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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Precipitation of dopants on acceptor-doped LaMnO3±δ revealed by defect chemistry from first principles

Perovskite oxides degrade at elevated temperatures while precipitating dopant-rich particles on the surface. A knowledge-based improvement of surface stability requires a fundamental and quantitative understanding of the dopant precipitation mechanism on these materials. We propose that dopant precipitation is a consequence of the variation of dopant solubility between calcination and operating conditions in solid oxide fuel cells (SOFCs) and electrolyzer cells (SOECs). To study dopant precipitation, we use 20% (D = Ca, Sr, Ba)-doped LaMnO3+δ (LDM20) as a model system. We employ a defect model taking input from density functional theory calculations. The defect model considers the equilibration of LDM20 with a reservoir consisting of dopant oxide (DO), peroxide (DO2), and O2 in the gas phase. The equilibrated non-stoichiometry of the A-site and B-site as a function of temperature, T, and oxygen partial pressure, p(O2), reveals three regimes for LDM20: A-site deficient (oxidizing conditions), A-site rich (atmospheric conditions), and near-stoichiometric (reducing conditions). Assuming an initial A/B non-stoichiometry, we compute the dopant precipitation boundaries in a p–T phase diagram. Our model predicts precipitation both under reducing (DO) and under highly oxidizing conditions (DO2). We found precipitation under anodic, SOEC conditions to be promoted by large dopant size, while under cathodic, SOFC conditions precipitation is promoted by initial A-site excess. The main driving forces for precipitation are oxygen uptake by the condensed phase under oxidizing conditions and oxygen release assisted by B-site vacancies under reducing conditions. Possible strategies for mitigating dopant precipitation under in electrolytic and fuel cell conditions are discussed.

Hess, Franziska (ORCID:000000024460643X)↗
DOE OSTI2024

Multi-Scale 3D Imaging for Machine Learning Property Upscaling: Mt. Simon Sandstone Case Study

Petrographic properties of principal target reservoirs for carbon sequestration, such as the Mt. Simon Sandstone, are relevant to broad interest groups. The Mt. Simon Sandstone is a deep, saline, regionally extensive Cambrian sandstone, overlain by low permeability sealing formations, making it one of the viable geologic carbon storage reservoirs in the Midwestern US. Its thickness (exceeding 2400 ft in some localities), depth, and lateral extent, combined with high porosity and permeability make it a high-priority target of multiple ongoing geologic carbon sequestration efforts in the United States of America. The National Energy Technology Laboratory in Morgantown, West Virginia, has been engaged in characterization efforts of the Mt. Simon for over a decade, with a strong focus on Computed Tomographic data acquisition. Data generated during this period has been hitherto not accessible to the public. This archival effort focused on preservation of historical CT data and associated metadata, and facilitating their accessibility, culminating with the publication of the entire dataset on NETL’s Energy Data eXchange (EDX) and the associated Gill et. al (2024) paper.

Gill, Magdalena K.↗
DOE OSTI2023

Enabling site-specific well leakage risk estimation during geologic carbon sequestration using a modular deep-learning-based wellbore leakage model

Geologic carbon sequestration (GCS) is a promising technology for mitigating net carbon emissions and growing climate concern by storing CO 2 in reservoirs. Oil and gas brownfields are an attractive option for CO 2 storage, but these sites have many historical wellbores from petroleum production and can be a potential leakage pathway for CO 2 or formation brine. Therefore, risk management of GCS operations requires an assessment of potential well leakage. Due to the high uncertainty of the system, stochastic approaches are ideal for quantifying the range of risk behaviors, but they must be computationally efficient in the face of complex physics. Here, we develop a new physics-centric deep learning wellbore model to predict the leakage of CO 2 and brine through leaky wellbores. Multi-physics numerical simulations were used to generate data sets, and physics-informed features were introduced. Neural networks were optimized with an automated searching algorithm. Feature analysis quantifies the impact of each feature on model prediction and confirms the role of physics-inspired parameters. The model shows high predictive performance across a wide range of geologic and injection conditions and well attributes. In conclusion, a case study illustrates how the model is applied to assess well leakage in GCS operations.

58 GEOSCIENCES↗
DOE OSTI2025

Deep Learning-based Surrogate Model for Efficient Reservoir Simulation in Large-scale Geological Carbon Storage: Application in IBDP Dataset

This project introduces an advanced deep learning (DL)-based surrogate modeling approach to enhance the efficiency and accuracy of large-scale geological carbon storage (GCS) simulations. Using the Illinois Basin Decatur Project (IBDP) dataset as training data, the study employs a residual U-Net architecture to predict critical state variables such as pressure and CO₂ saturation, as well as CO₂ plume migration. By incorporating key geological parameters (e.g., porosity, permeability, and rock facies) and physics-informed inputs like the diffusive time of flight and time step, the DL model effectively reduces computational complexity while maintaining robust physical constraints. Compared to traditional simulators like Eclipse, the DL model achieves remarkable accuracy, with a root mean square error (RMSE) of 1.57 psi for pressure and 0.007 for saturation, and dramatically reduces computational time from hours to just 69.9 seconds for 50-step simulations. These results demonstrate the potential of innovative DL methodologies to improve the predictivity and operational efficiency of GCS simulations, providing a reliable foundation for decision-making in CCS operations. Supported by the SMART initiative, this project underscores the success of leveraging computational innovations to advance CCS technologies.

advanced deep learning↗
DOE OSTI2020

Unification of nonequilibrium molecular dynamics and the mode-resolved phonon Boltzmann equation for thermal transport simulations

Nano-size confinement induces many intriguing non-Fourier heat conduction phenomena, such as nonlinear temperature gradients, temperature jumps near the contacts, and size-dependent thermal conductivity. Over the past decades, these effects have been studied and interpreted by nonequilibrium molecular dynamics (NEMD) and phonon Boltzmann transport equation (BTE) simulations separately, but no theory that unifies these two methods has ever been established. In this work, we unify these methods using a quantitative mode-level comparison and demonstrate that they are equivalent for various thermostats. We show that different thermostats result in different non-Fourier thermal transport characteristics due to the different mode-level phonon excitations inside the thermostats, which explains the different size-dependent thermal conductivities calculated using different reservoirs, even though they give the same bulk thermal conductivity. Specifically, the Langevin thermostat behaves like a thermalizing boundary in phonon BTE and provides mode-level thermal-equilibrium phonon outlets, while the Nose-Hoover chain thermostat and velocity rescaling method behave like biased reservoirs, which provide a spatially uniform heat generation and mode-level nonequilibrium phonon outlets. These findings explain why different experimental measurement methods can yield different size-dependent thermal conductivity. They also indicate that the thermal conductivity of materials can be tuned for various applications by specifically designing thermostats. Finally, the unification of NEMD and phonon BTE will largely facilitate the study of thermal transport in complex systems in the future by, e.g., replacing computationally unaffordable first-principles NEMD simulations with computationally less expensive spectral BTE simulations.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
NASA NTRS1979

Recycling isoelectric focusing with computer controlled data acquisition system

Isoelectric focusing is a high-resolution technique for separating and purifying large peptides, proteins, and other biomolecules. The apparatus described in the present paper constitutes a new approach to fluid stabilization and increased throughput. Stabilization is achieved by flowing the process fluid uniformly through an array of closely spaced filter elements oriented parallel both to the electrodes and the direction of the flow. This seems to overcome the major difficulties of parabolic flow and electroosmosis at the walls, while limiting the convection to chamber compartments defined by adjacent spacers. Increased throughput is achieved by recirculating the process fluid through external heat exchange reservoirs, where the Joule heat is dissipated.

Egen, N. B.↗
NASA NTRS1982

Cryogenic testing of mirrors for infrared space telescopes

The Shuttle IR Telescope Facility (SIRTF) test apparatus can test candidate mirror materials as large as 66 cm in diameter, at temperatures as low as about 10 K, and is accurate enough to detect optical figure changes as small as a fraction of a wavelength from the room temperature figure. The fused silica mirrors currently undergoing testing in the SIRTF are sunk into a liquid He reservoir with copper straps, whose individual strands are soldered to small silver spots diffused throughout the unfigured side of the mirror to accomplish fast conductive cooling. Optical access to the cold mirror is by means of a small glass port in the vacuum chamber. An interferometer is used to examine the mirror figure throughout the cool-down. Interferograms are photographed, fringe patterns are digitized, and mirror figure contour plots are calculated by means of a computer.

Miller, J. H.↗
DOE OSTI2022

Predicting variations of the least principal stress with depth: Application to unconventional oil and gas reservoirs using a log-based viscoelastic stress relaxation model

Knowledge of layer-to-layer variations of the least principal stress, S hmin , with depth is essential for optimization of multi-stage hydraulic fracturing in unconventional reservoirs. Utilizing a geomechanical model based on viscoelastic stress relaxation in relatively clay rich rocks, we present a new method for predicting continuous S hmin variations with depth. The method utilizes geophysical log data and S hmin measurements from routine diagnostic fracture injection tests (DFITs) at several depths for calibration. We consider a case study in the Wolfcamp formation in the Midland Basin, where both geophysical logs and values of S hmin from DFITs are available. We compute a continuous stress profile as a function of the well logs that fits all of the DFITs well. We utilized several machine learning technologies, such as bootstrap aggregation (or bagging), to improve the generalization of the model and demonstrate that the excellent fit between predicted and observed stress values is not the result of over-fitting the calibration points. The model is then validated by accurately predicting hold-out stress measurements from four wells within the study area and, without recalibration, accurately predicting stress as a function of depth in an offset pad about 6 miles away.

58 GEOSCIENCES↗
NASA NTRS1995

Aerothermodynamic Facilities And Measurement: Flow Characterization in Shock Tunnels

This presentation will examine the key performance aspects of shock tunnels as they relate to their use as aerothermodynamic flow simulation facilities. Assessment of shock tube reservoir conditions and flow contaminants generated in the shock tube will be presented along with their limiting impact on viable test envelopes, Facility nozzle performance as it pertains to test time assessment and nozzle exit flow quality (survey of pressure, temperature, and species) will be addressed. Also included will be a discussion of free stream flow diagnostics, both intrusive and nonintrusive, for measurement of critical flow properties not directly inferred from surface mounted transducers. The use of computational fluid dynamics for purposes of validating experimental measurements as well as predicting performance in regimes where measurements are not feasible or possible will be discussed. The use of CFD for facility research and design will also be presented.

Cavolowsky, John A.↗
NASA NTRS2016

Large-Scale and Global Hydrology

Powered by the sun, water moves continuously between and through Earths oceanic, atmospheric, and terrestrial reservoirs. It enables life, shapes Earths surface, and responds to and influences climate change. Scientists measure various features of the water cycle using a combination of ground, airborne, and space-based observations, and seek to characterize it at multiple scales with the aid of numerical models. Over time our understanding of the water cycle and ability to quantify it have improved, owing to advances in observational capabilities, the extension of the data record, and increases in computing power and storage. Here we present some of the most recent estimates of global and continental ocean basin scale water cycle stocks and fluxes and provide examples of modern numerical modeling systems and reanalyses.Further, we discuss prospects for predicting water cycle variability at seasonal and longer scales, which is complicated by a changing climate and direct human impacts related to water management and agriculture. Changes to the water cycle will be among the most obvious and important facets of climate change, thus it is crucial that we continue to invest in our ability to monitor it.

land hydrology↗
NASA NTRS1998

Issues in the reconstruction of environmental doses on the basis of thermoluminescence measurements in the Techa riverside

The potential of thermoluminescence measurements of bricks from the contaminated area of the Techa river valley, Southern Urals, Russia, for reconstructing external exposures of affected population groups has been studied. Thermoluminescence dating of background samples was used to evaluate the age of old buildings available on the river banks. The anthropogenic gamma dose accrued in exposed samples is determined by subtracting the natural radiation background dose for the corresponding age from the accumulated dose measured by thermoluminescence. For a site in the upper Techa river region, where the levels of external exposures were extremely high, the depth-dose distribution in bricks and the dependence of accidental dose on the height of the sampling position were determined. For the same site, Monte Carlo simulations of radiation transport were performed for different source configurations corresponding to the situation before and after the construction of a reservoir on the river and evacuation of the population in 1956. A comparison of the results provides an understanding of the features of the measured depth-dose distributions and height dependencies in terms of the source configurations and shows that bricks from the higher sampling positions are likely to have accrued a larger fraction of anthropogenic dose from the time before the construction of the reservoir. The applicability of the thermoluminescent dosimetry method to environmental dose reconstruction in the middle Techa region, where the external exposure was relatively low, was also investigated.

NASA Discipline Radiation Health↗
NASA NTRS1983

Oil Recovery System

A Downhole Steam Generation System brings oil up from deep reservoirs. The system, developed by Foster-Miller Associates consists of a steam generator, a "packer" that keeps the steam from leaking up the wellbore, and tube string that supplies air, fuel, water and hydraulics to the generator and packer; all are encased in a standard seven-inch well casing. Downhole means that the steam generator is located far down the well casing rather than on the surface. This design is more efficient than surface generated steam. A COSMIC (Computer Software Management and Information Center) program aided in the design.

Source record↗
NASA NTRS2012

Simplex GPS and InSAR Inversion Software

Changes in the shape of the Earth's surface can be routinely measured with precisions better than centimeters. Processes below the surface often drive these changes and as a result, investigators require models with inversion methods to characterize the sources. Simplex inverts any combination of GPS (global positioning system), UAVSAR (uninhabited aerial vehicle synthetic aperture radar), and InSAR (interferometric synthetic aperture radar) data simultaneously for elastic response from fault and fluid motions. It can be used to solve for multiple faults and parameters, all of which can be specified or allowed to vary. The software can be used to study long-term tectonic motions and the faults responsible for those motions, or can be used to invert for co-seismic slip from earthquakes. Solutions involving estimation of fault motion and changes in fluid reservoirs such as magma or water are possible. Any arbitrary number of faults or parameters can be considered. Simplex specifically solves for any of location, geometry, fault slip, and expansion/contraction of a single or multiple faults. It inverts GPS and InSAR data for elastic dislocations in a half-space. Slip parameters include strike slip, dip slip, and tensile dislocations. It includes a map interface for both setting up the models and viewing the results. Results, including faults, and observed, computed, and residual displacements, are output in text format, a map interface, and can be exported to KML. The software interfaces with the QuakeTables database allowing a user to select existing fault parameters or data. Simplex can be accessed through the QuakeSim portal graphical user interface or run from a UNIX command line.

Donnellan, Andrea↗
DOE OSTI2020

Protonic solid-state electrochemical synapse for physical neural networks

Physical neural networks made of analog resistive switching processors are promising platforms for analog computing. State-of-the-art resistive switches rely on either conductive filament formation or phase change. These processes suffer from poor reproducibility or high energy consumption, respectively. Herein, we demonstrate the behavior of an alternative synapse design that relies on a deterministic charge-controlled mechanism, modulated electrochemically in solid-state. The device operates by shuffling the smallest cation, the proton, in a three-terminal configuration. It has a channel of active material, WO 3 . A solid proton reservoir layer, PdH x , also serves as the gate terminal. A proton conducting solid electrolyte separates the channel and the reservoir. By protonation/deprotonation, we modulate the electronic conductivity of the channel over seven orders of magnitude, obtaining a continuum of resistance states. Proton intercalation increases the electronic conductivity of WO 3 by increasing both the carrier density and mobility. This switching mechanism offers low energy dissipation, good reversibility, and high symmetry in programming.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Simulating Fracture Sealing by Granular LCM Particles in Geothermal Drilling

Lost circulation occurs when the returned fluid is less than what is pumped into the well due to loss of fluid to pores or fractures. A lost-circulation event is a common occurrence in a geothermal well. Typical geothermal reservoirs are often under-pressured and have larger fracture apertures. A severe lost-circulation event is costly and may lead to stuck pipe, well instability, and well abandonment. One typical treatment is adding lost-circulation materials (LCMs) to seal fractures. Conventional LCMs fail to properly seal fractures because their mechanical limit is exceeded at elevated temperatures. In this paper, parametric studies in numerical simulations are conducted to better understand different thermal effects on the sealing mechanisms of LCMs. The computational fluid dynamics (CFDs) and the discrete element method (DEM) are coupled to accurately capture the true physics of sealing by granular materials. Due to computational limits, the traditional Eulerian–Eulerian approach treats solid particles as a group of continuum matter. With the advance of modern computational power, particle bridging is achievable with DEM to track individual particles by modeling their interactive forces between each other. Particle–fluid interactions can be modeled by coupling CFD algorithms. Fracture sealing capability is investigated by studying the effect of four individual properties including fluid viscosity, particle size, friction coefficient, and Young’s modulus. It is found that thermally degraded properties lead to inefficient fracture sealing.

15 GEOTHERMAL ENERGY↗
DOE OSTI2023

FY22 Laboratory Directed Research and Development Annual Report

The Laboratory Directed Research and Development (LDRD) program yields foundational scientific research and development (R&D) essential to growing SRNL’s core competencies, in alignment with SRNL’s Strategic Plan to provide long-term benefits to the Department of Energy (DOE), the National Nuclear Security Administration (NNSA), and other customers and stakeholders. Five strategic goals are outlined in SRNL’s strategic plan: 1) Provide applied science and engineering for EM’s active clean-up sites and LM’s post closure management sites; 2) Provide science-based solutions for gaps identified in nonproliferation strategic vision and support the government in actives impacting national security; 3) Lead ST&E as the central technical authority for processing tritium loaded reservoirs and support production of plutonium pits; 4) Align science and energy security programs by focusing modern modeling, simulation, and data analytics tools on materials engineering and performance applications; 5) Build a workforce for the future.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS1994

A statistical survey of the central plasma sheet

We present a statistical analysis of the ion plasma properties and magnetic field of the central plasma sheet at distances from 10 to 23 R(sub E) from the Earth. This study incorporates International Sun-Earth Explorer (ISEE) 1 plasma sheet samples accumulated with the University of Iowa Lepedea plasma analyzer during 1979. Our study of the bulk properties of the ion plasma shows that there is relatively little change as a result of increasing geomagnetic activity. The main macroscopic effect is an increase in plasma temperature. When the transverse profile of the plasma sheet is examined, it is clear that this increase in temperature occurs preferentially at high latitudes. The magnetic field shows an increase in magnitude in the vicinity of the neutral sheet periods of high geomagnetic activity. The radial profiles of the density, temperature, plasma, and magnetic pressures show a gradient in total energy density in the plasma sheet. The convective velocities are systematically directed earthward, and there is a bias toward positive V(sub y) on the duskside of the magnetotail. We infer that particle motion can be attributed to a pressure gradient, combined with steady state convective electric fields. We compute the convective electric fields from our measured bulk velocities, arriving at a pattern similar to that observed at low altitudes. The cross-tail electric field E(sub y) is dominant near the neutral sheet region, but at high latitudes E(sub z) is a major component. Our results suggest that the plasma sheet boundary layer, rather than the central plasma sheet, is a region of diverging electric fields. The picture of the plasma sheet that emerges from our study is that of a stable reservoir of hot plasma in which thermodynamic, rather than dynamic, processes is important.

Huang, C. Y.↗
DOE OSTI2020

Evolutionary Optimization for Neuromorphic Systems

Designing and training an appropriate spiking neural network for neuromorphic deployment remains an open challenge in neuromorphic computing. In 2016, we introduced an approach for utilizing evolutionary optimization to address this challenge called Evolutionary Optimization for Neuromorphic Systems (EONS). In this work, we present an improvement to this approach that enables rapid prototyping of new applications of spiking neural networks in neuromorphic systems. We discuss the overall EONS framework and its improvements over the previous implementation. We present several case studies of how EONS can be used, including to train spiking neural networks for classification and control tasks, to train under hardware constraints, to evolve a reservoir for a liquid state machine, and to evolve smaller networks using multi-objective optimization.

Schuman, Catherine↗