SO2 emissions at Volcan de Colima, 2003-2007

Volcán de Colima has been continuously erupting since the onset of dome growth in 1998. This period of unrest has had 4 prominent periods; 1998-1999, 2003, 2004-2005, and the current dome growth that began in February of 2007. Each of these episodes was marked by lava extrusion forming a dome and lava flows, followed by explosions that destroyed the dome. The Correlation Spectrometer (COSPEC) was used to determine SO2 emission rates on 164 days from May 2003 to February 2007, using both stationary ground based scans and some flight traverses. Scans were separated into the categories of explosive degassing and passive, or background degassing. These scans show variation in the SO2 flow rate from below detection limit (~3 t/d depending on environmental conditions) during background, passive emissions to a peak of 2949 t/d (34 kilograms/second) during an explosion on 9 October, 2004. Both passive and explosive degassing increased when there was lava extrusion in 2004 and with the increased explosive activity in 2005. These two different processes of degassing wax with each other when activity increases and wane together as well, indicating a parallel cyclicity in the volcanic eruption and degassing rates, where the conduit partially seals (pressurizes) between explosions. Colima’s gas and eruptive behavior is compared to similar systems such as Santiaguito and Soufrière Hills, Montserrat. About 2/3 of Colima’s SO2 degassing, amounting to 1.3 x 105 tonnes in 3.74 yrs has come in short lived small (VEI=0-1) vertical explosions that occurred at the rate of 100-3000explosions/ month, and the remaining third has occured in continuous passive degassing. Colima emits sulfur at a rate equivalent to about 0.04 to 0.08 wt % S, similar to other andesitic convergent plate boundary volcanoes. There has been an explosive destruction of the dome in every cycle for that past 5 years, and it is assumed that the current dome which began growth in February, 2007 (just at the end of this study) will be destroyed. Higher emission rates seen in the quiescence of 2006 may have eased the pressure at the time, resulting in the slow effusion of the current dome and lack of explosivity.

MODEL UPDATING AND STRUCTURAL HEALTH MONITORING OF HORIZONTAL AXIS WIND TURBINES VIA ADVANCED SPINNING FINITE ELEMENTS AND STOCHASTIC SUBSPACE IDENTIFICATION METHODS

Wind energy has been one of the most growing sectors of the nation’s renewable energy portfolio for the past decade, and the same tendency is being projected for the upcoming years given the aggressive governmental policies for the reduction of fossil fuel dependency. Great technological expectation and outstanding commercial penetration has shown the so called Horizontal Axis Wind Turbines (HAWT) technologies. Given its great acceptance, size evolution of wind turbines over time has increased exponentially. However, safety and economical concerns have emerged as a result of the newly design tendencies for massive scale wind turbine structures presenting high slenderness ratios and complex shapes, typically located in remote areas (e.g. offshore wind farms). In this regard, safety operation requires not only having first-hand information regarding actual structural dynamic conditions under aerodynamic action, but also a deep understanding of the environmental factors in which these multibody rotating structures operate. Given the cyclo-stochastic patterns of the wind loading exerting pressure on a HAWT, a probabilistic framework is appropriate to characterize the risk of failure in terms of resistance and serviceability conditions, at any given time. Furthermore, sources of uncertainty such as material imperfections, buffeting and flutter, aeroelastic damping, gyroscopic effects, turbulence, among others, have pleaded for the use of a more sophisticated mathematical framework that could properly handle all these sources of indetermination. The attainable modeling complexity that arises as a result of these characterizations demands a data-driven experimental validation methodology to calibrate and corroborate the model. For this aim, System Identification (SI) techniques offer a spectrum of well-established numerical methods appropriated for stationary, deterministic, and data-driven numerical schemes, capable of predicting actual dynamic states (eigenrealizations) of traditional time-invariant dynamic systems. As a consequence, it is proposed a modified data-driven SI metric based on the so called Subspace Realization Theory, now adapted for stochastic non-stationary and timevarying systems, as is the case of HAWT’s complex aerodynamics. Simultaneously, this investigation explores the characterization of the turbine loading and response envelopes for critical failure modes of the structural components the wind turbine is made of. In the long run, both aerodynamic framework (theoretical model) and system identification (experimental model) will be merged in a numerical engine formulated as a search algorithm for model updating, also known as Adaptive Simulated Annealing (ASA) process. This iterative engine is based on a set of function minimizations computed by a metric called Modal Assurance Criterion (MAC). In summary, the Thesis is composed of four major parts: (1) development of an analytical aerodynamic framework that predicts interacted wind-structure stochastic loads on wind turbine components; (2) development of a novel tapered-swept-corved Spinning Finite Element (SFE) that includes dampedgyroscopic effects and axial-flexural-torsional coupling; (3) a novel data-driven structural health monitoring (SHM) algorithm via stochastic subspace identification methods; and (4) a numerical search (optimization) engine based on ASA and MAC capable of updating the SFE aerodynamic model.