Browsing by Author "Reardon, Ken, committee member"

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Open Access
Adsorptive separations of phytocannabinoids and pesticides in the liquid phase
(Colorado State University. Libraries, 2022) Cuchiaro, Jamie H., author; Reynolds, Melissa, advisor; Farmer, Delphine, committee member; Chung, Jean, committee member; Reardon, Ken, committee member
To view the abstract, please see the full text of the document.
Open Access
Characterizing fuel reactivity in advanced internal combustion engines
(Colorado State University. Libraries, 2014) Baumgardner, Marc E., author; Marchese, Anthony J., advisor; Reardon, Ken, committee member; Olsen, Daniel, committee member; Gao, Xinfeng, committee member
The urgent need to increase efficiency and reduce exhaust emissions from internal combustion engines has resulted in an increased interest in alternative combustion modes. Premixed or partially premixed compression ignition modes, such as homogeneous-charge compression ignition (HCCI), reactivity-controlled compression ignition (RCCI) and multi-zone stratified compression ignition (MSCI) have been a particular focus because of their potential to deliver enhanced fuel efficiency and meet exhaust emissions mandates without the addition of costly after-treatment technologies. For HCCI and other single fuel, partially premixed compression ignition schemes such as MSCI, many studies have shown that fuels with characteristics intermediate between gasoline and diesel fuel are necessary. Many researchers have shown, however, that existing industry metrics such as Octane Number and Cetane Number are insufficient to represent fuel ignition characteristics for advanced engine combustion modes. In light of the poor performance of traditional metrics, new methods have been proposed to try and better characterize, order, and rank fuels used in HCCI operation. However, studies have since shown that when a broad array of fuels are considered, these recent metrics fail to adequately define a characteristic HCCI fuel index. Described in this work is an analysis of fuel reactivity in traditional and advanced internal combustion engines. Firstly, conventional engine regimes are broken down to their basic components, providing a framework for investigating the context of fuel reactivity. This analysis allows a novel equation to be formulated which links the historic metrics of Octane Number and Cetane Number. As part of this analysis a parameter, the knock length, is developed which explains the underlying principles of the Research and Motor Octane Number scales and further shows why some fuels test differently in these two methods. The knock length is also used to investigate unusual behavior observed in Methane Number reference fuels data - behavior which traditional concepts such as ignition delay and flame speed are unable to explain on their own. Secondly, this work focuses on the application of fuels such as bio-derived alcohols (ethanol and butanol) and fatty acid methyl esters in traditional and advanced combustion applications. Reactivity differences between alcohol and petroleum fuels are described and explained. Lastly, a new metric, the HCCI Number, is developed which allows the prediction of combustion timing in HCCI engines, and is highly amenable toward the development of bench-top laboratory apparatuses to facilitate practical adoption by fuel manufactures. Data from 23 different fuel blends tested in Cooperative Fuel Research (CFR) engines, a Fuel Ignition Tester, and a HCCI engine provide the experimental support for the theory presented herein. Additionally, a new chemical-kinetic mechanism is developed and used to describe combustion of n-butanol/n-heptane fuel mixtures in both conventional and advanced combustion applications (HCCI). Computational modeling is also used to examine the experiments presented herein: single and multi-zone (CHEMKIN) as well as system-level (GT Power) and multi-dimensional (CONVERGE) modeling approaches are developed and discussed. For the HCCI experiments conducted herein, an engine test-bed that allows HCCI examination across a wide array of conditions was also designed and fabricated. In summary, it is hoped that with better understanding of how fuels react in current and future engines, researchers can achieve the control necessary to bring higher performance engines to market and help the world take one step closer to addressing some of the pressing environmental and humanitarian issues at hand.
Open Access
End-gas autoignition propensity and flame propagation rate measurements in laser-ignited rapid compression machine experiments
(Colorado State University. Libraries, 2019) Zdanowicz, Andrew, author; Marchese, Anthony, advisor; Windom, Bret, committee member; Hampson, Greg, committee member; Reardon, Ken, committee member
Knock in spark-ignited (SI) engines is initiated by autoignition and detonation in the unburned gases upstream of spark-ignited, propagating, turbulent premixed flames. Knock propensity of fuel/air mixtures is typically quantified using research octane number (RON), motor octane number (MON), or methane number (MN; for gaseous fuels), which are measured using single-cylinder, variable compression ratio engines. In this study, knock propensity of SI fuels was quantified via observations of end-gas autoignition (EGAI) in unburned gases upstream of laser-ignited, premixed flames at elevated pressures and temperatures in a rapid compression machine. Stoichiometric primary reference fuel (PRF; n-heptane/isooctane) blends of varying reactivity (50 ≤ PRF ≤ 100) were ignited using an Nd:YAG laser over a range of temperatures and pressures, all in excess of 545 K and 16.1 bar. Laser-ignition produced outwardly-propagating premixed flames. High-speed pressure measurements and schlieren images indicated the presence of EGAI. The fraction of the total heat release attributed to EGAI (i.e., EGAI fraction) varied strongly with fuel reactivity (i.e., octane number) and the time-integrated temperature in the end-gas prior to ignition. Flame propagation rates, which were measured using schlieren images, did not vary strongly with octane number but were affected by turbulence caused by variation in piston timing. Under conditions of low turbulence, measured flame propagation rates agreed with the theoretical premixed laminar flame speeds quantified by 1-D calculations performed at the same conditions. Experiments were compared to a three-dimensional CONVERGE™ model with reduced chemical kinetics. Model results accurately captured the measured flame propagation rates, as well as the variation in EGAI fraction with fuel reactivity and time-integrated end-gas temperature. Model results also revealed low-temperature heat release and hydrogen peroxide formation in the end-gas upstream of the propagating laminar flame, which increased the temperature and degree of chain branching in the end-gas and ultimately led to EGAI.
Open Access
Evaluation of ethanol substitution in a compression ignition engine
(Colorado State University. Libraries, 2017) Van Roekel, Chris, author; Olsen, Daniel B., advisor; Bandhauer, Todd, committee member; Reardon, Ken, committee member
Heavy duty compression ignition engines rely on advanced emission control strategies to mitigate regulated emissions in compliance with requirements set by the Environmental Protection Agency. These strategies add significant cost and complexity to engine design. Previous work identified that a diesel-ethanol dual fuel combustion technique may be able to reduce diesel fuel consumption and supplement current emission control methods. The substitution of diesel fuel with a renewable, U.S. based fuel such as corn ethanol would also improve US energy security. A review of diesel-ethanol dual fuel combustion identified five possible methods of diesel-ethanol dual fuel combustion. They were ethanol-diesel emulsions, ethanol-diesel-additive blending, twin direct injection of ethanol and diesel, ethanol fumigation of intake air with standard diesel fuel injection, and full substitution of diesel with ethanol. Analysis of ethanol-diesel emulsions and ethanol-diesel-additive blending concluded that only low volumes of ethanol (<10%) could be blended in diesel fuel before the two fuels were immiscible. However, analysis using ternary phase diagrams showed that additives such as B100 biodiesel could be used to extend the substitution limit significantly such that at 25°C mixtures of 80% 200 proof ethanol, 10% B100 biodiesel, and 10% off-road diesel were visibly miscible. Miscible mixtures containing high volumes of ethanol underwent further analysis, which showed that these fuels were not suitable drop in replacements for diesel fuel due to poor cold flow properties. Based on fuel blending analysis and previously published literature ethanol fumigation of intake air was selected for an on-engine demonstration using a Cummins 6.7L QSB Tier 4 Final engine. Three ethanol based fuels were selected for this dual fuel combustion work: 200 proof ethanol, 190 proof ethanol, and a blend of 15% E0 gasoline and 85% 200 proof ethanol. Pre and post aftertreatment emission data and high speed combustion data were collected while operating the engine at ISO 8178 test points C1-7, C1-3, and C2-4. The maximum diesel substitution at each test point was similar among the three test fuels. and at moderate to high engine loads diesel substitution was limited to 25% and 39%, respectively due to engine knock . At low engine loads substitution was limited to 25% by exhaust emission requirements. Premixed ethanol combustion increased brake specific efficiency at moderate and high engine loads by 3% and 3.2%, respectively, but reduced efficiency at low engine loads by 1.4%. Finally, although the complete ISO 8178 test map was not completed the Tier 4 Final after treatment system was able to reduce ethanol premixed combustion emissions to at or below the diesel baseline emissions at nearly every test point.
Embargo
Interdisciplinary techniques in protein binding prediction and crystal engineering
(Colorado State University. Libraries, 2024) DeRoo, Jacob Benjamin, author; Reynolds, Melissa, advisor; Snow, Christopher D., advisor; Reardon, Ken, committee member; Zabel, Mark, committee member
This dissertation explores the integration of interdisciplinary methods such as advanced robotic automation, machine learning, and hybrid materials synthesis to dual protein engineering challenges: predicting protein-peptide binding specificity and the preparation of crystalline protein materials. The first chapter introduces a computational pipeline, PAbFold, based on AlphaFold2, designed to predict linear antibody epitopes from a given antigen sequence. This method provides a rapid and cost-effective alternative to traditional experimental techniques for epitope mapping, significantly lowering the financial barrier for laboratories. By accurately identifying binding sites on target proteins, PAbFold enhances the understanding of antibody-antigen interactions, facilitating the development of diagnostic and therapeutic antibodies in a more accessible manner. The second chapter presents an innovative approach to protein crystallization scale-up utilizing the Opentrons 2 liquid handling robot. This automation not only reduces manual labor and variability in crystallization experiments but also makes high-throughput crystallization more accessible to a broader range of laboratories by decreasing costs. Traditional high throughput protein crystallization liquid handling robots are priced around $75,000; the Opentrons 2 costs around $15,000. By employing Python scripts for precise control of the Opentrons 2, the study demonstrates successful crystallization of model and non-model proteins, highlighting the potential of automated systems in structural biochemistry to democratize access to high-quality protein crystals. The third chapter delves into the creation of hybrid materials by combining metal-organic frameworks (MOFs) with porous protein crystals. The research demonstrates the feasibility of embedding MOF domains within protein crystals, potentially opening new avenues for applications in catalysis, gas storage, and chemical warfare agent detoxification. By developing a new class of hybrid materials, this work contributes to making advanced structural biochemical research. Together, these chapters illustrate a modern interdisciplinary approach that embraces machine learning and automation in service of the engineering of peptide-binding proteins and crystalline protein materials. The integration of automation, computational predictions, and hybrid materials offers a promising path toward more efficient and innovative solutions in biochemical research, while significantly lowering the cost barriers, thereby increasing accessibility for researchers worldwide.