THERMAL CHARACTERIZATION OF COMBUSTION CHAMBER COMPONENTS IN A GASOLINE TURBOCHARGED DIRECT INJECTION (GTDI) ENGINE

This report is a PhD dissertation proposal to study the in-cylinder temperature and heat flux distributions within a gasoline turbocharged direct injection (GTDI) engine. Recent regulations requiring automotive manufacturers to increase the fuel efficiency of their vehicles has led to great technological achievements in internal combustion engines. These achievements have increased the power density of gasoline engines dramatically in the last two decades. Engine technologies such as variable valve timing (VVT), direct injection (DI), and turbocharging have significantly improved engine power-to-weight and power-to-displacement ratios. A popular trend for increasing vehicle fuel economy in recent years has been to downsize the engine and add VVT, DI, and turbocharging technologies so that a lighter more efficient engine can replace a larger, heavier one. With the added power density, thermal management of the engine becomes a more important issue. Engine components are being pushed to their temperature limits. Therefore it has become increasingly important to have a greater understanding of the parameters that affect in-cylinder temperatures and heat transfer. The proposed research will analyze the effects of engine speed, load, relative air-fuel ratio (AFR), and exhaust gas recirculation (EGR) on both in-cylinder and global temperature and heat transfer distributions. Additionally, the effect of knocking combustion and fuel spray impingement will be investigated. The proposed research will be conducted on a 3.5 L six cylinder GTDI engine. The research engine will be instrumented with a large number of sensors to measure in-cylinder temperatures and pressures, as well as, the temperature, pressure, and flow rates of energy streams into and out of the engine. One of the goals of this research is to create a model that will predict the energy distribution to the crankshaft, exhaust, and cooling system based on normalized values for engine speed, load, AFR, and EGR. The results could be used to aid in the engine design phase for turbocharger and cooling system sizing. Additionally, the data collected can be used for validation of engine simulation models, since in-cylinder temperature and heat flux data is not readily available in the literature..

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.