The pitting behavior of aluminum has been found to exhibit anisotropic characteristics, which means that the rate of corrosion is dependent on crystallographic orientation. Specifically, in pure aluminum, the (100) orientation has shown that it pits slower than other crystal orientations. Although the anisotropy of the pitting is not well understood, it has been found that the (100) orientation has a higher pitting potential than the other facets. Pure aluminum (99.999%) was obtained in both polycrystalline and single crystal form. The orientations of the single crystals are (100), (110) and (111). 3.5% NaCl was used to replicate the crystallographic attack of polycrystalline and single crystal aluminum. Using a scanning electron microscope, the crystallographic dependence of the pitting of the aluminum was easily visible. The inside and surrounding surfaces of the pits exhibited a strong faceted appearance. In addition, the metastable pitting was examined for single crystal and polycrystalline aluminum with potentiostatically applied potentials below the pitting potential. The extent of metastable pitting and the value of the pitting potential was measured to determine the extent of pit initiation for each crystal orientation. Stable pitting was induced by potentiostatically applying potentials above the pitting potential. The size of the pits was examined with scanning electron microscopy and stereo pair quantification in the polycrystalline aluminum to determine the extent of propagation for the various grain orientations. Simulation of the aggressive pit solution was accomplished using HCl. The corrosion rate and hydrogen reaction in the simulated pit solution was examined for the various single crystal orientations so that the propagation rates for the crystal orientations could be measured. Scanning electron microscopy was used to determine if damage had occurred during the cathodic hydrogen reaction and to determine the final orientations for the corrosion rate experiments. Using the data obtained from these tests, it has been determined that the (100) crystal orientation is the rate determining orientation, and a model has been constructed to better explain the faceted pitting of aluminum.
Thermophotovoltaics (TPV) is a relatively new science, which utilizes previous experience and technology from solar photovoltaics to convert heat energy directly into electricity. This technology offers the advantage of having a higher power density than other types of power generation systems currently in use. It also offers a distinct advantage of generating electricity with no moving parts which are subjected to wear, cause vibration and noise, and are prone to breakdown. Unlike solar photovoltaics, any high temperature heat source can provide energy for thermophotovoltaic cells.
The current project at the U.S. Naval Academy involved the development of a prototype TPV generator, which uses a General Electric T-58 gas turbine as the heat source. The combustion gas was tapped from the T-58's combustor through one of the two ignitor ports and extracted through a silicon carbide tube into the ceramic emitter. The emitter chosen was made from silicon carbide. However, the generator was designed to ease removal of the emitter so different materials could be tried at a later date. The ceramic emitter is heated by the combustion gas via convection, and then serves the TPV generator by radiating the heat outwards where it can be absorbed by thermophotovoltaic cells and converted directly into electricity. Radiant energy not absorbed by the cells is reflected back to the emitter by a selective coating applied directly to the cells and by gold foil located in the spaces between the cells. The gas turbine and generator module are monitored by National Instrument's Labview program which performs both data collection and control functions. This project details the design of the TPV generator and provides the results of the initial tests with the gas turbine driven TPV energy conversion system.
This project investigates the usefulness of the addition of a learning behavior to continuous localization. Continuous localization is a process that allows a robot to maintain an accurate estimate of its location. The learning component allows the system to record changes in the robot's environment by correcting the map stored by the robot.
For mobile robots to perform autonomously in environments in which objects move, they first need the ability to determine their location in the world. This includes the capacity to learn maps of their environment and to use these maps to localize themselves (i.e., to ascertain their location and orientation). Simple localization techniques do not provide accurate and reliable estimates of a robot's location and orientation. Continuous localization is an improvement over these simple techniques because it uses a robot's sensors to determine the current locations of all nearby objects. The learning behavior records the positions of these objects on the map the robot uses to update its localization. By updating this map continuously, the robot is provided with an accurate estimate of its localization.
Once a mobile robot can localize precisely, it can perform higher-level operations. One important higher-level function is a path planner, a process that determines the shortest path from the robot to a goal specified by a human. Wavefront propagation is a path planner that informs the robot of the correct direction to follow in order to arrive at the goal in the shortest possible time. Combining continuous localization with a wavefront propagation path planner provides a robust navigation system that allows the robot to avoid obstacles that were not on the original map.
The Severn River tributary of the Chesapeake Bay estuary is believed to maintain a distinct density stratification of its water column in the summer and early fall that diminishes as the season changes to winter. This layered effect is determined by many factors, including freshwater inflow rates, wind, subtidal currents, and heating by the sun's radiation.
As part of an effort to study the hydrodynamic behavior of the Severn, a mooring with several oceanographic sensors was deployed near the mouth of the river during the autumn of 1995. This mooring had two S4 InterOcean current meters located at depths of 2.3 and 4.7 meters which took readings of salinity, temperature, and current. Simultaneous wind speed and direction data was taken from the top of Michelson Hall at the U.S. Naval Academy.
The purpose of this project was to document the circulatory pattern of the Severn River estuary and to determine how the fall destratification occurs. The time series provided by the instruments from September to December of 1995 were low pass filtered in order to isolate the non-tidal components. Based on the non-tidal salinity and current data, it was determined that the Severn River estuary falls into the partially-mixed (2a) category according to the Hansen and Rattray criteria. The gradient Richardson Number was also calculated for the observation period; as a result a very clear pattern of vertical stratification/destratification emerged. An examination of the role of the local wind forcing in the occurrence of mixing events in the water column was also analyzed.
The primary purpose of this project was to determine the gas phase reactivities of N2O with the first-row transition metal atoms. Sc (a2D3/2), V (a4F3/2), Cr (a7S3), Co (a4F9/2), and Ni (a3F4, a3D3) were studied in the temperature range of 298K - 598K. At higher temperatures the precursors were found to thermally decompose. Sc, V, Cr, Co and Ni atoms were produced by the photodissociation of Sc(hfa)3, Sc(TMHD)3, V(CO)6, V(CO)4(C5H5), Cr(CO)6, Co(C5H5)(CO)2, and Ni(C5H5)2, respectively. Pseudo-first order conditions were maintained, ([Transition Metal] << [N2O]), and atoms were detected by laser-induced fluorescence using an excimer-pumped dye laser. Rate constants were determined for each of these metals from a measurement of the concentration of the metal as a function of laser delay. A delay of a few microseconds was often necessary to allow the highly excited atoms produced in the photodissociation process to relax back down to the ground state. Reactions of the ground states with N2O were temperature dependent. Reactions of Sc, V, Cr, and Co with N2O were found to be pressure independent, indicating a bimolecular abstraction mechanism. The rate constants for Sc, V, and Cr can be described in Arrhenius form (k = Aexp(-Ea/RT)) by k = 1.6E-10exp(-11.7kJ/mol/RT) cm3s-1, k = 4.6E-11exp(10.7 kJ/mol/RT) cm3s-1, and k = 4.2E-11exp(-20.4kJ/mol/RT) cm3s-1, respectively. The rate constants for Co are very temperature dependent resulting in an approximate activation energy of 50 kJ/mol. The rate constants for Ni were found to be pressure dependent at low temperatures. At higher temperatures, however, the rate constants increased with increasing temperature. This suggests a termolecular reaction at low temperatures; however, the abstraction channel becomes important at higher temperatures. The abstraction channel for Ni has an activation energy of approximately 30 kJ/mol.
Thermophotovoltaics (TPV) is an important, newly developed direct energy conversion process that possesses a wide range of commercial and military applications. TPV energy conversion is a process in which thermal energy is directly converted to electric current via thermophotoelectric diodes. It is analogous to solar power except that an emitter is providing thermal radiation rather than the sun. The goal of this research was to discover new materials that can be used as thermal emitters in a TPV generator.
The Department of Energy provided TPV cells that efficiently convert thermal energy into electric current. These cells are tuned to operate most efficiently with photons of a specific energy. When materials are heated, they radiate photons across a broad spectrum, but the peak emissions occur at one specific wavelength which is dictated by the temperature of the emitting material. According to this relationship, the TPV cells will operate most efficiently at an ideal emitter material temperature of 2400 degrees Fahrenheit.
The focus of the research in this project was to determine the ideal emitter material that can withstand 2400 degrees F, while possessing a near blackbody emissivity of 0.9. Additional concerns included the material's coefficient of thermal expansion, resistance to thermal shock and thermal cycling, creep resistance, and corrosion resistance. Unfortunately these material properties are mutually exclusive for most materials.
The thermal emitter was designed to operate in a TPV generator powered by the combustion gases of a T-58 gas turbine. This project expanded to include researching the ideal materials to serve as the structural components of the TPV generator itself, since components of the generator would experience temperatures as high as 3500 degrees Fahrenheit.
Speech is a natural, unspecialized method of communication that is perhaps the ultimate machine interface. The successes of providing such an interface, however, have been limited to pre-defined vocabularies of an artificial syntax. This project presents a method for speaker-dependent speech identification that uses a phonetic classification scheme rather than word identification.
Time slices of an analog speech signal are identified using a back-propagation neural network recognition system. By applying a homomorphic filtering process to sequential times slices of a speech signal, the vocal tract's impulse response is separated from the excitation source. The vocal tract changes slowly in time and can be modeled using only a small set of parameters that the impulse response provides. The frequency representation of this impulse response is applied to the input layer of the neural network. This network was previously trained on a set of speaker dependent phonemes, and now phonetically classifies new speech input.
This classification scheme could be used to translate linguistic messages into machine code with a very high data rate. This benefit would allow for real-time interaction with machines with no specialized training. Applications could be as simple as providing quick voice to text processing or as diverse as implementing a control system with response time tied to specified voice patterns.
Current advanced milling uses Computer Numerical Control (CNC) to make complex shapes. These shapes are frequently created by Computer Aided Design (CAD) and translated into a tool path by Computer Aided Machining (CAM). This tool path defines the mill commands used to move the end mill to create the part. Since CNC technology today uses hardwired equipment, the mill command structure cannot be updated or modified without removing the controller. Modern milling techniques such as newer curve fitting algorithms cannot be implemented on older controllers without losing some resolution. A possible solution is the implementation of the Personal Computer (PC) in the shop floor. A PC can easily handle the computing tasks of mill control, while also having the flexibility of being upgradable in implementing that control. New control codes or algorithms can be implemented by using new software, without the prohibitive cost of changing out expensive control equipment.
The additional advantage of utilizing the PC in the shop floor is the current research into Adaptive Controls (AC). AC represents an attempt by industrial researchers to optimize the milling process dynamically. Currently, mill technicians will optimize milling parameters such as depth of cut, feedrate, and spindle speed before any milling is done. During the milling process these parameters remain unchanged. The disadvantage of this situation is the requirement to set each parameter at its most conservative setting so as to allow the mill to safely remove material under all end mill interactions. The mill operates at worst case conditions during the entire milling process. Adaptive controls enable the mill to receive feedback on the milling process and change appropriate parameters. This dynamic control allows the mill to adapt to the current milling condition and fit milling parameters accordingly. In this project, an Acoustic Emission (AE) sensor returns strain information to the PC. This strain information is referenced to material removal, and from calculating the material removal rate, the PC will optimize feedrate. By adjusting the feedrate, the PC can speed up or slow down the end mill trajectory. This process will decrease overall milling time by allowing the mill to depart from worst case conditions during the milling process.