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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS2015

GeneLab: A Systems Biology Platform for Spaceflight Omics Data

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. Resources to support large numbers of spaceflight investigations are limited. NASA's GeneLab project is maximizing the science output from these experiments by: (1) developing a unique public bioinformatics database that includes space bioscience relevant "omics" data (genomics, transcriptomics, proteomics, and metabolomics) and experimental metadata; (2) partnering with NASA-funded flight experiments through bio-sample sharing or sample augmentation to expedite omics data input to the GeneLab database; and (3) developing community-driven reference flight experiments. The first database, GeneLab Data System Version 1.0, went online in April 2015. V1.0 contains numerous flight datasets and has search and download capabilities. Version 2.0 will be released in 2016 and will link to analytic tools. In 2015 Genelab partnered with two Biological Research in Canisters experiments (BBRIC-19 and BRIC-20) which examine responses of Arabidopsis thaliana to spaceflight. GeneLab also partnered with Rodent Research-1 (RR1), the maiden flight to test the newly developed rodent habitat. GeneLab developed protocols for maxiumum yield of RNA, DNA and protein from precious RR-1 tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected. GeneLab is establishing partnerships with at least three planned flights for 2016. Organism-specific nationwide Science Definition Teams (SDTs) will define future GeneLab dedicated missions and ensure the broader scientific impact of the GeneLab missions. GeneLab ensures prompt release and open access to all high-throughput omics data from spaceflight and ground-based simulations of microgravity and radiation. Overall, GeneLab will facilitate the generation and query of parallel multi-omics data, and deep curation of metadata for integrative analysis, allowing researchers to uncover cellular networks as observed in systems biology platforms. Consequently, the scientific community will have access to a more complete picture of functional and regulatory networks responsive to the spaceflight environment.. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and enable emerging terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space. As a result, open access to the data will foster new hypothesis-driven research for future spaceflight studies spanning basic science to translational science.

proteomics↗
NASA NTRS2016

Using Tabulated Experimental Data to Drive an Orthotropic Elasto-Plastic Three-Dimensional Model for Impact Analysis

An orthotropic elasto-plastic-damage three-dimensional model with tabulated input has been developed to analyze the impact response of composite materials. The theory has been implemented as MAT 213 into a tailored version of LS-DYNA being developed under a joint effort of the FAA and NASA and has the following features: (a) the theory addresses any composite architecture that can be experimentally characterized as an orthotropic material and includes rate and temperature sensitivities, (b) the formulation is applicable for solid as well as shell element implementations and utilizes input data in a tabulated form directly from processed experimental data, (c) deformation and damage mechanics are both accounted for within the material model, (d) failure criteria are established that are functions of strain and damage parameters, and mesh size dependence is included, and (e) the theory can be efficiently implemented into a commercial code for both sequential and parallel executions. The salient features of the theory as implemented in LS-DYNA are illustrated using a widely used composite - the T800S/3900-2B[P2352W-19] BMS8-276 Rev-H-Unitape fiber/resin unidirectional composite. First, the experimental tests to characterize the deformation, damage and failure parameters in the material behavior are discussed. Second, the MAT213 input model and implementation details are presented with particular attention given to procedures that have been incorporated to ensure that the yield surfaces in the rate and temperature dependent plasticity model are convex. Finally, the paper concludes with a validation test designed to test the stability, accuracy and efficiency of the implemented model.

Polymer Matrix Composites↗
NASA NTRS1981

Computer simulator for a mobile telephone system

A software simulator was developed to assist NASA in the design of the land mobile satellite service. Structured programming techniques were used by developing the algorithm using an ALCOL-like pseudo language and then encoding the algorithm into FORTRAN 4. The basic input data to the system is a sine wave signal although future plans call for actual sampled voice as the input signal. The simulator is capable of studying all the possible combinations of types and modes of calls through the use of five communication scenarios: single hop systems; double hop, signal gateway system; double hop, double gateway system; mobile to wireline system; and wireline to mobile system. The transmitter, fading channel, and interference source simulation are also discussed.

Schilling, D. L.↗
NASA NTRS1985

A case study of the effects of random errors in rawinsonde data on computations of ageostrophic winds

Data input for the AVE-SESAME I experiment are utilized to describe the effects of random errors in rawinsonde data on the computation of ageostrophic winds. Computer-generated random errors for wind direction and speed and temperature are introduced into the station soundings at 25 mb intervals from which isentropic data sets are created. Except for the isallobaric and the local wind tendency, all winds are computed for Apr. 10, 1979 at 2000 GMT. Divergence fields reveal that the isallobaric and inertial-geostrophic-advective divergences are less affected by rawinsonde random errors than the divergence of the local wind tendency or inertial-advective winds.

Moore, J. T.↗
NASA NTRS1995

The Development and Use of a Flight Optimization System Model of a C-130E Transport Aircraft

The Systems Analysis Branch at NASA Langley Research Center conducts a variety of aircraft design and analyses studies. These studies include the prediction of characteristics of a particular conceptual design, analyses of designs that already exist, and assessments of the impact of technology on current and future aircraft. The FLight OPtimization System (FLOPS) is a tool used for aircraft systems analysis and design. A baseline input model of a Lockheed C-130E was generated for the Flight Optimization System. This FLOPS model can be used to conduct design-trade studies and technology impact assessments. The input model was generated using standard input data such as basic geometries and mission specifications. All of the other data needed to determine the airplane performance is computed internally by FLOPS. The model was then calibrated to reproduce the actual airplane performance from flight test data. This allows a systems analyzer to change a specific item of geometry or mission definition in the FLOPS input file and evaluate the resulting change in performance from the output file. The baseline model of the C-130E was used to analyze the effects of implementing upper wing surface blowing on the airplane. This involved removing the turboprop engines that were on the C-130E and replacing them with turbofan engines. An investigation of the improvements in airplane performance with the new engines could be conducted within the Flight Optimization System. Although a thorough analysis was not completed, the impact of this change on basic mission performance was investigated.

Desch, Jeremy D.↗
NASA NTRS2005

An Interoperable, Agricultural Information System Based on Satellite Remote Sensing Data

Monitoring global agricultural crop conditions during the growing season and estimating potential seasonal production are critically important for market development of US. agricultural products and for global food security. The Goddard Space Flight Center Earth Sciences Data and Information Services Center Distributed Active Archive Center (GES DISC DAAC) is developing an Agricultural Information System (AIS), evolved from an existing TRMM Online Visualization and Analysis System (TOVAS), which will operationally provide satellite remote sensing data products (e.g., rainfall) and services. The data products will include crop condition and yield prediction maps, generated from a crop growth model with satellite data inputs, in collaboration with the USDA Agricultural Research Service. The AIS will enable the remote, interoperable access to distributed data, by using the GrADS-DODS Server (GDS) and by being compliant with Open GIS Consortium standards. Users will be able to download individual files, perform interactive online analysis, as well as receive operational data flows. AIS outputs will be integrated into existing operational decision support systems for global crop monitoring, such as those of the USDA Foreign Agricultural Service and the U.N. World Food Program.

Teng, William↗
DOE OSTI2022

Reactor physics benchmark experiments at the JSI TRIGA MARK II reactor - Current status and future outlook

Full text of publication follows. With the development of new high-fidelity computational methods, improvement of nuclear data, and multiphysics modelling, there is an increased need for benchmark experiments to experimentally validate the models, methods and input data. Many of the nuclear facilities designed to perform reactor physics benchmark experiments have been shut down. Therefore, research reactors offer a great opportunity for benchmark experiments, if they are well designed and performed with great care and accuracy. In this presentation we provide an overview of the past and ongoing activities related to benchmark experiments at the Jozef Stefan Institute TRIGA Mark II research reactor. The following experiments have been performed: criticality with fresh fuel, {sup 197}Au(n,γ) and {sup 27}Al(n,α) reaction rates in irradiation channels, absolute and relative {sup 197}Au(n,γ), {sup 235}U(n,f) and {sup 238}U(n,f) reaction rates in the core, burnup, kinetic parameters, control rod worth, isothermal reactivity coefficient, self-shielding, slow and fast (pulse) transients, nuclear heating, delayed and prompt gamma ray production, temperature profiles for multi-physics. Since the existing fleet of research reactors is ageing very rapidly and new experiments are needed, new research reactors should be designed and built to meet the needs of future advanced reactors, education and training, and other technologies in the coming years. We will review planned activities at the JSI TRIGA reactors and plans for the new research reactor in Slovenia. (authors)

21 SPECIFIC NUCLEAR REACTORS AND ASSOCIATED PLANTS↗
NASA NTRSDate not supplied

Conversion of Environment Files from ORDEM 3 & MEM 3 to STENVI

To perform Micrometeoroid and Orbital Debris (MMOD) analyses, NASA describes the orbital debris particle environment using the ORDEM 3 tool and the micrometeoroid environment using the MEM 3 tool. Both output files that detail the flux of particles as a function of several parameters. European MMOD analyses use different tools to perform similar functions, including the Meteoroid And Space debris Terrestrial Environment Reference (MASTER) environment model, which outputs flux data in the Standard Environment Interface (STENVI) format. The NASA Johnson Space Center Hypervelocity Impact Technology Group was asked to create a converter to translate flux data from the output file formats used by ORDEM 3.x and MEM 3 into the STENVI file format. Validation was performed to ensure that the converter was reproducing the input data correctly. The Bumper 3 MMOD risk assessment tool has a capability for reading ORDEM 3.x, MEM 3, and STENVI environment file formats and was used to compare MMOD risk results generated using the converter’s output STENVI files to the risk results generated using the input ORDEM 3.x and MEM 3 igloo files. Specific point comparisons validated that the converter translated the environment data correctly, producing the intended output. However, realistic risk assessments showed that the bin methodology employed by Bumper for determining particle flux as a function of particle size when reading files in STENVI format produces much higher MMOD risk than the point-interpolation methodology used in Bumper risk assessments using ORDEM and MEM. Modifications to how Bumper 3 reads STENVI files during analyses are considered to improve Bumper’s ability to perform useful analyses with environment files in this format.

STENVI↗
NASA NTRS2023

Conversion of ORDEM 3.X & MEM 3 Into STENVI File Format

To perform Micrometeoroid and Orbital Debris (MMOD) analyses, NASA describes the orbital debris particle environment using the ORDEM 3 tool and the micrometeoroid environment using the MEM 3 tool. Both output files that detail the flux of particles as a function of several parameters. European MMOD analyses use different tools to perform similar functions, including the Meteoroid And Space debris Terrestrial Environment Reference (MASTER) environment model, which outputs flux data in the Standard Environment Interface (STENVI) format. The NASA Johnson Space Center Hypervelocity Impact Technology Group was asked to create a converter to translate flux data from the output file formats used by ORDEM 3.x and MEM 3 into the STENVI file format. Validation was performed to ensure that the converter was reproducing the input data correctly. The Bumper 3 MMOD risk assessment tool has a capability for reading ORDEM 3.x, MEM 3, and STENVI environment file formats and was used to compare MMOD risk results generated using the converter’s output STENVI files to the risk results generated using the input ORDEM 3.x and MEM 3 igloo files. Specific point comparisons validated that the converter translated the environment data correctly, producing the intended output. However, realistic risk assessments showed that the bin methodology employed by Bumper for determining particle flux as a function of particle size when reading files in STENVI format produces much higher MMOD risk than the point-interpolation methodology used in Bumper risk assessments using ORDEM and MEM. Modifications to how Bumper 3 reads STENVI files during analyses are considered to improve Bumper’s ability to perform useful analyses with environment files in this format.

STENVI↗
DOE OSTI2023

Evaluating the Interplay between Trajectory Segmentation and Mode Inference Error

Travel behavior changes are essential to transportation decarbonization. Travel diaries, consisting of sequences of trips between places, are typically used to instrument human travel behavior. However, these diaries are only as accurate as the underlying methods used to construct them. Travel diary algorithms have been a popular research topic since the advent of Global Positioning System tracking surveys. These algorithms have typically been validated using prompted recall of presegmented trips, thus disregarding the continuity of mode inference. Phone operating systems have adopted battery-conserving techniques, but the resulting data collection errors have not been studied extensively. We introduce a framework to evaluate the accuracy of trip length computations and mode inference by analyzing continuous mode-segmented trajectories for groups of trips. We then use the framework to identify the input data quality and the impact of postprocessing. Our primary inputs to this evaluation are MobilityNet, a public dataset containing information from three artificial timelines covering 15 different travel modes, and sample open-source travel diary creation algorithms from the OpenPATH project. Our framework concretely shows that the variance of the distance error drops from (0.217, 0.0848) to (o.011, 0.0407) (Android, iOS) after postprocessing. Similarly, the weighted F-scores for mode inference increase from (0.25, 0.29) to (0.60, 0.74) (iOS, Android) between random forest and geographic information system-based models. We hope that this standardized method will be adapted to evaluate other, potentially proprietary, travel diary algorithms. Finally, the results can be used to understand and improve the state of the art in the travel diary creation field.

33 ADVANCED PROPULSION SYSTEMS↗
DOE OSTI2020

BEPAM Model Code and CABBI Simulation Results for "Assessing the Additional Carbon Savings with Biofuel"

This dataset consists of various input data that are used in the GAMS model. All the data are in the format of .inc which can be read within GAMS or Notepad. Main data sources include: acreage data (acre), crop budget data ($/acre), crop yield data (e.g. bushel/acre), Soil carbon sequestration data (KgCO2/ha/yr). Model details can be found in the "Assessing the Additional Carbon Savings with Biofuel" and GAMS model package. ## File Description (1) GAMS Model.zip: This includes all the input files and scripts for running the model (2) Table*.csv: These files include the data from the tables in the manuscript (3) Figure2_3_4.csv: This contains the data used to create the figures in the manuscript (4) BaselineResults.csv: This includes a summary of the model results. (5) SensitivityResults_*.csv: Model results from the various sensitivity analyses performed (6) LUC_emission.csv: land use change emissions by crop reporting district for changes of pasturelands to annual crops.

Anticipated baseline approach↗
NASA NTRS1992

PHOTCAL: The IRAF photometric calibration package

The IRAF photmetric calibration package PHOTCAL is discussed. PHOTCAL is a set of tasks designed to derive the transformation from the instrumental photometric system to the standard photometric system, and apply the transformation to the observations. The PHOTCAL package contains tasks for: (1) creating and/or editing standard star catalogs and observations catalogs, (2) creating, checking and editing the configuration file which specifies the format of the standard star and observations catalogs and the form of the transformation equations, (3) solving the transformation equations interactively or non-iteractively using non-linear least squares fitting routines, and (4) applying the transformation to the observations. PHOTCAL standard star and observations catalogs are simple text files, whose columns are delimited by whitespace, and whose first column contains the star names. This format makes it relatively easy to interface the output of non-IRAF photometry programs as well as the output of the IRAF APPHOT and DAOPHOT photometry packages to PHOTCAL. PHOTCAL maintains a standard star catalog directory for the convenience of the user, but users can easily create their own standard star catalogs and/or define their own standard star catalog directory. Separate observations files produced by APPHOT, DAOPHOT or a user program containing data for stellar fields taken through different filters, can be combined into observations catalogs using one of the PHOTCAL preprocessor tasks. The input configuration file required by PHOTCAL is a text file, consisting of a series of instructions written by the user in a mini-language understood by the PHOTCAL parser. These instructions: (1) assign names to the input data columns in the standard star and observations catalogs, (2) assign names and initial values to the parameters to be fit, (3) define and describe how to solve the transformation equations. The mini-language approach permits great flexibility in the format of the input catalogs and the form of the transformation equations.

Davis, L. E.↗
DOE OSTI2024

Application of Modified Meshgraphnets for Subsurface Prediction during CO2 Sequestration

In the face of the increasingly dire consequences of anthropogenic climate change, capturing and storing carbon dioxide is paramount. However, several impediments exist to the safe and effective subsurface storage of CO2, such as cost of transport, identification of suitable sites for subsurface storage, and assessment of long-term risk from storage in subsurface aquifers. Accurate subsurface modeling is necessary to ensure that CO2 storage is both safe and effective. Still, such modeling has traditionally required either substantial time and computational power (numerical simulation) or a substantial amount of pre-existing data for training (machine learning models). Additionally, these models lack flexibility in dealing with both changes in discretization of the input data and generalizability beyond the data on which they are trained. In order to address these issues, this research applies graph neural networks (GNNs) to predict subsurface saturation and pressure during CO₂ injection in a model of the Illinois Basin-Decatur Project (IBDP). GNNs provide a flexible, intuitive method for representing and manipulating complex unstructured data, which is often found in many practical domain problems such as fluid flow and subsurface characterization. These unstructured grids are easily represented in GNNs by representing spatially-localized features such as permeability, porosity, saturation, and pressure as nodes in a graph and relationships between these properties as edges connecting these nodes. This research applies a specific GNN model called MeshGraphNets (MGN) to model the change in CO2 saturation and pressure over a 50-month time period (36 months of injection, 14 months post-injection). The MGN model leverages a message passing process that allows the network to learn both the spatial and temporal dynamics of this system simultaneously. Additionally, training on a limited dataset (64 realizations, 20 time points each) resulted in a high degree of accuracy in saturation prediction both within the same timeframe as the training (20 months, 0.039 average RMSE) and when projecting out to the end of injection (36 months, 0.053 average RMSE). Temporal predictions such as those generated by MGNs and other similar models are prone to accumulated error over time; in order to address this, a multi-step rollout (MSR) training process was applied to calculate training loss. This method mimics the forward prediction during inference by “rolling out” multiple time points in a single training step using the previous prediction as input to the MGN model. By calculating the loss several time steps forward from the current prediction, the model is forced to find a more stable state over time. Application of MSR to the MGN model resulted in an average 15% reduction in inference error over time during forward prediction. This study showcases the immense potential of GNNs as a game-changing methodology for predicting pressure and saturation evolution in CCS projects, ultimately paving the way for more sustainable and effective carbon storage solutions. Presentation prepared for the 2024 AiChE Annual Meeting, October 27 to November 1 2024, San Diego, CA.

Holcomb, Paul↗
NASA NTRS2011

Classification

A supervised learning task involves constructing a mapping from input data (normally described by several features) to the appropriate outputs. Within supervised learning, one type of task is a classification learning task, in which each output is one or more classes to which the input belongs. In supervised learning, a set of training examples---examples with known output values---is used by a learning algorithm to generate a model. This model is intended to approximate the mapping between the inputs and outputs. This model can be used to generate predicted outputs for inputs that have not been seen before. For example, we may have data consisting of observations of sunspots. In a classification learning task, our goal may be to learn to classify sunspots into one of several types. Each example may correspond to one candidate sunspot with various measurements or just an image. A learning algorithm would use the supplied examples to generate a model that approximates the mapping between each supplied set of measurements and the type of sunspot. This model can then be used to classify previously unseen sunspots based on the candidate's measurements. This chapter discusses methods to perform machine learning, with examples involving astronomy.

Oza, Nikunj C.↗
DOE OSTI2024

Acoustic-based monitoring and machine learning of component status for microreactor applications

This report provides a description and assessment of recent efforts to couple acoustic-based experimental measurements and characterization with machine learning models in order to enhance structural health monitoring capabilities for nuclear microreactors. With resilient embedded sensors in development by others supported by programs funded by the US Department of Energy’s Office of Nuclear Energy, the work described herein builds upon ongoing efforts to improve non-destructive testing technology that relates measured acoustic signatures to component stresses and/or structural defects, using a combination of new experimental measurements and machine learning architectures. The experimental procedure remained similar to that developed for the previous year’s demonstration of damage detection by the authors, with the same damaged sample tested under similar applied stress conditions. Notably, a new mounting fixture was designed and implemented to improve measurement consistency and a more sophisticated laser Doppler vibrometer was employed to make high-fidelity vibration measurements. Two nominally identical sets of training data were collected for each experimental setup to better understand the repeatability of the experiment and to better test the generality of trained neural network models. Additionally, we obtained new high-quality 3D mode shapes of the damaged test article at various stress and excitation levels, providing greater insights into the physical response of the sample during testing. Previously, we demonstrated that a machine learning model based on a convolutional neural network can predict structural details of an artificially introduced interface (intact, rough cut, smooth cut), and the applied torque level. In this study, we have transitioned to graph-based neural network architectures to better develop and test a flexible framework that is more suitable to being transferred away from controlled benchtop experiments and into more applied settings where less-structured data inputs may be expected. In general, performance testing of a graph neural network on frequency-domain representations of the data indicates strong and consistent identification of test conditions for datasets recorded on damaged components. With goals of predicting damage location and other changing experimental conditions using limited datasets, predictive models using a graph neural network architecture correctly predicted the applied torque level with an accuracy of 85% using only a single measurement point and predicted within one torque level in 95% of test windows. Predictions of damage location had limited success due to the symmetry and minimal number of the damage scenarios presented during model training. Results were ambiguous as to whether the model could detect the location of the artificial damage, or if it was instead learning the location of a given measurement point on the part and subsequently detecting which points were closest to the location of the damage. This finding will be factored into upcoming planned work on damaged graphite components, where new experimental tests with a larger number and variety of damage scenarios are expected to provide improved validation of recent developments in monitoring methodology.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

Propagating Uncertainties in the SALT3 Model-training Process to Cosmological Constraints

Type Ia supernovae (SNe Ia) are standardizable candles that must be modeled empirically to yield cosmological constraints. To understand the robustness of this modeling to variations in the model-training procedure, we build an end-to-end pipeline to test the recently developed SALT3 model. We explore the consequences of removing pre-2000s low-z or poorly calibrated U-band data, adjusting the amount and fidelity of SN Ia spectra, and using a model-independent framework to simulate the training data. We find that the SALT3 model surfaces are improved by having additional spectra and U-band data, and can be shifted by ~5% if host-galaxy contamination is not sufficiently removed from SN spectra. We find that resulting measurements of w are consistent to within 2.5% for all of the training variants explored in this work, with the largest shifts coming from variants that add color-dependent calibration offsets or host-galaxy contamination to the training spectra and those that remove pre-2000s low-z data. These results demonstrate that the SALT3 model-training procedure is largely robust to reasonable variations in the training data, but that additional attention must be paid to the treatment of spectroscopic data in the training process. We also find that the training procedure is sensitive to the color distributions of the input data—the resulting w measurement can be biased by ~2% if the color distribution is not sufficiently wide. Future low-z data, particularly u-band observations and high signal-to-noise ratio SN Ia spectra, will help to significantly improve SN Ia modeling in the coming years.

79 ASTRONOMY AND ASTROPHYSICS↗
NASA NTRS1977

A keyboard interface for the JOLT microprocessor

A keyboard interface designed to allow data input without use of a teleprinter is described. In operation, the keyboard allows JOLT start-up and entry of data for LORAN or OMEGA navigation.

Wright, L.↗