Water and sanitation accessibility and the health of rural Ugandans

The lack of access to sufficient water and sanitation facilities is one of the largest hindrances towards the sustainable development of the poorest 2.2 billion people in the world. Rural Uganda is one of the areas where such inaccessibility is seriously hampering their efforts at development. Many rural Ugandans must travel several kilometers to fetch adequate water and many still do not have adequate sanitation facilities. Such poor access to clean water forces Ugandans to spend an inordinate amount of time and energy collecting water - time and energy that could be used for more useful endeavors. Furthermore, the difficulty in getting water means that people use less water than they need to for optimal health and well-being. Access to other sanitation facilities can also have a large impact, particularly on the health of young children and the elderly whose immune systems are less than optimal. Hand-washing, presence of a sanitary latrine, general household cleanliness, maintenance of the safe water chain and the households’ knowledge about and adherence to sound sanitation practices may be as important as access to clean water sources. This report investigates these problems using the results from two different studies. It first looks into how access to water affects peoples’ use of it. In particular it investigates how much water households use as a function of perceived effort to fetch it. Operationally, this was accomplished by surveying nearly 1,500 residents in three different districts around Uganda about their water usage and the time and distance they must travel to fetch it. The study found that there is no statistically significant correlation between a family’s water usage and the perceived effort they must put forth to have to fetch it. On average, people use around 15 liters per person per day. Rural Ugandan residents apparently require a certain amount of water and will travel as far or as long as necessary to collect it. Secondly, a study entitled “What Works Best in Diarrheal Disease Prevention?” was carried out to study the effectiveness of five different water and sanitation facilities in reducing diarrheal disease incidences amongst children under five. It did this by surveying five different communities before and after the implementation of improvements to find changes in diarrheal disease incidences amongst children under five years of age. It found that household water treatment devices provide the best means of preventing diarrheal diseases. This is likely because water often becomes contaminated before it is consumed even if it was collected from a protected source.

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.