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At least 289 records · Page 16

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NASA NTRS1981

Fuel/oxidizer-rich high-pressure preburners

The analyses, designs, fabrication, and cold-flow acceptance testing of LOX/RP-1 preburner components required for a high-pressure staged-combustion rocket engine are discussed. Separate designs of injectors, combustion chambers, turbine simulators, and hot-gas mixing devices are provided for fuel-rich and oxidizer-rich operation. The fuel-rich design addresses the problem of non-equilibrium LOX/RP-1 combustion. The development and use of a pseudo-kinetic combustion model for predicting operating efficiency, physical properties of the combustion products, and the potential for generating solid carbon is presented. The oxygen-rich design addresses the design criteria for the prevention of metal ignition. This is accomplished by the selection of materials and the generation of well-mixed gases. The combining of unique propellant injector element designs with secondary mixing devices is predicted to be the best approach.

Schoenman, L.↗

NASA NTRS1974

Diffusive ignition and combustion in a wall jet

Hydrogen is injected from a downstream facing step in a wall into a high-temperature stream. Temperature and hydroxyl radical concentration are measured downstream of the injection plane by means of ultraviolet absorption spectroscopy. The experimental results are compared with theory which is based on a finite-difference solution of boundary-layer equations. Finite-rate kinetics equations are included in the analysis. The analytic predictions are also compared with previously obtained experimental results which are based on probe measurements. Comparison is made between calculated and observed ignition distances.

Kurkov, A. P.↗

NASA NTRS1974

Diffusive ignition and combustion in a wall jet

Kurkov, A. P.↗

DOE OSTI2020

Probing the Effects of NO x and SO x Impurities on Oxy-Fuel Combustion in Supercritical CO 2 : Shock Tube Experiments and Chemical Kinetic Modeling

The direct-fired supercritical carbon dioxide cycles are one of the most promising power generation methods in terms of their efficiency and environmental friendliness. Two important challenges in implementing these cycles are the high pressure (300 bar) and high CO 2 dilution (>80%) in the combustor. The design and development of supercritical oxy-combustors for natural gas require accurate reaction kinetic models to predict the combustion outcomes. The presence of a small amount of impurities in natural gas and other feed streams to oxy-combustors makes these predictions even more complex. During oxy-combustion, trace amounts of nitrogen present in the oxidizer is converted to NO x and gets into the combustion chamber along with the recirculated CO 2 . Similarly, natural gas can contain a trace amount of ammonia and sulfurous impurities that get converted to NO x and SO x and get back into the combustion chamber with recirculated CO 2 . In this work, a reaction model is developed for predicting the effect of impurities such as NO x and SO x on supercritical methane combustion. The base mechanism used in this work is GRI Mech 3.0. H 2 S combustion chemistry is obtained from Bongartz et al. while NOx chemistry is from Konnov. The reaction model is then optimized for a pressure range of 30–300 bar using high-pressure shock tube data from the literature. It is then validated with data obtained from the literature for methane combustion, H 2 S oxidation, and NO x effects on ignition delay. The effect of impurities on CH 4 combustion up to 16 atm is validated using NO x -doped methane studies obtained from the literature. In order to validate the model for high-pressure conditions, experiments are conducted at the UCF shock tube facility using natural gas identical mixtures with N 2 O as an impurity at ~100 bar. Current results show that there is a significant change in ignition delay with the presence of impurities. A comparison is made with experimental data using the developed model and predictions are found to be in good agreement. The model developed was used to study the effect of impurities on CO formation from sCO 2 combustors. It was found that NO x helps in reducing CO formation while the presence of H 2 S results in the formation of more CO. The reaction mechanism developed herein can also be used as a base mechanism to develop reduced mechanisms for use in CFD simulations.

Energy & Fuels↗

NASA NTRSDate not supplied

Vehicle Modeling During the Burning of Thermally Thick Samples in the Saffire V Experiments

In order to design a safer spacecraft in terms of fire prevention, detection and cleanup, the transport of combustion products and heat must be predicted. Large-scale fire data was provided by the Saffire payload within Northrup Grumman’s Cygnus vehicle after a resupply mission to the ISS. Solid materials, including thick thermoplastic samples were ignited to study flame spread and the effect a fire has on a vehicle. Sensors that measured species and temperature were placed within Cygnus. A model using Pyrosim was developed to predict the thick fuel burn events from Saffire V by using sensor data from Cygnus. The heat transfer coefficient between the cabin air and cargo was previously calibrated using the cotton fuel burn event, and provided a good prediction of the temperature response within the vehicle during the two thick fuel experiments. The difference between the Saffire inlet CO 2 sensor, and the forward port standoff CO 2 sensor, which is the closest to the Saffire outlet, was used as an input into the model. This method provided an excellent prediction of the CO 2 response in the other parts of the Cygnus.

fire safety↗

NASA NTRS1999

Diffusive and Radiative Transport in Fires (DARTFire): Opposed-Flow Flame Spread in Low-Velocity Flows

For flames spreading into a low-velocity flow that can only be obtained in microgravity, we have observed behavior that is different from that which is obtained at higher velocities where radiative effects are unimportant and species transport is relatively fast. Unfortunately, lack of a large body of low-gravity flame spread experimental data inhibits progress in developing an understanding of the physics of low-velocity, opposed-flow flame spread phenomena. Recent DARTFire sounding rocket experimental studies though, coupled with developing theory and modelling, have allowed some strides in understanding to be made, on which we report here. Four launches to date have resulted in six experiments for opposed-flow flame spread over a thick PMMA sample. During the 6 min microgravity period, the PMMA samples were ignited, and steady flame spread was studied under varied flow velocity, oxidizer atmospheric conditions, and, because radiative heat transfer is so important in these slowly spreading flames, external radiant flux. These were the first attempts at such experimental control and measurement in microgravity. A recent reflight of the Solid Surface Combustion Experiment (SSCE) has demonstrated, as modelling predicts, that for the thick fuel of the DARTFire experiment, flame spread in a quiescent environment is a transient process evolving from ignition to extinction on the order of 600 s (Altenkirch et al., 1999). Further study then of the effects of radiation in the very low-velocity opposing flows is of interest in understanding the transition from steady, sustained spread to the unsteady evolution to extinction as the opposing flow is reduced further and eventually removed.

Altenkirch, R. A.↗

DOE OSTI2022

Co-optimized machine-learned manifold models for large eddy simulation of turbulent combustion

Many modeling approaches in large eddy simulation (LES) of turbulent combustion employ a projection of the thermochemical state onto a low-dimensional manifold within state space to reduce the number of transported variables and hence computational cost. Flamelet-generated manifolds (FGM) is an example of a well-established, physics-based approach, but increasingly, principal component analysis (PCA) is being used as a data-driven method for generating manifold models. For both approaches, the nonlinear relationship between the location on the predefined manifold and the outputs of interest, such as reaction rates, can be tabulated or encoded in a neural network. This work proposes a new approach for manifold modeling that extends these existing approaches. A modified neural network structure simultaneously encodes the definition of the manifold variables, the nonlinear mapping, and the subfilter closure for LES. This allows all three of these aspects of the model to be co-optimized, generating a model from any source of combustion thermochemical state data. The manifold parameterizing variables are constrained to be linear combinations of species, as in FGM and PCA-based models, to aid in interpretability and implementation. For LES, subfilter variances of the manifold variables are also included as inputs. Two types of a priori analysis are performed to evaluate the new approach. In the first, the model is trained on data from one-dimensional premixed flames. In this case, the approach recovers the behavior of flamelet-based manifold approaches, and in fact slightly improves performance by identifying an optimized progress variable. The approach is also applied to data from direct numerical simulations of spherical ignition kernels in isotropic turbulence. For any specified manifold dimensionality, the new approach provides substantially lower prediction errors than a PCA-based model developed from the same data set. Additionally, the LES formulation of the new approach can provide accurate predictions for filtered reaction rates across a variety of filter widths.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2020

National Ignition Campaign Data Analysis by an ICF Outsider

A simple DT burn model, with no alpha-particle warming feedback, using a combination of ICF inputs and the measured ion temperature, T, volume compression, C, ablator velocity, v, and burn width time, Δt, can reproduce the national-ignition campaign (NIC) fusion yields with a rms percent difference of ~40%. Analysis suggestion the fusion-yield reproducibility of NIC events with nominally identically measured T, C, v, and Δt is <30%. The highest fusion yields, at a fixed laser energy are obtained with depleted-uranium (DU) hohlraums and high-density carbon (HDC) ablators. Small diameter fit tubes (≤5 μm) enhance fusion yields by an average of 26±15% relative to 10-μm diameter fit tubes. A dependence of fusion yield on the diameter of the capsule relative to the diameter of the hohlraum is inferred for fixed T, C, v, and Δt. No evidence of conditions close to ignition, or alpha-particle warming is found in the existing NIC data with LEH< 2MJ. To obtain a predictive model for extrapolation proposes we map the T, C, and v into power laws as a function of LEH. The above mentioned extrapolatable model has a rms percent difference from the yields of the second half of the NIC of (56 ± 4)%. Higher (and significantly lower) DT temperatures are obtained with HDC ablators (and low-foot drives). The highest compressions are obtained via low-foot drives, but this does not compensate for the corresponding lower temperatures. An extrapolation of the presented model suggests significant alpha-particle heating at an LEH of ~5 MJ. It is hoped that others more familiar it the conditions needed for ignition will use to presented model to estimate the LEH required for ignition.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

Influence of NOx chemistry on the prediction of natural gas end-gas autoignition in CFD engine simulations

Natural gas (NG) represents a promising low-cost/low-emission alternative to diesel fuel when used in high-efficiency internal combustion engines. Advanced combustion strategies utilizing high EGR rates and controlled end-gas autoignition can be implemented with NG to achieve diesel-like efficiencies; however, to support the design of these next-generation NG ICEs, computational tools, including single- and multi-dimensional simulation packages will need to account for the complex chemistry that can occur between the reactive species found in EGR (including NOx) and the fuel. Research has shown that NOx plays an important role in the promotion/inhibition of large hydrocarbon autoignition and when accounted for in CFD engine simulations, can significantly improve the prediction of end-gas autoignition for these fuels. However, reduced NOx-enabled NG mechanisms for use in CFD engine simulations are lacking, and as a result, the influence of NOx chemistry on NG engine operation remains unknown. Here, we analyze the effects of NOx chemistry on the prediction of NG/oxidizer/EGR autoignition and generate a reduced mechanism of a suitable size to be used in engine simulations. Results indicate that NG ignition is sensitive to NOx chemistry, where it was observed that the addition of EGR, which included NOx, promoted NG autoignition. The modified mechanism captured well all trends and closely matched experimentally measured ignition delay times for a wide range of EGR rates and NG compositions. Here, the importance of C2-C3 chemistry is noted, especially for wet NG compositions containing high fractions of ethane and propane. Finally, when utilized in CFD simulations of a Cooperative Fuels Research (CFR) engine, the new reduced mechanism was able to predict the knock onset crank angle (KOCA) to within one crank angle degree of experimental data, a significant improvement compared to previous simulations without NOx chemistry.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2023

Artificial Neural Network Models for Octane Number and Octane Sensitivity: A Quantitative Structure Property Relationship Approach to Fuel Design

Octane sensitivity (OS), defined as the research octane number (RON) minus the motor octane number (MON) of a fuel, has gained interest among researchers due to its effect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially within advanced compression ignition engines. RON/MON must be experimentally tested to determine OS, requiring time, funding, and specialized equipment. Thus, predictive models trained with existing experimental data and molecular descriptors (via quantitative structure-property relationships (QSPRs)) would allow for the preemptive screening of compounds prior to performing these experiments. Here, the present work proposes two methods for predicting the OS of a given compound: using artificial neural networks (ANNs) trained with QSPR descriptors to predict RON and MON individually to compute OS (derived octane sensitivity (dOS)), and using ANNs trained with QSPR descriptors to directly predict OS. Twenty-five ANNs were trained for both RON and MON and their test sets achieved an overall 6.4% and 5.2% error, respectively. Twenty-five additional ANNs were trained for both dOS and OS; dOS calculations were found to have 15.3% error while predicting OS directly resulted in 9.9% error. A chemical analysis of the top QSPR descriptors for RON/MON and OS is conducted, highlighting desirable structural features for high-performing molecules and offering insight into the inner mathematical workings of ANNs; such chemical interpretations study the interconnections between structural features, descriptors, and fuel performance showing that connectivity, structural diversity, and atomic hybridization consistently drive fuel performance.

09 BIOMASS FUELS↗

NASA NTRS2019

Two-Stage Autoignition Dynamics of N-Dodecane Droplets Under Normal Gravity at High Pressures

Experimental observations of two-stage, autoignition characteristics of fiber-supported, n-dodecane fuel droplets under normal gravity are reported for a wide range of ambient temperatures, and pressures varying from 1 atm to 25 atm, which encompasses the fuel critical pressure (~ 18 atm). A high-speed camera is used to obtain backlit images of the autoignition process. During two-stage ignition, a cool-flame front forms below the droplet in the natural convective plume of the fuel vapor originating from the vaporizing droplet, and propagates upward, followed by the formation of a hot-flame kernel that expands rapidly and surrounds the droplet, eventually leading to the classical diffusion flame. Experimentally measured cool, and hot flame formation locations and propagation speeds are presented for varying initial temperatures and pressures. Measured first, and second stage ignition delay times are also reported. The influences of negative temperature coefficient (NTC) regime chemistry on flame starting location, propagation speeds, and ignition delay times are described. The first-stage cool flame characteristics are found to be controlled by both the chemistry, and physical aspects such as droplet evaporation and fuel-oxidizer mixing, while the second-stage hot flame ignition is primarily controlled by chemical kinetics. Further work is currently underway to develop models to quantitatively predict the observed behavior.

Rose, Evan↗

NASA NTRS2024

Nonlinear Dynamic Analyses of STS-1 Forward RCS Oxidizer Tank Structural Failure

At liftoff of the first Space Shuttle mission, a strong ignition overpressure pulse excited the stack. The frequency of the experienced pressure wave exceeded pre-launch predictions, and resulted in high, alternating normal accelerations along the length of the vehicle. As result of this unexpected loading, an oxidizer tank support strut in the orbiter’s forward reaction control system module failed in buckling under axial compression, which could have led to loss of the mission, crew, and vehicle. This incident is investigated in more detail using linear and nonlinear finite element analyses, complementing previous analyses with classical structural mechanics. The results of these analyses are examined to provide additional insight into the accident and to better inform future design decisions for modern space vehicle systems and structures.

Space Shuttle↗

NASA NTRS2024

Nonlinear Dynamic Analyses of STS-1 Forward RCS Oxidizer Tank Structural Failure

Space Shuttle↗

DOE OSTI2025

Predicting Fuel Properties and Emissions for Advanced Biofuels for Diesel Engines (CRADA Final Report)

The project will investigate variations in biofuel composition and optimize performance in combustion for conventional and future compression ignition engines. It will evaluate a variety of bio-derived molecules in the diesel range that can be produced using technology in ExxonMobil’s portfolio as well as fuels that cover the range of potential molecular structures for robust model development. Changes in fuel/air premixing and stratification in advanced engines could alter the relationship between fuel properties and performance in comparison to current generation spray combustion approaches. NREL experience in fuel and combustion modeling will enable development of general rules for predicting performance of a wide range of biofuel options.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2024

Demonstration of hot-spot fuel gain exceeding unity in direct-drive inertial confinement fusion implosions

Irradiating a small capsule containing deuterium and tritium fuel directly with intense laser light causes it to implode, which creates a plasma hot enough to initiate fusion reactions between the fuel nuclei. Here we report on such laser direct-drive experiments and observe that the fusion reactions produce more energy than the amount of energy in the central so-called hot-spot plasma. This condition is identified as having a hot-spot fuel gain greater than unity. A hot-spot fuel gain of around four was previously accomplished at the National Ignition Facility in indirect-drive inertial confinement fusion experiments where the capsule is irradiated by X-rays. In that case, up to 1.9 MJ of laser energy was used, but in contrast, our experiments on the OMEGA laser system require as little as 28 kJ. As the hot-spot fuel gain is predicted to grow with laser energy and target size, our work establishes the direct-drive approach to inertial fusion as a promising path towards burning and ignited plasmas in the laboratory. Additionally, we report a record (direct-drive) fusion yield of 0.9 kJ on OMEGA, which we achieved with thin-ice deuterium–tritium liner targets.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2022

The influence of thermochemistry on the reactivity of propane, the pentane isomers and $n$-heptane in the low temperature regime

In this study, the influence of thermochemistry on the reactivity of fuels at low temperatures (600–1000 K) is studied here. Specifically, the effect of different sets of thermochemistry on chemical model predictions is explored, where various sets are calculated at different levels of theory in addition to recently updated group additivity values. Experimentally measured ignition delay times for propane, the pentane isomers and n-heptane are simulated using NUIGMech1.2 and replacing the thermochemistry of the low-temperature species with the calculated values. For propane, three different thermochemistry sets were calculated, namely CCSD(T)-F12/TZ-F12//B2PLYP-D3/TZ//B2PLYP-D3/TZ (QM1), CCSD(T)-F12/TZ-F12//B2PLYP-D3/TZ//ωB97X-D/TZ (QM2) and B2PLYP-D3/TZ/ωB97X-D/6-31G*//ωB97X-D/6-31G* (QM3). The QM2 results provide parameters to optimize new group additivity (NGA) values which are used to calculate the fourth set of thermochemistry. The model predictions using these four sets are compared to those using NUIGMech1.2 for propane. As the QM1 and QM2 calculations are expensive, the thermochemistry calculated from the QM3 and NGA calculations are used in the pentane isomer and n-heptane models. For all of the models, it is found that the thermochemistry of the species involved in the low-temperature reaction sequence ($RH$, $\dot{R}$, $RO_2H$, $R\dot{O}_2$, $\dot{Q}OOH$ and $\dot{O}_2QOOH$ species) significantly affect fuel reactivity. The NGA values were developed based on all of these species except $\dot{O}_2QOOH$ radicals. The thermochemistry of $\dot{O}_2QOOH$ species cannot be accurately calculated with the NGA representations due to the importance of non-next-nearest neighbor interactions of –OOH substitution. Further development of the NGA method to capture such interactions is in progress. Overall, the model developed using the NGA thermochemistry shows better agreement with experimental data than the model using thermochemistry from affordable and prominent QM methods, such as QM3. Based on the results presented for propane, the pentane isomers and n-heptane, the thermochemistry calculated using the NGA method can be used to model the oxidation of higher order hydrocarbons at low temperatures.

33 ADVANCED PROPULSION SYSTEMS↗

DOE OSTI2020

Prediction of Research/Motor Octane Number and Octane Sensitivity Using Artificial Neural Networks

Octane sensitivity (OS), dfined as the research octane number (RON) minus the motor octane number (MON) of a given fuel, has gained interest among researchers due to its apparent ffect on knocking conditions in internal combustion engines. Compounds with a high OS enable higher efficiencies, especially with respect to advanced compression ignition engines. RON/MON must be experimentally tested to determine OS; however, the experimental methods utilized require a substantial amount of time, a significant monetary investment, and specialized equipment. To this end, predictive computational models trained with existing experimental data and molecular properties would allow for the preemptive screening of compounds prior to performing these experiments. The present work proposes two methods for predicting the OS of a given compound: using artficial neural networks (ANNs) trained with quantitative structure-property relationship (QSPR) descriptors to predict RON and MON individually to compute OS from RON/MON predictions (derived octane sensitivity, dOS), and using an ANN trained with QSPR descriptors to directly predict OS. ANNs trained to predict RON and MON achieved test set root-mean-square errors (RMSEs) of 10.499 and 7.551 respectively. dOS calculations were found to have a test set RMSE of 6.432 while predicting OS directly resulted in a test set RMSE of 7.019, showing it is more bene cial to obtain OS from RON/MON predictions than predicting it directly. Furthermore, relationships between individual QSPR descriptors and RON/MON/OS are discussed, highlighting correlations between specfic molecular features and these properties.

09 BIOMASS FUELS↗

DOE OSTI2025

Hydrodynamic Predictions for the Next Outburst of T Coronae Borealis: It Will Be the Brightest Classical or Recurrent Nova Ever Observed in X-Rays*

T Coronae Borealis (TCrB) is a recurrent nova with recorded outbursts in 1866 and 1946 and possible outbursts in 1217 and 1787. It is predicted to explode again in 2025 or 2026 based on multiple observational studies. The system consists of a massive ( M wd ≳ 1.35 M ⊙ ) white dwarf (WD) and a red giant (M3–M4 III). We have performed 1D hydrodynamic simulations with NOVA to predict the behavior of the next outburst. These simulations consist of a range of mass accretion rates onto ∼1.35 M ⊙ WDs, designed to bound the conditions necessary to achieve ignition of an explosion after an ≈80 yr interoutburst period. We have used both carbon–oxygen and oxygen–neon initial compositions, in order to include the possible ejecta abundances to be measured in the observations of the next outburst. As the WD in the TCrB system is observed to be massive, theoretical predictions reported here imply that the WD is growing in mass as a consequence of the thermonuclear runaway. Therefore, the secular evolution of the WD may allow it to approach the Chandrasekhar limit and either explode as a Type Ia supernova or undergo accretion-induced collapse, depending on its underlying composition. We have followed the evolution of just the WD, after removing the ejected matter from the surface layers. Our intent is to illuminate the mystery of the unique, second maximum in the two well-observed outbursts and we have found conditions that bracket the predictions.

Cataclysmic variable stars↗