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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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980 records · Page 55
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

A guided ion beam investigation of UO 2 + thermodynamics and f orbital participation: Reactions of U + + CO 2 , UO + + O 2 , and UO + + CO

A guided ion beam tandem mass spectrometer was employed to study the reactions of U + + CO 2 , UO + + O 2 , and the reverse of the former, UO + + CO. Reaction cross sections as a function of kinetic energy over about a three order of magnitude range were studied for all systems. The reaction of U + + CO 2 proceeds to form UO + + CO with an efficiency of 118% ± 24% as well as generating UO 2 + + C and UCO + + O. The reaction of UO + + O 2 forms UO 2 + in an exothermic, barrierless process and also results in the collision-induced dissociation of UO + to yield U + . In the UO + + CO reaction, the formation of UO 2 + in an endothermic process is the dominant reaction, but minor products of UCO + + O and U + + (O + CO) are also observed. Analysis of the kinetic energy dependences observed provides the bond energies, D 0 (U + –O) = 7.98 ± 0.22 and 8.05 ± 0.14 eV, D 0 (U + –CO) = 0.73 ± 0.13 eV, and D 0 (OU + –O) = 7.56 ± 0.12 eV. The values obtained for D 0 (U + –O) and D 0 (OU + –O) agree well with the previously reported literature values. To our knowledge, this is the first experimental measurement of D 0 (U + –CO). Furthermore, an analysis of the oxide bond energies shows that participation of 5f orbitals leads to a substantial increase in the thermodynamic stability of UO 2 + relative to ThO 2 + and especially transition metal dioxide cations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Integrated Bosch Process System Models for In-Situ Oxygen and Carbon Production

In-Situ Resource Utilization (ISRU) technology is a vital component to NASA’s mission of a sustainable presence on the Moon and Mars. Local resources can be leveraged to reduce resupply frequency and mass. Elements of the Bosch process, combined with the carbothermal reduction process, can produce oxygen on the lunar surface with minimal consumables. The Bosch process can also produce oxygen on the Martian surface by using the CO 2 -rich environment. Between both systems, adsorption pump, solar thermal energy, carbon formation reactor, and water recovery subsystems are modeled and integrated to create a functional model in MATLAB software. The model is used to simulate performance of the system and reduce mass, power, and volume requirements. This integrated system model provides a tool to scale ISRU technologies for oxygen and carbon production. The MATLAB model is created by developing a system of independent subsystem models that are solved for their quasi-steady state values which can be integrated with respect to time to determine the change in current states. A flexible time stepping method is used to ensure a high level of accuracy during periods of rapid change while still making use of a simple explicit integration method. The flexible time step is calculated for each independent subsystem and the minimum value from those is used as the overall time step. A flexible time step is calculated by dividing a resolution value, or the maximum change per time step, by the variables current rate of change. The maximum value from all points in space is used for subsystem models that contain multiple values. The process is done for every variable that is being monitored in each subsystem and the global minimum is used as that iteration’s timestep. Several assumptions used in the MATLAB model for fluid flow dynamics, such as 1-D gas flow through the sorption pump, are supported by modeling in Ansys Fluent software. The Lunar oxygen production system is outlined in Fig. 1. The carbothermal reduction subsystem uses solar energy to heat a mixture of lunar regolith and carbon powder to produce carbon monoxide. To begin, the carbon monoxide feeds to the modified Bosch subsystem along with hydrogen gas. The reactants then enter the carbon formation reactor where water and carbon powder are produced. Solar thermal energy is used to add energy to the reactor, but waste heat from the carbothermal process is another potential heat source. The water is collected and electrolyzed to produce hydrogen which reenters the Bosch subsystem, and the oxygen is stored for downstream use. The carbon powder is collected and feeds back into the carbothermal subsystem. The Martian oxygen production system uses the full Bosch process and is outlined in Fig 2. A CO 2 adsorption pump thermally cycles to scrub and pressurize CO 2 from the environment. Along with an initial supply of hydrogen, the reactants enter the Reverse Water Gas Shift Reactor (RWGSR) which produces carbon monoxide and water. Carbon monoxide and unreacted hydrogen enter the carbon formation reactor to produce water and carbon powder. The water is collected from both reactors and electrolyzed to reintroduce hydrogen and store oxygen for propellant production or life support. Carbon is removed from the carbon formation reactor and stored. The adsorption pump utilizes rapid cycle temperature swings within a stack of zeolite coated surfaces. The subsystem model solves 1-D quasi-steady conservation laws of the quasi-steady form, shown in Eq. 1, for the gas stream and heat exchange liquid to predict performance parameters such as breakthrough capacity and optimum cycle time. The source term S is used to capture interactions between the fluid flows and the sorbent. A quasi-steady-state scheme is used where no time derivatives appear in the governing equations, except for those in the source terms. This results in an autonomous system, where ∂F/∂x = ƒ(F). The fluxes F are provided at the inlet, and an explicit method is used to solve for the spatial distribution of F. The heat and mass flows to the sorbent are then extracted from the source terms. These flows are numerically integrated to produce a 1-D solution for the system’s state as a function of both time and space. The body of the adsorption pump is separated into two semi-independent models: the heat exchanger fluid flow and gas flow through the zeolite coated surfaces. Both models are solved using the above-described method to find a 1-D solution as a function of space and interact only once a timestep is taken. The interaction point is the sorbent through which all heat transfer between the two models must occur. Sorbent mass adsorption is calculated using the Lagergren model, shown in Eq. 2, where the transfer coefficient, λ D , is found by solving a system of nondimensionalized equations derived by using the heat and mass transfer analogy for transport phenomena. Using Grade 544 Type 13X zeolite as the sorbent material, the equilibrium concentration, θ eq , is calculated using the k-site Langmuir isotherm and fit parameters. Additionally, the enthalpy of adsorption used in the model is computed by interpolation of available data [1]. The subsystem model was validated using the Rapid Cycle Temperature Swing Adsorption (RC-TSA) pump. The solar thermal energy subsystem focuses on a solar concentrator concept with a heat exchanger to heat the reactants before entering the carbon formation reactor. The subsystem model assumes a fixed solar flux and reflector efficiency to calculate the reactant temperature given the incoming temperature, pressure, and exchanger geometry. The receiver is a custom manufactured series of copper blocks with serpentine channels to increase its surface area and the residence time of the reactants to heat up to 550 °C. The subsystem model was validated using a heat exchanger developed at NASA Glenn Research Center (GRC). The solar thermal energy subsystem focuses on a solar concentrator concept with a heat exchanger to heat the reactants before entering the carbon formation reactor. The subsystem model assumes a fixed solar flux and reflector efficiency to calculate the reactant temperature given the incoming temperature, pressure, and exchanger geometry. The receiver is a custom manufactured series of copper blocks with serpentine channels to increase its surface area and the residence time of the reactants to heat up to 550 °C. The subsystem model was validated using a heat exchanger developed at NASA Glenn Research Center (GRC).

In situ Resource Utilization↗
DOE OSTI2021

Exploring Explicit Uncertainty for Binary Analysis (EUBA)

Reverse engineering (RE) analysts struggle to address critical questions about the safety of binary code accurately and promptly, and their supporting program analysis tools are simply wrong sometimes. The analysis tools have to approximate in order to provide any information at all, but this means that they introduce uncertainty into their results. And those uncertainties chain from analysis to analysis. We hypothesize that exposing sources, impacts, and control of uncertainty to human binary analysts will allow the analysts to approach their hardest problems with high-powered analytic techniques that they know when to trust. Combining expertise in binary analysis algorithms, human cognition, uncertainty quantification, verification and validation, and visualization, we pursue research that should benefit binary software analysis efforts across the board. We find a strong analogy between RE and exploratory data analysis (EDA); we begin to characterize sources and types of uncertainty found in practice in RE (both in the process and in supporting analyses); we explore a domain-specific focus on uncertainty in pointer analysis, showing that more precise models do help analysts answer small information flow questions faster and more accurately; and we test a general population with domain-general sudoku problems, showing that adding "knobs" to an analysis does not significantly slow down performance. This document describes our explorations in uncertainty in binary analysis.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Reversible CO 2 Hydrogenation, Neutron Crystallography, and Hydride Reactivity of a Triiridium Heptahydride Complex

Abstract The authors report the structure, reactivity, and catalytic utility of a triiridium complex, [Ir 3 H 6 ( μ 3 ‐H)(PN) 3 ] 2+ ( 2‐H , PN = (2‐pyridyl)CH 2 PBu t 2 ). Despite its unusual stability to unsaturated organics, electrophiles, and even CF 3 SO 3 D, they find that complex 2‐H catalyzes hydrogenation of CO 2 to formate (TON Ir = 9600) and reverse formic acid dehydrogenation (TON Ir = 54 400). The hydrogenation operates via a reactive intermediate [Ir 3 H 4 ( μ ‐H) 4 (PN) 3 ] + ( 5 ). Neutron crystallography and DFT‐supported neutron vibrational spectroscopy of 2‐H reveal Ir─H bond lengths and elucidate the vibration modes within the Ir 3 H 7 core. Stoichiometric oxidation of 2‐H produces four classes of iridium complexes of varied nuclearity and hydride structure: tetra‐ and pentanuclear clusters [Ir 3 H 6 ( μ 3 ‐AuPPh 3 )(PN) 3 ] 2+ ( 2‐Au ) and [Ag{Ir 2 H 4 ( μ ‐OAc)(PN) 2 } 2 ] 3+ ( 6 ) are generated using AuPPh 3 + and AgOAc, respectively. Further oxidation to class [Ir 2 H 3 ( μ ‐X) 2 (PN) 2 ] + is possible with AgOAc, Hg(OAc) 2 , or I 2 . Finally, a TEMPO/HCl system completely oxidizes the hydrides and gives [Ir 2 Cl 4 ( μ ‐Cl) 2 (PN) 2 ] ( 11 ).

Cherepakhin, Valeriy [Loker Hydrocarbon Research I↗
DOE OSTI2023

Probing the Potential Energy Profile of the I + (H 2 O) 3 → HI + (H 2 O) 2 OH Forward and Reverse Reactions: High Level CCSD(T) Studies with Spin-Orbit Coupling Included

Three different pathways for the atomic iodine plus water trimer reaction I + (H 2 O) 3 → HI + (H 2 O) 2 OH were preliminarily examined by the DFT-MPW1K method. Related to previous predictions for the F/Cl/Br + (H 2 O) 3 reactions, three pathways for the I + (H 2 O) 3 reaction are linked in terms of geometry and energetics. To legitimize the results, the “gold standard” CCSD(T) method was employed to investigate the lowest-lying pathway with the correlation-consistent polarized valence basis set up to cc-pVQZ(-PP). According to the CCSD(T)/cc-pVQZ(-PP)//CCSD(T)/cc-pVTZ(-PP) results, the I + (H 2 O) 3 → HI + (H 2 O) 2 OH reaction is predicted to be endothermic by 47.0 kcal mol -1 . The submerged transition state is predicted to lie 43.7 kcal mol -1 above the separated reactants. The I···(H 2 O) 3 entrance complex lies below the separated reactants by 4.1 kcal mol -1 , and spin-orbit coupling has a significant impact on this dissociation energy. The HI···(H 2 O) 2 OH exit complex is bound by 4.3 kcal mol -1 in relation to the separated products. Compared with simpler I + (H 2 O) 2 and I + H 2 O reactions, the I + (H 2 O) 3 reaction is energetically between them in general. It is speculated that the reaction between the iodine atom and the larger water clusters may be energetically analogous to the I + (H 2 O) 3 reaction. The iodine reaction I + (H 2 O) 3 is connected with the analogous valence isoelectronic bromine/chlorine reactions Br/Cl + (H 2 O) 3 but much different from the F + (H 2 O) 3 reaction. Significant difference with other halogen systems, especially for barrier heights, are seen for the iodine systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2023

Propulsion-Airframe Integration for Conceptual Redesign of a Low-Boom Supersonic Transport

A low-boom supersonic transport was generated in a previous low-boom multidisciplinary optimization (MDO) study that used computational fluid dynamics (CFD) off-body pressure to compute the undertrack sonic boom ground signature and calibrated low-fidelity aerodynamics analyses to compute the mission performance metrics. This low-boom aircraft, referred to as the Mach 1.7 40-PAX concept, can carry 40 passengers for a low-boom overland mission with cruise Mach 1.7 for the airport pairs over the continental US and has the potential to achieve an undertrack sonic boom ground noise level below 70 perceived level of decibels at the start of overland cruise (SOC). The engine for the Mach 1.7 40-PAX concept was designed using the Numerical Propulsion System Simulation (NPSS) for the mission analysis and modeled as a flow-through nacelle for CFD-based sonic boom analysis. To understand how the engine plume affects the undertrack ground signature, the Mach 1.7 40-PAX concept is redesigned in this paper after replacing the flow-through nacelles by CFD engines for sonic boom analysis using aeropropulsive CFD simulation. A process is developed for approximation of the NPSS engine at SOC by a CFD engine for aeropropulsive CFD simulation. The generated CFD engine has the identical nozzle boundary conditions and approximately the same mass flow and thrust as those of the NPSS engine at SOC. Then, the outer mold line of the configuration with the CFD engines is redesigned to approximately restore the low-boom characteristics of the Mach 1.7 40-PAX concept at SOC. Finally, the CFD simulation data for the redesigned concept with the CFD engines is used to calibrate the low-fidelity aerodynamic analyses for mission analysis of this concept. The cyclic dependency of the involved disciplinary analyses is resolved using an iteration method for a consistent coupling of the mission analysis, NPSS engine analysis, and low-boom redesign using the aeropropulsive CFD simulation. This low-boom redesign study is used as an example to demonstrate how the propulsion-airframe integration could be implemented for conceptual design of supersonic transports that satisfy both the low-boom and mission performance requirements.

Low-boom supersonic transport↗
DOE OSTI2023

Improving Subsurface Stress Characterization for Carbon Dioxide Storage Projects by Incorporating Machine Learning Techniques

The overall objective of this project is to develop a framework for reliable characterization and prediction of the state of stress in the overburden and underburden (including the basement) in CO 2 storage reservoirs using machine learning and integrated geomechanics and geophysical methods. Specifically, we propose to develop workflow encompassing of technologies and/or methods to predict stress and pressure changes due to CO 2 injection in an active tertiary recovery site and their impacts on subtle fault activation, fractures and occurrence of microseismic events and compare responses to field observations. In this project, we anticipate using dataset from the Farnsworth field Unit (FWU) which is operated by Purdure Petroleum. A novel elastic-waveform VSP inversion technique will be used to estimate high-resolution spatial and temporal changes of elastic moduli in CO 2 storage reservoirs, which will be combined with velocity-stress relationship derived from laboratory tests to obtain subsurface pressure and stress. Clustered microseismic data will be jointly inverted for improved focal mechanisms. Least-squares reverse-time migration of microseismic waveform data will be performed to directly image fracture/fault zones. Additionally, a deep neural network machine learning technique with convolutional and recurrent layers will be used for learning the spectro-temporal structures in microseismic waveforms. The results of this geotechnical data analysis will be integrated to develop a high-resolution 3D mechanical earth model extending from the overburden sealing formations to the underburden including the basement. Mechanical properties will be derived through integration of mechanical logs, tests, available results from chemo-mechanical laboratory tests, and elastic inversion of seismic data using a combination of Bayesian and stochastic methods as well as machine learning technique. Failure features (faults/fractures) will be represented and/or modeled based on seismic and core data analysis. A transient hydrodynamic-geomechanical model will be developed through coupling with the calibrated FWU reservoir simulation model. The full physics coupled model will be used to train a reduced order proxy model using machine learning algorithm for estimating stress which will then be used with appropriate constitutive relationships and forward seismological models to simulate pressure changes and induced microseismicity. An advanced optimization framework will be developed to perform a history match to minimize error between field observations and simulated. The history matched proxy model will be verified against the full-physics equivalent. The field observations that will be used in the coupled model calibration process include pressure/stress inverted from VSP, moment magnitude from microseismic analysis, real time downhole pressure measurements, production and injection data. Parameter sensitivity and uncertainty analysis will be performed to characterize the impact of model parameter uncertainty on stress estimates. The proposed project will have significant impact on future field implementation of the proposed technology. Because the project field site is an ongoing CO 2 EOR development, the value of the new technology will be demonstrated in an operational context and evaluated as a viable risk mitigation strategy. Cost/benefit will be evaluated together with the various commercial incentives for CO 2 sequestration available to oil and gas operators. The extensive available dataset and ongoing data acquisition under the SWP Phase III work plan provides flexibility for investigation of multiple approaches and reduces technical risk.

58 GEOSCIENCES↗
DOE OSTI2026

The Levelized Cost of Exergy Framework

Exergy is the amount of energy within a substance or within a transfer of energy that can be used to produce work or some other useful output when interacting with some reference environment. Two different energy systems that produce the same output with the same exergetic efficiency must necessarily have the same amount of exergy input, even if the amount of energy input to the two systems is vastly different. For example, a low-grade heat-driven desalination would require far more energy input than a reverse osmosis (RO) plant producing the same amount of water, but if their exergetic efficiencies were the same, they would require the same amount of exergy input. Thus, comparisons between different energy sources on the basis of energy is not always appropriate. Instead, a comparison on a per unit exergy basis provides more insight on the cost-effectiveness of different energy sources and systems. In this presentation, we describe a framework for analyzing the levelized cost of exergy (LCOEx) for both inputs and outputs of various energy systems. Our framework illustrates how the cost per unit exergy of a system's energy source, as well as the exergetic efficiency of the system, greatly affect the cost of the system output. We use the levelized cost of electricity as a benchmark value for LCOEx, due to electricity's ubiquity as an energy source, and because it is relatively inexpensive on a per unit exergy basis. The LCOEx of various heat sources are then compared to the LCOEx of electricity. Medium- and high-grade industrial heat (> 150 degrees C) produced by natural gas tends to have an LCOEx on par with electricity. This is due to the low cost of natural gas, as well as the high exergy content of heat at higher temperatures. Meanwhile, low-grade heat tends to be an expensive exergy source, owing to the low exergy content of the low-grade heat. We first apply our framework to desalination, where RO has come to dominate, due to the low LCOEx of the energy source (electricity) and relatively high exergetic efficiency of RO compared to thermal desalination systems. We then use this framework to highlight an opportunity for dehumidification systems to experience a similar cost improvement as desalination has. If an electrically-driven, high exergetic efficiency dehumidification system were developed (such as the membrane-based dehumidification systems proposed in literature), it would use a low cost exergy source with a high exergetic efficiency and could potentially lower the cost of dehumidification in the way that RO has done for desalination. Finally, we apply our framework to various fuels (natural gas, hydrogen, gasoline, etc.) and energy systems across different sectors (desalination, dehumidification, vehicles, etc.) to understand the variation in the cost of exergy input and exergetic efficiency of different systems and technologies.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗