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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 91 records · Page 5
All sourcesNASA NTRSDOE OSTI
NASA NTRS1987

The use of airborne imaging spectrometer data to determine experimentally induced variation in coniferous canopy chemistry

It was experimentally determined whether induced differences in forest canopy chemical composition can be detected using data from the Airborne Imaging Spectrometer (AIS). Treatments were applied to an even-aged forest of Douglas fir trees. Work to date has stressed wet chemical analysis of foilage samples and correction of AIS data. Plot treatments were successful in providing a range of foliar N2 concentrations. Much time was spent investigating and correcting problems with the raw AIS data. Initial problems with groups of drop out lines in the AIS data were traced to the tape recorder and the tape drive. Custom adjustment of the tape drive led to recovery of most missing lines. Remaining individual drop out lines were replaced using average of adjacent lines. Application of a notch filter to the Fourier transform of the image in each band satisfactorily removed vertical striping. The aspect ratio was corrected by resampling the image in the line direction using nearest neighbor interpolation.

Swanberg, Nancy A.↗
DOE OSTI2022

ORNL Neutron Cross Section Measurements of 90 Zr

Nuclear criticality modeling and simulations rely on the quality of the existing evaluated nuclear data libraries such as Evaluated Nuclear Data File (ENDF)/B, the Joint Evaluated Fission and Fusion (JEFF) nuclear data library, or the Japanese Evaluated Nuclear Data Library (JENDL). In some cases, the cross-section evaluations of those libraries were found to be deficient in describing criticality benchmarks accurately. More than two decades ago, the US Nuclear Criticality Safety Program (NCSP) established a Nuclear Data (ND) task which encompassed experiments and evaluations. In response to this, the Oak Ridge National Laboratory (ORNL) formed a Nuclear Criticality and Data group which performed ND experiments, data analysis, and evaluations to produce ENDF files for the ND libraries as identified in the NCSP Five-Year Plan. Before being submitted to the ENDF library, files were processed and tested for performance by running benchmark calculations. This procedure was centralized in the ORNL group and is now often referred to as the ND pipeline. NCSP collaborates with the Joint Research Center (JRC) of the European Commission in Geel, Belgium, to perform high-resolution neutron-induced cross section measurements at the Geel Linear Accelerator (GELINA). The objective is to address emerging ND problems in criticality calculations. Difficulties with ND include insufficient neutron energy range, missing covariances, and previously unrecognized inaccuracies with experiments. New neutron total and capture cross sections of 90 Zr in the neutron energy range from 100 eV to several hundred keV were recently performed. These measured data will be used, together with existing high-resolution transmission data from a metallic 90 Zr sample, to improve representation of the cross sections.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

NeuroSEM: A hybrid framework for simulating multiphysics problems by coupling PINNs and spectral elements

Multiphysics problems that are characterized by complex interactions among fluid dynamics, heat transfer, structural mechanics, and electromagnetics, are inherently challenging due to their coupled nature. While experimental data on certain state variables may be available, integrating these data with numerical solvers remains a significant challenge. Physics-informed neural networks (PINNs) have shown promising results in various engineering disciplines, particularly in handling noisy data and solving inverse problems in partial differential equations (PDEs). However, their effectiveness in forecasting nonlinear phenomena in multiphysics regimes, particularly involving turbulence, is yet to be fully established. Here, this study introduces NeuroSEM, a hybrid framework integrating PINNs with the highfidelity Spectral Element Method (SEM) solver, Nektar++. NeuroSEM leverages the strengths of both PINNs and SEM, providing robust solutions for multiphysics problems. PINNs are trained to assimilate data and model physical phenomena in specific subdomains, which are then integrated into the Nektar++ solver. We demonstrate the efficiency and accuracy of NeuroSEM for thermal convection in cavity flow and flow past a cylinder. The framework effectively handles data assimilation by addressing those subdomains and state variables where the data is available. We applied NeuroSEM to the Rayleigh-B´enard convection system, including cases with missing thermal boundary conditions and noisy datasets. Finally, we applied the proposed NeuroSEM framework to real particle image velocimetry (PIV) data to capture flow patterns characterized by horseshoe vortical structures. Our results indicate that NeuroSEM accurately models the physical phenomena and assimilates the data within the specified subdomains. The framework’s plug-and-play nature facilitates its extension to other multiphysics or multiscale problems. Furthermore, NeuroSEM is optimized for efficient execution on emerging integrated GPU-CPU architectures. This hybrid approach enhances the accuracy and efficiency of simulations, making it a powerful tool for tackling complex engineering challenges in various scientific domains.

42 ENGINEERING↗
NASA NTRS1986

The International Halley Watch

The International Halley Watch has been designed to maximize the scientific value of ground-based observations of Halley's Comet. Important in their own right, such observations have also enhanced the value of space observations, setting the brief duration flyby data in the context of the overall apparition, placing the extremely high resolution encounter data into the normal scale of observations, and filling in missing data. The IHW has standardized observing techniques wherever useful and possible, coordinated the observing, and is now collecting data from publication in a comprehensive Halley Archieve. The IHW has been designed to avoid the problems of 1910 where the two major monographs on Halley were not published until 21 and 24 years later and where much data remains unpublished to this day. The Giacobini-Zinner Watch preovided support to the ICE mission and complements the IHW by using the same ground-based techniques at the same time of study another very different comet for comparison.

Newburn, R. L., Jr.↗
NASA NTRS2023

Using Random Hiveminds to Predict Solar Energetic Particles (SEPs)

The Problem: The use of conventional neural networks (CoNNs) to predict SEPs has become popular, but neural network models do not follow one-size-fits-all approaches and their chaotic natures can yield completely different results on identical data sets. Committees of neural networks identical in input features have been used to solve this problem by (Aminalragia et al., 2021), but they have the possibility of all agreeing together in lockstep and missing crucial information. The Solution: (O’Keefe et al., 2023) propose a solution consisting of neural network estimators in an ensemble, but with features randomly removed from them in a layout known as a random hivemind (RH). The decision weight, learning rate, and epoch count of each member in this ensemble are boosted in relation to how well its individual features perform in a chi-square test.

SMD↗
DOE OSTI2021

Verification and Validation Tests of Gamma Library of MC2-3 for Coupled Neutron and Gamma Heating Calculation

For the accurate assessment of the heat generation rate in fast reactors, the gamma library of MC2 -3 and the MC2 -3 + GAMSOR procedure employing the coupled neutron and gamma heating calculation has been thoroughly verified against Monte Carlo results and validated using the ZPPR-15D gamma dose measurement data. NJOY outputs are post-processed in a consistent way with the NJOY procedure to avoid any missing data or double counting of data. Prompt heating for 379 out of 391 isotopes in the gamma library was verified against MCNP6.2 to within 1% relative error in total heating for most isotopes. For both a simple one-dimensional slab problem representing a sodium cooled fast reactor and the Experimental Breeder Reactor II (EBR-II) benchmark problem, the root-mean-square values of assembly power error were less than 0.5% for fuel assemblies, ~1% in blankets and ~1 to ~3% in reflectors compared to MCNP6.2 results. The most plausible cause for the 3% error in a reflector assembly is believed to be the error in the multigroup neutron cross sections for the reflector assembly. For validation, gamma doses measured with thermoluminiscent dosimeters (TLDs) in the ZPPR-15D experiment were calculated using GAMSOR. Due to the uncertainty in the TLD measurement with regards to the energy deposition of photons and neutrons, the validation data leads to a 12.7% uncertainty on the experimental measurement. With this uncertainty bound, the calculated doses all fell within one standard deviation of the measured value. Combined with the accurate calculation of reaction rate distributions and neutron spectrum measurements, these results indicate good agreement for the neutron and gamma heating calculations that were performed.

coupled neutron and gamma heating↗
DOE OSTI2021

Investigating Application of LiDAR for Nuclear Power Plants

Many evaluation, assessment, and modeling tasks at nuclear power plants require spatial in-formation; this often requires physical visits to locations within the facility because the 2D or 3D schematics and current models do not contain enough detail or do not capture as-built and real-world conditions. These visits require extensive manual labor for not only the requesting party, but also support groups, such as security. Light Detection and Ranging (LiDAR) mapping is trying to solve that problem by providing very detailed 3D models for low costs. However, the use of these models can be very limited because either component reference information is missing and too costly to add or there is no way to extract specific spatial data needed for other tools. This report outlines two main efforts. First, to reduce the effort of “Tagging” data in large 3D models, a general Application Programming Interface (API) was developed to import a variety of existing plant database information into a 3D-visualization engine. Filters allow the user to have only zone-specific items listed; then, they can simply click and assign the information to a specific spot or component in the 3D model. The second part of the work is the development of an interface for importing pieces from the3D-LiDAR model into other systems needed for modeling and simulation, outlined around fire modeling. This interface allows for the retrieval of item location and boundaries, enabling the auto generation of models for varying tools.

3D Modelling↗
DOE OSTI2026

Merged Aerosol Value-Added Product Report

The Merged Aerosol Value-Added Product (VAP) simplifies scientists’ use of Atmospheric Radiation Measurement (ARM) User Facility aerosol data by performing several tedious, time-consuming tasks for the users. First, the VAP identifies the best data available when multiple datastreams exist for a single geophysical quantity so that ARM users do not have to research this for themselves. Second, the VAP consolidates multiple ARM aerosol datastreams into a single file for ARM data users so that they do not have to download, open, and read multiple files for their analysis. Next, the VAP transforms all measurements onto a common one-hour timestamp. The one-hour resolution matches the time resolution of the slowest instrument. Instruments with faster sampling rates than one measurement per hour are averaged over the time interval. Finally, the VAP reads the QA/QC variables and marks data with known issues as missing, so that users do not have to spend excessive time cleaning data. This includes incorporating Data Quality Reports (DQRs) that exist at the time when the VAP data is generated. DQRs are reports filed by instrument mentors or data users that indicate a problem with the output data of individual instruments.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS1993

The development and application of the self-adaptive grid code, SAGE

The multidimensional self-adaptive grid code, SAGE, has proven to be a flexible and useful tool in the solution of complex flow problems. Both 2- and 3-D examples given in this report show the code to be reliable and to substantially improve flowfield solutions. Since the adaptive procedure is a marching scheme the code is extremely fast and uses insignificant CPU time compared to the corresponding flow solver. The SAGE program is also machine and flow solver independent. Significant effort was made to simplify user interaction, though some parameters still need to be chosen with care. It is also difficult to tell when the adaption process has provided its best possible solution. This is particularly true if no experimental data are available or if there is a lack of theoretical understanding of the flow. Another difficulty occurs if local features are important but missing in the original grid; the adaption to this solution will not result in any improvement, and only grid refinement can result in an improved solution. These are complex issues that need to be explored within the context of each specific problem.

Davies, Carol B.↗
DOE OSTI2021

Challenging problems of quality assurance and quality control (QA/QC) of meteorological time series data

Abstract Representativeness and quality of collected meteorological data impact accuracy and precision of climate, hydrological, and biogeochemical analyses and predictions. We developed a comprehensive Quality Assurance (QA) and Quality Control (QC) statistical framework, consisting of three major phases: Phase I—Preliminary data exploration, i.e., processing of raw datasets, with the challenging problems of time formatting and combining datasets of different lengths and different time intervals; Phase II—QA of the datasets, including detecting and flagging of duplicates, outliers, and extreme data; and Phase III—the development of time series of a desired frequency, imputation of missing values, visualization and a final statistical summary. The paper includes two use cases based on the time series data collected at the Billy Barr meteorological station (East River Watershed, Colorado), and the Barro Colorado Island (BCI, Panama) meteorological station. The developed statistical framework is suitable for both real-time and post-data-collection QA/QC analysis of meteorological datasets.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

White Paper to Justify the Use of Strip Effluent in the Defense Waste Processing Facility During Slurry Mix Evaporator Processing

At the request of Savannah River Remediation (SRR), a white paper was written to assess whether it would be acceptable to process Strip Effluent (SE) in both the Sludge Receipt and Adjustment Tank (SRAT) and Slurry Mix Evaporator (SME) in the Defense Waste Processing Facility (DWPF) for the nitric-glycolic acid flowsheet. Savannah River National Laboratory (SRNL) experimental data and DWPF Sludge Batch 9 (SB9) process data was reviewed, looking at batches which included Precipitate Reactor Feed Tank (PRFT) and/or Strip Effluent Feed Tank (SEFT) feeds that led to long processing times. These batches were reviewed looking for processing problems such as melter feed trips, foamovers, heating rod or steam coil fouling, missed Reduction/Oxidation (REDOX) targets, and other process anomalies.

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRS2004

The Latitude Dependence of the Effect of Pinatubo on Stratospheric Ozone

Statistical analysis of TOMS and SBLT total ozone data indicate that the eruption of Pinatubo in 1991 led to a significant decrease in ozone at northern midlatitudes with little or no effect at southern midlatitudes. We argue that this puzzling absence of a southern hemisphere effect may be an artifact of the statistical analysis. We have run a 3D CTM simulation of the past 30 years of stratospheric photochemistry with variable forcing due to chlorine/bromine compounds, solar ultraviolet radiation, and volcanic aerosols. This integration used winds from the FVGCM, which has similar interannual variability to the atmosphere. When this CTM output was examined with a standard time-series analysis, we found an effect of Pinatubo in the southern hemisphere, but not in the northern hemisphere. We then reran the CTM without volcanic aerosols. The subtraction of the two simulations indicated that, as expected, that Pinatubo affected both hemispheres in the model. This means that the northern hemisphere effect was in the model but did not show up in the statistical analysis. We also had an on-line parameterized chemical ozone tracer with seasonally repeating production and loss over the simulation. We used this as a dynamical surrogate to remove interannual variability from the original model output. The residual time series was then analyzed for the Pinatubo effect and we were able to find it in both hemispheres. We suggest that the combination of the two volcanoes, El Chichon and Pinatubo, with the solar cycle and interannual variability led to this problem of analysis in the northern hemisphere of our model. We furthermore suggest that a similar think may be occurring in the southern hemisphere of the data. An analysis of the atmosphere's southern hemisphere with a good dynamical surrogate may solve the mystery of the missing southern hemisphere effect of Pinatubo on ozone.

Stolarski, Richard S.↗
NASA NTRS2010

Multi-Objective Multi-User Scheduling for Space Science Missions

We have developed an architecture called MUSE (Multi-User Scheduling Environment) to enable the integration of multi-objective evolutionary algorithms with existing domain planning and scheduling tools. Our approach is intended to make it possible to re-use existing software, while obtaining the advantages of multi-objective optimization algorithms. This approach enables multiple participants to actively engage in the optimization process, each representing one or more objectives in the optimization problem. As initial applications, we apply our approach to scheduling the James Webb Space Telescope, where three objectives are modeled: minimizing wasted time, minimizing the number of observations that miss their last planning opportunity in a year, and minimizing the (vector) build up of angular momentum that would necessitate the use of mission critical propellant to dump the momentum. As a second application area, we model aspects of the Cassini science planning process, including the trade-off between collecting data (subject to onboard recorder capacity) and transmitting saved data to Earth. A third mission application is that of scheduling the Cluster 4-spacecraft constellation plasma experiment. In this paper we describe our overall architecture and our adaptations for these different application domains. We also describe our plans for applying this approach to other science mission planning and scheduling problems in the future.

science planning↗
NASA NTRS1994

Is xenon eldest?

It is well known that the solubility of noble gases in magmas decreases with increasing atomic weight. Xenon, the weightiest of the stable noble gases, is the least soluble atmospheric gas in magma. It is not unreasonable to suppose that the noble gases should have degassed from (or equilibrated with) a bubbling mantle in order of increasing solubility, such that xenon was the most rapidly degassed and helium the least. The apparent relative ages of the famous radiogenic noble gas isotopes agrees, at least qualitatively, with this premise. When atmospheric loss processes are assigned their proper place, several long-standing xenonological puzzles become added evidence for xenon's relative antiquity. Xenon being the afore-mentioned sense the oldest atmospheric gas, will have been most greatly subject to escape, be it impact-driven or EUV-driven. Nonradiogenic xenon's pronounced isotopic fractionation has already been attributed to escape; why it should be more fractionated than krypton would be assigned to xenon's greater atmospheric age. The small atmospheric inventory of xenon relative to the other nonradiogenic noblegases, known as the 'missing xenon' problem, could easily be explained by differential escape. The relatively tiny atmospheric inventories of the radiogenic daughter products of 129 Iodine and 244 Plutonium, both much smaller than would be expected from the inferred abundances of the parents in meteorites, offer a third and fourth data to support the hypothesis that Earth has lost most of its xenon.

Zahnle, K.↗
NASA NTRSDate not supplied

Reinforcement Learning for Spacecraft Navigation & Environment Characterization in the Planar-Restricted Two-Body Problem

During mission planning and execution, spacecraft operators must balance data collection and downlink, systems constraints, human factors, and navigation. As missions become increasingly complex and ambitious, these factors become more intricately entwined and conflicted. For example, a spacecraft’s position must be known accurately in order to point to and image a target. Large position errors may cause missed observations or require additional scanning that increases operations complexity and data volume. Some observations require imaging from specific relative geometries which adds orbit control and timing considerations. Adjusting the orbit may allow for optimal observability of environmental parameters and/or enable more efficient sensor coverage, but maneuver execution error adds uncertainty to the current state which impacts both characterization and coverage objectives.

Navigation↗
DOE OSTI2025

Missing baryons recovered: A measurement of the gas fraction in galaxies and groups with the kinematic Sunyaev-Zel’dovich effect and CMB lensing

We present new constraints on the halo masses and matter density profiles of DESI galaxy groups by cross-correlating samples of luminous red galaxies (LRGs) and bright galaxy survey (BGS) galaxies with the publicly available CMB lensing convergence map from ACT DR6. This provides an independent, lensing-based calibration of halo masses, complementary to methods relying on clustering or dynamics. We derive constraints on the mean halo mass for three DESI-selected samples, finding log⁡(𝑀 halo /(𝑀 ⊙ /ℎ)) ≈ 13.18, 13.03 and 13.02 for the main LRG, extended LRG, and BGS samples, respectively. Using a halo model approach, we also compare the projected galaxy-matter density profiles with previously reported gas profiles inferred from measurements of the kinematic Sunyaev-Zel’dovich (kSZ) effect. This work addresses one of the key uncertainties in interpreting kSZ signals—the unknown host halo mass distribution—by providing an independent and consistent mass calibration. The agreement between the gas and total mass profiles at large aperture suggests that sufficiently far from the group center (2–3 virial radii), we recover all the baryons, offering a resolution to the missing baryon problem. We further study the cumulative gas fractions for all galaxies as well as for the most massive galaxy groups in the sample [log⁡(𝑀 halo /(𝑀 ⊙ /ℎ)) ≈ 13.5], finding values that are physically sensible and in agreement with previous findings using kSZ and x-ray data: compared to the TNG300 simulation, the observed gas fractions are systematically lower at fixed radius by ≳ 4⁢𝜎, providing compelling, independent evidence for stronger baryonic feedback in the real Universe. These findings highlight the power of combining CMB lensing with galaxy surveys to probe the interplay between baryons and dark matter in group-sized halos.

Hadzhiyska, Boryana [Lawrence Berkeley National La↗
DOE OSTI2026

Multi-Modal Bayesian Neural Network Surrogates with Conjugate Last-Layer Estimation

As data collection and simulation capabilities advance, multi-modal learning, the task of learning from multiple modalities and sources of data, is becoming an increasingly important area of research. Surrogate models that learn from data of multiple auxiliary modalities to support the modeling of a highly expensive quantity of interest have the potential to aid outer loop applications such as optimization, inverse problems, or sensitivity analyses when multi-modal data are available. We develop two multi-modal Bayesian neural network surrogate models and leverage conditionally conjugate distributions in the last layer to estimate model parameters using stochastic variational inference (SVI). We provide a method to perform this conjugate SVI estimation in the presence of partially missing observations. Here, we demonstrate improved prediction accuracy and uncertainty quantification compared to unimodal surrogate models for both scalar and time series data.

97 MATHEMATICS AND COMPUTING↗