Antonio Velazquez

Gyroscopic Effects of Horizontal Axis Wind Turbines Using Stochastic Aeroelasticity via Spinning Finite Elements

Horizontal axis wind turbine (HAWTs) structures, throughout the years, have presumed to be of relatively simple construction, but wind-induced aerodynamic vibrations, wind-field conditions, and power requirements tend to lead to the need for increasingly complicated designs. One phenomenon that requires special attention is the gyroscopic or Coriolis effect. In general, blades design codes are written to optimize for lightness and slenderness, but also to withstand excitations at high frequency. As a result, gyroscopic motion derives as a nonlinear dynamic condition in the out-of-plane direction that is difficult to characterize by means of the well-known vibrational theory that has been established for their design and analysis. The present study develops and presents a probabilistic analysis of the precession — gyroscopic — effects of a wind turbine model developed for tapered-swept cross-sections of nt degree with nonlinear variations of mass and geometry along the body of the blade. A dynamic orthogonal decoupling method is utilized to successfully perform the aeroelastic analysis by decoupling the damped-gyroscopic equations of motion, as a result of the addition of Rayleigh damping — symmetric proportional mass and stiffness — within the linear system in study. Results are valid for yaw-free rotor configurations by means of unknown and random (though bounded) yaw rates. Simultaneously, those results can easily be expanded for yaw-controlled mechanisms. The yaw-free assumption presents a higher risk of potential reliability expectations, given the stochastic impairment of the gyroscopic nature that is present for out-of-plane axis motions, requiring special attention at higher frequencies. This impairment becomes particularly troublesome for blade profiles with tapered-swept cross-section variations. This uncertainty can be minimized by incorporating a mathematical framework capable of characterizing properly the yaw action such that gyroscopic effects can be fully interpreted and diagnosed. In summary, the main goal is to decipher the complexity of gyroscopic patterns of flexible rotor blades with complex shape configurations, but also to provide substantial elements to successfully approach yaw-mechanics of tapered-swept rotor blades.Copyright

Probabilistic characterization of wind turbine blades via aeroelasticity and spinning finite element formulation

Wind energy is an increasingly important component of this nations renewable energy portfolio, however safe and economical wind turbine operation is a critical need to ensure continued adoption. Safe operation of wind turbine structures requires not only information regarding their condition, but their operational environment. Given the difficulty inherent in SHM processes for wind turbines (damage detection, location, and characterization), some uncertainty in conditional assessment is expected. Furthermore, given the stochastic nature of the loading on turbine structures, a probabilistic framework is appropriate to characterize their risk of failure at a given time. Such information will be invaluable to turbine controllers, allowing them to operate the structures within acceptable risk profiles. This study explores the characterization of the turbine loading and response envelopes for critical failure modes of the turbine blade structures. A framework is presented to develop an analytical estimation of the loading environment (including loading effects) based on the dynamic behavior of the blades. This is influenced by behaviors including along and across-wind aero-elastic effects, wind shear gradient, tower shadow effects, and centrifugal stiffening effects. The proposed solution includes methods that are based on modal decomposition of the blades and require frequent updates to the estimated modal properties to account for the time-varying nature of the turbine and its environment. The estimated demand statistics are compared to a code-based resistance curve to determine a probabilistic estimate of the risk of blade failure given the loading environment.

Probabilistic analysis of mean-response along-wind induced vibrations on wind turbine towers using wireless network data sensors

Wind turbine systems are attracting considerable attention due to concerns regarding global energy consumption as well as sustainability. Advances in wind turbine technology promote the tendency to improve efficiency in the structure that support and produce this renewable power source, tending toward more slender and larger towers, larger gear boxes, and larger, lighter blades. The structural design optimization process must account for uncertainties and nonlinear effects (such as wind-induced vibrations, unmeasured disturbances, and material and geometric variabilities). In this study, a probabilistic monitoring approach is developed that measures the response of the turbine tower to stochastic loading, estimates peak demand, and structural resistance (in terms of serviceability). The proposed monitoring system can provide a real-time estimate of the probability of exceedance of design serviceability conditions based on data collected in-situ. Special attention is paid to wind and aerodynamic characteristics that are intrinsically present (although sometimes neglected in health monitoring analysis) and derived from observations or experiments. In particular, little attention has been devoted to buffeting, usually non-catastrophic but directly impacting the serviceability of the operating wind turbine. As a result, modal-based analysis methods for the study and derivation of flutter instability, and buffeting response, have been successfully applied to the assessment of the susceptibility of high-rise slender structures, including wind turbine towers. A detailed finite element model has been developed to generate data (calibrated to published experimental and analytical results). Risk assessment is performed for the effects of along wind forces in a framework of quantitative risk analysis. Both structural resistance and wind load demands were considered probabilistic with the latter assessed by dynamic analyses.

Monitoring of Hurricane Wind Pressures and Wind Speeds on a Residential Home Roof with Wireless Instrumentation

A sensor network used for characterization of the impact from hurricane storms on man made structures is described. The network consists of many sensor units attached to the structure under consideration, a base unit which orchestrates the measurements over a wireless interface and appropriate wireless and wireline backhaul that delivers the data to a central server. At the central server, the measurements are analyzed and presented in a near “real time” fashion. Unlike most of the sensor networks used for wind monitoring which are hardwired, the network described in this paper uses wireless communication interfaces. This allows for a flexible and fast network deployment that is non-intrusive to the monitored structure. The network is designed to collect pressure readings from sensor units, wind speed, wind direction and temperature during the entire period of the hurricane storm. The empirical data collected by the network are used for hurricane characterization and provide a valuable experimental and simulation basis for evaluation of the existing building codes.

Low-force magneto-rheological damper design for small-scale structural control experimentation

Experimental validation of novel structural control algorithms is a vital step in both developing and building acceptance for this technology. Small-scale experimental test-beds fulfill an important role in the validation of multiple-degree-offreedom (MDOF) and distributed semi-active control systems, allowing researchers to test the control algorithms, communication topologies, and timing-critical aspects of structural control systems that do not require full-scale specimens. In addition, small-scale building specimens can be useful in combined structural health monitoring (SHM) and LQG control studies, diminishing safety concerns during experiments by using benchtop-scale rather than largescale specimens. Development of such small-scale test-beds is hampered by difficulties in actuator construction. In order to be a useful analog to full-scale structures, actuators for small-scale test-beds should exhibit similar features and limitations as their full-scale counterparts. In particular, semi-active devices, such as magneto-rheological (MR) fluid dampers, with limited authority (versus active mass dampers) and nonlinear behavior are difficult to mimic over small force scales due to issues related to fluid containment and friction. In this study, a novel extraction-type small-force (0- 10 N) MR-fluid damper which exhibits nonlinear hysteresis similar to a full-scale, MR-device is proposed. This actuator is a key development to enable the function of a small-scale structural control test-bed intended for wireless control validation studies. Experimental validation of this prototype is conducted using a 3-story scale structure subjected to simulated single-axis seismic excitation. The actuator affects the structural response commanded by a control computer that executes an LQG state feedback control law and a modified Bouc-Wen lookup table that was previously developed for full-scale MR-applications. In addition, damper dynamic limitations are characterized and presented including force output magnitude and frequency characteristics.

Operational model updating of low-order horizontal axis wind turbine models for structural health monitoring applications

Rotational machinery such as horizontal axis wind turbines exhibits complex and nonlinear dynamics (e.g. precession and Coriolis effects, torsional coupling) and is subjected to nonlinear constrained conditions (i.e. aeroelastic interaction). For those reasons, aeroelastic and computer-aided models reproduced under controlled conditions may fail to predict the correct non-stationary loading and resistance patterns of wind turbines in actual operation. Operational techniques for extracting modal properties under actual non-stationary loadings are needed in order to improve computer-aided elasto-aerodynamic models to better characterize the actual behavior of horizontal axis wind turbines in operational scenarios, monitor and diagnose the system for integrity and damage through time, and optimize control systems. For structural health monitoring applications, model updating of stochastic aerodynamic problems has gained interest over the past decades. A probability theory framework is employed in this study to update a horizontal axis wind turbine model using such a stochastic global optimization approach. Structural identification is addressed under regular wind turbine operation conditions for non-stationary, unmeasured, and uncontrolled excitations by means of stochastic subspace identification techniques. This numerical framework is then coupled with an adaptive simulated annealing numerical engine for solving the problem of model updating. Numerical results are presented for an experimental deployment of a small horizontal axis wind turbine structure.

Operational model updating of spinning finite element models for HAWT blades

Structural health monitoring (SHM) relies on collection and interrogation of operational data from the monitored structure. To make this data meaningful, a means of understanding how damage sensitive data features relate to the physical condition of the structure is required. Model-driven SHM applications achieve this goal through model updating. This study proposed a novel approach for updating of aero-elastic turbine blade vibrational models for operational horizontal-axis wind turbines (HAWTs). The proposed approach updates estimates of modal properties for spinning HAWT blades intended for use in SHM and load estimation of these structures. Spinning structures present additional challenges for model updating due to spinning effects, dependence of modal properties on rotational velocity, and gyroscopic effects that lead to complex mode shapes. A cyclo-stationary stochastic-based eigensystem realization algorithm (ERA) is applied to operational turbine data to identify data-driven modal properties including frequencies and mode shapes. Model-driven modal properties are derived through modal condensation of spinning finite element models with variable physical parameters. Complex modes are converted into equivalent real modes through reduction transformation. Model updating is achieved through use of an adaptive simulated annealing search process, via Modal Assurance Criterion (MAC) with complex-conjugate modes, to find the physical parameters that best match the experimentally derived data.

Modeling stability of flap-enabled HAWT blades using spinning finite elements

Horizontal-axis wind turbines (HAWTs) are growing in size and popularity for the generation of renewable energy to meet the world’s ever increasing demand. Long-term safety and stability are major concerns related to the construction and use-phase of these structures. Braking and active pitch control are important tools to help maintain safe and stable operation, however variable cross-section control represents another possible tool as well. To properly evaluate the usefulness of this approach, modeling tools capable of representing the dynamic behavior of blades with conformable cross sections are necessary. In this study, a modeling method for representing turbine blades as a series of interconnected spinning finite elements (SPEs) is presented where the aerodynamic properties of individual elements may be altered to represent changes in the cross section due to conformability (e.g., use of a mechanical flap or a “smart” conformable surface). Such a model is expected to be highly valuable in design of control rules for HAWT blades with conformable elements. Sensitivity and stability of the modeling approach are explored.

Operational Model Updating of Low-Order HAWT Models for Structural Health Monitoring Applications

For the past decade, wind turbines have become the largest source of installed renewable-energy capacity in the United States. Economical, maintenance and operation are critical issues when dealing with such large slender structures, particularly when these structures are sited remotely. Because of the chaotic nature of non-stationary rotating-machinery systems such as the horizontal-axis wind turbines (HAWTs), in-operation modeling and computer-aided numerical characterization is typically troublesome, and tends to be imprecise while predicting the real content of the actual aerodynamic loading. Loading environment under operation conditions is usually substantially different from those driven by modal testing or computer-aided model characterization and difficult to measure directly in the field. In addition, rotational machinery such as HAWTs exhibit complex and nonlinear dynamics (i.e., precession and Coriolis effects, torsional coupling, nonlinear geometries, plasticity of composite materials); and are subjected to nonlinear constrained conditions (i.e., aeroelastic interaction). For those reasons, modal-aeroelastic and computer-aided models reproduced under controlled conditions may fail to predict the correct non-stationary loading and resistance patterns of wind turbines in actual operation. Operational techniques for extracting modal properties under actual non-stationary loadings are needed in order to (1) improve computer-aided elasto-aerodynamic models to better characterize the actual behavior of HAWTs in operational scenarios, (2) improve and correlate models, (3) monitor and diagnose the system for integrity and damage through time, or even (4) optimize control systems. For structural health monitoring (SHM) applications, model updating of stochastic aerodynamic problems has gained interest over the past decades. For situations where optimizing objective functions are not differentiable, convex or continuous in nature that is the case of gradient methods such as Modal Assurance Criterion (MAC), global optimization (metaheurstic) methods based on probability principles have emerged. These search engine techniques are promising suitable to cope with non-stationary-stochastic system identification methods for model updating of HAWT systems. A probability theory framework is employed in this study to update the wind turbine model using such a stochastic global optimization approach. Structural identification is addressed under regular wind turbine operation conditions for non-stationary, unmeasured, and uncontrolled excitations by means of the eigensystem realization theory (ERA). This numerical framework is then tied up with an adaptive simulated annealing (ASA) numerical engine for solving the problem of model updating. Numerical results are presented for an experimental deployment of a small HAWT structure. Results are benchmarked and validated with other empirical mode-decomposition and time-domain solutions.Copyright

Cyclo-stationary linear parameter time-varying subspace realization method applied for identification of horizontal-axis wind turbines

Wind energy is becoming increasingly important worldwide as an alternative renewable energy source. Economical, maintenance and operation are critical issues for large slender dynamic structures, especially for remote offshore wind farms. Health monitoring systems are very promising instruments to assure reliability and good performance of the structure. These sensing and control technologies are typically informed by models based on mechanics or data-driven identification techniques in the time and/or frequency domain. Frequency response functions are popular but are difficult to realize autonomously for structures of higher order and having overlapping frequency content. Instead, time-domain techniques have shown powerful advantages from a practical point of view (e.g. embedded algorithms in wireless-sensor networks), being more suitable to differentiate closely-related modes. Customarily, time-varying effects are often neglected or dismissed to simplify the analysis, but such is not the case for wind loaded structures with spinning multibodies. A more complex scenario is constituted when dealing with both periodic mechanisms responsible for the vibration shaft of the rotor-blade system, and the wind tower substructure interaction. Transformations of the cyclic effects on the vibration data can be applied to isolate inertia quantities different from rotating-generated forces that are typically non-stationary in nature. After applying these transformations, structural identification can be carried out by stationary techniques via data-correlated Eigensystem realizations. In this paper an exploration of a periodic stationary or cyclo-stationary subspace identification technique is presented here by means of a modified Eigensystem Realization Algorithm (ERA) via Stochastic Subspace Identification (SSI) and Linear Parameter Time-Varying (LPTV) techniques. Structural response is assumed under stationary ambient excitation produced by a Gaussian (white) noise assembled in the operative range bandwidth of horizontal-axis wind turbines. ERA-OKID analysis is driven by correlation-function matrices from the stationary ambient response aiming to reduce noise effects. Singular value decomposition (SVD) and eigenvalue analysis are computed in a last stage to get frequencies and mode shapes. Proposed assumptions are carefully weighted to account for the uncertainty of the environment the wind turbines are subjected to. A numerical example is presented based on data acquisition carried out in a BWC XL.1 low power wind turbine device installed in University of California at Davis. Finally, comments and observations are provided on how this subspace realization technique can be extended for modal-parameter identification using exclusively ambient vibration data.