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NASA NTRS1985

Analyses via automated mass spectrometry (MS/DS)

New or improved uses of the Finnigan 4000 quadrupole mass spectrometer (MS) with its associated INCOS data system (DS) were investigated. The first phase involved an evaluative activity in which specific problems with miscalibration and gas chromatographic column conditioning were identified. This also revealed one solution to the problem of detection of substances not visible in the gas chromatography detection mode. A second phase was to seek useful applications of the direct inlet systems. This mode of sample introduction has not been previously utilized on the existing equipment and was successfully applied to the analysis of the components of TONOX 60/40 and in the thermal degradation products of some polymeric materials. Suggestions are made for improving and expanding the use of the MS/DS system in materials development and testing.

Koenig, T.↗

NASA NTRS1992

Measured rotational and vibrational temperature differences in arc jet shock layers

Shock-layer-radiation emission spectra from the first negative system of the nitrogen molecular ion N2(+) are analyzed over the wavelength region between 370 and 430 nm. The shock layer is produced by a blunt body placed in the expanded flow from an arc heater employing either pure nitrogen or a nitrogen-oxygen air mixture. A rotational temperature is obtained from analysis of (0,1) band emission from N2(+). Vibrational temperatures are extracted from population distributions obtained through spectral fitting of the N2(+) (B-X) band sequences. Rotational temperatures up to 9500 K near the front of the shocks are determined for different energy and flow conditions. The rotational temperatures are found to be lower than those obtained for vibration confirming previous measurements and contrary to expectation based on theory.

Blackwell, Harvel E.↗

NASA NTRS1994

Controllability of Free-piston Stirling Engine/linear Alternator Driving a Dynamic Load

This paper presents the dynamic behavior of a Free-Piston Stirling Engine/linear alternator (FPSE/LA) driving a single-phase fractional horse-power induction motor. The controllability and dynamic stability of the system are discussed by means of sensitivity effects of variations in system parameters, engine controller, operating conditions, and mechanical loading on the induction motor. The approach used expands on a combined mechanical and thermodynamic formulation employed in a previous paper. The application of state-space technique and frequency domain analysis enhances understanding of the dynamic interactions. Engine-alternator parametric sensitivity studies, similar to those of the previous paper, are summarized. Detailed discussions are provided for parametric variations which relate to the engine controller and system operating conditions. The results suggest that the controllability of a FPSE-based power system is enhanced by proper operating conditions and built-in controls.

Kankam, M. David↗

NASA NTRS2003

Algorithmic Enhancements to the VULCAN Navier-Stokes Solver

VULCAN (Viscous Upwind aLgorithm for Complex flow ANalysis) is a cell centered, finite volume code used to solve high speed flows related to hypersonic vehicles. Two algorithms are presented for expanding the range of applications of the current Navier-Stokes solver implemented in VULCAN. The first addition is a highly implicit approach that uses subiterations to enhance block to block connectivity between adjacent subdomains. The addition of this scheme allows more efficient solution of viscous flows on highly-stretched meshes. The second algorithm addresses the shortcomings associated with density-based schemes by the addition of a time-derivative preconditioning strategy. High speed, compressible flows are typically solved with density based schemes, which show a high level of degradation in accuracy and convergence at low Mach numbers (M less than or equal to 0.1). With the addition of preconditioning and associated modifications to the numerical discretization scheme, the eigenvalues will scale with the local velocity, and the above problems will be eliminated. With these additions, VULCAN now has improved convergence behavior for multi-block, highly-stretched meshes and also can solve the Navier-Stokes equations for very low Mach numbers.

Litton, D. K.↗

NASA NTRS2016

Mapping Past, Present, and Future Climatic Suitability for Invasive Aedes Aegypti and Aedes Albopictus in the United States: A Process-Based Modeling Approach Using CMIP5 Downscaled Climate Scenarios

The ongoing spread of the mosquitoes, Aedes aegypti and Aedes albopictus, in the continental United States leaves new areas at risk for local transmission of dengue, chikungunya, and Zika viruses. All three viruses have caused major disease outbreaks in the Americas with infected travelers returning regularly to the U.S. The expanding range of these mosquitoes raises questions about whether recent spread has been enabled by climate change or other anthropogenic influences. In this analysis, we used downscaled climate scenarios from the NASA Earth Exchange Global Daily Downscaled Projections (NEX GDDP) dataset to model Ae. aegypti and Ae. albopictus population growth rates across the United States. We used a stage-structured matrix population model to understand past and present climatic suitability for these vectors, and to project future suitability under CMIP5 climate change scenarios. Our results indicate that much of the southern U.S. is suitable for both Ae. aegypti and Ae. albopictus year-round. In addition, a large proportion of the U.S. is seasonally suitable for mosquito population growth, creating the potential for periodic incursions into new areas. Changes in climatic suitability in recent decades for Ae. aegypti and Ae. albopictus have occurred already in many regions of the U.S., and model projections of future climate suggest that climate change will continue to reshape the range of Ae. aegypti and Ae. albopictus in the U.S., and potentially the risk of the viruses they transmit.

process-based modeling↗

NASA NTRSDate not supplied

A Look at the Truths and Misconceptions of the Variational Quantum Eigensolver and the Implications of Overparameterization

In this work, we investigate loss landscapes of the variational quantum eigensolver (VQE) by quantifying the number of local minima through empirical analyses. We focus on minimal models in chemistry and physics so that we can do a complete analysis using more computationally expensive tools. We employ Hessian eigenvalue calculations and the nudged elastic band algorithm to characterize these landscapes. Our results expand upon the existing literature by highlighting the optimization challenges faced by VQE. We find that, as the number of parameters in our ansatz increases, the number of basins increases while the corresponding loss function values converge toward the global minimum value. This observation implies that overparameterization may lead to an ``effective convexity'' in VQE loss landscapes, a phenomenon supported by theoretical and numerical work in classical machine learning.

quantum computing↗

DOE OSTI2020

Efficient cosmological analysis of the SDSS/BOSS data from the Effective Field Theory of Large-Scale Structure

The precision of the cosmological data allows us to accurately approximate the predictions for cosmological observables by Taylor expanding up to a low order the dependence on the cosmological parameters around a reference cosmology. By applying this observation to the redshift-space one-loop galaxy power spectrum of the Effective Field Theory of Large-Scale Structure, we analyze the BOSS DR12 data by scanning over all the parameters of ΛCDM cosmology with massive neutrinos. Furthermore, we impose several sets of priors, the widest of which is just a Big Bang Nucleosynthesis prior on the current fractional energy density of baryons, Ω b h 2 , and a bound on the sum of neutrino masses to be less than 0.9 eV. In this case we measure the primordial amplitude of the power spectrum, A s , the abundance of matter, Ω m , the Hubble parameter, H 0 , and the tilt of the primordial power spectrum, n s , to about 19%, 5.7%, 2.2% and 7.3% respectively, obtaining ln (10 10 A s )=2.91± 0.19, Ω m =0.314± 0.018, H 0 =68.7± 1.5 km/(s Mpc) and n s =0.979± 0.071 at 68% confidence level.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS2012

Improvement to Airport Throughput Using Intelligent Arrival Scheduling and an Expanded Planning Horizon

The first phase of this study investigated the amount of time a flight can be delayed or expedited within the Terminal Airspace using only speed changes. The Arrival Capacity Calculator analysis tool was used to predict the time adjustment envelope for standard descent arrivals and then for CDA arrivals. Results ranged from 0.77 to 5.38 minutes. STAR routes were configured for the ACES simulation, and a validation of the ACC results was conducted comparing the maximum predicted time adjustments to those seen in ACES. The final phase investigated full runway-to-runway trajectories using ACES. The radial distance used by the arrival scheduler was incrementally increased from 50 to 150 nautical miles (nmi). The increased Planning Horizon radii allowed the arrival scheduler to arrange, path stretch, and speed-adjust flights to more fully load the arrival stream. The average throughput for the high volume portion of the day increased from 30 aircraft per runway for the 50 nmi radius to 40 aircraft per runway for the 150 nmi radius for a traffic set representative of high volume 2018. The recommended radius for the arrival scheduler s Planning Horizon was found to be 130 nmi, which allowed more than 95% loading of the arrival stream.

Glaab, Patricia C.↗

NASA NTRS1984

Noise produced by the interaction of a rotor wake with a swept stator blade

An analysis is developed for the noise generated by the interaction of rotor viscous wakes and a single swept stator vane. The stator vane spans a channel with infinite parallel walls which contains a uniform subsonic mean flow. High frequency wakes, for which the noise generation is concentrated at the vane leading edge, are considered. The general wake pattern is expanded in spanwise modes and solutions for each mode are derived using the Wiener-Hopf technique applied to the equations in the nonorthogonal coordinates. Closed form expressions for the acoustic farfield are obtained. The results of the analysis are used in parametric calculations of rotor viscous wake-stator vane interactions in order to study the effectiveness of sweep as a noise reduction mechanism. For the cases studied, moderate stator sweep angles produce sizeable reductions in the level of the farfield noise. The presence of rotor wake circumferential lean actually increases the noise reduction produced by moderate stator sweep angles.

Envia, E.↗

DOE OSTI2024

Expanded genome and proteome reallocation in a novel, robust Bacillus coagulans strain capable of utilizing pentose and hexose sugars

Bacillus coagulans, a Gram-positive thermophilic bacterium, is recognized for its probiotic properties and recent development as a microbial cell factory. Despite its importance for biotechnological applications, the current understanding of B. coagulans’ robustness is limited, especially for undomesticated strains. To fill this knowledge gap, we characterized the metabolic capability and performed functional genomics and systems analysis of a novel, robust strain, B. coagulans B-768. Genome sequencing revealed that B-768 has the largest B. coagulans genome known to date (3.94 Mbp), about 0.63 Mbp larger than the average genome of sequenced B. coagulans strains, with expanded carbohydrate metabolism and mobilome. Functional genomics identified a well-equipped genetic portfolio for utilizing a wide range of C5 (xylose, arabinose), C6 (glucose, mannose, galactose), and C12 (cellobiose) sugars present in biomass hydrolysates, which was validated experimentally. For growth on individual xylose and glucose, the dominant sugars in biomass hydrolysates, B-768 exhibited distinct phenotypes and proteome profiles. Faster growth and glucose uptake rates resulted in lactate overflow metabolism, which makes B. coagulans a lactate overproducer; however, slower growth and xylose uptake diminished overflow metabolism due to the high energy demand for sugar assimilation. Carbohydrate Transport and Metabolism (COG-G), Translation (COG-J), and Energy Conversion and Production (COG-C) made up 60%–65% of the measured proteomes but were allocated differently when growing on xylose and glucose. The trade-off in proteome reallocation, with high investment in COG-C over COG-G, explains the xylose growth phenotype with significant upregulation of xylose metabolism, pyruvate metabolism, and tricarboxylic acid (TCA) cycle. Strain B-768 tolerates and effectively utilizes inhibitory biomass hydrolysates containing mixed sugars and exhibits hierarchical sugar utilization with glucose as the preferential substrate.

carbohydrate metabolism↗

DOE OSTI2024

Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) for Prospective Impact Analysis of Novel Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics. The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM (Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

decarbonizing↗

DOE OSTI2024

Towards Prospective LCA Using Life-Cycle Assessment Integration into Scalable Open-Source Numerical Models (LiAISON) Framework for Analyzing Emerging Low-Carbon Technologies

Decarbonizing the industrial sector is a significant challenge in achieving a net-zero greenhouse gas (GHG) emissions economy by 2050 and the Paris Agreement, i.e., a global climate change mitigation target of achieving a maximum average temperature change potential of 1.5 Degrees Celsius or less by 2100 with respect to pre-industrial levels. In the United States (US), the industrial sector accounts for 23% of total GHG emissions and is home to a number of hard-to-electrify activities. The chemicals subsector has the single largest subsector emissions profile after direct emissions from fossil fuel combustion and leakage from fossil fuel distribution systems. Within the chemicals subsector, many processes depend on hydrogen or ammonia precursors. Decarbonizing these two commodities would contribute significantly to decarbonizing the industrial sector as hydrogen could also be used for low carbon steel production (e.g., hydrogen-based direct reduction of iron) and other industrial applications. Emerging technologies require the application of prospective life cycle assessment (LCA), which can account for technology (foreground) scaling and process improvements via learning-by-doing, among others. In many cases, the future system context (background) in which the technologies are assumed to operate in is equally relevant. Background scenarios generated by integrated assessment models (IAM) can coherently incorporate potential future dynamics of the energy-climate-human-land system. Further, IAM scenarios are harmonized across socioeconomic and climate change mitigation pathways, which facilitates the comparability of prospective LCAs using different IAMs. We introduce an open source prospective LCA framework, the Life-cycle Assessment Integration into Scalable Open-source Numerical models (LiAISON), to analyze the non-linear relationships between technology foreground and the future energy system background across a series of midpoint and resource use metrics The integration of LCA and IAM data is achieved using prospective environmental Impact assessment (PREMISE). We showcase it by assessing two Power-to-Hydrogen (PtH2) processes, namely Solid Oxide Electrolysis (SOE) and Polymer Electrolyte Membrane Electrolysis (PEME). We compare the technologies to a baseline of hydrogen production via natural gas-based Steam Methane Reforming (SMR) in a US context of multiple energy system and climate change mitigation futures. Besides providing an analysis that specifies the LCA results ranges with temporal and geospatial explicitness across the two technologies, metrics, and impact assessment methods, this research also aims to establish a base framework that can be expanded to use other IAM generated scenarios and US open-source life cycle inventory (LCI) databases. We find that the temporal environmental performance of either technology or their difference to SMR is directly influenced by the underlying background dynamics. Additionally we compare our results by linking two other prospective models with LiAISON - GCAM(Global Change Assessment Model) and ReEDS (Regional Energy Deployment System) to analyze the effect of changing background scenarios using varying predictions in life cycle analysis.

emissions↗

NASA NTRSDate not supplied

Applicability of Loads Estimation Techniques Using Sparse Acceleration Sensor Data to Spacecraft Structural Health Monitoring

The use of structural health monitoring systems on spacecraft structures can play a crucial role in ensuring the safety, reliability, and longevity of the structure by gathering and analyzing onboard sensor data. Of specific importance is monitoring for excessive loading at critical interfaces as any off-nominal structural excitations experienced by spacecraft structures can cause early unpredicted high structural life consumption or damage. The availability and cost of flight-certified sensors along with the size of spacecraft structures and allowable payload mass drives the need for a method to estimate loads using sparsely-located sensors. Numerous approaches such as physics-based, statistical learning, and physics-enhanced statistical learning algorithms have gained popularity among structural prognostics applications. However, developing noise-robust prediction models to assess loads and structural life predictions from a sparse multi-sensor data acquisition system can be a challenging task. This paper discusses the evaluation of physics-based versus machine-learning algorithms for predicting loads and structural life at mission critical locations on the spacecraft structure using a finite element loads analysis with the application of simulated noise and noise reduction techniques. To estimate the loads from accelerations, the physics-based algorithm leverages a loads transformation matrix from a Craig-Bampton reduced finite element model. A System Equivalent Reduction Expansion Process (SEREP) and a pseudo-inverse approach are considered to expand from the onboard sensor degrees of freedom to the Craig-Bampton model degrees of freedom. The machine learning algorithm provides a data driven solution/mapping of the sensor accelerations to the loads at the mission critical locations using a high dimensionality analysis. Although these strategies produce comparable loads prediction without noise, the limitations of these strategies with incorporating simulated noise and noise reduction techniques with low signal to noise ratio signals are evaluated. The study demonstrates the immense potential of statistical learning algorithms for sparse structural prognostic models and enhancing signal denoising techniques. These findings also highlight the need for noise-resilient prognostic models and low-noise data acquisition systems onboard spacecraft structures.

Spacecraft Structural Health Monitoring↗

DOE OSTI2021

Cellular, molecular, and therapeutic characterization of pilocarpine-induced temporal lobe epilepsy

Animal models have expanded our understanding of temporal lobe epilepsy (TLE). However, translating these to cell-specific druggable hypotheses is not explored. Herein, we conducted an integrative insilico-analysis of an available transcriptomics dataset obtained from animals with pilocarpine-induced-TLE. A set of 119 genes with subtle-to-moderate impact predicted most forms of epilepsy with ~ 97% accuracy and characteristically mapped to upregulated homeostatic and downregulated synaptic pathways. The deconvolution of cellular proportions revealed opposing changes in diverse cell types. The proportion of nonneuronal cells increased whereas that of interneurons, except for those expressing vasoactive intestinal peptide (Vip), decreased, and pyramidal neurons of the cornu-ammonis (CA) subfields showed the highest variation in proportion. A probabilistic Bayesian-network demonstrated an aberrant and oscillating physiological interaction between nonneuronal cells involved in the blood–brain-barrier and Vip interneurons in driving seizures, and their role was evaluated insilico using transcriptomic changes induced by valproic-acid, which showed opposing effects in the two cell-types. Additionally, we revealed novel epileptic and antiepileptic mechanisms and predicted drugs using causal inference, outperforming the present drug repurposing approaches. These well-powered findings not only expand the understanding of TLE and seizure oscillation, but also provide predictive biomarkers of epilepsy, cellular and causal micro-circuitry changes associated with it, and a drug-discovery method focusing on these events.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2015

Incorporating Agricultural Management Practices into the Assessment of Soil Carbon Change and Life-Cycle Greenhouse Gas Emissions of Corn Stover Ethanol Production

Land management practices such as cover crop adoption or manure application that can increase soil organic carbon (SOC) may provide a way to counter SOC loss upon removal of stover from corn fields for use as a biofuel feedstock. This report documents the data, methodology, and assumptions behind the incorporation of land management practices into corn-soybean systems that dominate U.S. grain production using varying levels of stover removal in the GREET TM (Greenhouse gases, Regulated Emissions, and Energy use in Transportation) model and its CCLUB (Carbon Calculator for Land Use change from Biofuels production) module. Tillage (i.e., conventional, reduced and no tillage), corn stover removal (i.e., at 0, 30% and 60% removal rate), and organic matter input techniques (i.e., cover crop and manure application) are included in the analysis as major land management practices. Soil carbon changes associated with land management changes were modeled with a surrogate CENTURY model. The resulting SOC changes were incorporated into CCLUB while GREET was expanded to include energy and material consumption associated with cover crop adoption and manure application. Life-cycle greenhouse gas (GHG) emissions of stover ethanol were estimated using a marginal approach (all burdens and benefits assigned to corn stover ethanol) and an energy allocation approach (burdens and benefits divided between grain and stover ethanol). In the latter case, we considered corn grain and corn stover ethanol to be produced at an integrated facility. Life-cycle GHG emissions of corn stover ethanol are dependent upon the analysis approach selected (marginal versus allocation) and the land management techniques applied. The expansion of CCLUB and GREET to accommodate land management techniques can produce a wide range of results because users can select from multiple scenario options such as choosing tillage levels, stover removal rates, and whether crop yields increase annually or remain constant. In a scenario with conventional tillage and a 30% stover removal rate, life-cycle GHG emissions for a combined gallon of corn grain and stover ethanol without cover crop adoption or manure application are 49 g CO 2 eq MJ -1 , in comparison with 91 g CO 2 eq MJ -1 for petroleum gasoline. Adopting a cover crop or applying manure reduces the former ethanol life-cycle GHG emissions by 8% and 10%, respectively. We considered two different life cycle analysis approaches to develop estimates of life-cycle GHG emissions for corn stover ethanol, marginal analysis and energy allocation. In the same scenario, this fuel has GHG emissions of 12 – 20 g CO 2 eq MJ -1 (for manure and cover crop application, respectively) and 45 – 48 g CO 2 eq MJ -1 with the marginal approach and the energy allocation approach, respectively.

09 BIOMASS FUELS↗

NASA NTRS1992

Tubular copper thrust chamber design study

The use of copper tubular thrust chambers is particularly important in high performance expander cycle space engines. Tubular chambers have more surface area than flat wall chambers, and this extra surface area provides enhanced heat transfer for additional energy to power the cycle. This paper was divided into two sections: (1) a thermal analysis and sensitivity study; and (2) a preliminary design of a selected thrust chamber configuration. The thermal analysis consisted of a statistical optimization to determine the optimum tube geometry, tube booking, thrust chamber geometry, and cooling routing to achieve the maximum upper limit chamber pressure for a 25,000 pound thrust engine. The preliminary design effort produced a layout drawing of a tubular thrust chamber that is three inches shorter than the Advanced Expander Test Bed (AETB) milled channel chamber but is predicted to provide a five percent increase in heat transfer. Testing this chamber in the AETB would confirm the inherent advantages of tubular chamber construction and heat transfer.

Masters, A. I.↗

NASA NTRS2001

Quantifying Residual Stresses by Means of Thermoelastic Stress Analysis

This study focused on the application of the Thermoelastic Stress Analysis (TSA) technique as a tool for assessing the residual stress state of structures. TSA is based on the fact that materials experience small temperature changes when compressed or expanded. When a structure is cyclically loaded, a surface temperature profile results which correlates to the surface stresses. The cyclic surface temperature is measured with an infrared camera. Traditionally, the amplitude of a TSA signal was theoretically defined to be linearly dependent on the cyclic stress amplitude. Recent studies have established that the temperature response is also dependent on the cyclic mean stress (i.e., the static stress state of the structure). In a previous study by the authors, it was shown that mean stresses significantly influenced the TSA results for titanium- and nickel-based alloys. This study continued the effort of accurate direct measurements of the mean stress effect by implementing various experimental modifications. In addition, a more in-depth analysis was conducted which involved analyzing the second harmonic of the temperature response. By obtaining the amplitudes of the first and second harmonics, the stress amplitude and the mean stress at a given point on a structure subjected to a cyclic load can be simultaneously obtained. The experimental results showed good agreement with the theoretical predictions for both the first and second harmonics of the temperature response. As a result, confidence was achieved concerning the ability to simultaneously obtain values for the static stress state as well as the cyclic stress amplitude of structures subjected to cyclic loads using the TSA technique. With continued research, it is now feasible to establish a protocol that would enable the monitoring of residual stresses in structures utilizing TSA.

Gyekenyesi, Andrew L.↗

DOE OSTI2020

Combustion of Silane-Hydrogen-Nitrous oxide Mixtures: Analysis of Laminar Flame Propagation and Condensed Products

The laminar burning rate, the explosion pressure, and the pressure rise coefficient have been measured for silane-nitrous oxide-argon mixtures using the spherically expanding flame technique in a constant volume combustion chamber. For these three parameters, the values obtained were higher than for hydrogen-nitrous oxide-argon and typical hydrocarbon-based mixtures. As an example, a maximum burning rate of 1800 g/m 2 s was measured at 101 kPa, whereas under similar conditions, a maximum burning rate around 950 g/m 2 s has been reported for hydrogen-nitrous oxide-argon mixtures. Here, the condensed combustion products formed in the silane-nitrous oxide-argon flames were sampled and characterized using electron micrograph, electronic diffraction, energy-dispersive spectroscopy, and X-ray powder diffraction. For all equivalence ratios, silica spherical particles with a mean diameter in the range 200-300 nm were observed. In addition, for mixtures with Φ ≥2.2, silicon nanowires were formed. X-ray diffraction experiments showed that the silicon nanowires are composed of a material referred to as “metal silicon” characterized by a cubic structure (lattice parameter: a = 5.425 Å) with the Fm-3m space group.

42 ENGINEERING↗