The Low Temperature Oxidation of 2,7-Dimethyloctane in a Pressurized Flow Reactor

The complexity of real fuels has fostered the use of simple mixtures of hydrocarbons whose combustion behavior approximates that of real fuels in both experimental and computational studies to develop models of the combustion of the real fuel. These simple mixtures have been called surrogates. Lightly branched paraffins are an important class of constituents in gasoline, diesel and aviation turbine fuels and therefore are primary candidates for use as a component in a surrogate. Unfortunately, fundamental studies on combustion characteristics of high molecular weight mono- and di-methylated iso-paraffins are scarce. Therefore, this study was designed to investigate the low-temperature oxidation of 2,7-dimethyloctane (2,7-DMO) (C10H22), a lightly branched isomer of decane. Replicate 2,7-DMO oxidation experiments were conducted in a pressurized flow reactor (PFR) over the temperature range of 550 - 850 K, at a pressure of 8 atm and an equivalence ratio of 0.3 in 4.21% oxygen / nitrogen. The reactivity was mapped by continuous monitoring of CO, CO2, and O2 using a non-dispersive infrared (NDIR) carbon monoxide / carbon dioxide analyzer and an electrochemical oxygen sensor. For examining the underlying reaction chemistry, detailed speciation of samples was performed at selected temperatures using a gas chromatograph with a flame ionization detector coupled to a mass spectrometer. Comparable oxidation experiments for n-decane were carried out to examine the unique effects of branching on fuel reactivity and distribution of major stable intermediates. For both isomers, the onset of negative temperature coefficient (NTC) region was observed near 700 K, with the reactivity decreasing with increasing the temperature. The flow reactor study of n-decane oxidation confirmed that the isomerization reduces the amount of CO produced at peak reactivity. In addition to reaction inhibition, branching affected the distribution of C2-C4 olefin intermediates. While the oxidation of n-decane resulted primarily in the formation of ethene near the NTC start, propene and isobutene were the major olefins produced from 2,7-DMO. A comparative analysis of experimental data with respect to a detailed chemical kinetic model for 2,7-DMO was performed and discrepancies were noted. Based on these results, a collaborative effort with Dr. Charles Westbrook (Lawrence Livermore National Laboratory) was initiated to refine the model predictions in the low temperature and NTC regimes. The effort resulted in an updated version of the 2,7-DMO mechanism, improving some of the key features such as calculated CO2 profile and final yields of iso-butene over the studied range of temperature. Fuel pyrolysis in the intermediate temperature regime, 850 - 1000 K, also was investigated for the first time in the PFR facility. However, preliminary n-decane experiments measured only a small amount of fuel decomposition, indicating that higher temperature operation would be beneficial. The major species produced from n-decane decomposition, in descending order of molar fraction, were ethene, propene, and 1-butene. These results were compared with the predictions of two existing chemical kinetic models and the sources of variations between the experiments and the models as well as among the mechanisms were investigated. At 1000 K, the mechanisms predicted higher levels of fuel depletion and ethene production. Also, while the mechanisms were similar in their predicted pathways for fuel depletion and formation of ethene, inconsistencies were observed in relative contribution of these pathways to the final yields as well as the rate parameter determination for several sensitive reactions with respect to n-decane and ethene. Overall, the research aided in achieving a data set quantifying the oxidation characteristics of 2,7-DMO (and n-decane for comparison) as well as an elucidation of critical reaction pathways based on experimental results. Preliminary pyrolysis experiments were carried out using n-decane and the limitations on companion 2,7-DMO pyrolysis experiments were established. The data was compared with the predictions of several chemical kinetic mechanisms and, using tools such as rate of production analysis and sensitivity analysis, the sources of deviations from experimental data as well as possible areas of improvement were identified. The findings from 2,7-DMO study was directly used to refine an existing chemical kinetic model for 2,7-DMO, in line with the ultimate goal of feeding the much needed experimental database for validation and refinement of kinetic models of jet fuel surrogates.Ph.D., Mechanical Engineering and Mechanics -- Drexel University, 201

The Low to Intermediate Temperature Oxidation of n Propylcyclohexanein a Pressurized Flow Reactor

Currently computational capabilities for next generation, air-breathing propulsion systems are underutilized in terms of combustion. This lack thereof represents an area of immense research that has ignited a profound interest within the combustion community. However, major hurdles exist that obstruct the community’s pathway to this goal. The important problems that need to be addressed can be grouped into two categories of project goals. First, the combustion properties of practical fuels and their associated surrogate components and mixtures used in air-breathing combustion systems must be understood and quantified. Second, the development of detailed reaction models and strategies for model reduction for use in large-scale simulations must be addressed. These project goals present a daunting task because of the large number of chemical components and classes contained in practical jet fuels derived from petroleum or alternative resources, such as natural gas and coal. It is well accepted that the solution to this problem is to develop surrogates for real jet fuels that contain a reduced amount of chemical components and classes. These surrogates are developed to match the physical properties and chemical kinetics of the practical jet fuels such that the combustion phenomena of the surrogates mimic that of the real jet fuel. Currently the combustion properties of practical jet fuels remain poorly understood and surrogate development is an ongoing process. The desired outcome of this effort is the improved qualitative understanding and quantitative predictability of the combustion properties of practical jet fuels and their surrogates, and the development of reliable kinetic models that may be used in practical combustion applications for design purposes. The JP-8 jet fuel cycloalkane surrogate component n-propylcyclohexane was oxidized in the Drexel Pressurized Flow Reactor (PFR) to gain further insight into its associated combustion kinetics

A comprehensive combustion chemistry study of n-propylcyclohexane

Alkylated cycloalkanes are vital components in gasoline, aviation, and diesel fuels; however, their combustion chemistry has been less investigated compared to other hydrocarbon classes. In this work, the combustion kinetics of n-propylcyclohexane (n-Pch) was studied across a range of experiments including pressurized flow reactor (PFR), jet stirred reactor (JSR), shock tube (ST), and rapid compression machine (RCM). These experiments cover a wide range of conditions spanning low to intermediate to high temperatures, low to high pressures at lean to rich equivalence ratios. Stable intermediate species were measured in PFR over a temperature range of 550–850 K, pressure of 8.0 bar, equivalence ratio (φ) of 0.27, and constant residence time of 120 ms. The JSR was utilized to measure the speciation during oxidation of n-Pch at φ of 0.5–2.0, at atmospheric pressure, and across temperature range of 550–800 K. Ignition delay times (IDTs) for n-Pch were measured in the RCM and ST at temperatures ranging from 650 to 1200 K, at pressures of 20 and 40 bar, at φ=0.5,1.0. In addition, a comprehensive detailed chemical kinetic model was developed and validated against the measured experimental data. The new kinetic model, coupled with the breadth of data from various experiments, provides an improved understanding of n-Pch combustion

Decoding Clinical Biomarker Space of COVID-19: Exploring Matrix Factorization-based Feature Selection Methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases

The low and intermediate temperature oxidation of JP-8 and its surrogate components

Currently computational capabilities for next generation, air-breathing propulsion systems are underutilized. This lack thereof represents an area of immense research that has ignited a profound interest within the combustion community. However, major hurdles exist that obstruct the community’s pathway to this goal. The important problems that need to be addressed can be grouped into two categories of project goals. First, the combustion properties of practical fuels and their associated surrogate components and mixtures used in air-breathing combustion systems must be understood and quantified. Second, the development of detailed reaction models and strategies for model reduction for use in large-scale simulations must be addressed. These project goals present a daunting task because of the large number of chemical components and classes contained in practical jet fuels derived from petroleum or alternative resources, such as natural gas and coal. It is well accepted that the solution to this problem is to develop surrogates for real jet fuels that contain a reduced amount of chemical components and classes. These surrogates are developed to match the physical properties and chemical kinetics of the practical jet fuels such that the combustion phenomena of the surrogates mimic that of the real jet fuel. Currently the combustion properties of practical jet fuels remain poorly understood and surrogate development is an ongoing process. The desired outcome of this effort is the improved qualitative understanding and quantitative predictability of the combustion properties of practical jet fuels and their surrogates, and the development of reliable kinetic models that may be used in practical combustion applications for design purposes

Decoding clinical biomarker space of COVID-19: Exploring matrix factorization-based feature selection methods

One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O2 Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases.</p

Decoding clinical biomarker space of COVID-19:exploring matrix factorization-based feature selection methods

Abstract One of the most critical challenges in managing complex diseases like COVID-19 is to establish an intelligent triage system that can optimize the clinical decision-making at the time of a global pandemic. The clinical presentation and patients’ characteristics are usually utilized to identify those patients who need more critical care. However, the clinical evidence shows an unmet need to determine more accurate and optimal clinical biomarkers to triage patients under a condition like the COVID-19 crisis. Here we have presented a machine learning approach to find a group of clinical indicators from the blood tests of a set of COVID-19 patients that are predictive of poor prognosis and morbidity. Our approach consists of two interconnected schemes: Feature Selection and Prognosis Classification. The former is based on different Matrix Factorization (MF)-based methods, and the latter is performed using Random Forest algorithm. Our model reveals that Arterial Blood Gas (ABG) O₂ Saturation and C-Reactive Protein (CRP) are the most important clinical biomarkers determining the poor prognosis in these patients. Our approach paves the path of building quantitative and optimized clinical management systems for COVID-19 and similar diseases