Browsing by Author "Lei, Yafeng"
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Item Combustion and direct energy conversion in a micro-combustor(Texas A&M University, 2006-10-30) Lei, YafengThe push toward the miniaturization of electromechanical devices and the resulting need for micro-power generation (milliwatts to watts) with low-weight, long-life devices has led to the recent development of the field of micro-scale combustion. Since batteries have low specific energy (~200 kJ/kg) and liquid hydrocarbon fuels have a very high specific energy (~50000 kJ/kg), a miniaturized power-generating device, even with a relatively inefficient conversion of hydrocarbon fuels to power, would result in increased lifetime and/or reduced weight of an electronic or mechanical system that currently requires batteries for power. Energy conversion from chemical energy to electrical energy without any moving parts can be achieved by a thermophotovoltaic (TPV) system. The TPV system requires a radiation source which is provided by a micro-combustor. Because of the high surface area to volume ratio for micro-combustor, there is high heat loss (proportional to area) compared to heat generation (proportional to volume). Thus the quenching and flammability problems are more critical in a micro-scale combustor. Hence innovative schemes are required to improve the performance of micro-combustion. In the current study, a micro-scale counter flow combustor with heat recirculation is adapted to improve the flame stability in combustion modeled for possible application to a TPV system. The micro-combustor consists of two annular tubes with an inner tube of diameter 3 mm and 30 mm long and an outer tube of 4.2 mm diameter and 30 mm long. The inner tube is supplied with a cold premixed combustible mixture, ignited and burnt. The hot produced gases are then allowed to flow through outer tube which supplies heat to inner tube via convection and conduction. The hot outer tube radiates heat to the TPV system. Methane is selected as the fuel. The model parameters include the following: diameter d , inlet velocity u , equivalence ratio ???? and heat recirculation efficiency ???? between the hot outer flow and cold inner flow. The predicted performance results are as followings: the lean flammability limit increased from 7.69% to 7.86% and the quenching diameter decreased from 1.3 mm to 0.9 mm when heat recirculation was employed. The overall energy conversion efficiency of current configuration is about 2.56.Item Wavelets, Self-organizing Maps and Artificial Neural Nets for Predicting Energy Use and Estimating Uncertainties in Energy Savings in Commercial Buildings(2010-01-14) Lei, YafengThis dissertation develops a "neighborhood" based neural network model utilizing wavelet analysis and Self-organizing Map (SOM) to predict building baseline energy use. Wavelet analysis was used for feature extraction of the daily weather profiles. The resulting few significant wavelet coefficients represent not only average but also variation of the weather components. A SOM is used for clustering and projecting high-dimensional data into usually a one or two dimensional map to reveal the data structure which is not clear by visual inspection. In this study, neighborhoods that contain days with similar meteorological conditions are classified by a SOM using significant wavelet coefficients; a baseline model is then developed for each neighborhood. In each neighborhood, modeling is more robust without unnecessary compromises that occur in global predictor regression models. This method was applied to the Energy Predictor Shootout II dataset and compared with the winning entries for hourly energy use predictions. A comparison between the "neighborhood" based linear regression model and the change-point model for daily energy use prediction was also performed. We also studied the application of the non-parametric nearest neighborhood points approach in determining the uncertainty of energy use prediction. The uncertainty from "local" system behavior rather than from global statistical indices such as root mean square error and other measures is shown to be more realistic and credible than the statistical approaches currently used. In general, a baseline model developed by local system behavior is more reliable than a global baseline model. The "neighborhood" based neural network model was found to predict building baseline energy use more accurately and achieve more reliable estimation of energy savings as well as the associated uncertainties in energy savings from building retrofits.