Fluidic oscillator design for water removal enhancement in a PEM fuel cell

This research initiative was triggered by the problems of water management of Polymer Electrolyte Membrane Fuel Cell (PEMFC). In low temperature fuel cells such as PEMFC, some of the water produced after the chemical reaction remains in its liquid state. Excess water produced by the fuel cell must be removed from the system to avoid flooding of the gas diffusion layers (GDL). The GDL is responsible for the transport of reactant gas to the active sites and remove the water produced from the sites. If the GDL is flooded, the supply gas will not be able to reach the reactive sites and the fuel cell fails. The choice of water removal method in this research is to exert a variable asymmetrical force on a liquid droplet. As the drop of liquid is subjected to an external vibrational force in the form of periodic wave, it will begin to oscillate. A fluidic oscillator is capable to produce a pulsating flow using simple balance of momentum fluxes between three impinging jets. By connecting the outputs of the oscillator to the gas channels of a fuel cell, a flow pulsation can be imposed on a water droplet formed within the gas channel during fuel cell operation. The lowest frequency produced by this design is approximately 202 Hz when a 20 inches feed-back port length was used and a supply pressure of 5 psig was introduced. This information was found by setting up a fluidic network with appropriate data acquisition. The components include a fluidic amplifier, valves and fittings, flow meters, a pressure gage, NI-DAQ system, Siglab®, Matlab software and four PCB microphones. The operating environment of the water droplet was reviewed, speed of the sound pressure which travels down the square channel was precisely estimated, and measurement devices were carefully selected. Applicable alternative measurement devices and its application to pressure wave measurement was considered. Methods for experimental setup and possible approaches were recommended, with some discussion of potential problems with implementation of this technique. Some computational fluid dynamic was also performed as an approach to oscillator design.

DEVELOPMENT OF AN OPTIMIZATION MODEL FOR BIOFUEL FACILITY SIZE AND LOCATION AND A SIMULATION MODEL FOR DESIGN OF A BIOFUEL SUPPLY CHAIN

A range of societal issues have been caused by fossil fuel consumption in the transportation sector in the United States (U.S.), including health related air pollution, climate change, the dependence on imported oil, and other oil related national security concerns. Biofuels production from various lignocellulosic biomass types such as wood, forest residues, and agriculture residues have the potential to replace a substantial portion of the total fossil fuel consumption. This research focuses on locating biofuel facilities and designing the biofuel supply chain to minimize the overall cost. For this purpose an integrated methodology was proposed by combining the GIS technology with simulation and optimization modeling methods. The GIS based methodology was used as a precursor for selecting biofuel facility locations by employing a series of decision factors. The resulted candidate sites for biofuel production served as inputs for simulation and optimization modeling. As a precursor to simulation or optimization modeling, the GIS-based methodology was used to preselect potential biofuel facility locations for biofuel production from forest biomass. Candidate locations were selected based on a set of evaluation criteria, including: county boundaries, a railroad transportation network, a state/federal road transportation network, water body (rivers, lakes, etc.) dispersion, city and village dispersion, a population census, biomass production, and no co-location with co-fired power plants. The simulation and optimization models were built around key supply activities including biomass harvesting/forwarding, transportation and storage. The built onsite storage served for spring breakup period where road restrictions were in place and truck transportation on certain roads was limited. Both models were evaluated using multiple performance indicators, including cost (consisting of the delivered feedstock cost, and inventory holding cost), energy consumption, and GHG emissions. The impact of energy consumption and GHG emissions were expressed in monetary terms to keep consistent with cost. Compared with the optimization model, the simulation model represents a more dynamic look at a 20-year operation by considering the impacts associated with building inventory at the biorefinery to address the limited availability of biomass feedstock during the spring breakup period. The number of trucks required per day was estimated and the inventory level all year around was tracked. Through the exchange of information across different procedures (harvesting, transportation, and biomass feedstock processing procedures), a smooth flow of biomass from harvesting areas to a biofuel facility was implemented. The optimization model was developed to address issues related to locating multiple biofuel facilities simultaneously. The size of the potential biofuel facility is set up with an upper bound of 50 MGY and a lower bound of 30 MGY. The optimization model is a static, Mathematical Programming Language (MPL)-based application which allows for sensitivity analysis by changing inputs to evaluate different scenarios. It was found that annual biofuel demand and biomass availability impacts the optimal results of biofuel facility locations and sizes.