Planting and production of switchgrass (Panicum virgatum L.) as a bioenergy crop in Michigan's Upper Peninsula

Switchgrass (Panicum virgatum L.) is a perennial grass holding great promise as a biofuel resource. While Michigan’s Upper Peninsula has an appropriate land base and climatic conditions, there is little research exploring the possibilities of switchgrass production. The overall objectives of this research were to investigate switchgrass establishment in the northern edge of its distribution through: investigating the effects of competition on the germination and establishment of switchgrass through the developmental and competitive characteristics of Cave-in-Rock switchgrass and large crabgrass (Digitaria sanguinalis L.) in Michigan’s Upper Peninsula; and, determining the optimum planting depths and timing for switchgrass in Michigan’s Upper Peninsula. For the competition study, a randomized complete block design was installed June 2009 at two locations in Michigan’s Upper Peninsula. Four treatments (0, 1, 4, and 8 plants/m2) of crabgrass were planted with one switchgrass plant. There was a significant difference between switchgrass biomass produced in year one, as a function of crabgrass weed pressure. There was no significant difference between the switchgrass biomass produced in year two versus previous crabgrass weed pressure. There is a significant difference between switchgrass biomass produced in year one and two. For the depth and timing study, a completely randomized design was installed at two locations in Michigan’s Upper Peninsula on seven planting dates (three fall 2009, and four spring 2010); 25 seeds were planted 2 cm apart along 0.5 m rows at depths of: 0.6 cm, 1.3 cm, and 1.9 cm. Emergence and biomass yields were compared by planting date, and depths. A greenhouse seeding experiment was established using the same planting depths and parameters as the field study. The number of seedlings was tallied daily for 30 days. There was a significant difference in survivorship between the fall and spring planting dates, with the spring being more successful. Of the four spring planting dates, there was a significant difference between May and June in emergence and biomass yield. June planting dates had the most percent emergence and total survivorship. There is no significant difference between planting switchgrass at depths of 0.6 cm, 1.3 cm, and 1.9 cm. In conclusion, switchgrass showed no signs of a legacy effect of competition from year one, on biomass production. Overall, an antagonistic effect on switchgrass biomass yield during the establishment period has been observed as a result of increasing competing weed pressure. When planting switchgrass in Michigan’s Upper Peninsula, it should be done in the spring, within the first two weeks of June, at any depth ranging from 0.6 cm to 1.9 cm.

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.