Development of a digital wind tunnel for heliostats wind load analysis
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Martell, Javier Alberto↗
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Analytical Method to Determine a Tip Loss Factor for Highly-Loaded Wind Turbines.
Abstract not provided.
Schmitz, Sven↗
The effects of parameter variation on inflight wind loading
Optimization of payload for space shuttle by reducing structural loading and weight
Mackey, A. C.↗
Effect of astronaut control on launch vehicle wind loads
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Sleeper, R. K.↗
Adaptive stratified importance sampling: hybridization of extrapolation and importance sampling Monte Carlo methods for estimation of wind turbine extreme loads
Wind turbine extreme load estimation is especially difficult because turbulent inflow drives nonlinear turbine physics and control strategies; thus there can be huge differences in turbine response to essentially equivalent environmental conditions. The two main current approaches, extrapolation and Monte Carlo sampling, are both unsatisfying: extrapolation-based methods are dangerous because by definition they make predictions outside the range of available data, but Monte Carlo methods converge too slowly to routinely reach the desired 50-year return period estimates. Thus a search for a better method is warranted. We introduce an adaptive stratified importance sampling approach that allows for treating the choice of environmental conditions at which to run simulations as a stochastic optimization problem that minimizes the variance of unbiased estimates of extreme loads. Furthermore, the framework, built on the traditional bin-based approach used in extrapolation methods, provides a close connection between sampling and extrapolation, and thus allows the solution of the stochastic optimization (i.e., the optimal distribution of simulations in different wind speed bins) to guide and recalibrate the extrapolation. Results show that indeed this is a promising approach, as the variance of both the Monte Carlo and extrapolation estimates are reduced quickly by the adaptive procedure. We conclude, however, that due to the extreme response variability in turbine loads to the same environmental conditions, our method and any similar method quickly reaches its fundamental limits, and that therefore our efforts going forward are best spent elucidating the underlying causes of the response variability.
17 WIND ENERGY↗
Correlation of flight-test loads with wind- tunnel predicted loads on three lifting body vehicles
An essential area of research with the unique M2-F2, HL-10, and X-24A lifting body configurations is the assessment of the ability to predict flight loads from wind- tunnel tests. Flight measurements and correlation with predictions are necessary in verifying the structural integrity of existing vehicles and establishing the groundwork for weight savings on future vehicles of similar shapes. As part of the overall lifting body flight investigation at the Flight Research Center, detailed aerodynamic-load studies are being made on each of the three vehicles. This paper presents the preliminary results from these studies
Ming H Tang↗
Assessment of Wind Parameter Sensitivity on Ultimate and Fatigue Wind Turbine Loads: Preprint
Wind turbines are designed using a set of simulations to ascertain the structural loads that the turbine could encounter. While mean hub-height wind speed is considered to vary, other wind parameters such as turbulence spectra, sheer, veer, spatial coherence, and component correlation are fixed or conditional values that, in reality, could have different characteristics at different sites and have a significant effect on the resulting loads. This paper therefore seeks to assess the sensitivity of different wind parameters on the resulting ultimate and fatigue loads on the turbine during normal operational conditions. Eighteen different wind parameters are screened using an Elementary Effects approach with radial points. As expected, the results show a high sensitivity of the loads to the turbulence standard deviation in the primary wind direction, but the sensitivity to wind shear is often much greater. To a lesser extent, other wind parameters that drive loads include the coherence in the primary wind direction and veer.
17 WIND ENERGY↗
Effect of the integral length scales of turbulent inflows on wind turbine loads
As wind turbines become larger, the fluctuations in the inflow become increasingly influential in the turbine structural loading. These fluctuations are characterized by the integral length scale, which represents the average size of the largest energy-containing turbulent eddies. Current design standards neglect the varying integral length scales that characterize inflows of wind turbines in operation. Using large-eddy simulations, we generate turbulent inflows of varying integral length scales and quantify the loads of the IEA 15-MW reference wind turbine. Results illustrate that the impact of turbulence on rotor and tower loads is up to 10 times greater than the impact of the mean shear profile. Increasing integral length scales from 0.3x to 0.5x the rotor diameter reduces blade root flapwise moments and rotor and tower loads. Increasing integral length scales from 0.5x to 1.7x the rotor diameter increases the rotor aerodynamic thrust force and the blade root flapwise shear loads and decreases the tower base torsional moment and the tilting and yawing rotor aerodynamic moments. Additionally, turbulence intensity has a greater impact on wind turbine loads than integral length scales. In conclusion, findings indicate that design standards should consider varying integral length scales for accurate wind turbine loading characterization in turbulent inflow conditions.
17 WIND ENERGY↗
Sensitivity analysis of the effect of wind characteristics and turbine properties on wind turbine loads
Proper wind turbine design relies on the ability to accurately predict ultimate and fatigue loads of turbines. The load analysis process requires precise knowledge of the expected wind-inflow conditions as well as turbine structural and aerodynamic properties. However, uncertainty in most parameters is inevitable. It is therefore important to understand the impact such uncertainties have on the resulting loads. The goal of this work is to assess which input parameters have the greatest influence on turbine power, fatigue loads, and ultimate loads during normal turbine operation. An elementary effects sensitivity analysis is performed to identify the most sensitive parameters. Separate case studies are performed on (1) wind-inflow conditions and (2) turbine structural and aerodynamic properties, both cases using the National Renewable Energy Laboratory 5 MW baseline wind turbine. The Veers model was used to generate synthetic International Electrotechnical Commission (IEC) Kaimal turbulence spectrum inflow. The focus is on individual parameter sensitivity, though interactions between parameters are considered. The results of this work show that for wind-inflow conditions, turbulence in the primary wind direction and shear are the most sensitive parameters for turbine loads, which is expected. Secondary parameters of importance are identified as veer, u -direction integral length, and u components of the IEC coherence model, as well as the exponent. For the turbine properties, the most sensitive parameters are yaw misalignment and outboard lift coefficient distribution; secondary parameters of importance are inboard lift distribution, blade-twist distribution, and blade mass imbalance. This information can be used to help establish uncertainty bars around the predictions of engineering models during validation efforts, and provide insight to probabilistic design methods and site-suitability analyses.
17 WIND ENERGY↗
Liquid Rocket Booster (LRB) for the Space Transportation System (STS) systems study. Appendix E: Pressure-fed booster test bed for the liquid rocket booster study
The stress analysis/structural design of the Pressure-Fed Booster Engine Test Bed using the existing F-1 Test Facility Test Stand at Huntsville, Alabama is described. The analysis has been coded and set up for solution on NASTRAN. A separate stress program was established to take the NASTRAN output and perform stress checks on the members. Joint checks and other necessary additional checks were performed by hand. The notes include a brief description of other programs which assist in reproducing and reviewing the NASTRAN results. The redesign of the test stand members and the stress analysis was performed per the A.I.S.C. Code. Loads on the stand consist of the loaded run tanks; wind loads; seismic loads; live loads consisting of snow and ice: live and dead loads of steel; and loaded pressurant bottle. In combining loads, wind loads and seismic loads were each combined with full live loads. Wind and seismic loads were not combined. No one third increase in allowables was taken for the environmental loads except at decks 147 and 214, where the increase was used when considering the stay rods, brackets and stay beams. Wind and seismic loads were considered from each of the four coordinate directions (i.e. N,S,E,W) to give eight basic conditions. The analysis was run with the pressurant tank mounted at level 125. One seismic condition was also run with the tank mounted at levels 169 and 214. No failures were noted with mounting at level 169, but extensive deck failure with mounting at level 214 (the loadsets used are included on the tape, but no detailed results are included in the package). Decking support beams at levels 147 and 214 are not included in the model. The stress program thus does not reduce strut lengths to the length between support beams (the struts are attached to the beams at intersection points) and gives stress ratios larger than one for some of the struts. The affected members were therefore checked by hand.
Source record↗
Sensitivity of Uncertainty in Wind Characteristics and Wind Turbine Properties on Wind Turbine Extreme and Fatigue Loads
Wind turbine design relies on the ability to accurately predict turbine ultimate and fatigue loads. The loads analysis process requires precise knowledge of the expected wind-inflow conditions as well as turbine structural and aerodynamic properties. However, uncertainty in most parameters is inevitable. It is therefore important to understand the impact such uncertainties have on the resulting loads. The goal of this work is to assess which input parameters have the greatest influence on turbine power, fatigue loads, and ultimate loads during normal turbine operation. An Elementary Effects (EE) sensitivity analysis is performed to identify the most sensitive parameters. Separate case studies are performed on (1) wind-inflow conditions and (2) turbine structural and aerodynamic properties, both using the National Renewable Energy Laboratory (NREL) 5-MW baseline wind turbine. The focus is on individual parameter sensitivity, though interactions between parameters are considered.
17 WIND ENERGY↗
Sensitivity analysis of the effect of wind and wake characteristics on wind turbine loads in a small wind farm
Abstract. Wind turbines are designed using a set of simulations to determine the fatigue and ultimate loads, which are typically focused solely on unwaked wind turbine operation. These structural loads can be significantly influenced by the wind inflow conditions. Turbines experience altered inflow conditions when placed in the wake of upstream turbines, which can additionally influence the fatigue and ultimate loads. It is important to understand the impact of uncertainty on the resulting loads of both unwaked and waked turbines. The goal of this work is to assess which wind-inflow-related and wake-related parameters have the greatest influence on fatigue and ultimate loads during normal operation for turbines in a three-turbine wind farm. Twenty-eight wind inflow and wake parameters are screened using an elementary effects sensitivity analysis approach to identify the parameters that lead to the largest variation in the fatigue and ultimate loads of each turbine. This study uses the National Renewable Energy Laboratory (NREL) 5 MW baseline wind turbine, simulated with OpenFAST and synthetically generated inflow based on the International Electrotechnical Commission (IEC) Kaimal turbulence spectrum with the IEC exponential coherence model using the NREL tool TurbSim. The focus is on sensitivity to individual parameters, though interactions between parameters are considered, and how sensitivity differs between waked and unwaked turbines. The results of this work show that for both waked and unwaked turbines, ambient turbulence in the primary wind direction and shear are the most sensitive parameters for turbine fatigue and ultimate loads. Secondary parameters of importance for all turbines are identified as yaw misalignment, streamwise integral length, and the exponent and streamwise components of the IEC coherence model. The tertiary parameters of importance differ between waked and unwaked turbines. Tertiary effects account for up to 9.0 % of the significant events for waked turbine ultimate loads and include veer, non-streamwise components of the IEC coherence model, Reynolds stresses, wind direction, air density, and several wake calibration parameters. For fatigue loads, tertiary effects account for up to 5.4 % of the significant events and include vertical turbulence standard deviation, lateral and vertical wind integral lengths, non-streamwise components of the IEC coherence model, Reynolds stresses, wind direction, and all wake calibration parameters. This information shows the increased importance of non-streamwise wind components and wake parameters in the fatigue and ultimate load sensitivity of downstream turbines.
17 WIND ENERGY↗
A Comparison of Forecast Error Generators for Modeling Wind and Load Uncertainty
This paper presents four algorithms to generate random forecast error time series, including a truncated-normal distribution model, a state-space based Markov model, a seasonal autoregressive moving average (ARMA) model, and a stochastic-optimization based model. The error time series are used to create real-time (RT), hour-ahead (HA), and day-ahead (DA) wind and load forecast time series that statistically match historically observed forecasting data sets, used for variable generation integration studies. A comparison is made using historical DA load forecast and actual load values to generate new sets of DA forecasts with similar stoical forecast error characteristics. This paper discusses and compares the capabilities of each algorithm to preserve the characteristics of the historical forecast data sets.
Lu, Ning↗
A model of rotationally-sampled wind turbulence for predicting fatigue loads in wind turbines
Empirical equations are presented with which to model rotationally-sampled (R-S) turbulence for input to structural-dynamic computer codes and the calculation of wind turbine fatigue loads. These equations are derived from R-S turbulence data which were measured at the vertical-plane array in Clayton, New Mexico. For validation, the equations are applied to the calculation of cyclic flapwise blade loads for the NASA/DOE Mod-2 2.5-MW experimental HAWT's (horizontal-axis wind turbines), and the results compared to measured cyclic loads. Good correlation is achieved, indicating that the R-S turbulence model developed in this study contains the characteristics of the wind which produce many of the fatigue loads sustained by wind turbines. Empirical factors are included which permit the prediction of load levels at specified percentiles of occurrence, which is required for the generation of fatigue load spectra and the prediction of the fatigue lifetime of structures.
Spera, David A.↗
Sensitivity analysis of numerical modeling input parameters on floating offshore wind turbine loads
Abstract. Floating wind turbines must withstand a unique and challenging set of loads from the wind and ocean environment. To de-risk development, accurate predictions of these loads are necessary. Uncertainty in modeling predictions leads to larger required safety factors, increasing project costs and the levelized cost of energy. Complex aero-hydro-elastic modeling tools use many input parameters to represent the wind, waves, current, aerodynamic loads, hydrodynamic loads, and structural properties. It is helpful to understand which of these parameters ultimately drives a design. In this work, an ultimate and fatigue-proxy load sensitivity analysis was performed with 35 different input parameters, using an elementary effects approach to identify the most influential parameters for a case study involving the National Renewable Energy Laboratory (NREL) 5 MW baseline wind turbine atop the OC4-DeepCwind semisubmersible during normal operation. The importance of each parameter was evaluated using 14 response quantities of interest across three operational wind speed conditions. The study concludes that turbulent wind velocity standard deviation is the parameter with the strongest sensitivity; this value is important not just for turbine loads, but also for the global system response. The system center of mass in the wind direction is found to have the highest impact on the system rotation and tower loads. The current velocity is found to be the most dominating parameter for the system global motion and consequently the mooring loads. All tested wind turbulence parameters in addition to the standard deviation are also found to be influential. Wave characteristics are influential for some fatigue-proxy loading but do not significantly impact the extreme ultimate loads in these operational load cases. The required number of random seeds for stochastic environmental conditions is considered to ensure that the sensitivities are due to the input parameters and not due to the seed. The required number of analysis points in the parameter space is identified so that the conclusions represent a global sensitivity. The results are specific to the platform, turbine, and choice of parameter ranges, but the demonstrated approach can be applied widely to guide focus in parameter uncertainty.
17 WIND ENERGY↗
Toward Development of a Stochastic Wake Model: Validation Using LES and Turbine Loads
Wind turbines within an array do not experience free-stream undisturbed flow fields. Rather, the flow fields on internal turbines are influenced by wakes generated by upwind unit and exhibit different dynamic characteristics relative to the free stream. The International Electrotechnical Commission (IEC) standard 61400-1 for the design of wind turbines only considers a deterministic wake model for the design of a wind plant. This study is focused on the development of a stochastic model for waked wind fields. First, high-fidelity physics-based waked wind velocity fields are generated using Large-Eddy Simulation (LES). Stochastic characteristics of these LES waked wind velocity field, including mean and turbulence components, are analyzed. Wake-related mean and turbulence field-related parameters are then estimated for use with a stochastic model, using Multivariate Multiple Linear Regression (MMLR) with the LES data. To validate the simulated wind fields based on the stochastic model, wind turbine tower and blade loads are generated using aeroelastic simulation for utility-scale wind turbine models and compared with those based directly on the LES inflow. The study’s overall objective is to offer efficient and validated stochastic approaches that are computationally tractable for assessing the performance and loads of turbines operating in wakes.
17 WIND ENERGY↗
Distributed Wind for Commercial Loads
Distributed wind can help meet on site energy and resilience needs for a wide variety of commercial loads .While it is a variable resource, it has predictable daily and annual production trends. The presence of on-site renewable generation can enhance resilience, supplying power for long periods of time without worrying about conserving fuel supply. The addition of properly sized storage or complementary solar resources can further reduce the challenges of intermittency and reduce the need for fuel-limited backup power during outage situations. This fact sheet explores further the energy and resilience requirements of commercial loads and how distributed wind may be a good match to meet their load needs.
20 FOSSIL-FUELED POWER PLANTS↗