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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 NTRSDate not supplied

Modeling of Cryogenic Heated-Tube Flow Boiling Experiments of Hydrogen and Helium With the Generalized Fluid System Simulation Program

Accurate modeling of cryogenic boiling heat transfer is vital for the development of extended-duration space missions. Such missions may require the transfer of cryogenic propellants from in-space storage depots or the cooling of nuclear reactors. Purdue University in collaboration with NASA has assembled a database of cryogenic flow boiling data points from heated-tube experiments dating back to 1959, which has been used to develop new flow boiling correlations specifically for cryogens. Computational models of several of these experiments have been constructed in the Generalized Fluid System Simulation Program (GFSSP), a network flow code developed at NASA’s Marshall Space Flight Center. The new Purdue-developed universal correlations cover the full boiling curve: onset of nucleate boiling, nucleate boiling, critical heat flux, and film boiling. These correlations have been coded into GFSSP user subroutines. The fluids modeled in this study are liquid hydrogen and liquid helium. Predictions of local wall temperature and pressure drop are presented and compared to the test data.

multi-phase flow↗
DOE OSTI2025

Deployment of neural-network-based neutron microscopic cross sections in the Griffin reactor physics application

The capability to utilize neural networks to predict macroscopic and microscopic cross section parametric spaces has been developed for the Griffin reactor physics application. The LibTorch interface enables Griffin's MOOSE-based materials to interact with LibTorch-trained models, allowing for the evaluation of complex macroscopic or microscopic cross section spaces, which are then used to evaluate the neutronic properties of the Griffin finite element model. This study benchmarks traditional ISOXML-formatted tabulation libraries against neural network-based models for 279 nuclides on 20,160 grid points for zero-dimensional and two-dimensional reactor models. Benchmark metrics include the fundamental mode eigenvalue, fission and absorption rates, and various temperature coefficients of reactivity (isothermal, fuel, and moderator). From the perspective of storage space, the complete set of LibTorch models uses 11 MB on disk, compared to the 10 GB for the ISOXML multigroup library that covers the same grid space. For the two-dimensional performance case considered in Griffin, the Torch model uses 97% less RAM than the reference ISOXML dataset while runtime increases by a factor of 3 when using the LibTorch model compared to the ISOXML dataset with multi-linear interpolation. The LibTorch model consistently yields errors within 0.01% for most analyzed quantities except for the temperature coefficients of reactivity where the maximum discrepancies are up to 0.3 $\frac{pcm}{K}$. Due to the neural network attempting to best predict quantities with no regard for a positive or negative bias for any given quantity, predictions may experience random fluctuations, resulting in both positive and negative errors. Future work will entail both depletion and coupled transient analysis to determine the predictive capabilities of Griffin with neural network-based cross sections.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
NASA NTRS2007

Ultra-Lightweight Hybrid Thin-Film Solar Cells: A Survey of Enabling Technologies for Space Power Applications

The development of hybrid inorganic/organic thin-film solar cells on flexible, lightweight, space-qualified, durable substrates provides an attractive solution for fabricating solar arrays with high mass specific power (W/kg). Next generation thin-film technologies may well involve a revolutionary change in materials to organic-based devices. The high-volume, low-cost fabrication potential of organic cells will allow for square miles of solar cell production at one-tenth the cost of conventional inorganic materials. Plastic solar cells take a minimum of storage space and can be inflated or unrolled for deployment. We will explore a cross-section of in-house and sponsored research efforts that aim to provide new hybrid technologies that include both inorganic and polymer materials as active and substrate materials. Research at University of Texas at Arlington focuses on the fabrication and use of poly(isothianaphthene-3,6-diyl) in solar cells. We describe efforts at Norfolk State University to design, synthesize and characterize block copolymers. A collaborative team between EIC Laboratories, Inc. and the University of Florida is investigating multijunction polymer solar cells to more effectively utilize solar radiation. The National Aeronautics and Space Administration (NASA)/Ohio Aerospace Institute (OAI) group has undertaken a thermal analysis of potential metallized substrates as well as production of nanoparticles of CuInS2 and CuInSe2 in good yield at moderate temperatures via decomposition of single-source precursors. Finally, preliminary work at the Rochester Institute of Technology (R.I.T.) to assess the impact on performance of solar cells of temperature and carbon nanotubes is reported. Technologies that must be developed to enable ultra-lightweight solar arrays include: monolithic interconnects, lightweight array structures, and new ultra-light support and deployment mechanisms. For NASA applications, any solar cell or array technology must not only meet weight and AMO efficiency goals, but also must be durable enough to survive launch conditions and space environments.

Hepp, Aloysius F.↗
NASA NTRS1986

Space Technology Plasma Issues in 2001

The purpose of the workshop was to identify and discuss plasma issues that need to be resolved during the next 10 to 20 years (circa 2001) to facilitate the development of the advanced space technology that will be required 20 or 30 years into the future. The workshop consisted of 2 days of invited papers and 2 sessions of contributed poster papers. During the third day the meeting broke into 5 working groups, each of which held discussions and then reported back to the conference as a whole. The five panels were: Measurements Technology and Active Experiments Working Group; Advanced High-Voltage, High-Power and Energy-Storage Space Systems Working Group; Large Structures and Tethers Working Group; Plasma Interactions and Surface/Materials Effects Working Group; and Beam Plasmas, Electronic Propulsion and Active Experiments Using Beams Working Group.

Garrett, Henry↗
NASA NTRS2012

Portable Intravenous Fluid Production Device for Ground Use

There are several medical conditions that require intravenous (IV) fluids. Limitations of mass, volume, storage space, shelf-life, transportation, and local resources can restrict the availability of such important fluids. These limitations are expected in long-duration space exploration missions and in remote or austere environments on Earth. Current IV fluid production requires large factory-based processes. Easy, portable, on-site production of IV fluids can eliminate these limitations. Based on experience gained in developing a device for spaceflight, a ground-use device was developed. This design uses regular drinking water that is pumped through two filters to produce, in minutes, sterile, ultrapure water that meets the stringent quality standards of the United States Pharmacopeia for Water for Injection (Total Bacteria, Conductivity, Endotoxins, Total Organic Carbon). The device weighs 2.2 lb (1 kg) and is 10 in. long, 5 in. wide, and 3 in. high (.25, 13, and 7.5 cm, respectively) in its storage configuration. This handheld device produces one liter of medical-grade water in 21 minutes. Total production capacity for this innovation is expected to be in the hundreds of liters.

Scarpa, Philip J.↗
NASA NTRS2010

Curation of Antarctic Meteorites at NASA Johnson Space Center

The U.S. Antarctic meteorite program began in the 1970 s and has provided more than 18,000 samples in over three decades. The program is based on a three agency agreement between NASA, the National Science Foundation, and the Smithsonian Institution. The collection, stored at the Johnson Space Center and the Smithsonian, is one of the largest collections of meteorites in the world and features samples from the moon and Mars, asteroids, and material from the early solar system. A brief consideration of the collection shows that it contains 92.2% ordinary chondrites, 3.2% carbonaceous chondrites, 3.7% achondrites (1.7% HED), as well as many puzzling ungrouped meteorites. JSC has sent splits of nearly 20,000 meteorite samples to more than 500 scientists around the world since 1977. After the meteorites are collected in Antarctica, they are shipped frozen to JSC in Houston, usually arriving in April following the field season. The Astromaterials Curation Office at JSC is responsible for: - receiving the frozen meteorites. - staging: repackaging and changing the samples field identification numbers with official names. - submitting the names to the Nomenclature Committee of the Meteoritical Society for approval as new meteorites. - initial processing: weighing, measuring, describing and photographing the sample and providing a chip for classification to the Smithsonian Institution staff. - the issuing of two newsletters per year, announcing hundreds of new meteorites. - the handling of requests from the scientific community and the allocation of those requests that are approved. - providing supplies and tools for the field team such as teflon bags and tape, aluminum foil, clean tweezers and tongs. - maintaining the meteorite database with more than 76,000 sample splits. - making petrographic thin and thick sections for the JSC library and scientific investigators. - providing storage and handling of the meteorites in a class 10,000 clean room. Samples that have not been requested for three years are sent to the Smithsonian for permanent storage. There have been several seasons in recent years with two field teams, increasing the number of acquired samples (>1000 in a few seasons). We have recently increased our storage space and additional staff to better serve the scientific community.

McBride, K. M.↗
DOE OSTI2020

Alternative Treatment of Defense Waste Processing Facility Recycle via Reuse of Existing Liquid Waste Facilities - 20097

The Defense Waste Processing Facility (DWPF) at Savannah River Site (SRS) has been immobilizing high level waste since 1996. The chemical process within DWPF generates a large volume of condensate, which is recycled to the SRS H-Area Tank Farm. The recycle stream includes a small quantity of sludge solids, as well as soluble cesium that is volatilized during melter operation. The recycle waste is currently received into a large, underground waste tank that separates insoluble solids via decanting. The supernate is treated by an evaporator with a concentrate stream that is stored for future processing. The evaporator overheads are collected and sent to the Effluent Treatment Project (ETP) for final polishing and testing prior to discharge to local surface water. Recycle storage and treatment as described above complicates the overall mission within the tank farms, which are primarily engaged in waste retrieval and preparation activities that support sludge and salt disposition, as well as tank characterization and closure. The need to devote a portion of available storage space to recycle treatment limits operational flexibility, and ultimately the DWPF recycle stream must be diverted to fully close all the SRS waste tanks. An alternative treatment process is being explored to decouple the recycle stream from the tank farm. The proposed treatment process will accomplish solids separation via crossflow filtration and will utilize a wiped film evaporator to volume-reduce the filtrate stream. Evaporator overheads will continue to be further processed in ETP while the solids stream and evaporator concentrate stream will be returned for reprocessing with the DWPF and Salt Waste Processing Facility (SWPF). This process will utilize existing facilities within DWPF and the tank farm that were previously dedicated to Interim Salt Disposition (ISD), but no longer have an identified mission with the startup of SWPF. The reuse of these facilities will remove several constraints from the current Liquid Waste (LW) System Plan without expanding the current footprint of Department of Energy Environmental Management (EM) infrastructure within LW. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
NASA NTRSDate not supplied

Risk Characterization Research for Artemis II: Human Factors and Behavioral Performance

BACKGROUND Artemis II will be the first time NASA astronauts go beyond low-Earth orbit (LEO) since the Apollo era, and the first astronauts heading into space in the Orion vehicle. As such, it provides a critical opportunity to refine our understanding of the likelihood and consequences associated with the Behavioral Medicine (BMed), Team, Human System Integration Architecture (HSIA), and Sleep Risks, and prepare for future Moon and Mars missions. However, Artemis II research efforts are uniquely shaped by in-mission data collection constraints. There is currently no in-mission crew time available to complete measures. In-mission data will need to be collected unobtrusively from available data streams (e.g., audiovisual, existing records such as schedules, and actigraphy). Accordingly, the overarching goal of our research is to utilize Artemis II data to further define the likelihood and consequences of these risks, and to create an unobtrusive research infrastructure that can be expanded to include future Artemis missions. This goal spans four aims across three research phases: (1) identify and operationally define key performances metrics and constructs across the four aforementioned risks, (2) develop an unobtrusive methodology and coding scheme for in-mission data collection, (3) characterize performance decrements due to Bmed, Team, HSIA, and Sleep Risks, and (4) develop a data infrastructure for future Artemis missions. The following details results of Phase I efforts in which we address Aims 1 and 2 to develop an unobtrusive measurement plan and coding scheme to capture key constructs, contributing factors, and performance decrements across each risk area. METHOD As part of Phase I, we conducted an interdisciplinary literature review and consulted with SMEs to identify unobtrusive methodologies that leverage text, audio, and/or video data as well as conceptualize key performance metrics, contributing factors, and BMed, Team, HSIA, and Sleep risk constructs related to performance decrements. The Phase I effort resulted in a finalized pre- and post-mission protocol for Artemis II, along with a measurement and coding scheme for in-mission Artemis II data. Phase II will involve data collection from the upcoming Artemis II mission. Phase III will include data processing, coding, depiction, analysis, and report writing of the Artemis II data. RESULTS & DISCUSSION To date, we have completed Phase I efforts. Specifically, we identified BMed, Team, HSIA, and Sleep risk constructs related to performance metrics, summarized how these constructs can be measured using audiovisual data collected during the mission, and worked with NASA’s HFBP Element to finalize a data collection protocol that leverages audiovisual input from the Orion spacecraft system. Our protocol includes novel unobtrusive methodologies that adhere to in-mission data streams and subsequent constraints (e.g., limited storage space on GoPro cameras, ambient noise impeding audio files) to best capture in-mission phenomena across each risk area. We will present our results from Phase I efforts, namely best practices for unobtrusive measurement as identified through literature reviews and SME consultation as well as codebook excerpts for use in Artemis II. We will include a description of planned work as we prepare for Phase II and Phase III of this research plan and the Artemis II mission itself. SUMMARY We describe progress on our Human Factors and Behavioral Performance Research for Artemis II study.

behavioral health↗
NASA NTRS1996

Pilot Project for Spaceborne Massive Optical Storage Devices

A space bound storage device has many special requirements. In addition to large storage capacity, fas read/ write time, and high reliability, it also needs to have small volume, light weight, low power consumption, radiation hardening, ability to operate in extreme temperature ranges, etc. Holographic optical recording technology, which has been making major advancements in recent years, is an extremely promising candidate. The goal of this pilot project is to demonstrate a laboratory bench-top holographic optical recording storage system (HORSS) based on nonlinear polymer films 1 and/or other advanced photo-refractive materials. This system will be used as a research vehicle to study relevant optical properties of novel holographic optical materials, to explore massive optical storage technologies based on the photo-refractive effect and to evaluate the feasibility of developing a massive storage system, based on holographic optical recording technology, for a space bound experiment in the near future.

Chen, Y. J.↗
DOE OSTI2020

Super resolution for root imaging

Premise High‐resolution cameras are very helpful for plant phenotyping as their images enable tasks such as target vs. background discrimination and the measurement and analysis of fine above‐ground plant attributes. However, the acquisition of high‐resolution images of plant roots is more challenging than above‐ground data collection. An effective super‐resolution (SR) algorithm is therefore needed for overcoming the resolution limitations of sensors, reducing storage space requirements, and boosting the performance of subsequent analyses. Methods We propose an SR framework for enhancing images of plant roots using convolutional neural networks. We compare three alternatives for training the SR model: (i) training with non‐plant‐root images, (ii) training with plant‐root images, and (iii) pretraining the model with non‐plant‐root images and fine‐tuning with plant‐root images. The architectures of the SR models were based on two state‐of‐the‐art deep learning approaches: a fast SR convolutional neural network and an SR generative adversarial network. Results In our experiments, we observed that the SR models improved the quality of low‐resolution images of plant roots in an unseen data set in terms of the signal‐to‐noise ratio. We used a collection of publicly available data sets to demonstrate that the SR models outperform the basic bicubic interpolation, even when trained with non‐root data sets. Discussion The incorporation of a deep learning–based SR model in the imaging process enhances the quality of low‐resolution images of plant roots. We demonstrate that SR preprocessing boosts the performance of a machine learning system trained to separate plant roots from their background. Our segmentation experiments also show that high performance on this task can be achieved independently of the signal‐to‐noise ratio. We therefore conclude that the quality of the image enhancement depends on the desired application.

Ruiz‐Munoz, Jose F.↗
DOE OSTI2024

Photometric redshifts probability density estimation from recurrent neural networks in the DECam local volume exploration survey data release 2

Photometric wide-field surveys are imaging the sky in unprecedented detail. These surveys face a significant challenge in efficiently estimating galactic photometric redshifts while accurately quantifying associated uncertainties. In this work, we address this challenge by exploring the estimation of Probability Density Functions (PDFs) for the photometric redshifts of galaxies across a vast area of 17,000 square degrees, encompassing objects with a median 5 σ point-source depth of g = 24.3, r = 23 . 9 , i = 23.5, and z = 22.8 mag. Our approach uses deep learning, specifically integrating a Recurrent Neural Network architecture with a Mixture Density Network, to leverage magnitudes and colors as input features for constructing photometric redshift PDFs across the whole DECam Local Volume Exploration (DELVE) survey sky footprint. Subsequently, we rigorously evaluate the reliability and robustness of our estimation methodology, gauging its performance against other well-established machine learning methods to ensure the quality of our redshift estimations. Our best results constrain photometric redshifts with the bias of − 0 . 0013 , a scatter of 0.0293, and an outlier fraction of 5.1%. These point estimates are accompanied by well-calibrated PDFs evaluated using diagnostic tools such as Probability Integral Transform and Odds distribution. We also address the problem of the accessibility of PDFs in terms of disk space storage and the time demand required to generate their corresponding parameters.We present a novel Autoencoder model that reduces the size of PDF parameter arrays to one-sixth of their original length, significantly decreasing the time required for PDF generation to one-eighth of the time needed when generating PDFs directly from the magnitudes.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2024

Machine learning-based discovery of molecular descriptors that control polymer gas permeation

While machine learning has found increasing use in predicting the properties of polymeric materials with only a knowledge of chain architecture, determining the molecular factors underpinning properties (“interpretable AI”) has remained less well explored. We show that encoding chain chemistry in commonly employed formats, e.g., binary-valued fingerprints, leads to uniqueness issues during the hashing process to save storage space. This is because the hashing algorithm can map several chemical moieties into the same bit. These issues carry over into the ML algorithms, especially for “inverse” design and interpretable AI, and cannot be avoided by changing the length of the fingerprint. Using MACCS key featurizations of monomer repeats resolves some of these issues, and we show that a few substructures consistently appear in top features for maximizing permeability across several gases and ML models. These are carbon–carbon double bonds (as in polyacetylenes) especially when they are associated with methyl groups (found in branching architectures). Here these results, derived from the limited data set of ~ 500 polymers with experimental gas permeation data, are in agreement with physical insight and thus provide a robust foundation which could further enable study of these material classes through detailed experiments and simulations.

36 MATERIALS SCIENCE↗
DOE OSTI2020

Total cost of carbon capture and storage implemented at a regional scale: northeastern and midwestern United States

We model the costs of carbon capture and storage (CCS) in subsurface geological formations for emissions from 138 northeastern and midwestern electricity-generating power plants. The analysis suggests coal-sourced CO 2 emissions can be stored in this region at a cost of $52–$60 ton -1 , whereas the cost to store emission from natural-gas-fired plants ranges from approximately $80 to $90. Storing emissions offshore increases the lowest total costs of CCS to over $60 per ton of CO 2 for coal. Because there apparently is sufficient onshore storage in the northeastern and midwestern United States, offshore storage is not necessary or economical unless there are additional costs or suitability issues associated with the onshore reservoirs. For example, if formation pressures are prohibitive in a large-scale deployment of onshore CCS, or if there is opposition to onshore storage, offshore storage space could probably store emissions at an additional cost of less than $10 ton -1 . Finally, it is likely that more than 8 Gt of total CO 2 emissions from this region can be stored for less $60 ton -1 , slightly more than the $50 ton -1 Section 45Q tax credits incentivizing CCS.

Life Sciences & Biomedicine - Other Topics↗
DOE OSTI2022

A Multi-Branch Decoder Network Approach to Adaptive Temporal Data Selection and Reconstruction for Big Scientific Simulation Data

A key challenge in scientific simulation is that the simulation outputs often require intensive I/O and storage space to store the results for effective post hoc analysis. This article focuses on a quality-aware adaptive temporal data selection and reconstruction problem where the goal is to adaptively select simulation data samples at certain key timesteps in situ and reconstruct the discarded samples with quality assurance during post hoc analysis. This problem is motivated by the limitation of current solutions that a significant amount of simulation data samples are either discarded or aggregated during the sampling process, leading to inaccurate modeling of the simulated phenomena. Two unique challenges exist: 1) the sampling decisions have to be made in situ and adapted to the dynamics of the complex scientific simulation data; 2) the reconstruction error must be strictly bounded to meet the application requirement. To address the above challenges, we develop DeepSample , an error-controlled convolutional neural network framework, that jointly integrates a set of coherent multi-branch deep decoders to effectively reconstruct the simulation data with rigorous quality assurance. The results on two real-world scientific simulation applications show that DeepSample significantly outperforms other state-of-the-art methods on both sampling efficiency and reconstructed simulation data quality.

Zhang, Yang↗
DOE OSTI2022

SZ3: A Modular Framework for Composing Prediction-Based Error-Bounded Lossy Compressors

Today's scientific simulations require a significant reduction of data volume because of extremely large amounts of data they produce and the limited I/O bandwidth and storage space. Here, error-bounded lossy compression has been considered one of the most effective solutions to the above problem. In practice, however, the best-fit compression method often needs to be customized or optimized in particular because of diverse characteristics in different datasets and various user requirements on the compression quality and performance. In this paper, we address this issue with a novel modular, composable compression framework named SZ3. Our contributions are four-folds. (1) We develop SZ3 which features an innovative modular abstraction for the prediction-based compression framework, such that compression modules can be plugged in easily to create new compressors based on characteristics of data and user requirements. (2) We create a new compression pipeline by SZ3 for GAMESS data, which significantly improves the compression ratios over state-of-the-art compressors. (3) We develop an adaptive compression pipeline by SZ3 for APS data with minimal efforts, which leads to the best rate-distortion among all existing error-bounded lossy compressors for any bit-rate. (4) We compare the sustainability of SZ3 with leading error-bounded prediction-based compressors, and then demonstrate the necessity of diverse pipelines by integrating and evaluating several compression pipelines on diverse scientific datasets from multiple disciplines. Experiments show that SZ3 incurs very limited overhead in compressor integration and our customized compression pipelines lead to up to 20% improvement in compression ratios under the same data distortion, when compared with the best existing approach.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Performance Optimization for Relative-Error-Bounded Lossy Compression on Scientific Data

Scientific simulations in high-performance computing (HPC) environments generate vast volume of data, which may cause a severe I/O bottleneck at runtime and a huge burden on storage space for postanalysis. Unlike traditional data reduction schemes such as deduplication or lossless compression, not only can error-controlled lossy compression significantly reduce the data size but it also holds the promise to satisfy user demand on error control. Pointwise relative error bounds (i.e., compression errors depends on the data values) are widely used by many scientific applications with lossy compression since error control can adapt to the error bound in the dataset automatically. Pointwise relative-error-bounded compression is complicated and time consuming. In this article, we develop efficient precomputation-based mechanisms based on the SZ lossy compression framework. Our mechanisms can avoid costly logarithmic transformation and identify quantization factor values via a fast table lookup, greatly accelerating the relative-error-bounded compression with excellent compression ratios. In addition, we reduce traversing operations for Huffman decoding, significantly accelerating the decompression process in SZ. Experiments with eight well-known real-world scientific simulation datasets show that our solution can improve the compression and decompression rates (i.e., the speed) by about 40 and 80 p, respectively, in most of cases, making our designed lossy compression strategy the best-in-class solution in most cases.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗