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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 901 records · Page 50
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Modeling and Simulation of the Off-gas in an Electric Arc Furnace

The following paper describes an approach to process modeling and simulation of the gas phase in an electric arc furnace (EAF). The work presented represents the continuation of research by Logar, Dovžan, and Škrjanc on modeling the heat and mass transfer and the thermochemistry in an EAF. Due to the lack of off-gas measurements, Logar et al. modeled a simplified gas phase under consideration of five gas components and simplified chemical reactions. The off-gas is one of the main continuously measurable EAF process values and the off-gas flow represents a heat loss up to 30 pct of the entire EAF energy input. Therefore, gas phase modeling offers further development opportunities for future EAF optimization. This paper presents the enhancement of the previous EAF gas phase modeling by the consideration of additional gas components and a more detailed heat and mass transfer modeling. In order to avoid the increase of simulation time due to more complex modeling, the EAF model has been newly implemented to use an efficient numerical solver for ordinary differential equations. Compared to the original model, the chemical components H{sub 2}, H{sub 2}O, and CH{sub 4} are included in the gas phase and equilibrium reactions are implemented. The results show high levels of similarity between the measured operational data from an industrial scale EAF and the theoretical data from the simulation within a reasonable simulation time. In the future, the dynamic EAF model will be applicable for on- and offline optimizations, e.g., to analyze alternative input materials and mode of operations.

42 ENGINEERING↗
DOE OSTI2023

Physics informed deep neural network embedded in a chemical transport model for the Amazon rainforest

Secondary organic aerosols (SOA) are fine particles in the atmosphere, which interact with clouds, radiation and affect the Earth’s energy budget. SOA formation involves chemistry in gas phase, aqueous aerosols, and clouds. Simulating these chemical processes involve solving a stiff set of differential equations, which are computationally expensive steps for three-dimensional chemical transport models. Deep neural networks (DNNs) are universal function approximators that could be used to represent the complex nonlinear changes in aerosol physical and chemical processes; however, key challenges such as generalizability to extended time periods, preservation of mass balance, simulating sparse model outputs, and maintaining physical constraints have limited their use in atmospheric chemistry. Here, we develop an approach of using a physics-informed DNN that overcomes previous such challenges and demonstrates its applicability for the chemical formation processes of isoprene epoxydiol SOA (IEPOX-SOA) over the Amazon rainforest. The DNN is trained with data generated by simulating IEPOX-SOA over the entire atmospheric column, using the Weather Research and Forecasting Model coupled with Chemistry (WRF-Chem). The trained DNN is then embedded within WRF-Chem to replace the computationally expensive default solver of IEPOX-SOA formation. The trained DNN predictions generalizes well with the default model simulation of the IEPOX-SOA mass concentrations and its size distribution (20 size bins) over several days of simulations in both dry and wet seasons. The embedded DNN reduces the computational expense of WRF-Chem by a factor of 2. Our approach shows promise in terms of application to other computationally expensive chemistry solvers in climate models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Atomic masses with machine learning for the astrophysical r process

The astrophysical r process plays a vital role in the production of heavy elements. Modeling of the r process is sensitive to masses and further requires knowledge of masses beyond current experimental reach. Therefore, simulations of the r process offer a unique test bed for predicting mass extrapolations. We take a Machine-Learning (ML) approach to model the masses across the entire chart of nuclides. For the first time, we simulate r-process nucleosynthesis with a physics-based ML mass model. We compare simulated abundances to solar data in order to evaluate the model's performance far from stability. The resulting r-process abundances up to thorium and uranium qualitatively match those of the observed solar system abundance pattern, with the characteristic peaks well positioned. We propagate the mass uncertainties obtained from the ML model to r-process abundance yields to estimate an uncertainty band associated with our approach. The size of the uncertainty band is approximately one order of magnitude which aligns with the uncertainty reported using alternative techniques.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2021

Process-based and observation-constrained SOA simulations in China: the role of semivolatile and intermediate-volatility organic compounds and OH levels

Organic aerosol (OA) is a major component of tropospheric submicron aerosol that contributes to air pollution and causes adverse effects on human health. Chemical transport models have difficulties in reproducing the variability in OA concentrations in polluted areas, hindering understanding of the OA budget and sources. Herein, we apply both process-based and observation-constrained schemes to simulate OA in GEOS-Chem. Comprehensive data sets of surface OA, OA components, secondary organic aerosol (SOA) precursors, and oxidants were used for model–observation comparisons. The base models generally underestimate the SOA concentrations in China. In the revised schemes, updates were made on the emissions, volatility distributions, and SOA yields of semivolatile and intermediate-volatility organic compounds (SVOCs and IVOCs) and additional nitrous acid sources. With all the model improvements, both the process-based and observation-constrained SOA schemes can reproduce the observed mass concentrations of SOA and show spatial and seasonal consistency with each other. Our best model simulations suggest that anthropogenic SVOCs and IVOCs are the dominant source of SOA, with a contribution of over 50% in most of China, which should be considered for pollution mitigation in the future. The residential sector may be the predominant source of SVOCs and IVOCs in winter, despite large uncertainty remaining in the emissions of IVOCs from the residential sector in northern China. The industry sector is also an important source of IVOCs, especially in summer. More SVOC and IVOC measurements are needed to constrain their emissions. Besides, the results highlight the sensitivity of SOA to hydroxyl radical (OH) levels in winter in polluted environments. The addition of nitrous acid sources can lead to over 30% greater SOA mass concentrations in winter in northern China. It is important to have good OH simulations in air quality models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2019

Numerical simulation of Water-alternating-gas Process for Optimizing EOR and Carbon Storage

Carbon dioxide (CO 2 ) generated by fossil fuel combustion can have serious consequences for humans and environments. Carbon capture, utilization and geological storage (CCUS) is a key approach to reduce the emissions of the anthropogenic CO 2 into the atmosphere. Of those, sequestering the CO 2 into depleted hydrocarbon reservoirs with associated enhanced oil recovery is the most achievable approach under current economic constraints since it increases recovery of existing oil reserves, and bridges the gap between nonprofit geological CO 2 sequestration and profit CO 2 -EOR. The Upper Devonian fluvial sandstone reservoir in Jacksonburg-Stringtown oil field in West Virginia, which has produced over 22 million barrels of oil since 1895, is an ideal candidate for CO 2 sequestration coupled with EOR. This research illustrates an example of CCUS, in which CO 2 is sequestered into underground meanwhile enhanced oil recovery from the depleted hydrocarbon reservoir by water alternating gas (WAG) strategy. Three CO 2 storage mechanisms including structural/stratigraphic trapping, dissolution trapping, and residual trapping are considered. A 3D geological model is constructed based on existing geological data from the Jacksonburg-Stringtown oil field. A 3D composition fluid model with 0.4 pore volume (PV) of water injection before WAG process is considered as a benchmark of this study. Several factors including WAG cycle period, injection time ratio, CO 2 injection rate are evaluated. After injection wells are completely shut-in, stored CO 2 is monitored for additional 200 years as a function of various storage mechanisms. The result shows that over 6 years of WAG injection, total 12 - 40 MMlb-mol CO 2 is sequestrated and oil recovery factor can achieve 28%. As a conclusion, this research constructs a basic workflow for CO 2 storage coupled with CO 2 -EOR which can be applied to other super-mature oil fields.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2016

Post-processing interstitialcy diffusion from molecular dynamics simulations

An algorithm to rigorously trace the interstitialcy diffusion trajectory in crystals is developed. The algorithm incorporates unsupervised learning and graph optimization which obviate the need to input extra domain specific information depending on crystal or temperature of the simulation. The algorithm is implemented in a flexible framework as a post-processor to molecular dynamics (MD) simulations. We describe in detail the reduction of interstitialcy diffusion into known computational problems of unsupervised clustering and graph optimization. We also discuss the steps, computational efficiency and key components of the algorithm. Using the algorithm, thermal interstitialcy diffusion from low to near-melting point temperatures is studied. We encapsulate the algorithms in a modular framework with functionality to calculate diffusion coefficients, migration energies and other trajectory properties. The study validates the algorithm by establishing the conformity of output parameters with experimental values and provides detailed insights for the interstitialcy diffusion mechanism. The algorithm along with the help of supporting visualizations and analysis gives convincing details and a new approach to quantifying diffusion jumps, jump-lengths, time between jumps and to identify interstitials from lattice atoms. -- Graphical abstract:.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2018

Particle-in-cell simulations of magnetically driven reconnection using laser-powered capacitor coils

In this study, we propose an experimental scheme to fulfill magnetically driven reconnections. Here, two laser beams are focused on a capacitor-coil target and then strong currents are wired in two parallel circular coils. Magnetic reconnection occurs between the two magnetic bubbles created by the currents in the two parallel circular coils. A two-dimensional particle-in-cell simulation model in the cylindrical coordinate is used to investigate such a process, and the simulations are performed in the ( r , z ) plane. The results show that with the increase of the currents in the two coils, the associated magnetic bubbles expand and a current sheet is formed between the two bubbles. Magnetic reconnection occurs when the current sheet is sufficiently thin. A quadrupole structure of the magnetic field in the θ direction ( B θ ) is generated in the diffusion region and a strong electron current along the r direction ( J e r ) is also formed due to the existence of the high-speed electron flow away from the X line in the center of the outflow region. Finally, because the X line is a circle along the θ direction, the convergence of the plasma flow around r = 0 will lead to the asymmetry of J e r and B θ between the two outflow regions of magnetic reconnection.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2016

Discrimination of correlated and entangling quantum channels with selective process tomography

The accurate and reliable characterization of quantum dynamical processes underlies efforts to validate quantum technologies, where discrimination between competing models of observed behaviors inform efforts to fabricate and operate qubit devices. We present a protocol for quantum channel discrimination that leverages advances in direct characterization of quantum dynamics (DCQD) codes. We demonstrate that DCQD codes enable selective process tomography to improve discrimination between entangling and correlated quantum dynamics. Numerical simulations show selective process tomography requires only a few measurement configurations to achieve a low false alarm rate and that the DCQD encoding improves the resilience of the protocol to hidden sources of noise. Lastly, our results show that selective process tomography with DCQD codes is useful for efficiently distinguishing sources of correlated crosstalk from uncorrelated noise in current and future experimental platforms.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2016

Optimized Gen-II FeCrAl cladding production in large quantity for campaign testing

There are two major objectives in this report; (1) to optimize microstructure control of ATF FeCrAl alloys during tube drawing processes, and (2) to provide an update on the progress of ATF FeCrAl tube production via commercial manufacturers. Experimental efforts have been made to optimize the process parameters balancing the tube fabricability, especially for tube drawing processes, and microstructure control of the final tube products. Lab-scale sheet materials of Gen II FeCrAl alloys (Mo-containing and Nb-containing FeCrAl alloys) were used in the study, combined with a stepwise warm-rolling process and intermediate annealing, aiming to simulate the tube drawing process in a commercial tube manufacturer. The intermediate annealing at 650ºC for 1h was suggested for the tube-drawing process of Mo-containing FeCrAl alloys because it successfully softened the material by recovering the work hardening introduced through the rolling step, without inducing grain coarsening due to recrystallization. The final tube product is expected to have stabilized deformed microstructure providing the improved tensile properties with sufficient ductility. Optimization efforts on Nb-containing FeCrAl alloys focused on the effect of alloying additions and annealing conditions on the stability of deformed microstructure. Relationships between the second-phase precipitates (Fe 2 Nb-Laves phase) and microstructure stability are discussed. FeCrAl tube production through commercial tube manufacturers is currently in progress. Three different manufacturers, Century Tubes, Inc. (CTI), Rhenium Alloys, Inc. (RAI), and Superior Tube Company, Inc. (STC), are providing capabilities for cold-drawing, warm-drawing, and HPTR cold-pilgering, respectively. The first two companies are currently working on large quantity tube production (expected 250 ft length) of Gen I model FeCrAl alloy (B136Y3, at CTI) and Gen II (C35M4, at RAI), with the process parameters obtained from the experimental efforts. The expected delivery dates are at the end of July, 2016, and the middle of June, 2016, respectively. Tube production at STC would be the first attempt to apply cold-pilgering to the FeCrAl alloys. Communication has been initiated, and the materials have been machined for the cold-pilgering process.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Ab initio implementation of quantum trajectory mean-field approach and dynamical simulation of the N{sub 2}CO photodissociation

In this work, the recently introduced quantum trajectory mean-field (QTMF) approach is implemented and employed to explore photodissociation dynamics of diazirinone (N{sub 2}CO), which are based on the high-level ab initio calculation. For comparison, the photodissociation process has been simulated as well with the fewest-switches surface hopping (FSSH) and the ab initio multiple spawning (AIMS) methods. Overall, the dynamical behavior predicted by the three methods is consistent. The N{sub 2}CO photodissociation at λ > 335 nm is an ultrafast process and the two C—N bonds are broken in a stepwise way, giving birth to CO and N{sub 2} as the final products in the ground state. Meanwhile, some noticeable differences were found in the QTMF, FSSH, and AIMS simulated time constants for fission of the C—N bonds, excited-state lifetime, and nonadiabatic transition ratios in different intersection regions. These have been discussed in detail. The present study provides a clear evidence that direct ab initio QTMF approach is one of the reliable tools for simulating nonadiabatic dynamics processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗