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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Echo state network for coarsening dynamics of charge density waves

An echo state network (ESN) is a type of reservoir computer that uses a recurrent neural network with a sparsely connected hidden layer. Compared with other recurrent neural networks, one great advantage of ESN is the simplicity of its training process. Yet, despite the seemingly restricted learnable parameters, ESN has been shown to successfully capture the spatial-temporal dynamics of complex patterns. Here we build an ESN to model the coarsening dynamics of charge-density waves (CDWs) in a semiclassical Holstein model, which exhibits a checkerboard electron density modulation at half-filling stabilized by a commensurate lattice distortion. The inputs to the ESN are local CDW order parameters in a finite neighborhood centered around a given site, while the output is the predicted CDW order of the center site at the next time step. Special care is taken in the design of couplings between hidden layer and input nodes to ensure lattice symmetries are properly incorporated into the ESN model. Since the model predictions depend only on CDW configurations of a finite domain, the ESN is scalable and transferrable in the sense that a model trained on dataset from a small system can be directly applied to dynamical simulations on larger lattices. Furthermore, our work opens avenues for efficient dynamical modeling of pattern formations in functional electron materials.

2-dimensional systems↗
DOE OSTI2024

xesn: Echo state networks powered by Xarray and Dask

Xesn is a Python package that allows scientists to easily design Echo State Networks (ESNs) for forecasting problems. ESNs are a Recurrent Neural Network architecture introduced by Jaeger (2001) that are part of a class of techniques termed Reservoir Computing. One defining characteristic of these techniques is that all internal weights are determined by a handful of global, scalar parameters, thereby avoiding problems during backpropagation and reducing training time significantly. Because this architecture is conceptually simple, many scientists implement ESNs from scratch, leading to questions about computational performance. Xesn offers a straightforward, standard implementation of ESNs that operates efficiently on CPU and GPU hardware. The package leverages optimization tools to automate the parameter selection process, so that scientists can reduce the time finding a good architecture and focus on using ESNs for their domain application. Importantly, the package flexibly handles forecasting tasks for out-of-core, multi-dimensional datasets, eliminating the need to write parallel programming code. Xesn was initially developed to handle the problem of forecasting weather dynamics, and so it integrates naturally with Python packages that have become familiar to weather and climate scientists such as Xarray (Hoyer & Hamman, 2017). However, the software is ultimately general enough to be utilized in other domains where ESNs have been useful, such as in signal processing (Jaeger & Haas, 2004).

97 MATHEMATICS AND COMPUTING↗
NASA NTRS1975

LANDSAT-1 data as it has been applied for land use and water quality data by the Virginia State Water Control Board. 1: The state project. 2: Monitoring water quality from LANDSAT

LANDSAT-1 imagery has been used for water quality and land use monitoring in and around the Swift Creek and Lake Chesdin Reservoirs in Virginia. This has proved useful by (1) helping determine valid reservoir sampling stations, (2) monitoring areas not accessible by land or water, (3) giving the State a viable means of measuring Secchi depth readings in these inaccessible areas, (4) giving an overview of trends in changing sedimentation loadings over a given time period and classifying these waters into various categories, (5) enabling the State to inventory all major lakes and reservoirs and computing their acreage, (6) monitoring land use changes in any specific area, (7) evaluating possible long-term environmental effects of nearby developments, and (8) monitoring and predicting population shifts with possible impact on water quality problems. The main problems in the long-term use of such imagery appear to be cost and lack of consistency due to cloud cover limitations.

Trexler, P. L.↗
NASA NTRS2007

Radiolytic Gas-Driven Cryovolcanism in the Outer Solar System

Water ices in surface crusts of Europa, Enceladus, Saturn's main rings, and Kuiper Belt Objects can become heavily oxidized from radiolytic chemical alteration of near-surface water ice by space environment irradiation. Oxidant accumulations and gas production are manifested in part through observed H2O2 on Europa. tentatively also on Enceladus, and found elsewhere in gaseous or condensed phases at moons and rings of Jupiter and Saturn. On subsequent chemical contact in sub-surface environments with significant concentrations of primordially abundant reductants such as NH3 and CH4, oxidants of radiolytic origin can react exothermically to power gas-driven cryovolcanism. The gas-piston effect enormously amplifies the mass flow output in the case of gas formation at basal thermal margins of incompressible fluid reservoirs. Surface irradiation, H2O2 production, NH3 oxidation, and resultant heat, gas, and gas-driven mass flow rates are computed in the fluid reservoir case for selected bodies. At Enceladus the oxidant power inputs are comparable to limits on nonthermal kinetic power for the south polar plumes. Total heat output and plume gas abundance may be accounted for at Enceladus if plume activity is cyclic in high and low "Old Faithful" phases, so that oxidants can accumulate during low activity phases. Interior upwelling of primordially abundant NH3 and CH4 hydrates is assumed to resupply the reductant fuels. Much lower irradiation fluxes on Kuiper Belt Objects require correspondingly larger times for accumulation of oxidants to produce comparable resurfacing, but brightness and surface composition of some objects suggest that such activity may be ongoing.

Cooper, John F.↗
DOE OSTI2024

Accelerating Multiphase Simulations With Denoising Diffusion Model Driven Initializations

This study introduces a hybrid fluid simulation approach that integrates generative diffusion models with physics‐based simulations, aiming at reducing the computational costs of flow simulations while still honoring all the physical properties of interest. Pore‐scale simulations enhance our understanding of applications such as assessing hydrogen and storage efficiency in underground reservoirs. Nevertheless, they are computationally expensive and the presence of non‐unique solutions can require multiple simulations within a single geometry. To overcome the computational cost hurdle, we propose a method that couples generative diffusion models and physics‐based simulations. While training the data‐driven model, we simultaneously generate initial conditions and perform physics‐based simulations using these. This integrated approach enables us to receive real‐time feedback on a single compute node equipped with both CPUs and GPUs. By efficiently managing these processes within a single compute node, we can continuously monitor performance and halt training once the model meets the specified criteria. To test our model, we generate realizations in a real Berea sandstone fracture which shows that our technique is up to 4.4 times faster than commonly used flow simulation initializations.

36 MATERIALS SCIENCE↗
NASA NTRS1974

Quantitative water quality with ERTS-1

Analyses of ERTS-1 MSS computer compatible tapes of reservoir scenes in Kansas along with ground truth show that MSS bands and band ratios can be used for reliable prediction of suspended loads up to at least 900 ppm. The major reservoirs in Kansas, as well as in other Great Plains states, are playing increasingly important roles in flood control, recreation, agriculture, and urban water supply. Satellite imagery is proving useful for acquiring timely low cost water quality data required for optimum management of these fresh water resources.

Yarger, H. L.↗
DOE OSTI2025

Computer Programs For Analysis Of Thermally Reactive Tracer Tests In Geothermal Reservoirs

The Tracer Analysis Toolbox (TAT) is a stand-alone executable software package that focuses on reservoir monitoring, using changes in concentration of thermally degrading tracers between and injection well and a production well to reflect the temperature-time histories along flow paths through the reservoir. By conducting such tests periodically, the thermal evolution of the flow paths connecting injection wells to production wells can be monitored. Because this approach is sensitive to temperature along the whole path, it can detect changes well before they occur at the distal end of the path (i.e., the production well). Thus, the thermally degrading tracer method provides early detection of thermal decline before it impacts that well. The goal of TAT is to simplify planning of a reactive tracer monitoring campaign and interpretation of results by providing: (1) tracer test planning and analysis tools to define the necessary kinetic parameters, injection concentrations, and other tracer parameters that are suitable for the expected reservoir residence time and temperature, (2) identification of tracers with suitable characteristics (defined by (1)) and (3) a suite of analysis programs to analyze tracer test results.

Plummer, MitchellAaron [Idaho National Laboratory ↗
NASA NTRS1932

Effect of the Reservoir Volume on the Discharge Pressures in the Injection System of the N.A.C.A. Spray Photography Equipment

Tests were made to determine the effect of the reservoir volume on the discharge pressures in the injection system of the N.A.C.A. spray photography equipment. The data obtained are applicable to the design of a common rail fuel-injection system. The data show that an injection system of the type described can be designed so that not more than full load fuel quantity can be injected into the engine cylinders, and so that the fuel spray characteristics remain constant over a large range of engine speeds. Formulas are presented for computing the volume of the reservoir and the diameter of the discharge orifice.

Rothrock, A M↗
NASA NTRS1977

Fluid-line math model

Simplified mathematical model simulates large hydraulic systems on either analog or digital computers. Models of pumps, servoactuators, reservoirs, accumulators, and valves are connected generating systems containing six hundred elements.

Kandelman, A.↗
NASA NTRS2012

Enabling Earth Science Through Cloud Computing

Cloud Computing holds tremendous potential for missions across the National Aeronautics and Space Administration. Several flight missions are already benefiting from an investment in cloud computing for mission critical pipelines and services through faster processing time, higher availability, and drastically lower costs available on cloud systems. However, these processes do not currently extend to general scientific algorithms relevant to earth science missions. The members of the Airborne Cloud Computing Environment task at the Jet Propulsion Laboratory have worked closely with the Carbon in Arctic Reservoirs Vulnerability Experiment (CARVE) mission to integrate cloud computing into their science data processing pipeline. This paper details the efforts involved in deploying a science data system for the CARVE mission, evaluating and integrating cloud computing solutions with the system and porting their science algorithms for execution in a cloud environment.

science data system↗
NASA NTRS1980

Trophic classification of Tennessee Valley area reservoirs derived from LANDSAT multispectral scanner data

LANDSAT MSS data from four different dates were extracted from computer tapes using a semiautomated digital data handling and analysis system. Reservoirs were extracted from the surrounding land matrix by using a Band 7 density level slice of 3; and descriptive statistics to include mean, variance, and ratio between bands for each of the four bands were calculated. Significant correlations ( 0.80) were identified between the MSS statistics and many trophic indicators from ground truth water quality data collected at 35 reservoirs in the greater Tennessee Valley region. Regression models were developed which gave significant estimates of each reservoir's trophic state as defined by its trophic state index and explained in all four LANDSAT frames at least 85 percent of the variability in the data. To illustrate the spatial variations within reservoirs as well as the relative variations between reservoirs, a table look up elliptical classification was used in conjunction with each reservoir's trophic state index to classify each reservoir on a pixel by pixel basis and produce color coded thematic representations.

Meinert, D. L.↗
NASA NTRS1980

Tres Marias Reservoir, Minas Gerais State: Study of the dispersion of suspended sediments in surface waters using orbital images

Computer compatible tapes from LANDSAT were used to compartmentalize the Ires Marias reservoir according to respective grey level spectral response. Interactive and automatic, supervised classification, was executed from the IMAGE-100 system. From the simple correlation analysis and graphic representation, it is shown that grey tone levels are inversely proportional to Secchi Depth values. It is further shown that the most favorable period to conduct an analysis of this type is during the rainy season.

Dejesusparada, N.↗
NASA NTRS1994

Computation of nonequilibrium viscous flows in arc-jet wind tunnel nozzles

The axisymmetric Navier-Stokes equations are numerically solved for air flows in converging-diverging conical nozzles. A nonequilibrium formulation with a two-temperature thermochemical model has been employed for vibrationally excited, partially dissociated and ionized air. Evaluations of the formulation is made through comparisons of the computed flow quantities with the experimental data obtained in expanding arc-jet nozzle flows. Two sets of experimental data obtained in conical nozzles with geometric exit-to-throat area ratios of 625 and 8100 cover reservoir pressures ranging from 5 to 95 atm and enthalpies from 5 to 10 MJ/kg. The computed results are in good agreement with local mass flux, Pitot pressure, and static wall pressure measurements at the nozzle exit. For low pressure reservoir conditions, comparisons of computations with the experimental data indicate that the wall catalycity is very important in predicting species mole fraction at the nozzle exit since gas phase chemistry is frozen. The current computations overpredict the atomic oxygen concentrations which in turn results in underprediction of the vibrational temperature.

Gokcen, Tahir↗
DOE OSTI2025

CO2 Plume Imaging with Accelerated Deep Learning-based Data Assimilation Considering Multiple Realizations: Application to the Illinois Basin-Decatur Carbon Sequestration Project

We propose a fast and efficient deep learning workflow for near real-time data assimilation, forecasting and visualization of CO2 plume evolution in saline aquifer and demonstrate its application at a field site. Unlike the previous work, this study incorporates the impact of spatial heterogeneity using multiple realizations. In the proposed workflow, a neural network model utilizes available monitoring data such as downhole pressure measurements as input and predicts the propagating pressure ‘front’ using the diffusive time of flight (DTOF) map which is considered as representative reservoir image of the flow field. The DTOF is the arrival time of pressure front propagation, which can be computed by the Fast Marching Method rapidly without flow simulations. Reservoir model calibration can be implemented by selecting the training data samples that describe the predicted DTOF map based on observed data. The power and efficacy of our workflow is demonstrated by application to the Illinois Basin-Decatur Project.

CO2 plume imaging↗
DOE OSTI2026

A Boundary Element Model for Assessing Large‐Scale Pressurization in Faulted Geological Storage Systems

Assessing large-scale pressurization at the regional scale—a possible outcome of large subsurface storage applications such as wastewater injection and geological carbon sequestration—presents significant computational challenges. These challenges are particularly pronounced when accounting for complex geologic structures with multiple reservoir and caprock layers, fault zones, and wells. This study introduces a computationally efficient model that integrates single-phase semi-analytical solutions with a boundary element (BE) approach. The model simulates pressure propagation in multilayered 3D systems, including vertical faults, caprock, basement, and confining units. We apply this new model to a representative scenario involving CO 2 injection near a partially sealing fault with verification against an independent two-phase flow model. Results demonstrate that our model accurately captures far-field pressure responses and that, outside the CO 2 plume zone, pressure predictions from single-phase and two-phase models are nearly identical. This supports the use of single-phase models like ours for efficient estimation of far-field pressure changes. Additionally, we demonstrate its effectiveness at a large scale, incorporating multiple wells and faults. With its ability to represent multiple wells, fault zones, and geological heterogeneity, our model is well suited for assessments of basin-scale pressurization. Its computational efficiency also makes it a promising tool for integration with optimization frameworks aimed at designing and managing injection strategies in faulted storage systems.

Cihan, A. [Lawrence Berkeley National Laboratory (↗
DOE OSTI2024

Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects

This is the conference paper accompanying an oral presentation “Unveiling the Potential of MeshGraphNets for Predicting Subsurface Evolution in Carbon Storage Projects” at the 17th International Conference on Greenhouse Gas Control Technologies GHGT-17 held in Calgary, Canada, October 20-24 , 2024. Carbon capture and storage (CCS) technology is critical for mitigating climate change but requires effective subsurface reservoir management to ensure safe containment of injected CO2. Accurate predictions of reservoir pressure and saturation are essential for assessing long-term CCS performance. Traditional numerical simulations, while effective, are computationally intensive, time-consuming, and constrained by data discretization. Previous work has shown the effectiveness of MeshGraphNets (MGN), a graph-based machine learning framework, as an innovative alternative for predicting reservoir behavior. MGN leverages graph neural networks (GNNs) and mesh representations to model complex geological formations, offering superior adaptability across different discretizations and reservoir configurations. Classic MGN implementations utilize an autoregressive technique to predict future behavior based on current predictions, but this technique is hampered by error accumulation over time. To enhance the model accuracy in time-series predictions, this study implemented a multi-step rollout strategy that integrates autoregressive predictions during training to stabilize prediction of saturation over time. Using the Illinois Basin – Decatur Project (IBDP) dataset, comprising 100 simulations of CO2 injection, pressure, and saturation changes, the framework demonstrated its ability to learn spatial dependencies and temporal dynamics. With inputs including permeabilities, porosities, and injection rates, MGN accurately predicted CO2 plume evolution over time, even with limited training data. Moreover, the addition of a multi-step rollout procedure during training improved the ability of MGN to predict stably over time by ~15%. This research positions MGN, enhanced with multi-step rollout capabilities, as a robust and efficient tool for CCS applications. It advances the field by enabling precise, computationally efficient predictions of reservoir behavior, providing a foundation for the broader adoption of machine learning frameworks in CCS and other geoscience domains.

Holcomb, Paul↗
NASA NTRS1977

The use of the LANDSAT data collection system and imagery in reservoir management and operation

The author has identified the following significant results. An increase in the data collection system's (DCS) ability to function in the flood control mission with no additional manpower was demonstrated during the storms which struck New England during April and May of 1975 and August 1976. It was found that for this watershed, creditable flood hydrographs could be generated from DCS data. It was concluded that an ideal DCS for reservoir regulation would draw features from LANDSAT and GOES. MSS grayscale computer printout and a USGS topographic map were compared, yielding an optimum computer classification map of the wetland areas of the Merrimack River estuary. A classification accuracy of 75% was obtained for the wetlands unit, taking into account the misclassified and the unclassified pixels. The MSS band 7 grayscale printouts of the Franklin Falls reservoir showed good agreement to USGS topographic maps in total area of water depicted at the low water reservoir stage and at the maximum inundation level. Preliminary analysis of the LANDSAT digital data using the GISS computer algorithms showed that the radiance of snow cover/vegetation varied from approximately 20 mW/sq cm sr in nonvegetated areas to less than 4 mW/sq cm sr for densely covered forested area.

Cooper, S.↗