Search NASA⌕ Search

SEARCH · Search NASA

Results for “soot quantification”

Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

14 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Extinction Imaging Diagnostics for In Situ Quantification of Soot within Explosively Generated Fireballs

Fireballs produced from the detonation of high explosives often contain particulates primarily composed of various phases of carbon soot. The transport and concentration of these particulates is of interest for model validation and emission characterization. Here, this work proposes ultra-high-speed imaging techniques to observe a fireball's structure and optical depth. An extinction-based diagnostic applied at two wavelengths indicates that extinction scales inversely with wavelength, consistent with particles in the Rayleigh limit and dimensionless extinction coefficients which are independent of wavelength. Within current confidence bounds, the extinction-derived soot mass concentrations agree with expectations based upon literature reported soot yields. Results also identify areas of high uncertainty where additional work is recommended.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2022

Diffuse back-illumination temperature imaging (DBI-TI), a novel soot thermometry technique

To meet stringent emissions regulations on soot emissions, it is critical to further advance the fundamental understanding of in-cylinder soot formation and oxidation processes. Among several optical techniques for soot quantification, diffuse back-illumination extinction imaging (DBI-EI) has recently gained traction mainly due to its ability to compensate for beam steering, which if not addressed, can cause unacceptably high measurement uncertainty. Until now, DBI-EI has only been used to measure the amount of soot along the line of sight, and here, we extend the capabilities of a DBI-EI setup to also measure in-cylinder soot temperature. This proof of concept of diffuse back-illumination temperature imaging (DBI-TI) as a soot thermometry technique is presented by implementing DBI-TI in a single cylinder, heavy-duty, optical diesel engine to provide 2-D line-of-sight integrated soot temperature maps. The potential of DBI-TI to be an accurate thermometry technique for use in optical engines is analyzed. The achievable accuracy is due in part to simultaneous measurement of the soot extinction, which circumvents the uncertainty in dispersion coefficients that depend on the optical properties of soot and the wavelength of light utilized. Analysis shows that DBI-TI provides temperature estimates that are closer to the mass-averaged soot temperature when compared to other thermometry techniques that are more sensitive to soot temperature closer to the detector. Furthermore, uncertainty analysis and Monte Carlo (MC) simulations provide estimates of the temperature measurement errors associated with this technique. The MC simulations reveal that for the light intensities and optical densities encountered in these experiments, the accuracy of the DBI-TI technique is comparable or even better than other established optical thermometry techniques. Thus, DBI-TI promises to be an easily implementable extension to the existing DBI-EI technique, thereby extending its ability to provide comprehensive line-of-sight integrated information on soot.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Robust two-colour pyrometry uncertainty analysis to acquire spatially-resolved measurements

Two-colour pyrometry (2CP) has been used over several decades to study engine-relevant combustion processes, but results are generally regarded as qualitative or semi-quantitative. In many current 2CP systems, large measurement errors are introduced by parallax because the two measured wavelengths are not from the same line of sight. Here this work presents a spatially-resolved 2CP system with the objective of quantifying and reducing measurement uncertainty. An optical setup that eliminates parallax in 2CP is used together with pixel-by-pixel calibration of the camera sensor to increase measurement accuracy. Primary uncertainty terms are identified, and an error propagation analysis is performed to compute uncertainties in the final results of soot temperature, soot concentration parameter, KL, and soot mass. These methodologies are applied to investigate an auto-igniting fuel spray in a constant pressure flow rig at diesel-like conditions of high ambient pressure and temperature. Results show bias uncertainty of around 200 K (≈10%) for temperature and about 40%–60% for KL. High uncertainty was found to occur on the diffusion flame front where both optical thickness and soot concentrations are small. However, these uncertain measurement zones with relatively low soot concentrations contribute minimally to the total soot mass present in the reacting jet during the temporal evolution of the flame.

42 ENGINEERING↗
DOE OSTI2024

Quantification of carbon phases in detonation soot

Soot is a powdery substance composed of solid residues that results from the incomplete combustion of hydrocarbons. Soot holds valuable information because its chemical makeup can provide answers for condensed carbon kinetics in HE detonations by quantitively identifying what carbon species remain. For carbon-rich explosives, this is particularly important because carbon kinetics is a major pathway for energy release. Current efforts have been able to qualitatively identify the carbons and confirm their presence, but we haven’t been able to successfully quantify them. For example, small angle x-ray diffraction has been fielded to qualitatively identify carbon phases as a function of time during a detonation (work by Trevor Willey and Mike Nielson). Other work (ice experiments) has focused on measuring the “effective” static cold curve equation of state (EOS) of the recovered carbon soots.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Experimental and theoretical study of the soot-forming tendencies of furans as potential biofuels

Sustainably-produced biofuels, including furans derived from lignocellulosic biomass, are being studied as potential next-generation fuels. Biofuels have unique structures that are absent in conventional fuels and they potentially emit fewer particulates than conventional fuels because of the presence of oxygen atoms. In this work, we focus on quantification and measurement of sooting tendency. A set of furans was selected and their sooting tendencies were characterized by Yield Sooting Index (YSI), which was obtained by measuring the line-of-sight spectral radiance (LSSR) of a coflow laminar diffusion flame doped with 1000 ppm of each test fuel. The measured YSIs are lower than traditional gasoline fuels and benzenoid aromatics (e.g., YSIbenzene = 100). We also performed numerical simulations with a detailed chemical kinetic mechanism to analyze the flame chemistry of 2-methylfuran (2MF). 4→5 H-atom migration in the furan ring causes 31.0% of the total decomposition and is the dominant fuel consumption pathway leading to soot. DFT calculations via the G4 composite method were carried out to obtain activation energies (Ea) and bond dissociation energies (BDE). The results showed that the 4→5 H-atom migration in the furan ring has the second lowest Ea and subsequently forms a 1,2-diene-substituted ketone derivative as a stable species. The ring-opening products of the 4→5 H-atom migration are propyne + a linear ketene for 2-n-alkylfurans, and 1-butyne + a methyl-substituted ketene for 2-t-butylfuran. These results can explain the trends of the measured YSIs of 2-alkylfurans.

09 BIOMASS FUELS↗
NASA NTRS2002

Carbon Nanostructure Examined by Lattice Fringe Analysis of High Resolution Transmission Electron Microscopy Images

The dimensions of graphitic layer planes directly affect the reactivity of soot towards oxidation and growth. Quantification of graphitic structure could be used to develop and test correlations between the soot nanostructure and its reactivity. Based upon transmission electron microscopy images, this paper provides a demonstration of the robustness of a fringe image analysis code for determining the level of graphitic structure within nanoscale carbon, i.e. soot. Results, in the form of histograms of graphitic layer plane lengths, are compared to their determination through Raman analysis.

VanderWal, Randy L.↗
NASA NTRS2003

Carbon Nanostructure Examined by Lattice Fringe Analysis of High Resolution Transmission Electron Microscopy Images

The dimensions of graphitic layer planes directly affect the reactivity of soot towards oxidation and growth. Quantification of graphitic structure could be used to develop and test correlations between the soot nanostructure and its reactivity. Based upon transmission electron microscopy images, this paper provides a demonstration of the robustness of a fringe image analysis code for determining the level of graphitic structure within nanoscale carbon, i.e., soot. Results, in the form of histograms of graphitic layer plane lengths, are compared to their determination through Raman analysis.

VanderWal, Randy L.↗
DOE OSTI2025

Investigating Kinetic Mechanisms of Soot Formation in Plasma Pyrolysis of Methane via Active Learning (Final Technical Report)

Plasma pyrolysis of methane is an effective route for zero-carbon hydrogen production. Yet, soot generated from pyrolysis of hydrocarbons is detrimental to the climate and human health. There is ample experimental and theoretical evidence that suggests polycyclic aromatic hydrocarbons (PAHs) are the molecular precursors to soot particles. The reaction pathways of PAH formation are intricately dependent on a multitude of process parameters, whose kinetic mechanisms are not well-understood in plasma pyrolysis. This project aims to leverage advances in the kinetic modeling of soot formation in combustion, as well as in surrogate modeling and active learning, to systematically investigate the effects of process parameter on the kinetics of PAH formation in plasma pyrolysis of methane. To this end, we propose to use the PAH formation kinetics model developed by the PPPL/PU group based on the well-established ABF and HACA mechanisms, coupled with low-temperature plasma models. We will develop an active learning (AL) framework based on Bayesian optimization to systematically and data-efficiently explore the complex and multivariable parameter space of plasma pyrolysis in order to quantify the effects of plasma and feed parameters on the ABF and HACA kinetic pathways. AL is the branch of machine learning concerned with systematically querying samples from a system (experimental or computational) to train a data-driven model that maps design parameters to a performance criterion. We will use the data generated via AL to perform global sensitivity analysis, combined with uncertainty quantification, to elucidate the impact of different reaction pathways on minimizing formation of soot precursors. This study will result in an improved understanding of kinetics of PAH formation in plasma pyrolysis and can pave the way for more advanced mechanistic studies (e.g., soot nucleation mechanisms). Additionally, the findings will be useful for establishing practical strategies for increasing the pyrolysis efficiency and producing high-grade carbon for synthesis of nanomaterials.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2020

A computationally-efficient method for flamelet calculations

A new open-source code for the simulation of the diffusion flamelet equations is proposed. Emphasis is placed on using an approximate Jacobian to reduce the computational cost of the matrix operations. Performance of the proposed solvers is tested by performing flamelet calculations with kinetic mechanisms of varying sizes. For the unity Lewis number equations, the present iterative Newton solver using an approximate Jacobian greatly outperforms direct Newton solvers using exact Jacobians. The computation cost scales linearly with the number of species, leading to a reduction in solution times by two orders of magnitude for mechanisms containing thousands of species. The applicability of the Jacobian approximations to the solution of the non-unity Lewis number flamelet equations is assessed. The approximations are generally inadequate to solve the full non-unity Lewis number equations but can be used in some applications depending on the balance of terms in the flamelet equations. As an example, the flamelet solver is applied to the study of sooting tendencies in laminar co-flow diffusion flames where modified non-unity Lewis number flamelet equations, previously shown to accurately reproduce experimentally-measured Yield Sooting Indices (YSI), are solved. Here, the accelerated flamelet solver is well suited for sensitivity analysis and uncertainty quantification with large detailed kinetic mechanisms, tasks for which the computational cost was previously prohibitive.

42 ENGINEERING↗
DOE OSTI2021

Application of machine learning to estimate fireball characteristics and their uncertainty from infrared spectral data

Experiments or events involving high explosives (HE) can be monitored remotely by infrared (IR) sensors to gather information about the configuration or materials involved in the device. Researchers at the Air Force Institute of Technology (AFIT) developed a phenomenological model for HE fireball spectra in the IR range that allows for parameters to be extracted from Fourier transform infrared (FTIR) data. This model includes parameters tied to physical characteristics of the fireball: temperature, size, soot, and gas concentrations. Previous works have sought to recover these parameters by the fitting of either whole spectra or select wavenumber bands to this phenomenological model. Difficulties arise due to the complex relationships between the parameters to be fit. Uncertainty quantification of the estimated fireball parameters is also problematic since HE experiments do not have any ground truth information on the parameters. It is suggested that artificial neural network (ANN) based approaches may be well suited to this problem, because of their ability to capture complex and highly nonlinear relationships. As such, this work seeks to explore the efficacy of deep artificial neural networks (DNNs) for this problem of parameter recovery from spectra and to also investigate the uncertainty of recovering the fireball parameters from FTIR data. Networks are designed using the hyperparameter optimization tool Hyperopt and trained/tested on artificial data generated using the phenomenological model developed by AFIT. The results of applying the network to the artificial data set are compared to a physics-based band approach that uses a selected number of bands based on their physical properties. Information on the uncertainty of estimating parameters from remotely sensed experimental data is obtained by treating the accuracy of the DNN model on artificial data as an upper bound and by examining the impact of emissivity due to soot on parameter estimation error; the results for artificial data are likely to be optimistic as compared to recovering parameters from experimental data.

42 ENGINEERING↗
DOE OSTI2023

Uncertainty quantification of a deep learning fuel property prediction model

Deep learning models are being widely used in the field of combustion. Given the black-box nature of typical neural network based models, uncertainty quantification (UQ) is critical to ensure the reliability of predictions as well as the training datasets, and for a principled quantification of noise and its various sources. Deep learning surrogate models for predicting properties of chemical compounds and mixtures have been recently shown to be promising for enabling data-driven fuel design and optimization, with the ultimate goal of improving efficiency and lowering emissions from combustion engines. In this study, UQ is performed for a multi-task deep learning model that simultaneously predicts the research octane number (RON), Motor Octane Number (MON), and Yield Sooting Index (YSI) of pure components and multicomponent blends. The deep learning model is comprised of three smaller networks: Extractor 1, Extractor 2, and Predictor, and a mixing operator. The molecular fingerprints of individual components are encoded via Extractor 1 and Extractor 2, the mixing operator generates fingerprints for mixtures/blends based on linear mixing operation, and the predictor maps the fingerprint to the target properties. Two different classes of UQ methods, Monte Carlo ensemble methods and Bayesian neural networks (BNNs), are employed for quantifying the epistemic uncertainty. Combinations of Bernoulli and Gaussian distributions with DropConnect and DropOut techniques are explored as ensemble methods. All the DropConnect, DropOut and Bayesian layers are applied to the predictor network. Aleatoric uncertainty is modeled by assuming that each data point has an independent uncertainty associated with it. The results of the UQ study are further analyzed to compare the performance of BNN and ensemble methods. Although this study is confined to UQ of fuel property prediction, the methodologies are applicable to other deep learning frameworks that are being widely used in the combustion community.

33 ADVANCED PROPULSION SYSTEMS↗
NASA NTRS2015

Quantitative Measurements of CH* Concentration in Normal Gravity and Microgravity Coflow Laminar Diffusion Flames

With the conclusion of the SLICE campaign aboard the ISS in 2012, a large amount of data was made available for the analysis of the effect of microgravity on laminar coflow diffusion flames. Previous work focused on the study of sooty flames in microgravity as well as the ability of numerical models to predict its formation in a simplified buoyancy-free environment. The current work shifts the investigation to soot-free flames, putting an emphasis on the chemiluminescence emission from electronically excited CH (CH*). This radical species is of significant interest in combustion studies: it has been shown that the CH* spatial distribution is indicative of the flame front position and, given the relatively simple diagnostic involved with its measurement, several works have been done trying to understand the ability of CH* chemiluminescence to predict the total and local flame heat release rate. In this work, a subset of the SLICE nitrogen-diluted methane flames has been considered, and the effect of fuel and coflow velocity on CH* concentration is discussed and compared with both normal gravity results and numerical simulations. Experimentally, the spectral characterization of the DSLR color camera used to acquire the flame images allowed the signal collected by the blue channel to be considered representative of the CH* emission centered around 431 nm. Due to the axisymmetric flame structure, an Abel deconvolution of the line-of-sight chemiluminescence was used to obtain the radial intensity profile and, thanks to an absolute light intensity calibration, a quantification of the CH* concentration was possible. Results show that, in microgravity, the maximum flame CH* concentration increases with the coflow velocity, but it is weakly dependent on the fuel velocity; normal gravity flames, if not lifted, tend to follow the same trend, albeit with different peak concentrations. Comparisons with numerical simulations display reasonably good agreement between measured and computed flame lengths and radii, and it is shown that the integrated CH* emission scales proportionally to the computed total heat release rate; the two-dimensional CH* spatial distribution, however, does not appear to be a good marker for the local heat release rate.

microgravity↗
NASA NTRS2015

Quantitative Measurements of Electronically Excited CH Concentration in Normal Gravity and Microgravity Coflow Laminar Diffusion Flames

With the conclusion of the SLICE campaign aboard the ISS in 2012, a large amount of data was made available for the analysis of the effect of microgravity on laminar coflow diffusion flames. Previous work focused on the study of sooty flames in microgravity as well as the ability of numerical models to predict its formation in a simplified buoyancy-free environment. The current work shifts the investigation to soot-free flames, putting an emphasis on the chemiluminescence emission from electronically excited CH (CH*). This radical species is of significant interest in combustion studies: it has been shown that the electronically excited CH spatial distribution is indicative of the flame front position and, given the relatively simple diagnostic involved with its measurement, several works have been done trying to understand the ability of electronically excited CH chemiluminescence to predict the total and local flame heat release rate. In this work, a subset of the SLICE nitrogen-diluted methane flames has been considered, and the effect of fuel and coflow velocity on electronically excited CH concentration is discussed and compared with both normal gravity results and numerical simulations. Experimentally, the spectral characterization of the DSLR color camera used to acquire the flame images allowed the signal collected by the blue channel to be considered representative of the electronically excited CH emission centered around 431 nm. Due to the axisymmetric flame structure, an Abel deconvolution of the line-of-sight chemiluminescence was used to obtain the radial intensity profile and, thanks to an absolute light intensity calibration, a quantification of the electronically excited CH concentration was possible. Results show that, in microgravity, the maximum flame electronically excited CH concentration increases with the coflow velocity, but it is weakly dependent on the fuel velocity; normal gravity flames, if not lifted, tend to follow the same trend, albeit with different peak concentrations. Comparisons with numerical simulations display reasonably good agreement between measured and computed flame lengths and radii, and it is shown that the integrated electronically excited CH emission scales proportionally to the computed total heat release rate; the two-dimensional electronically excited CH spatial distribution, however, does not appear to be a good marker for the local heat release rate.

chemiluminescence↗
DOE OSTI2024

EPCAPE-PT-LANL Measurements: Single Particle Soot Photometer

Coastal cities offer a unique environment for studying aerosol-cloud interactions and the effects of urban emissions on cloud properties. As part of the Eastern Pacific Cloud Aerosol Precipitation Experiment (EPCAPE), the Partitioning Thrust by Los Alamos National Laboratory (EPCAPE-PT-LANL) was conducted. Our campaign focused on measuring the optical and chemical properties of aerosols and their interactions within marine stratocumulus clouds in La Jolla, California. EPCAPE-PT-LANL enhances the primary goals of EPCAPE through innovative observations of vapor-phase transitions between aerosols and cloud droplets, the impact of black carbon on aerosol-cloud dynamics, and the effects of cloud processing on aerosol optical properties. Instrument: Single Particle Soot Photometer (Droplet Measurements Technology) Data Notes: Contact us if you want additional data products from this instrument. Reported data is the black carbon (rBC) number and mass concentration with diagnostic flags. The SP2 measures incandescence from particles that is induced with a 1064 Nd-YAG laser. Particles that absorb the laser energy and then emit radiation is assumed to contain black carbon. The amount of intensity of radiation is related to the mass of absorbing material in the particle. Here, we calibrate the incandescence intensity to size selected Regal Black (Cabot) nebulized from solution. Data provided for the EPCAPE campaign used the combined broadband high-gain + low-gain channels (BHBL). Each channel has a lower threshold of detection equivalent to 2-s of the respective channel noise. Thresholding has been applied to select between the high-gain and low-gain channels. The detection limit is 80-540 nm (Deq) or 0.36- 55 fg. It is assumed that rBC is the only aerosol type that is in significant concentration in the sampled atmosphere that absorbs the laser energy. Particle data are integrated over 10 second windows to calculate a rBC number and mass concentration. QC/QA: • Diagnostic flags that impacted measured concentration: - Sample, Sheath, and Purge Flow Rates: Despite observing fluctuations in all flows, the rBC detection and mass quantification are generally observed to be stable, although large changes in sample flow rate did impact detection efficiency. A flag was implemented for sample flow deviations >12 cm3/min from the set point averaged over 30 seconds - Laser Power: Detection efficiency decreases with laser power and deviations in laser power also affect mass quantification. Flag for laser power is set for deviations in laser current from the set value >5 mA. - Primary Detector Threshold: Primary thresholding is the signal value which determines whether a “particle” is recorded. Thresholding errors occur when the threshold value is too HIGH and real particles are ignored. • Several periods without data: - 16-18 Nov: Ultra Zero Air generator failure, flows deviated significantly from set points. rBC # conc recovered but questionable. - 20-21 Nov: Offline for calibrations for several hours each day. - 25-27 Nov: Data is missing. - 30 Nov: Power outage 3 Dec: SP2 hard-drive full. - 3 Dec: Power Outage Header: - BHBL_NumbConc[#/cc]: Refers to the number concentration of refractory black carbon (rBC) measured from combined broadband high-gain and low-gain channels, expressed in particles per cubic centimeter. - BHBL_BCMass_Conc[ug/m3]: Refers to the mass concentration of refractory black carbon (rBC) measured from combined broadband high-gain and low-gain channels, expressed in micrograms per cubic meter. - NoData_Flag[bool]: A boolean flag that indicates whether no data was recorded during a measurement. - NoBC_Flag[bool]: A boolean flag indicating whether no black carbon particles were recorded during the measurement. - Laser_Flag[bool]: A boolean flag indicating deviations in laser current during the measurement. - SampleFlow_Flag[bool]: A boolean flag indicating deviations from the set sample flow rate during the measurement. - PrimThresh_Flag[bool]: A boolean flag indicating deviations from the set primary threshold during the measurement, potentially ignoring real particles. - Manual_Flag[bool]: A boolean flag indicating manual intervention or adjustments during the measurement. - CVI_Flag[bool]: A boolean flag indicating whether the Counterflow Virtual Impactor (CVI) was active or inactive during the measurement.

54 ENVIRONMENTAL SCIENCES↗