Eungsoo Kim

Calibration of Live-Load Factor in LRFD Bridge Design Specifications Based on State-Specific Traffic Environments

In this paper, the live load factor in the Strength I Limit State in the AASHTO LRFD Bridge Design Specifications is calibrated based on state-specific traffic environments and bridge configurations. As the initial development of the live load factor in the LRFD specifications was intended to be applied at the national level, state-specific traffic conditions, such as traffic volume, truck load, or bridge configurations, were not considered in the development process. In addition, due to the lack of reliable U.S. truck weight data in the early 1990s, truck data from Ontario, Canada, collected in the 1970s were used for the initial AASHTO calibration. Hence, the application of the live load factor in the LRFD specifications may result in over- or under-designed bridges for a specific state. Through reliability analysis of bridges based on state-specific traffic and bridge conditions, the live load factor can be recalibrated to achieve both reliable and economical bridge design. In this study, the traffic d...

Hurricane-Induced Loads on Offshore Wind Turbines with Considerations for Nacelle Yaw and Blade Pitch Control

During extreme tropical storm systems such as hurricanes, offshore wind turbines are required to have adequate structural integrity in parked condition and with blades pitched to feather. Such turbine states are preferred in order to mitigate loads on the turbine blades; simultaneously, yaw control is required so as to track the changing wind direction in this configuration. During a hurricane, however, it is possible that a turbines yaw control system might operate abnormally due to damage of the control and protection system or due to loss of the electric grid and/or insufficient backup power. In earlier studies, the authors have shown that feathered blades can lead to higher tower bending moments in the side-to-side (lateral) direction rather than in the fore-aft (longitudinal) direction. In the present study, we carry out an in-depth investigation of the effect of several alternative parked configurations on an offshore turbines response using numerical simulations with coupled wind-wave fields during a hurricane. We use these output wind-wave fields obtained from the University of Miami Coupled Model (UMCM), a fully coupled atmosphere-wave-ocean model that is used to simulate the storm and associated environmental fields and is able to represent relevant physical processes at the air-sea interface. In this study, we evaluate: (1) the effect of different nacelle yaw angles relative to the wind direction (i.e., different amounts of yaw error or misalignment); (2) the effect of different blade pitch angles; (3) the effect of different turbine parking strategies—e.g., parked and at a standstill or in an idling state; and (4) load maxima at different blade azimuthal configurations while the turbine is in a standstill state. The effects of the different turbine parked configurations on turbine response are evaluated under only wind loads first; later, wave loads are included to reflect possible joint metocean conditions likely to be encountered during a hurricane.

A Framework for Hurricane Risk Assessment of Offshore Wind Farms

We present a framework aimed at estimating the potential damage to an offshore wind farm from hurricanes. Our approach is related to assessing risks that are assumed to be fundamentally related to the estimation of wind speed exceedance probabilities at selected hub heights of wind turbines in the farm and of associated wind turbine loads. As part of this preliminary framework for risk assessment, synthetic storm tracks are first simulated over the ocean using available historical tropical storm data; then, a hurricane intensity evolution model based on thermodynamic and atmospheric environmental variables is developed for each of the tracks as they get to regions within the proximity of the chosen wind farm site. Based on this intensity model, a turbulent wind field can be simulated at locations of interest along the hurricane track. The simulated turbulent wind field may then be used to estimate wind speed exceedance probability distributions and, when combined with correlated waves, it can also be used in analysis of the response of individual turbines in a wind farm. The framework for the overall risk assessment is presented; the individual components that comprise such an assessment are described briefly in illustrative applications.© 2012 ASME

On the Extreme Rotor and Support Structure Response of an Offshore Wind Turbine in an Evolving Hurricane

This study examines extreme response statistics for a monopile-supported 5-MW offshore wind turbine in 20 meters of water that is subjected to coupled wind and wave input fields during a hurricane. Over approximately 120 hours, these hurricane-induced input fields yield changing characteristics of the excitation and the response of a parked turbine. As the storm evolves, the directionality of the wind and waves changes; short-crested waves are simulated and associated wind velocity fields are generated. Aerodynamic loads on the rotor and hydrodynamic loads on the support structure are simulated in coupled response analyses. Because yaw control backup power is not assured during the hurricane, different assumptions on yaw misalignment are assumed in the turbine response simulations. Time series of various turbine response measures are evaluated. Response extremes are of particular interest; we discuss the relative importance of wind and waves on the overall turbine performance during the storm. We also assess the role of yaw control systems and the effect of loss of power to such systems during tropical storms by examining the turbine response for alternative situations of turbine misalignment. Ultimately, this study seeks to provide the framework for assessing turbine designs for tropical cyclone conditions.Copyright

Hurricane risk assessment for offshore wind plants

This study describes a framework for estimation of structural damage to offshore wind plants during hurricanes. Related risk assessment is fundamentally dependent on the estimation of hurricane-generated wind speed exceedance probabilities at selected hub heights of wind turbines in the plant and on estimation of associated wind turbine loads. As part of a framework for risk assessment introduced here, synthetic storm tracks are first simulated over the ocean using available historical tropical storm data; then, a hurricane intensity evolution model based on thermodynamic and atmospheric environmental variables is developed for each simulated track as it approaches the chosen wind plant site. Based on this intensity model, a turbulent wind field can be simulated at any location of interest along the hurricane track. The simulated turbulent wind field can then be used to estimate wind speed exceedance probability distributions and, when combined with partially correlated waves, it can also be used in analyzing the response of individual turbines in a wind plant. A framework for the overall risk assessment is presented; individual components that are part of such a framework are described briefly in illustrative applications. Finally, a brief discussion is presented that addresses issues related to the concept of “robustness” checks that are often considered in the context of safe performance-based design. This concept employed in the oil and gas industries provides for a secondary margin for “beyond design-level” external conditions out to more extreme conditions as might accompany hurricanes.

Erratum for “Calibration of Live-Load Factor in LRFD Bridge Design Specifications Based on State-Specific Traffic Environments” by Oh-Sung Kwon, Eungsoo Kim, and Sarah Orton

This erratum provides corrections to the results published in the paper by the authors. In the paper, it is assumed that the maximum live loads in 75 years of bridge lifespan follows a Gumbel type I distribution. The parameters for the distribution were estimated based on the 100 days of simulation results of live load using weigh-inmotion (WIM) data collected inMissouri. During the batch processing of the data to calculate the reliability indices of the selected bridges, Gumbel type I distribution for minimum values wasmistakenly used rather than that for maximum values. Because the effect of live load was underestimated by using minimum value distribution, the reliability index reported in Fig. 6 in the original paper was overestimated. The reliability indices are recalculated after correcting the issue. Figs. 6 and 7 in the original paper should be replaced with the new figures provided here. As observed in Figs. 6 and 7, the reliability index is somewhat lower than the target reliability index of 3.5. The relatively low reliability index is primarily the result of the adoption of the Gumbel type I distribution, which was used to evaluate 75-year maximum live load effect. In the original study for the development of the LRFD specification (Nowak 1999), the tail portion of the cumulative distribution was subjectively extrapolated on normal probability paper. This method tends to result in a lower maximum response than the Gumbel type I distribution, as discussed in the report published by Kwon et al. (2011; Section 4.4.5). Because the reliability index highly depends on the adopted projection method, rather than calibrating the live load factor to achieve a reliability index of 3.5, the live load factor is calibrated to achieve the reliability index of bridges when the average daily truck traffic (ADTT) is 5,000, which is used for the latest study on load factor calibration (Kulicki et al. 2007). It is expected that the bridges designed with the live load calibration factor will have a uniform reliability index regardless of ADTT. The revised live load calibration factor is presented in Table 2. Table 2 in the original paper should be replaced with the new table provided here. More detailed information can be found in the revised report by the authors (Kwon et al. 2011).