Tropospheric ozone and CO over North Atlantic for the past decade

We investigate how declines in US emissions of CO and O3 precursors have impacted the lower free troposphere over the North Atlantic. We use seasonal observations for O3 and CO from the PICO-NARE project for the period covering 2001 to 2010. Observations are used to verify model output generated by the GEOS-Chem 3-D global chemical transport model. Additional satellite data for CO from AIRS/Aqua and for O3 from TES/Aura were also used to provide additional comparisons; particularly for fall, winter, and spring when PICO-NARE coverage is sparse. We find GEOS-Chem captures the seasonal cycle for CO and O3 well compared to PICO-NARE data. For CO, GEOS-Chem is biased low, particularly in spring which is in agreement with findings from previous studies. GEOS-Chem is 24.7 +/- 5.2 ppbv (1-σ) low compared to PICO-NARE summer CO data while AIRS is 14.2 +/- 6.6 ppbv high. AIRS does not show nearly as much variation as seen with GEOS-Chem or the Pico data, and goes from being lower than PICO-NARE data in winter and spring, to higher in summer and fall. Both TES and GEOS-Chem match the seasonal ozone cycle well for all seasons when compared with observations. Model results for O3 show GEOS-Chem is 6.67 +/- 2.63 ppbv high compared to PICO-NARE summer measurements and TES was 3.91 +/- 4.2 ppbv higher. Pico data, model results, and AIRS all show declines in CO and O3 for the summer period from 2001 to 2010. Limited availability of TES data prevents us from using it in trend analysis. For summer CO Pico, GEOS-Chem, and AIRS results show declines of 1.32, 0.368, and 0.548 ppbv/year respectively. For summer O3, Pico and GEOS-Chem show declines of -0.726 and -0.583 ppbv/year respectively. In other seasons, both model and AIRS show declining CO, particularly in the fall. GEOS-Chem results show a fall decline of 0.798 ppbv/year and AIRS shows a decline of 0.8372 ppbv/year. Winter and spring CO declines are 0.393 and 0.307 for GEOS-Chem, and 0.455 and 0.566 for AIRS. GEOS-Chem shows declining O3 in other seasons as well; with fall being the season of greatest decrease and winter being the least. Model results for fall, winter, and spring are 0.856, 0.117, and 0.570 ppbv/year respectively. Given the availability of data we are most confident in summer results and thus find that summer CO and O3 have declined in lower free troposphere of the North Atlantic region of the Azores. Sensitivity studies for CO and O3 at Pico were conducted by turning off North American fossil fuel emissions in GEOS-Chem. Model results show that North America fossil fuel emissions contribute 8.57 ppbv CO and 4.03 ppbv O3 to Pico. The magnitude of modeled trends declines in all seasons without North American fossil fuel emissions except for summer CO. The increase in summer CO declines may be due to a decline of 5.24 ppbv/year trend in biomass burning emissions over the study period; this is higher than the 2.33 ppbv/year North American anthropogenic CO model decline. Winter O3 is the only season which goes from showing a negative trend to a positive trend.

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.

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.

Induction of Microalgal Lipids for Biodiesel Production in Tandem with Sequestration of High Carbon Dioxide Concentration

There is no doubt that sufficient energy supply is indispensable for the fulfillment of our fossil fuel crises in a stainable fashion. There have been many attempts in deriving biodiesel fuel from different bioenergy crops including corn, canola, soybean, palm, sugar cane and vegetable oil. However, there are some significant challenges, including depleting feedstock supplies, land use change impacts and food use competition, which lead to high prices and inability to completely displace fossil fuel [1-2]. In recent years, use of microalgae as an alternative biodiesel feedstock has gained renewed interest as these fuels are becoming increasingly economically viable, renewable, and carbon-neutral energy sources. One reason for this renewed interest derives from its promising growth giving it the ability to meet global transport fuel demand constraints with fewer energy supplies without compromising the global food supply. In this study, Chlorella protothecoides microalgae were cultivated under different conditions to produce high-yield biomass with high lipid content which would be converted into biodiesel fuel in tandem with the mitigation of high carbon dioxide concentration. The effects of CO2 using atmospheric and 15% CO2 concentration and light intensity of 35 and 140 µmol m-2s-1 on the microalgae growth and lipid induction were studied. The approach used was to culture microalgal Chlorella protothecoides with inoculation of 1×105 cells/ml in a 250-ml Erlenmeyer flask, irradiated with cool white fluorescent light at ambient temperature. Using these conditions we were able to determine the most suitable operating conditions for cultivating the green microalgae to produce high biomass and lipids. Nile red dye was used as a hydrophobic fluorescent probe to detect the induced intracellular lipids. Also, gas chromatograph mass spectroscopy was used to determine the CO2 concentrations in each culture flask using the closed continuous loop system. The goal was to study how the 15% CO2 concentration was being used up by the microalgae during cultivation. The results show that the condition of high light intensity of 140 µmol m-2s-1 with 15% CO2 concentration obtain high cell concentration of 7 x 105 cells mL-1 after culturing Chlorella protothecoides for 9 to 10 day in both open and closed systems respectively. Higher lipid content was estimated as indicated by fluorescence intensity with 1.3 to 2.5 times CO2 reduction emitted by power plants. The particle size of Chlorella protothecoides increased as well due to induction of lipid accumulation by the cells when culture under these condition (140 µmol m-2s-1 with 15% CO2 concentration).

ELECTROCATALYTIC PROCESSING OF RENEWABLE BIOMASS-DERIVED COMPOUNDS FOR PRODUCTION OF CHEMICALS, FUELS AND ELECTRICITY

The dual problems of sustaining the fast growth of human society and preserving the environment for future generations urge us to shift our focus from exploiting fossil oils to researching and developing more affordable, reliable and clean energy sources. Human beings had a long history that depended on meeting our energy demands with plant biomass, and the modern biorefinery technologies realize the effective conversion of biomass to production of transportation fuels, bulk and fine chemicals so to alleviate our reliance on fossil fuel resources of declining supply. With the aim of replacing as much non-renewable carbon from fossil oils with renewable carbon from biomass as possible, innovative R&D activities must strive to enhance the current biorefinery process and secure our energy future. Much of my Ph.D. research effort is centered on the study of electrocatalytic conversion of biomass-derived compounds to produce value-added chemicals, biofuels and electrical energy on model electrocatalysts in AEM/PEM-based continuous flow electrolysis cell and fuel cell reactors. High electricity generation performance was obtained when glycerol or crude glycerol was employed as fuels in AEMFCs. The study on selective electrocatalytic oxidation of glycerol shows an electrode potential-regulated product distribution where tartronate and mesoxalate can be selectively produced with electrode potential switch. This finding then led to the development of AEMFCs with selective production of valuable tartronate or mesoxalate with high selectivity and yield and cogeneration of electricity. Reaction mechanisms of electrocatalytic oxidation of ethylene glycol and 1,2-propanediol were further elucidated by means of an on-line sample collection technique and DFT modeling. Besides electro-oxidation of biorenewable alcohols to chemicals and electricity, electrocatalytic reduction of keto acids (e.g. levulinic acid) was also studied for upgrading biomass-based feedstock to biofuels while achieving renewable electricity storage. Meanwhile, ORR that is often coupled in AEMFCs on the cathode was investigated on non-PGM electrocatalyst with comparable activity to commercial Pt/C. The electro-biorefinery process could be coupled with traditional biorefinery operation and will play a significant role in our energy and chemical landscape.

Local degradation of structural, mechanical, electrical and chemical properties of membrane electrode assembly in polymer electrolyte fuel cell

Polymer electrolyte fuel cell (PEMFC) is promising source of clean power in many applications ranging from portable electronics to automotive and land-based power generation. However, widespread commercialization of PEMFC is primarily challenged by degradation. The mechanisms of fuel cell degradation are not well understood. Even though the numbers of installed units around the world continue to increase and dominate the pre-markets, the present lifetime requirements for fuel cells cannot be guarantee, creating the need for a more comprehensive knowledge of material’s ageing mechanism. The objective of this project is to conduct experiments on membrane electrode assembly (MEA) components of PEMFC to study structural, mechanical, electrical and chemical changes during ageing and understanding failure/degradation mechanism. The first part of this project was devoted to surface roughness analysis on catalyst layer (CL) and gas diffusion layer (GDL) using surface mapping microscopy. This study was motivated by the need to have a quantitative understanding of the GDL and CL surface morphology at the submicron level to predict interfacial contact resistance. Nanoindentation studies using atomic force microscope (AFM) were introduced to investigate the effect of degradation on mechanical properties of CL. The elastic modulus was decreased by 45 % in end of life (EOL) CL as compare to beginning of life (BOL) CL. In another set of experiment, conductive AFM (cAFM) was used to probe the local electric current in CL. The conductivity drops by 62 % in EOL CL. The future task will include characterization of MEA degradation using Raman and Fourier transform infrared (FTIR) spectroscopy. Raman spectroscopy will help to detect degree of structural disorder in CL during degradation. FTIR will help to study the effect of CO in CL. XRD will be used to determine Pt particle size and its crystallinity. In-situ conductive AFM studies using electrochemical cell on CL to correlate its structure with oxygen reduction reaction (ORR) reactivity

Diesel spray mixing limited vaporization with non-ideal and multi-component fuel thermophysical property effects

Internal combustion engines are, and will continue to be, a primary mode of power generation for ground transportation. Challenges exist in meeting fuel consumption regulations and emission standards while upholding performance, as fuel prices rise, and resource depletion and environmental impacts are of increasing concern. Diesel engines are advantageous due to their inherent efficiency advantage over spark ignition engines; however, their NOx and soot emissions can be difficult to control and reduce due to an inherent tradeoff. Diesel combustion is spray and mixing controlled providing an intrinsic link between spray and emissions, motivating detailed, fundamental studies on spray, vaporization, mixing, and combustion characteristics under engine relevant conditions. An optical combustion vessel facility has been developed at Michigan Technological University for these studies, with detailed tests and analysis being conducted. In this combustion vessel facility a preburn procedure for thermodynamic state generation is used, and validated using chemical kinetics modeling both for the MTU vessel, and institutions comprising the Engine Combustion Network international collaborative research initiative. It is shown that minor species produced are representative of modern diesel engines running exhaust gas recirculation and do not impact the autoignition of n-heptane. Diesel spray testing of a high-pressure (2000 bar) multi-hole injector is undertaken including non-vaporizing, vaporizing, and combusting tests, with sprays characterized using Mie back scatter imaging diagnostics. Liquid phase spray parameter trends agree with literature. Fluctuations in liquid length about a quasi-steady value are quantified, along with plume to plume variations. Hypotheses are developed for their causes including fuel pressure fluctuations, nozzle cavitation, internal injector flow and geometry, chamber temperature gradients, and turbulence. These are explored using a mixing limited vaporization model with an equation of state approach for thermopyhysical properties. This model is also applied to single and multi-component surrogates. Results include the development of the combustion research facility and validated thermodynamic state generation procedure. The developed equation of state approach provides application for improving surrogate fuels, both single and multi-component, in terms of diesel spray liquid length, with knowledge of only critical fuel properties. Experimental studies are coupled with modeling incorporating improved thermodynamic non-ideal gas and fuel

Study of the effect of biodiesel fuel on passive oxidation in a catalyzed particulate filter

A 2007 Cummins ISL 8.9L direct-injection common rail diesel engine rated at 272 kW (365 hp) and 317 kW (425 hp) was used to load the filter to 2.2 g/L and passively oxidize particulate matter (PM) within an aftertreatment system consisting of a diesel oxidation catalyst (DOC) and catalyzed particulate filter (CPF). The tests conducted with the engine rated at 365 hp used a 2007 DOC and CPF. The tests conducted with the engine rated at 425 hp used a 2010 DOC and 2007 CPF. Understanding the passive NO2 oxidation kinetics of PM within the CPF allows for reducing the frequency of active regenerations (hydrocarbon injection) and the associated fuel penalties. Modeling the passive oxidation of accumulated PM in the CPF will lead to creating accurate state estimation strategies. The MTU 1-D CPF model will be used to simulate data collected from this study to examine differences in the PM oxidation kinetics when soy methyl ester (SME) biodiesel is used as the source of fuel for the engine, and when the engine is operated at a higher power rating. A test procedure developed by Hutton et al. [1, 2] was modified to improve the ability to model the experimental data and provide additional insight into passively oxidized PM in a partially regenerated CPF. A test procedure was developed to allow PM oxidation rates by NO2 to be determined from engine test cell data. An experimental matrix consisting of CPF inlet temperatures from 250 to 450 °C with varying NOX/PM from 25 to 583and NO2/PM ratios from 5 to 240 was used. SME biodiesel was volumetrically blended with ULSD in 10% (B10) and 20% (B20) portions. This blended fuel was then used to evaluate the effect of biodiesel on passive oxidation rates. Four tests were performed with B10 and four tests with B20. Gathering data to determine the effect of fuel type (ULSD and biodiesel blends) on PM oxidation is the primary goal. The engine used for this testing was then configured to a higher power rating and one of the tests planned was performed. Additional testing is scheduled to take place with ULSD fuel to determine the affect the engine rating has on the PM oxidation. The experimental reaction rates during passive oxidation varied based upon the average CPF temperature, NO2 concentrations, and the NOX/PM ratios for each engine rating and with all fuels. The data analysis requires a high fidelity model that includes NO2 and thermal oxidation mechanisms and back diffusion to determine the details of the PM oxidation process.

Development and modeling of thermally conductive resins for fuel cell bipolar plate applications

Thermally conductive resins are a class of material that show promise in many different applications. One growing field for their use is in the area of bipolar plate technology for fuel cell applications. In this work, a LCP was mixed with different types of carbon fillers to determine the effects of the individual carbon fillers on the thermal conductivity of the composite resin. In addition, mathematical modeling was performed on the thermal conductivity data with the goal of developing predictive models for the thermal conductivity of highly filled composite resins.

Implementation of a phenomenological evaporation model into a porous network simulation for water management in low temperature fuel cells

A phenomenological transition film evaporation model was introduced to a pore network model with the consideration of pore radius, contact angle, non-isothermal interface temperature, microscale fluid flows and heat and mass transfers. This was achieved by modeling the transition film region of the menisci in each pore throughout the porous transport layer of a half-cell polymer electrolyte membrane (PEM) fuel cell. The model presented in this research is compared with the standard diffusive fuel cell modeling approach to evaporation and shown to surpass the conventional modeling approach in terms of predicting the evaporation rates in porous media. The current diffusive evaporation models used in many fuel cell transport models assumes a constant evaporation rate across the entire liquid-air interface. The transition film model was implemented into the pore network model to address this issue and create a pore size dependency on the evaporation rates. This is accomplished by evaluating the transition film evaporation rates determined by the kinetic model for every pore containing liquid water in the porous transport layer (PTL). The comparison of a transition film and diffusive evaporation model shows an increase in predicted evaporation rates for smaller pore sizes with the transition film model. This is an important parameter when considering the micro-scaled pore sizes seen in the PTL and becomes even more substantial when considering transport in fuel cells containing an MPL, or a large variance in pore size. Experimentation was performed to validate the transition film model by monitoring evaporation rates from a non-zero contact angle water droplet on a heated substrate. The substrate was a glass plate with a hydrophobic coating to reduce wettability. The tests were performed at a constant substrate temperature and relative humidity. The transition film model was able to accurately predict the drop volume as time elapsed. By implementing the transition film model to a pore network model the evaporation rates present in the PTL can be more accurately modeled. This improves the ability of a pore network model to predict the distribution of liquid water and ultimately the level of flooding exhibited in a PTL for various operating conditions.

Characterization of thermal and mechanical properties of polypropylene-based composites for fuel cell bipolar plates and development of educational tools in hydrogen and fuel cell technologies

In this project we developed conductive thermoplastic resins by adding varying amounts of three different carbon fillers: carbon black (CB), synthetic graphite (SG) and multi-walled carbon nanotubes (CNT) to a polypropylene matrix for application as fuel cell bipolar plates. This component of fuel cells provides mechanical support to the stack, circulates the gases that participate in the electrochemical reaction within the fuel cell and allows for removal of the excess heat from the system. The materials fabricated in this work were tested to determine their mechanical and thermal properties. These materials were produced by adding varying amounts of single carbon fillers to a polypropylene matrix (2.5 to 15 wt.% Ketjenblack EC-600 JD carbon black, 10 to 80 wt.% Asbury Carbon's Thermocarb TC-300 synthetic graphite, and 2.5 to 15 wt.% of Hyperion Catalysis International's FIBRILTM multi-walled carbon nanotubes) In addition, composite materials containing combinations of these three fillers were produced. The thermal conductivity results showed an increase in both through-plane and in-plane thermal conductivities, with the largest increase observed for synthetic graphite. The Department of Energy (DOE) had previously set a thermal conductivity goal of 20 W/m·K, which was surpassed by formulations containing 75 wt.% and 80 wt.% SG, yielding in-plane thermal conductivity values of 24.4 W/m·K and 33.6 W/m·K, respectively. In addition, composites containing 2.5 wt.% CB, 65 wt.% SG, and 6 wt.% CNT in PP had an in–plane thermal conductivity of 37 W/m·K. Flexural and tensile tests were conducted. All composite formulations exceeded the flexural strength target of 25 MPa set by DOE. The tensile and flexural modulus of the composites increased with higher concentration of carbon fillers. Carbon black and synthetic graphite caused a decrease in the tensile and flexural strengths of the composites. However, carbon nanotubes increased the composite tensile and flexural strengths. Mathematical models were applied to estimate through-plane and in-plane thermal conductivities of single and multiple filler formulations, and tensile modulus of single-filler formulations. For thermal conductivity, Nielsen's model yielded accurate thermal conductivity values when compared to experimental results obtained through the Flash method. For prediction of tensile modulus Nielsen's model yielded the smallest error between the predicted and experimental values. The second part of this project consisted of the development of a curriculum in Fuel Cell and Hydrogen Technologies to address different educational barriers identified by the Department of Energy. By the creation of new courses and enterprise programs in the areas of fuel cells and the use of hydrogen as an energy carrier, we introduced engineering students to the new technologies, policies and challenges present with this alternative energy. Feedback provided by students participating in these courses and enterprise programs indicate positive acceptance of the different educational tools. Results obtained from a survey applied to students after participating in these courses showed an increase in the knowledge and awareness of energy fundamentals, which indicates the modules developed in this project are effective in introducing students to alternative energy sources.

Analysis of spring break-up and its effects on a biomass feedstock supply chain in northern Michigan

Demand for bio-fuels is expected to increase, due to rising prices of fossil fuels and concerns over greenhouse gas emissions and energy security. The overall cost of biomass energy generation is primarily related to biomass harvesting activity, transportation, and storage. With a commercial-scale cellulosic ethanol processing facility in Kinross Township of Chippewa County, Michigan about to be built, models including a simulation model and an optimization model have been developed to provide decision support for the facility. Both models track cost, emissions and energy consumption. While the optimization model provides guidance for a long-term strategic plan, the simulation model aims to present detailed output for specified operational scenarios over an annual period. Most importantly, the simulation model considers the uncertainty of spring break-up timing, i.e., seasonal road restrictions. Spring break-up timing is important because it will impact the feasibility of harvesting activity and the time duration of transportation restrictions, which significantly changes the availability of feedstock for the processing facility. This thesis focuses on the statistical model of spring break-up used in the simulation model. Spring break-up timing depends on various factors, including temperature, road conditions and soil type, as well as individual decision making processes at the county level. The spring break-up model, based on the historical spring break-up data from 27 counties over the period of 2002-2010, starts by specifying the probability distribution of a particular county’s spring break-up start day and end day, and then relates the spring break-up timing of the other counties in the harvesting zone to the first county. In order to estimate the dependence relationship between counties, regression analyses, including standard linear regression and reduced major axis regression, are conducted. Using realizations (scenarios) of spring break-up generated by the statistical spring breakup model, the simulation model is able to probabilistically evaluate different harvesting and transportation plans to help the bio-fuel facility select the most effective strategy. For early spring break-up, which usually indicates a longer than average break-up period, more log storage is required, total cost increases, and the probability of plant closure increases. The risk of plant closure may be partially offset through increased use of rail transportation, which is not subject to spring break-up restrictions. However, rail availability and rail yard storage may then become limiting factors in the supply chain. Rail use will impact total cost, energy consumption, system-wide CO2 emissions, and the reliability of providing feedstock to the bio-fuel processing facility.

Static and dynamic contact angle measurement on rough surfaces using sessile drop profile analysis with application to water management in low temperature fuel cells

Fuel Cells are a promising alternative energy technology. One of the biggest problems that exists in fuel cell is that of water management. A better understanding of wettability characteristics in the fuel cells is needed to alleviate the problem of water management. Contact angle data on gas diffusion layers (GDL) of the fuel cells can be used to characterize the wettability of GDL in fuel cells. A contact angle measurement program has been developed to measure the contact angle of sessile drops from drop images. Digitization of drop images induces pixel errors in the contact angle measurement process. The resulting uncertainty in contact angle measurement has been analyzed. An experimental apparatus has been developed for contact angle measurements at different temperature, with the feature to measure advancing and receding contact angles on gas diffusion layers of fuel cells.

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.

Water transport in complex, non-wetting porous layers with applications to water management in low temperature fuel cells

An experimental setup was designed to visualize water percolation inside the porous transport layer, PTL, of proton exchange membrane, PEM, fuel cells and identify the relevant characterization parameters. In parallel with the observation of the water movement, the injection pressure (pressure required to transport water through the PTL) was measured. A new scaling for the drainage in porous media has been proposed based on the ratio between the input and the dissipated energies during percolation. A proportional dependency was obtained between the energy ratio and a non-dimensional time and this relationship is not dependent on the flow regime; stable displacement or capillary fingering. Experimental results show that for different PTL samples (from different manufacturers) the proportionality is different. The identification of this proportionality allows a unique characterization of PTLs with respect to water transport. This scaling has relevance in porous media flows ranging far beyond fuel cells. In parallel with the experimental analysis, a two-dimensional numerical model was developed in order to simulate the phenomena observed in the experiments. The stochastic nature of the pore size distribution, the role of the PTL wettability and morphology properties on the water transport were analyzed. The effect of a second porous layer placed between the porous transport layer and the catalyst layer called microporous layer, MPL, was also studied. It was found that the presence of the MPL significantly reduced the water content on the PTL by enhancing fingering formation. Moreover, the presence of small defects (cracks) within the MPL was shown to enhance water management. Finally, a corroboration of the numerical simulation was carried out. A threedimensional version of the network model was developed mimicking the experimental conditions. The morphology and wettability of the PTL are tuned to the experiment data by using the new energy scaling of drainage in porous media. Once the fit between numerical and experimental data is obtained, the computational PTL structure can be used in different types of simulations where the conditions are representative of the fuel cell operating conditions.