bridge system. Recently, considerable efforts have been made to study the force applied and the response of coastal bridge systems during extreme wave loading conditions. Although remarkable progress can be found in the quantification of load and response of coastal superstructures, very few studies assessed coastal bridge resiliency against extreme wave-induced loads. This paper adopts a simplified and practical technique to analyze and assess the resilience of coastal bridges exposed to extreme waves. Component-level and system-level fragility analyses form the basis of the resiliency analysis where the recovery functions are adopted based on the damage levels. It is shown that wave period has the highest contribution to the variation of bridge resiliency. Moreover, this study presents the uncertainty quantification in resiliency variation due to changes in wave load intensity. Results show that the bridge resiliency becomes more uncertain as the intensity of wave parameters increases. Finally, possible restoration strategies based on the desired resilience level and the attitude of decision-makers are also discussed.

Detecting abnormal small-scale events by nacelle lidar at the AD8-180 prototype turbine

Identifying anomalous wind events that lead to high aerodynamic loads is essential for mitigating extreme loading on wind turbines. Building upon a recently published method, we herein introduce an extended methodology for early small-scale abnormal wind event detection employing a pulsed nacelle-lidar, potentially triggering preemptive control actions within the wind turbine system. The extended method integrates the consideration of multiple measurement ranges within the induction zone and the evaluation of a horizontally asymmetric wind detection parametrized through a difference in lidar line-of-sight (LoS) measurements. A comparative assessment of various detection algorithms is conducted, leading to the identification of optimal parameter configurations and an evaluation of their reliability. With the proposed methodology, the false positive rate can be reduced with respect to the original methodology, although not all extreme loading events are detected.

On the Development of a Near-Shore Pivoting Wave Energy Converter

Numerous offshore wave energy converter (WEC) designs have been invented; however, none has achieved full commercialization so far. The primary obstacle impeding WEC commercialization is the elevated levelized cost of energy (LCOE). Consequently, there exists a pressing need to innovate and swiftly diminish the LCOE. A critical challenge faced by WECs is their susceptibility to extreme wave loads during storms. Promising concepts must demonstrate robust design features to ensure resilience in adverse conditions, while maintaining efficiency in harnessing power under normal sea states. It is anticipated that the initial commercial endeavors will concentrate on near-shore WEC technologies due to the cost advantages associated with proximity to the coastline, facilitating more affordable power transmission and maintenance. In response, this manuscript proposes a pioneering near-shore WEC concept designed with a survivability mode that is engineered to mitigate wave loads during severe sea conditions. Moreover, prior investigations have highlighted favorable resonance properties of this novel concept, enhancing wave power extraction during recurrent energetic sea states. This study employs numerical and physical modelling techniques to evaluate wave loads on the proposed WEC. The results indicate a remarkable 65% reduction in wave loads on the moving floater of the WEC during a range of sea states under the implemented survivability mode.

The Predictors of Osteoarthritis Among U.S. Adults: National Health and Nutrition Examination Survey, 2005 to 2018.

Background Osteoarthritis (OA) is a leading cause of disability in the United States (U.S.) population, and its prevalence continues to rise. Traditionally, extreme joint loading was described as the leading cause of OA; however, recent studies suggest OA may arise from more complex mechanisms. This study aimed to identify the association between OA and various health predictors among U.S. adults. Methods National Health and Nutrition Examination Survey (NHANES) data of adult participants from 2005 to 2018 was reviewed. OA diagnosis was patient-reported, and other health variables were assessed based on patient-reported, laboratory, and examination data. A multivariable survey logistic regression model was used to estimate adjusted odds ratios and confidence intervals (95% CIs).Stratified analysis based on BMI category was additionally performed to assess the modifying effect of obesity on the association between OA and health predictors. Results A total of 42,143 participants were included in this study. OA prevalence was highest in patients ages [Formula: see text]65 years, females, non-obese individuals, non-Hispanic Whites, and those with at least college education. After controlling for multiple confounding demographic variables and comorbidities, the odds of OA increased with aging, female sex, obesity, high cholesterol, hypertension, diabetes, depression, and thyroid disease. Non-Hispanic White patients and those with less than a high school education also had higher odds of OA. After stratified analysis, aging, female sex, and severe depression demonstrated similar associations with OA across each BMI strata. Having at least a college-level education additionally conferred a similar association with OA across each BMI strata. Conclusion The odds of OA increased with aging, female sex, obesity, less than high school education, high cholesterol, hypertension, diabetes, depression, and thyroid disease. Further studies are needed to characterize the mechanisms of these associations. Given the myriad of factors that influence OA development and progression, the utilization of multidisciplinary and holistic care of OA patients is recommended to limit the influence of other health predictors and reduce ensuing pain, disability, and other complications that result from OA.

Robustness of LiDAR-assisted controller towards measurement uncertainty

Many studies have concluded that LiDAR-assisted control can be used to increase annual energy production (AEP) as well as reduce extreme loads and fatigue damage on blades, tower, and main bearing components. However, most studies assume perfect LiDAR measurements and do not consider large measurement uncertainties on LiDAR measurements. In this study we evaluate the potential benefits in terms of fatigue load reduction from using a simple robust feedforward controller. In addition, we evaluate the impact of measurement uncertainties exemplified by a time shift between the expected time the wind hits the rotor and the actual time it hits the rotor as well as a mean wind speed measurement bias. Results shows that fatigue loads can be reduced up to 7% on the tower sections and up to 4% on other main components. However, these results only applies when the LiDAR is assumed to give perfect information about what is hitting the rotor plane. The load reduction degrades with the magnitude of a time shift of the arriving LiDAR wind measurements. A delay up to 2.5 seconds still gives reasonable load reductions. Furthermore, a positive wind speed bias does not affect the performance of the LiDAR-assisted controller, whereas a negative bias significantly deteriorates performance.

A New Perspective on Offshore Wind Turbine Certification Using High Performance Computing

A correct estimation of fatigue and ultimate loads on the structure is key for wind turbine design and certification. In a greater perspective, wind turbines are large structures placed in the natural environment and are thus subject to environmental loads that are stochastic in nature. In the case of offshore turbines, the design space is even vaster, as wind speed, turbulence intensity, wave height and period, and wind/wave direction need to be considered. Due to this complexity, standardization is a challenge, and current design standards prescribe load calculations to be performed on a site-per-site basis. Performing this task requires obtaining a long-term statistical representation of the installation site, which can be complex. Moreover, this process is affected by uncertainties. This work explores an alternative to this approach, i.e., partially, or entirely simulating the lifetime of the offshore asset. Results show how this method can reliably predict fatigue loads even using as little as one year of data. On the other hand, prediction of extreme loads is influenced by the sample size and time-period.

Deep learning based fault detection of automobile dry clutch system using spectrogram plots

Dry friction clutches are extremely important in the context of power transmission systems. Continuous exposure to extreme heat and loading makes clutch extremely susceptible to various faults. The timely detection and diagnosis of such faults are of utmost importance to prevent any damage to internal components and also helps in avoiding transmission system failures. In this research study, a novel approach that leverages the power of transfer learning (a famous deep learning technique) is proposed to diagnose multiple types of clutch faults including, worn release fingers, fractured pressure plates, deteriorated pressure plates, loss of friction material and distorted tangential strips using spectrogram plots. To train and validate the diagnostic system, vibration readings were taken from a specially designed test rig with the help of piezoelectric accelerometer while the clutch system was operated under different load conditions of 0 (no load), 5 and 10 kg This procedure of data collection was then repeated to acquire the vibration data for all of the fault conditions by replacing the good with fault components individually. These vibration signals were further processed and transformed into spectrogram plots that serves as the input data for the deep learning models considered. Fine-tuning techniques were applied on pretrained networks to maximise the prediction accuracy of the models to effectively determine and diagnose faults in the clutch system. For this study 12 pre-trained networks were chosen namely, Xception, InceptionResNet, DenseNet, AlexNet, VGG16, GoogLeNet, VGG19, ResNet101, ResNet50, InceptionV3, MobileNetV2 and ShuffleNet. To optimize the performance of deep learning models, a systematic adjustment of hyperparameters such as the train-test split ratio, learning rate, optimizer and batch size for each network model was carried out. Through careful experimentation and analysis, significant improvements in fault classification accuracy were achieved thereby enhancing the reliability and effectiveness of the diagnostic system. From the results it was noted that 100% classification accuracy was displayed by AlexNet (for the no load condition and the 10 kg load condition) and GoogLeNet (for 5 kg load condition) with extremely low computation times.

Triple-layer joint optimization of capacity and operation for integrated energy systems by coordination on multiple timescales

The introduction of energy conversion and storage devices, such as power-to-gas (P2G) and seasonal energy storage, can realize the multi-timescale electricity, heat and natural gas complementarity for integrated energy systems (IESs), promoting renewable energy consumption. However, this brings a large number of decision variables, strong coupling, and nonlinearity, increasing the complexity and difficulty of the optimal design for IESs. Therefore, this study proposes a generic triple-layer optimal design framework for IESs via coordination on multiple timescales. The proposed framework decomposes the optimal design of IESs into long-, medium-, and short-timescale optimization layers that are tractable. The long-timescale optimization layer realizes the seasonal source–load matching by incorporating a refined electricity–gas complementarity model; medium-timescale optimization layer utilizes the inter-day energy transfer to deal with extreme loads caused by rare weather conditions; and short-timescale optimization layer aims to realize real-time energy balances and gain economic benefits by intraday peak shaving. Each layer not only optimizes related capacity and operation variables jointly for its own timescale but also exchanges coupling information with adjacent layers via designed interactive iteration mechanisms. The implementation of the proposed framework is further investigated in the design of an electricity–gas–heating–cooling coupled IES. Finally, a case study is performed to verify the feasibility and effectiveness of the proposed optimal design method. Simulation results show that the proposed method realizes source–load matching across multiple timescales, reducing the annualized total cost of the IES by 2.4 % and 3.3 % compared to the conventional single-timescale optimal design method under the grid connection and off-grid modes, respectively. The renewable energy consumption and device utilization rate of the optimized IES exhibit obvious improvements. In particular, the wind energy consumption of the IES increases by 15 times after introducing P2G and seasonal energy storage. Furthermore, sensitivity analyses indicate that the natural gas price considerably affects the optimization of the capacity configuration and economic performance of the IES, far exceeding that of the electricity price.

An Overview of Digital Engineering Methods for Platform Integration of Power and Energy Systems

US Navy ships, and combatant ships in particular, have requirements for integrated systems that are designed and configured for operational efficiency, redundancy, and survivability. Mission systems today and in the future will not always come with their own energy and many may, at times, require extreme pulse power loads. In addition, the migration away from fossil fuels to hybrid systems with energy storage, or the requirements for autonomous platforms, is challenging our ability to design platforms for these systems. Understanding the interdependencies between components, the systems they support, and the energy domains they are member of is a critical ontological design requirement. This paper will address how we applied digital engineering principles and computer science to the design of ontologies that allow for the modeling of complex operational systems riding on the same shipboard energy network. This system network must support varying levels of detail needed during design while at the same time understanding the impacts of that design on ship system operations and their energy loads over time.

Enhancing Tribological Performance of Self-Lubricating Composite via Hybrid 3D Printing and In Situ Spraying.

In this work, a self-lubricating composite was manufactured using a novel hybrid 3D printing/in situ spraying process that involved the printing of an acrylonitrile butadiene styrene (ABS) matrix using fused deposition modeling (FDM), along with the in situ spraying of alumina (Al2O3) and hexagonal boron nitride (hBN) reinforcements during 3D printing. The results revealed that the addition of the reinforcement induced an extensive formation of micropores throughout the ABS structure. Under tensile-loading conditions, the mechanical strength and cohesive interlayer bonding of the composites were diminished due to the presence of these micropores. However, under tribological conditions, the presence of the Al2O3 and hBN reinforcement improved the frictional resistance of ABS in extreme loading conditions. This improvement in frictional resistance was attributed to the ability of the Al2O3 reinforcement to support the external tribo-load and the shearing-like ability of hBN reinforcement during sliding. Collectively, this work provides novel insights into the possibility of designing tribologically robust ABS components through the addition of in situ-sprayed ceramic and solid-lubricant reinforcements.

Kinematic and Physiological Analysis of Medieval Combat Sport Using Motion Analysis, Blood Lactate Measurement, and Heart Rate Monitoring: A Case Study.

Medieval combat sport is a form of mixed martial art in which combatants engage in fighting using offensive and defensive equipment while dressed in full armor. The sport is considered extremely taxing, making it nearly impossible to maintain the same level of performance. However, this form of sport has not been thoroughly analyzed, and its impact on human physical response is largely unknown. To address this gap, the study reported here aimed to introduce and test a procedure for analyzing human physical responses within the framework of the sport. To accomplish this, two experienced combatants were asked to engage in a series of strikes, performed in the form of a set duel simulating a professional fight competition. The kinematic aspect of the procedure was examined using motion analysis with the help of an IMU suit, while the physiological aspect was evaluated based on blood lactate levels and heart rate measurements. Furthermore, an ergometer test conducted in a laboratory setting aimed to determine the lactate threshold. The duel results showed noticeable decreases in the kinematic aspects of the strikes, such as the velocity of impact, and a dramatic rise in physiological aspects, such as heart rate and blood lactate levels. During the duel sets, the blood lactate surpassed the threshold level, and at the end, the heart rate exceeded the maximum age-related level. Practicing medieval combat sport has been shown to impose an extreme physical load on the bodies of combatants, noticeably affecting their performance levels.

Integrating physics-based simulations with gaussian processes for enhanced safety assessment of offshore installations

Installing large floating objects during offshore operations is a challenging and failure-prone task, especially when passing through the splash zone due to extreme lifting loads on the wire and the payload. For a safe operation, it is essential to predict the peak loads on the installation system and create an early decision-making scenario for the installation vessel before starting the real operation on site. To this end, the extreme loads that can lead to unsatisfactory performance of the system must be evaluated accurately; however, the operation involves a great deal of uncertainty and physics complexity that can lead to unreliable decision-making. It is also challenging to perform numerical calculations to support ongoing marine operations, as it usually takes hours to evaluate different environmental load cases. Thus, it is essential to create an efficient prediction method associated with the environment and the corresponding response levels. In this study, a model is proposed that integrates physics-based simulations with Gaussian Processes, for estimating peak loads in lifting wires. The model offers the advantage of addressing shorter simulation times while still maintaining accuracy in predicting extreme response levels and quantifying the loads uncertainty during the operation. Bayesian Inference is used to incorporate the uncertainty, estimating hyper-parameters and predict the peak loads for various marine environmental conditions. A real case study is considered to demonstrate the application of the proposed model. The results show good agreement with the simulations obtained from time-domain dynamic analysis. The current study provide insights for both onboard and pre-planned decision-making on installation conditions, thereby enhancing predictive accuracy and improving safety in complex marine lifting operations.

Optimization of the Traffic Load Model for Suspenders of a Super-Long-Span Suspension Bridge Considering Influence Line Geometry and Extreme Load Effect Scenarios

Optimization of the Traffic Load Model for Suspenders of a Super-Long-Span Suspension Bridge Considering Influence Line Geometry and Extreme Load Effect Scenarios