Phase Lags in the Optical-Infrared Light Curves of AGB Stars

To search for phase lags in the optical-infrared light curves of asymptotic giant branch stars, we have compared infrared data from the COBE DIRBE satellite with optical light curves from the AAVSO and other sources. We found 17 examples of phase lags in the time of maximum in the infrared vs. that in the optical, and 4 stars with no observed lags. There is a clear difference between the Mira variables and the semi-regulars in the sample, with the maximum in the optical preceding that in the near-infrared in the Miras, while in most of the semi-regulars no lags are observed. Comparison to published theoretical models indicates that the phase lags in the Miras are due to strong titanium oxide absorption in the visual at stellar maximum, and suggests that Miras pulsate in the fundamental mode, while at least some semi-regulars are first overtone pulsators. There is a clear optical-near-infrared phase lag in the carbon-rich Mira V CrB; this is likely due to C2 and CN absorption variations in the optical.Comment: AJ, in pres

Supervisor Hostile Environment Sexual Harassment Claims, Liability Insurance, and the Trend Towards Negligence

A lack of settled standards for determining liability in supervisor hostile environment sexual harassment lawsuits combined with similar uncertainty in the context of employer liability insurance coverage has resulted in increased litigation in this area. This Note argues that the current predominant standard in the employer liability context, which is based on negligence principle should be rejected in favor of an apparent authority standard, which more appropriately strikes a balance between encouraging employers to identify harassing behaviors and exonerating them from liability when they do so and take appropriate remedial action. It further argues that in order to develop effective mechanisms for preventing supervisor hostile environment sexual harassment and to adequately compensate victims courts should consider the cost-shifting effects of employer liability insurance coverage and also the conduct that substantive standards will encourage or discourage

Characterizing the relative timing and conditions of gold and base-metal deposition in the northern part of the Yellowknife Greenstone Belt, Northwest Territories, Canada

Title from PDF of title page (University of Missouri--Columbia, viewed on July 11, 2011).The entire thesis text is included in the research.pdf file; the official abstract appears in the short.pdf file; a non-technical public abstract appears in the public.pdf file.Thesis advisor: Dr. Kevin L. Shelton.Includes bibliographical references.M.S. University of Missouri--Columbia 2011.A complexity of gold-mineralization styles is recognized in the north end of the Yellowknife Greenstone Belt (YGB), [about]30 km north of Yellowknife. These include volcanogenic massive sulfides, sulfide zones at intersections of shear zones, and quartz veins crosscutting metavolcanic and intrusive rocks. Gold-mineralized areas are hosted in the Kam Group and Banting Group metavolcanic and metasedimentary rocks, which are older and younger, respectively, than rocks that host major ore bodies in Yellowknife. Ore petrology of each group shows early arsenopyrite-pyrite-gold deposition followed by later base-metal sulfide overprinting. Mineralization in the Banting Group is dominated by abundant pyrrhotite, a feature not observed in the Kam Group. This may indicate that chemically unique ore-depositing systems operated within the Kam and Banting Groups. Fluid inclusion, cathodoluminescence and stable isotope studies allow documentation of the nature of multiple fluids that affected these rocks. Data are interpreted to indicate that distinct styles of gold mineralization in the Kam and Banting Groups formed from their own individual hydrothermal systems

Performance modeling and parametric study of a stratified water thermal storage tank

pre-printThermal energy storage (TES) can significantly increase the overall effciency and operational flexibility of adistributed generation system. A sensible water storage tank is an attractive option for integration in building energy systems, due to its low cost and high heat capacity. As such, this paper presents a model for stratified water storage that can be used in building energy simulations and distributed generation simulations. The presented model considers a pressurized water tank with two heat exchangers supplying hot and cold water respectively, where 1-D, transient heat balance equations are used to determine the temperature profiles at a given vertical locations. The paper computationally investigates the effect of variable flow-rates inside the heat exchangers, effect of transient heat source, and buoyancy inside the tank induced by location and length of the heat exchangers. The model al so considers variation in thermophysical properties and heat loss to the ambient. TES simulation results compare favorably with similar 1-D water storage tank simulations, and the buoyancy model presented agrees with COMSOL 3-D simulations. The analysis shows that when the inlet hot fluid temperature is time dependent, there is a phase lag between the stored water and the hot fluid temperature. Furthermore, it was observed that an increase in flow-rate inside the hot eat exchanger increases the stored water and the cold water outlet temperature; however, the increment in temperature observes diminishing returns with increasing flow-rate of hot fluid. It was also noted that for either heat exchanger, increasing the vertical height of the heat exchanger above a certain value does not significantly increase the cold fluid outlet temperature. Results from the model simulations can assist building designers to determine the size and configurations of a thermal storage tank suited for a given distributed generation system, as well as allowing them to accurately predict the fraction of heat generated by the system that could be stored in the tank at a given time when charging, or the fraction of heating load that could be met by the tank when discharging

Predicting fuel consumption for commercial building with machine learning algorithms

pre-printThis paper presents a modeling framework that uses machine learning algorithms to make longterm, i.e. one year-ahead predictions, of fuel consumption in multiple types of commercial prototype buildings at one-hour resolutions. Weather and schedule variables were used as model inputs, and the hourly fuel consumption simulated with EnergyPlus provided target values. The data was partitioned on a monthly basis, and a feature selection method was incorporated as part of the model to select the best subset of input variables for a given month. Neural networks (NN) and Gaussian process (GP) regression were shown to perform better than multivariate linear regression and ridge regression, and as such, were included as part of the model. The modeling framework was applied to make predictions about fuel consumption in a small office, supermarket, and restaurant in multiple climate zone. It was shown that for all climate zones for all months, the maximum errors pertaining to one year-ahead forecasts of fuel consumption made by the ML model are 15.7 MJ (14,880 Btu), 284.3 MJ (268,516 Btu) and 74.0 MJ (70,138 Btu) respectively. The methods and results from this study can be used to estimate on-site fuel consumption and emissions from; buildings, thereby enabling improved decisions pertaining to building efficiency with respect to fuel use

Improving water heaters for sustainability

posterBuildings use about 40% of the total U.S. energy demand. Water heaters provide hot water for a variety of building uses including sinks, showers, dishwashers, washing machines, and space heating. Water heaters are the second most energy intensive appliances in a common household. Typically a home water heater's energy sources is natural gas. There are other types of tank water heaters including ultra low NOx, and electric resistance. Figure 1 displays the differences in water heater types. Electric resistance water heaters use electrical grid power. Building owners burdent thec cost of water heating through the initial water heater cost, energy bills, and the communal air pollution they breathe. Burning and extracting non-renewable fuels including natural gas leads to climate change. Water heaters noticeably attribute air pollution to winter inversions have adverse affects on human health. Combusted air pollutants include Carbon Dioxide (CO2), Nitorgen oxides (NOx), and Sulfur oxides (SOx). Pushing towards the future Salt Lake City has set a goal to reduce 80% of green house gases emissions by the year 2040 setting a demand for water heater emissions reduction

Predicting heating demand and sizing a stratified thermal storage tank using deep learning algorithms

journal articleThis paper evaluates the performance of deep recurrent neural networks in predicting heating demand for a commercial building over a medium-to-long term time horizon (≥ 1 week), and proposes a modeling framework to demonstrate how these longer-term predictions can be used to aid design of a stratified thermal storage tank. The building sector contributes significantly to primary energy consumption in the U.S, and as such, there is a need to predict heating demand in buildings over longer time horizons, and to develop methods that can facilitate installation, planning and management of distributed generation and thermal storage to meet these heating demands. Key objectives of this paper are: (a) Investigate how a deep recurrent neural network model performs in predicting heating demand in campus buildings at University of Utah over multiple weeks, and (b) Develop an optimization framework that which can provide definitive guidelines on sizing a stratified thermal storage tank without requiring high performance computing resources. The results showed that the predictions by the deep RNN are comparatively more accurate than those by a 3-layer MLP, and that these deep RNN predictions can adequately serve as proxy for future demand while considering sizing in the design of a complementary stratified thermal storage tank

Deep recurrent neural networks for building energy prediction

posterThis poster illustrates the development of a deep recurrent neural network (RNN) model using long-short-term memory (LSTM) cells to predict energy consumption in buildings at one-hour time resolution over medium-to-long term time horizons ( greater than or equal to 1 week)

Energy modeling framework for optimizing heat recovery in a seasonal food processing facility

pre-printSocietal, cultural and economic factors are driving food processors to reduce energy consumed per unit mass of food. This presents a unique problem because time variant batch processing using low to medium grade heat is common in food production facilities. Heat recovery methods may be implemented by food processors to reduce energy consumption; however, temporal variance in the process and utility flow require the development of a robust, easily implemented energy model to accurately determine system effectiveness and economic incentive. A bottom-up modular computational framework is proposed to model the energy consumption of a cannery. The model predicts that the cannery will require 612 kJ gas/kg product produced, which is within the ranges provided in previous literature. Results show that adding a globally optimized indirect heat recovery system will reduce the gas consumption by 6% annually. The proposed framework, used here to represent a cannery, may be adapted to many different types of food processing facilities. With a clear picture of energy consumption by device, and the ability to predict the impact of process modification or heat recovery, plant-level energy usage for food processing may be significantly reduced

System scaling approach and thermoeconomic analysis of a pressure retarded osmosis system for power production with hypersaline draw solution: A Great Salt Lake study

pre-printOsmotic power with pressure retarded osmosis (PRO) is an emerging renewable energy option for locations where fresh water and salt water mix. Energy can be recovered from the salinity gradient between the solutions. This study provides a comprehensive feasibility analysis for a PRO power plant in a hypersaline environment. A sensitivity analysis investigates the effects of key technical and financial parameters on energy and economic performances. A case study is developed for the Great Salt Lake in Utah, USA (which has an average 24% salt concentration). A 25 MW PRO power plant is investigated to analyze the necessary components and their performances. With currently available technologies, the power plant would require 1.54 m3/s (24,410 GPM) fresh water flow rate and 3.08 m3/s (48,820 GPM) salt water flow rate. The net annual energy production is projected to be 154,249 MWh, with capital cost of $238.0 million, and operations and maintenance cost of$35.5 million per year. The levelized cost of electricity (LCOE) would be $0.2025/kWh, but; further design improvements would reduce the LCOE to$0.1034/kWh. The high salinity of the Great Salt Lake is a critical factor toward making the osmotic power plant economically feasible