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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.

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At least 181 records · Page 10
All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

CFD Analysis of Emissions for a Candidate N+3 Combustor

An effort was undertaken to analyze the performance of a model Lean-Direct Injection (LDI) combustor designed to meet emissions and performance goals for NASA's N+3 program. Computational predictions of Emissions Index (EINOx) and combustor exit temperature were obtained for operation at typical power conditions expected of a small-core, high pressure-ratio (greater than 50), high T3 inlet temperature (greater than 950K) N+3 combustor. Reacting-flow computations were performed with the National Combustion Code (NCC) for a model N+3 LDI combustor, which consisted of a nine-element LDI flame-tube derived from a previous generation (N+2) thirteen-element LDI design. A consistent approach to mesh-optimization, spray-modeling and kinetics-modeling was used, in order to leverage the lessons learned from previous N+2 flame-tube analysis with the NCC. The NCC predictions for the current, non-optimized N+3 combustor operating indicated a 74% increase in NOx emissions as compared to that of the emissions-optimized, parent N+2 LDI combustor.

combustion chambers↗
NASA NTRS2019

CFD Evaluation of Lean-Direct Injection Combustors for Commercial Supersonics Technology

An overview is given of an effort that focused on using CFD analysis to complement design and configuration definition of Lean-Direct Injection (LDI) combustion concepts for NASA's Commercial Supersonic Transport (CST) program. The National Combustion Code (OpenNCC) was used to perform non-reacting and two-phase reacting flow computations for second and third generation LDI configurations at CST cruise conditions. All computations were performed with a consistent approach of mesh-generation, spray modeling, ignition and kinetics modeling. Emissions (EINOx) characteristics were predicted for CST cruise conditions, and compared with emissions data from experimental measurements to evaluate the fidelity of the CFD modeling approach to predict emissions changes in response to changes in supersonic cycle conditions.

nitrogen oxides injectors↗
NASA NTRS2019

Pilot Injector Redesign to Reduce N+3 Cycle Emissions for a Gas-Turbine Combustor

An overview is given of an effort for the use of CFD analysis to complement design and configuration definition of third generation Lean-Direct Injection combustion concepts (LDI-3) for NASA’s N+3 program. The National Combustion Code (OpenNCC) was used to perform non-reacting and two-phase reacting flow computations for a three-cup, nineteen-element flame tube array with redesigned pilot injectors to improve spray and emissions characteristics when compared to a previous LDI-3 design. All computations were performed with a consistent approach to mesh-generation, spray modeling, ignition and kinetics modeling for a ‘medium-power’ cycle condition. Computational predictions of the aerodynamics of a new pre-filming pilot injector were used to arrive at an optimized aerothermal design that meets effective area and fuel-air mixing criteria. The newly designed pilot injectors were shown to provide considerable improvements in aerodynamic stability, flame-tube pattern factor and NOx emissions, when compared to the original design.

nitrogen oxides injectors↗
NASA NTRS2019

Pilot Injector Redesign to Reduce N+3 Cycle Emissions for a Gas-Turbine Combustor

An overview is given of an effort for the use of CFD analysis to complement design and configuration definition of third generation Lean-Direct Injection combustion concepts (LDI-3) for NASA's N plus 3 program. The National Combustion Code (OpenNCC) was used to perform non-reacting and two-phase reacting flow computations for a three-cup, nineteen-element flametube array with redesigned pilot injectors to improve spray and emissions characteristics when compared to a previous LDI-3 design. All computations were performed with a consistent approach to mesh-generation, spray modeling, ignition and kinetics modeling for a "medium-power" cycle condition. Computational predictions of the aerodynamics of a new pre-filming pilot injector were used to arrive at an optimized aerothermal design that meets effective area and fuel-air mixing criteria. The newly designed pilot injectors were shown to provide considerable improvements in aerodynamic stability, flame-tube pattern factor and NOx emissions, when compared to the original design.

Nitrogen Oxides↗
NASA NTRS2019

CFD Evaluation Of Lean-Direct Injection Combustors for Commercial Supersonics Technology

An overview is given of an effort that focused on using CFD analysis to complement design and configuration definition of Lean-Direct Injection (LDI) combustion concepts for NASA's Commercial Supersonic Transport (CST) program. The National Combustion Code (OpenNCC) was used to perform non-reacting and two-phase reacting flow computations for second and third generation LDI configurations at CST cruise conditions. All computations were performed with a consistent approach of mesh-generation, spray modeling, ignition and kinetics modeling. Emissions (EINOx) characteristics were predicted for CST cruise conditions, and compared with emissions data from experimental measurements to evaluate the fidelity of the CFD modeling approach to predict emissions changes in response to changes in supersonic cycle conditions.

Ajmani, Kumud↗
NASA NTRS2018

Tuning Process for the Modified Magnussen Combustion Model

In the application of CFD to turbulent reacting flows, one of the main limitations to predictive accuracy is the chemistry model. Using a full or skeletal kinetics model may provide good predictive ability, however, at considerable computational cost. Adding the ability to account for the interaction between turbulence and chemistry improves the overall fidelity of a simulation but adds to this cost. An alternative is the use of simple models, such as the Magnussen model, which has negligible computational overhead, but lacks general predictive ability except for cases that can be tuned to the flow being solved. In this paper, a technique will be described that allows the tuning of the Magnussen model for an arbitrary fuel and flow geometry without the need to have experimental data for a particular case. The tuning is based on comparing the results of the Magnussen model and full finite-rate chemistry when applied to perfectly and partially stirred reactor sim- ulations. In addition, a modification to the Magnussen model is proposed that allows the upper kinetic limit for the reaction rate to be set, giving better physical agreement with full kinetic mechanisms. In order to improve the agreement with flame temperatures, the thermal properties of the product species is adjusted to better match the mixture proper- ties of the full mechanism. The combustion model is then applied to the simulation of a representative scramjet flowpath, and the results compared to experimental data and other kinetic models. This procedure allows a simple reacting model to be used in a predictive manner, and affords significant savings in computational costs for CFD simulations.

Norris, A. T.↗
NASA NTRS2019

Considerations for Femtosecond Laser Electronic Excitation Tagging in High-Speed Flows

Femtosecond laser electronic excitation tagging (FLEET) is an unseeded method for molecular tagging which offers valuable opportunities for measurement of high-speed (transonic, supersonic or hypersonic) flows. The unique nature of high-speed testing demands certain performance from FLEET such as satisfactory signal-to-noise ratio (SNR) at depressed static conditions (i.e., low temperatures, pressures and densities), wide dynamic range for velocity determination (especially single-shot), and measurements with acceptable accuracy and precision. This dissertation strives to evaluate FLEET in those regards and provide strategies to maximize the method's capabilities. A zero-dimensional kinetics model in nitrogen explains FLEET signal changes with pressure/density and/or temperature in terms of plasma-chemical reactions. Poorly known rate coefficients are tuned by comparing model output to measurements, with temporal agreement up to several hundred nanoseconds. Modeling reveals that initial signal peaks at reduced density because of slowed temporal evolution (and decay) of excited populations. Low temperatures enhance signal by enlarging cluster ion populations which contribute to excited species via electron-ion dissociative recombination. A purpose-built free jet facility provides experimental validation of the kinetics model and assesses FLEET velocimetry in low temperature and pressure/density conditions. Signal, lifetime, accuracy and precision results are obtained from unheated subsonic through Mach 4.0 operation of the facility, with best results noted. FLEET measurements of a sweeping jet (SWJ) actuator in compressible operation showcase its advantages in a highly unsteady jet containing subsonic through supersonic velocities. FLEET velocimetry is performed in the device's internal and external flow fields, with the latter compared to hot-wire anemometry. Internal measurements reveal the absence of shockwaves theorized to occur at high pressure ratios. Simultaneous qualitative measurements of compressible jet mixing are shown as a proof-of-concept. Overall, the work demonstrates that previous understanding of SWJ incompressible operation readily extends into the compressible realm. Practical aspects of performing FLEET velocimetry are detailed, along with strategies for improving measurement quality. Determination of a fundamental precision in nitrogen and air is attempted. Experiments show that increasing time delay and/or SNR improves velocimetry precision. A comparison of five camera systems indicates sensors with larger pixels capture higher SNR data and produce more precise results.

FLEET↗
NASA NTRS1989

Kinetic theory model for the flow of a simple gas from a two-dimensional nozzle

A system of nonlinear integral equations equivalent to the Krook kinetic equation for the steady state is the mathematical basis used to develop a computer code to model the flowfields for low-thrust two-dimensional nozzles. The method of characteristics was used to solve numerically by an iteration process the approximated Boltzmann equation for the number density, temperature, and velocity profiles of a simple gas as it exhausts into a vacuum. Results predict backscatter and show the effect of the inside wall boundary layer on the flowfields external to the nozzle.

Riley, B. R.↗
NASA NTRS1991

Kinetic theory model for the flow of a simple gas from a three-dimensional axisymmetric nozzle

A system of nonlinear integral equations equivalent to the Krook kinetic equations for the steady state is the mathematical basis used to develop a computer code to model the flowfields for low-thrust three-dimensional axisymmetric nozzles. The method of characteristics is used to solve numerically by an iteration process the approximated Boltzmann equation for the number density, temperature, and velocity profiles of a simple gas as it expands into a vacuum. Results predict backscatter and show the effect of the nozzle wall boundary layer on the external flowfields.

Riley, B. R.↗
DOE OSTI2025

A neural master equation framework for multiscale modeling of molecular processes: application to atomic-scale plasma processes

Plasma-surface interactions (PSI) play a crucial role in microelectronics fabrication; however, their multiscale nature and array of complex, often unknown interactions make computational modeling of PSIs extremely difficult. To this end, we propose a general neural master equation (NME) framework that uses master equations to describe the dynamics of a molecular process, wherein neural networks learned from atomistic simulations represent unknown transitions between different system states. By leveraging the physics-based structure of master equations and data-driven state transitions, the NME framework promotes generalizability and physics interpretability, and can bridge disparate length and time scales. The framework is demonstrated for multiscale modeling of Si atomic layer etching and reactive ion etching, where the learned NME-based surface kinetic models exhibit good predictive and extrapolative capabilities for predicting experimentally relevant observables as a function of process parameters. The NME-based surface kinetic models obey physical constraints, which are violated in models based on neural ordinary differential equations. The proposed NME framework for multiscale modeling of molecular processes can pave the way for the discovery of new chemistries and materials in atomic-scale plasma processes.

Chemical engineering↗
NASA NTRS1993

Dispersed ion structures at the poleward edge of the auroral oval: Low-altitude observations and numerical modeling

We have compared the AUREOL 3 (A3) observations of auroral ion precipitation, particularly ion beams, with the results from the global kinetic model of magnetotail plasma of Ashour-Abdalla et al. (1993). We have identified 101 energetic keV H(+) velocity dispersed precipitating ion structures (VDIS) with fluxes above 10(exp -3) ergs./sq cm./s in the A3 record between the end of 1981 and mid-1984. These beams display a systematic increase in energy with increasing latitude and were observed in a narrow region within less than 1 deg in latitude of the polar cap boundary. The VDIS are the most distinctive feature in the auroral zone of the plasma sheet boundary layer. We report first on a statistical analysis of the possible ralationships between magnetic activity or substorm phase and the VDIS properties. Our particle simulations of the precipitating ions have been extended by using a series of modified versions of the Tsyganenko (1989) magnetic field model and by varying the cross-magnetosphere electric field. In the simulations, plasma from a mantle source is subject to strong nonlinear acceleration, forming beams which flow along the PSBL. Only 3 to 4% of these beams precipitate into the ionosphere to form the VDIS while the majority return to the equatorial plane after mirroring and form the thermalized central plasma sheet. The final energy and the dispersion of the beams in the model depend on the amplitude of the cross-tail electric field. Two unsual observations of low-energy (less than 5 keV) O(+) VDIS, shifted by 4 deg 5 deg in invariant latitude equatorward of H(+) VDIS are analyzed in detail. The sparsity of such O(+) events and the absence of the changes in the flux and frequency of occurrence indicate a solar wind origin for the plasma. Finally, large-scale kinetic modeling, even with its simplifications and assumptions (e.g., static magnetic field, solar wind source), reproduces low-altitude auroral ion features fairly well; it may therefore be presented as an appropriate framework into which data on energization and transport of the hot plasma, obtained in the equatorial plane, could be inserted in the near future.

Bosqued, J. M.↗
DOE OSTI2024

Rotating cylinder electrode in reactive CO 2 capture: Identifying active C species via transport, VLE models and kinetics

Here, this article explores technical challenges and potential methodologies for understanding electrochemical Reactive CO 2 Capture (RCC) mechanisms. RCC offers potential energy cost advantages by directly converting captured CO 2 into fuels and chemicals, unlike traditional carbon capture and utilization (CCU) processes that require sequential capture, concentration, and compression. However, direct conversion of captured CO 2 introduces complexity due to additional equilibrium buffer reactions, making it challenging to identify active species for reduction in electrochemical studies. This article discusses methods to integrate transport, thermodynamics, and kinetics concepts to identify active carbon sources in RCC. Vapor‐Liquid Equilibrium (VLE) and transport models are validated against experimental results obtained in a gastight rotating cylinder electrode reactor and are shown as useful tools for studying RCC in heterogeneous electrocatalysts across different capture agents, solvents, and temperatures. This article establishes an experimental framework for advancing research in electrochemical RCC.

Electrocatalysis↗
DOE OSTI2025

Optimizing ensemble NV − spin properties of fluorescent diamond microparticles by systematic low pressure high temperature annealing

Low pressure high temperature annealing is a means for driving nitrogen and defect diffusion in diamond to reduce internal lattice damage without the need for technically complicated high-pressure cells. Herein, we perform a systematic time (5, 15, and 30 min) and temperature (1200 °C–1800 °C) study of effects of low-pressure high temperature annealing on photoluminescence, spin concentrations, and spin relaxation properties of NV centers in ca. 3 μm synthetic type 1b diamond particles. Annealing in the temperature range of ca. 1400 °C–1700 °C for even 5 min leads to a higher optically detected magnetic resonance contrast as compared to standard annealing at 900 °C for 2 h. Particles annealed at 1700 °C for 5 min exhibit a contrast close to about 13% as compared to about 9% for those annealed at 900 °C for 2 h. A reduction in the zero-field splitting strain parameter from E ≈ 4.5 MHz to ≈ 2.5 MHz and spectral linewidth from Δν ≈ 7 MHz to ≈ 4 MHz are observed even after 5 min annealing at 1700 °C. Improvements in these spectral parameters resulted in a roughly 2-fold reduction in the noise level of temperature monitoring experiment utilizing an ensemble of NV centers in the particles. Annealing in the temperature range of 1600 °C for 15 or 30 min or 1700 °C for 5 min resulted in NV T 1 relaxation times approaching ca. 5 ms typically observed for bulk diamond. Quantitative electron paramagnetic resonance (EPR) allowed for estimations of thermal activation energies of paramagnetic center annihilation. Monitoring the primary defect concentration (P1 and other defects with half integer spins) and utilizing second order kinetic modeling, an activation energy of 3.63 ± 0.28 eV was estimated. Alternatively, using the NV half field EPR signal and first order kinetic modeling, a similar activation energy 3.89 ± 0.29 eV was estimated.

NV centers↗
DOE OSTI2024

Distinguishing Surface and Bulk Reactivity: Concentration-Dependent Kinetics of Iodide Oxidation by Ozone in Microdroplets

Iodine oxidation reactions play an important role in environmental, biological, and industrial contexts. The multiphase reaction between aqueous iodide and ozone is of particular interest due to its prevalence in the marine atmosphere and unique reactivity at the air–water interface. Here, we explore the concentration dependence of the I – + O 3 reaction in levitated microdroplets under both acidic and basic conditions. To interpret the experimental kinetics, molecular simulations are used to benchmark a kinetic model, which enables insight into the reactivity of the interface, the nanometer-scale subsurface region, and the bulk interior of the droplet. For all experiments, a kinetic description of gas- and liquid-phase diffusion is critical to interpreting the results. We find that the surface dominates the iodide oxidation kinetics under concentrated and acidic conditions, with the reactive uptake coefficient approaching an upper limit of 10 –2 at pH 3. In contrast, reactions in the subsurface dominate under more dilute and alkaline conditions, with inhibition of the surface reaction at pH 12 and an uptake coefficient that is 10× smaller. In conclusion, the origin of a changing surface mechanism with pH is explored and compared to previous ozone-dependent measurements.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Enhancing Drinking Water Quality Modeling: Leveraging Physics Informed Neural Networks for Learning with Imperfect Reaction Models and Partial Data

Chemical kinetics models, typically formulated as systems of ordinary or partial differential equations, are valuable tools for simulating drinking water quality. However, these models often face inaccuracies due to discrepancies between the laboratory and the real-world conditions, as well as limitations in experimental analytical methods, hindering the accurate representation of the true underlying chemical mechanisms. In this study, we propose a Physics Informed Neural Network (PINN), using the eXtreme Theory of Functional Connections, to improve the prediction of chemical concentrations over time. The PINN method accounts for imperfect chemical models and incorporates partial data to improve predictions. Focusing on reactions describing water disinfection residual and disinfectant byproduct formation, which are crucial for public health and regulatory compliance, we demonstrate that the PINN model is able to accurately predict the concentrations of chemical species across various pH values. Notably, the model extends its accuracy to predict concentrations of chemical species not originally included in its training data. The developed method can be extended to a variety of chemical systems, offering a wide array of potential applications.

13 HYDRO ENERGY↗
DOE OSTI2025

Kinetic Deep Learning v0.1

Here, we present a method that uses protein levels to predict times series of metabolite concentrations. Understanding this type of pathway dynamics is important in order to predict the behavior of the pathway and, more pragmatically, to be able to design biological systems (such as strains bioengineered to produce chemical products) reliably. Typically, for this purpose, kinetic models consisting of differential equations based on the Michaelis-Menten dynamics have been used in the past. However, these methods can rarely produce good fits to measured data time series. Possibly, this happens because the kinetic constants are unknown or are different from the ones measured in vivo, or perhaps because Michaelis-Menten dynamics is not a satisfactory description. In order to improve the predictive nature of these kinetic models we have eliminated the Michaelis-Menten description of pathway dynamics and we have substituted it by algorithms that automatically learn these dynamics from previously obtained metabolomics and proteomics data using machine learning approaches. Specifically, kinetic deep learning uses deep learning to map proteomics time series to metabolite concentration time series, instead of learning the first metabolite derivative and integrating in (as in the first version of kinetic learning). This approach is shown to provide good to excellent results with a data set specifically collected for this purpose.

Garcia Martin, Hector [Joint BioEnergy Institute (↗
DOE OSTI2025

Portal matter models of kinetic mixing with two light dark gauge bosons

The kinetic mixing (KM) portal mandates the existence of at least one new gauge boson, the dark photon (DP) based on the group 𝑈⁢(1) 𝐷 , which mixes with the Standard Model (SM) photon via loops of other new heavy particles carrying both SM and dark charges called portal matter (PM). Arguments exist based on the renormalization group equations running of the 𝑈⁢(1) 𝐷 gauge coupling suggesting that at higher scales 𝑈⁢(1) 𝐷 becomes part of a more complex non-Abelian group, a simple example being just the SM-like 𝐺 𝐷 =𝑆⁢𝑈⁢(2)𝐼 ×𝑈⁢(1) 𝑌 𝐼 . In our past analyses, it was always assumed that 𝐺 𝐷 broke in a SM-like manner directly to the DP’s 𝑈⁢(1) 𝐷 which then subsequently broke at low energies ≲1 GeV. However, this need not be the case, and 𝐺 𝐷 can instead break to 𝑈⁢(1) 𝑇 3⁢𝐼 ×𝑈⁢(1) 𝑌 𝐼 , with 𝑇 3⁢𝐼 being the diagonal generator of 𝑆⁢𝑈⁢(2) 𝐼 , now producing two light gauge bosons which obtain masses at the ≲1 GeV scale. In this paper, we explore the phenomenology of a very simple realization of this kind of alternative setup employing non-Abelian KM and having a minimal, leptonlike PM sector, demonstrating its distinctive nature in comparison to the previously examined symmetry breaking path. The effects of interference between these gauge bosons on thermal dark matter annihilation, the production of new heavy gauge, and Higgs and PM states at colliders, as well as the corresponding signatures for the light dark gauge bosons are examined. Collider signatures of this setup are found to be particularly challenging.

extensions of Higgs sector↗
NASA NTRS2004

The Decompression Sickness and Venous Gas Emboli Consequences of Air Breaks During 100% Oxygen Prebreathe

Not enough is known about the increased risk of hypobaric decompression sickness (DCS) and production of venous (VGE) and arterial (AGE) gas emboli following an air break in an otherwise normal 100% resting oxygen (O2) prebreathe (PB), and certainly a break in PB when exercise is used to accelerate nitrogen (N2) elimination from the tissues. Current Aeromedical Flight Rules at the Johnson Space Center about additional PB payback times are untested, possibly too conservative, and therefore not optimized for operational use. A 10 min air break at 90 min into a 120 min PB that includes initial dual-cycle ergometry for 10 min will show a measurable increase in the risk of DCS and VGE after ascent to 4.3 psia compared to a 10 min break at 15 min into the PB, or when there is no break in PB. Data collection with humans begins in 2005, but here we first evaluate the hypothesis using three models of tissue N2 kinetics: Model I is a simple single half-time compartment exponential model, Model II is a three compartment half-time exponential model, and Model III is a variable half-time compartment model where the percentage of maximum O2 consumption for the subject during dual-cycle ergometry exercise defines the half-time compartment. Model I with large rate constants to simulate an exercise effect always showed a late break in PB had the greatest consequence. Model II showed an early break had the greatest consequence. Model III showed there was no difference between early or late break in exercise PB. Only one of these outcomes will be observed when humans are tested. Our results will favor one of these models, and so advance our understanding of tissue N2 kinetics, and of altitude DCS after an air break in PB.

Conkin, J.↗