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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 1,639 records · Page 91
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

The Analysis and Support for LANL Technology Testbed: High Altitude Balloon Gondola and Orion Eagle Gamma Ray Detection Instrument

The Analysis and Support for LANL Technology Testbed: High Altitude Balloon Gondola and Orion Eagle Gamma-Ray Detection Instrument is a project where Los Alamos National Laboratory (LANL), in conjunction with the University of Michigan, are looking into gamma-ray detection in a space-like environment. The purpose of the detection is to work on instruments that can assist with planetary exploration and nuclear deterrents using a High-Altitude Balloon (HAB). For this paper, the main focus is on the mechanical aspects related to the structural analysis of the gondola. It is vital to understand the structural analysis because it helps determine if the gondola can survive the changes in the environment and the different forces or shocks that it will experience when the parachute is deployed and at landing. Additionally, NASA's Balloon Program Office (BPO) has specific requirements to meet to achieve flight certifications. Part of those certifications requires the gondola to simulate different scenarios at specific conditions with various forces applied and orientations. Therefore, it is necessary to adjust the calculations of each method and the force applied to get the right results. Once that is done, the information is inputted into the structural analysis modeling system. The system will aid the mechanical team in assessing the conditions of the gondola at each scenario and determine its margin of safety. After the gondola and instrument are certified, the payload will be able to fly. The HAB and payload are launched from NASA's Columbia Scientific Balloon Facility (CSBF) at Fort Sumner, NM. The official flight was on September 26th, 2021.

42 ENGINEERING↗
DOE OSTI2024

The Scientific Case for Concurrent Neutron and X-ray Scattering and Spectroscopy

The interrogation of materials with X-rays or neutrons to determine the structure, energetics, and dynamics of materials is fundamental to advancing materials' physical and chemical science and developing innovative material technologies. A transcending challenge in developing novel materials is that progress hinges on understanding the structure and dynamics across multiple time and length scales in complex materials that feature multiple components, interfaces, and compositions. Despite the ever-growing demands on materials’ characterization, existing approaches are almost exclusively based on isolated X-ray or neutron scattering, i.e., an approach commensurate with the more narrowly defined needs of fifty years ago. A three-day workshop sponsored by the U.S. National Science Foundation (NSF) analyzed the demand for concurrent neutron and X-ray (NeX) experiments. It was held at the Spring Hill Suites, San Jose, California, from June 2 to 4, 2022. In this workshop, 70 national and international experts ascertained the crucial need to establish NeX capabilities to advance the science of complex materials and systems in the US. Here, we illustrate the need for NeX scattering and spectroscopy experiments by showcasing examples that span areas as diverse as biomaterials, energy science, soft matter, and nanomaterials. To provide NeX capability will require new instrumentation that enables concurrent experiments. Affected areas include chemistry, soft matter, quantum materials, pure and applied chemistry, bioscience, geoscience, and applied materials. NeX benefits research outcomes due to the complementarity of the two techniques, which is essential for better model refinement. While joint refinement of data from separate neutron and X-ray experiments is critical to avoid ambiguities, especially in multiphase-multicomponent materials, concurrent experiments overcome scientific and technical barriers associated with single measurements, separated by location and, thus, time. Among all the examples, these factors introduce uncertainties in the results that complicate data analysis. [1,2] [3] While models are strongly sample-dependent, the principles of joint refinement are generally applicable to these disciplines, including the development of advanced parameterization, modeling, and analysis techniques that also consider the temporal and spatial resolutions of the two methods, leading to unambiguous data interpretation. Solutions for technical barriers must be found to realize NeX experiments, including developing robust sample environments that meet the optical requirements of neutrons and X-rays.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A Multi-Sensor Approach for Measuring Bird and Bat Collisions with Offshore Wind Turbines (Final Technical Report)

Collision of birds and bats with wind turbines is a conservation concern for both land-based and offshore wind projects. The fatality rates of birds and bats at land-based turbines are well documented. The measurement strategies on land focus on finding carcasses following collision, estimating the number of carcasses missed through searcher efficiency, carcass persistence trials and carcass fall distributions, and modeling statistically robust fatality rates. Few technologies have been developed to monitor offshore bird and bat collisions, and many that have been developed focused on detecting collisions with large birds. The few studies that have attempted to document collisions at offshore turbines do not account for smaller bodied animals or for collisions that might be missed, which prevents the calculation of statistically robust fatality rates. The overall goal of this report, A Multi-Sensor Approach for Measuring Bird and Bat Collisions with Offshore Wind Turbines (Project), was to develop an effective multi-sensor system for quantifying bird and bat collision rates, specifically for offshore wind facilities. The Project goal and resulting automated collision detection system was achieved through two major technological advancements: 1) refining The Netherlands Organisation for Applied Scientific Research’s (TNO’s) existing WT-Bird® vibration sensing system, that had successfully detected large bird collisions during daytime, to allow for improved detection of smaller birds and bats during both daytime and nighttime hours and 2) improving image processing systems and developing and integrating machine learning algorithms to automatically detect and classify small and large bird and bat collisions with offshore turbines. This final technical report (FTR) summarizes Methods , Results , Conclusions , and Lessons Learned during each of the five Tasks identified for this research and development effort. This FTR includes summaries of the following: Task 1. Initial Engineering Tests to Improve WT-Bird® Task 2. Installation of WT‐Bird® on a Utility-scale Turbine at the National Wind Technology Center – National Renewable Energy Laboratory Task 3. Field Tests and Refinement of the Object Detection System Task 4. Validation of WT-Bird® on a Land-based Turbine Task 5. Preparation for the Implementation of WT-Bird® on an Offshore Turbine. This research and development effort documented successful improvement of the WT Bird® collision detection system to detect small birds and bats, and WT-Bird® is the first collision detection system to validate results compared to land-based post-construction monitoring. The collision trials provide estimates of missed targets that can be used to estimate fatality rates, a significant improvement relative to other offshore collision monitoring systems. Advances were made in developing an edge-processing solution to reduce data storage requirements, which is important if the system is deployed for long periods of time at offshore turbines. The improved WT-Bird® system also provides an important option for wind operators on land or offshore who need to document specific details about when collisions occur, particularly efforts to further research on bat impact minimization, or when standard fatality searches are impractical (e.g. offshore) or inadequate (e.g. challenging locations on land).

17 WIND ENERGY↗
NASA NTRS1991

Computational Fluid Dynamics In Aerospace

Report reviews recent progress in computational fluid dynamics (CFD). Addresses state of art in terms of methods, examples of solutions, and computer technology. Provides examples of CFD solutions of aerospace and fluid-physics problems to illustrate recent progress. Aerospace solutions include rotor/stator velocity vectors from three-dimensional Navier-Stokes code for turbomachinery, pressure distributions from simulation of aeroelasticity of wing surface-pressure contours on four-bladed helicopter rotor in forward flight, and particle traces about several aerospace vehicles. Computer technology covers supercomputers, scientific work stations, local supercomputing networks, and remote access to supercomputers.

Kutler, Paul↗
NASA NTRS1987

Flow physics

A discussion is presented on the coupling of computational analysis and experiment. It is believed that this coupling is critical in developing new aerodynamic insights. Additionally, new methods for analyzing and interpreting data are discussed. These methods need to be developed in small-scale research studies and then applied to large-scale technology programs. The specific objectives of this program are threefold: (1) provide definitive data sets for the assessment of numerical simulations to the Navier-Stokes equations; (2) incorporate advanced instrumentation to measure the spatial and temporal structure of fluid flows; and (3) develop true parallelism between computational and experimental research using the 'scientific workstation' concept. The discussion is presented in viewgraph form.

Davis, Sanford↗
NASA NTRS1993

A reference model for scientific information interchange

This paper presents an overview of an Information Interchange Reference Model (IIRM) currently being developed by individuals participating in the Consultative Committee for Space Data Systems (CCSDS) Panel 2, the Planetary Data Systems (PDS), and the Committee on Earth Observing Satellites (CEOS). This is an ongoing research activity and is not an official position by these bodies. This reference model provides a framework for describing and assessing current and proposed methodologies for information interchange within and among the space agencies. It is hoped that this model will improve interoperability between the various methodologies. As such, this model attempts to address key information interchange issues as seen by the producers and users of space-related data and to put them into a coherent framework. Information is understood as the knowledge (e.g., the scientific content) represented by data. Therefore, concern is not primarily on mechanisms for transferring data from user to user (e.g., compact disk read-only memory (CD-ROM), wide-area networks, optical tape, and so forth) but on how information is encoded as data and how the information content is maintained with minimal loss or distortion during transmittal. The model assumes open systems, which means that the protocols or methods used should be fully described and the descriptions publicly available. Ideally these protocols are promoted by recognized standards organizations using processes that permit involvement by those most likely to be affected, thereby enhancing the protocol's stability and the likelihood of wide support.

Reich, Lou↗
NASA NTRS2000

Visualization, Extraction and Quantification of Discontinuities in Compressible Flows

Scientific visualizations of two-dimensional compressible flow of a gas with discontinuities are presented. The numerical analogue to experimental techniques such as schlieren imaging, shadowgraphs, and interferograms are discussed. Edge detection techniques are utilized to identify the discontinuities. In particular, the zero crossing of the Laplacian of a field (usually density) is recommended for extracting the discontinuities. An algorithm to extract and quantify the discontinuities is presented. To illustrate the methods developed in the report, the example chosen is that of an unsteady interaction of a shock wave with a contact discontinuity.

Samtaney, Ravi↗
NASA NTRS2005

NASA Space Science and a Search for Ram-Pressure Stripping in the Hydra I Cluster

The NASA Goddard Space Flight Center's Sciences and Exploration Directorate seeks to expand scientific knowledge through observational and theoretical research in the study of the Earth-Sun system, the solar system and the origins of life, and the birth and evolution of the universe. This talk will discuss some of the cutting-edge space science research being conducted at Goddard. In addition, I will discuss my research on ram-pressure stripping in cluster elliptical galaxies. Ram-pressure stripping is a method by which hot interstellar gas can be removed from a galaxy moving through a group or cluster of galaxies. Indirect evidence of ram-pressure stripping includes lowered X-ray brightness in a galaxy due to less X-ray emitting gas remaining in the galaxy. Here we present the initial results of our program to determine whether cluster elliptical galaxies have lower hot gas masses than their counterparts in less rich environments. This test requires the use of the high-resolution imaging of the Chandra Observatory and we present our analysis of the galaxies in the nearby cluster Hydra I.

Brown, Beth↗
NASA NTRS2012

Materials Advance Chemical Propulsion Technology

In the future, the Planetary Science Division of NASA's Science Mission Directorate hopes to use better-performing and lower-cost propulsion systems to send rovers, probes, and observers to places like Mars, Jupiter, and Saturn. For such purposes, a new propulsion technology called the Advanced Materials Bipropellant Rocket (AMBR) was developed under NASA's In-Space Propulsion Technology (ISPT) project, located at Glenn Research Center. As an advanced chemical propulsion system, AMBR uses nitrogen tetroxide oxidizer and hydrazine fuel to propel a spacecraft. Based on current research and development efforts, the technology shows great promise for increasing engine operation and engine lifespan, as well as lowering manufacturing costs. In developing AMBR, ISPT has several goals: to decrease the time it takes for a spacecraft to travel to its destination, reduce the cost of making the propulsion system, and lessen the weight of the propulsion system. If goals like these are met, it could result in greater capabilities for in-space science investigations. For example, if the amount (and weight) of propellant required on a spacecraft is reduced, more scientific instruments (and weight) could be added to the spacecraft. To achieve AMBR s maximum potential performance, the engine needed to be capable of operating at extremely high temperatures and pressure. To this end, ISPT required engine chambers made of iridium-coated rhenium (strong, high-temperature metallic elements) that allowed operation at temperatures close to 4,000 F. In addition, ISPT needed an advanced manufacturing technique for better coating methods to increase the strength of the engine chamber without increasing the costs of fabricating the chamber.

Source record↗
NASA NTRSDate not supplied

Developing Standards for AI/ML Systems in Civil Aviation: Challenges and Barriers

The inability to establish appropriate assurance methods for AI/ML components in safety critical systems leaves us unable to effectively manage the risks and benefits of such systems. It drives cost of development for systems with AI/ML components uneconomically high, it delays the adoption of systems with AI/ML components at scale, and it can result in catastrophic consequences in terms of the safety of systems with AI/ML components. In this presentation we will explore what constitutes sufficient scientific-based evidence to substantiate a safety claim related to an AI/ML component performing a safety-critical function.

AI/ML Standards↗
DOE OSTI2023

Towards Lightweight Data Integration Using Multi-Workflow Provenance and Data Observability

Modern large-scale scientific discovery requires multidisciplinary collaboration across diverse computing facilities, including High Performance Computing (HPC) machines and the Edge-to-Cloud continuum. Integrated data analysis plays a crucial role in scientific discovery, especially in the current AI era, by enabling Responsible AI development, FAIR, Reproducibility, and User Steering. However, the heterogeneous nature of science poses challenges such as dealing with multiple supporting tools, cross-facility environments, and efficient HPC execution. Building on data observability, adapter system design, and provenance, we propose MIDA: an approach for lightweight runtime Multi-workflow Integrated Data Analysis. MIDA defines data observability strategies and adaptability methods for various parallel systems and machine learning tools. With observability, it intercepts the dataflows in the background without requiring instrumentation while integrating domain, provenance, and telemetry data at runtime into a unified database ready for user steering queries. We conduct experiments showing end-to-end multi-workflow analysis integrating data from Dask and MLFlow in a real distributed deep learning use case for materials science that runs on multiple environments with up to 276 GPUs in parallel. We show near-zero overhead running up to 100,000 tasks on 1,680 CPU cores on the Summit supercomputer.

Santos Souza, Renan↗
DOE OSTI2024

Speeding up and reducing memory usage for scientific machine learning via mixed precision

Scientific machine learning (SciML) has emerged as a versatile approach to address complex computational science and engineering problems. Within this field, physics-informed neural networks (PINNs) and deep operator networks (DeepONets) stand out as the leading techniques for solving partial differential equations by incorporating both physical equations and experimental data. However, training PINNs and DeepONets require significant computational resources, including long computational times and large amounts of memory. In search of computational efficiency, training neural networks using half precision (float16) rather than the conventional single (float32) or double (float64) precision has gained substantial interest, given the inherent benefits of reduced computational time and memory consumed. However, we find that float16 cannot be applied to SciML methods, because of gradient divergence at the start of training, weight updates going to zero, and the inability to converge to a local minima. To overcome these limitations, we explore mixed precision, which is an approach that combines the float16 and float32 numerical formats to reduce memory usage and increase computational speed. Our experiments showcase that mixed precision training not only substantially decreases training times and memory demands but also maintains model accuracy. Here, we also reinforce our empirical observations with a theoretical analysis. The research has broad implications for SciML in various computational applications.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

STSR-INR: Spatiotemporal super-resolution for multivariate time-varying volumetric data via implicit neural representation

Implicit neural representation (INR) has surfaced as a promising direction for solving different scientific visualization tasks due to its continuous representation and flexible input and output settings. We present STSR-INR, an INR solution for generating simultaneous spatiotemporal super-resolution for multivariate time-varying volumetric data. Inheriting the benefits of the INR-based approach, STSR-INR supports unsupervised learning and permits data upscaling with arbitrary spatial and temporal scale factors. Unlike existing GAN- or INR-based super-resolution methods, STSR-INR focuses on tackling variables or ensembles and enabling joint training across datasets of various spatiotemporal resolutions. Here we achieve this capability via a variable embedding scheme that learns latent vectors for different variables. In conjunction with a modulated structure in the network design, we employ a variational auto-decoder to optimize the learnable latent vectors to enable latent-space interpolation. To combat the slow training of INR, we leverage a multi-head strategy to improve training and inference speed with significant speedup. We demonstrate the effectiveness of STSR-INR with multiple scalar field datasets and compare it with conventional tricubic+linear interpolation and state-of-the-art deep-learning-based solutions (STNet and CoordNet).

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Graph convolutional networks applied to unstructured flow field data

Abstract Many scientific and engineering processes produce spatially unstructured data. However, most data-driven models require a feature matrix that enforces both a set number and order of features for each sample. They thus cannot be easily constructed for an unstructured dataset. Therefore, a graph based data-driven model to perform inference on fields defined on an unstructured mesh, using a graph convolutional neural network (GCNN) is presented. The ability of the method to predict global properties from spatially irregular measurements with high accuracy is demonstrated by predicting the drag force associated with laminar flow around airfoils from scattered velocity measurements. The network can infer from field samples at different resolutions, and is invariant to the order in which the measurements within each sample are presented. The GCNN method, using inductive convolutional layers and adaptive pooling, is able to predict this quantity with a validation R 2 above 0.98, and a Normalized Mean Squared Error below 0.01, without relying on spatial structure.

Ogoke, Francis (ORCID:0000000224327783)↗
DOE OSTI2020

Electrolyte Solvation and Ionic Association. VII. Correlating Raman Spectroscopic Data with Solvate Species

Raman spectroscopy is a widely utilized method for determining information about the solvate species present in electrolyte solutions. Difficulties arise, however, when interpreting the Raman data in terms of correlating the spectroscopic vibrational bands with specific solvate species. Examples are provided to demonstrate how erroneous conclusions may be drawn with regard to the identity and population (distribution) of the solvent-separated ion pair (SSIP), contact ion pair (CIP) and aggregate (AGG) solvate species frequently mentioned in the scientific literature.

contact ion pairs↗
DOE OSTI2024

Live-Cell Quantum Dot-Based Tracking of Plant & Microbial Extracellular Vesicles

All cellular organisms examined to date release various types of small vesicles called extracellular vesicles (EVs). They were originally thought to be unimportant, but research into EVs as carriers of key messages for communication within large, multicellular organisms or between interacting organisms has gained interest in many different scientific areas of study. Recently, it was discovered that both plants and their pathogenic fungi generate EVs and they are important vehicles for sending plant signals to thwart invasion by fungal pathogens, and in response, fungi use EVs to help evade plant defenses. This project aimed to develop new ways to visualize EVs and their potential nucleic acid cargo isolated from the crop plant, sorghum. We adapted a click chemistry-based method for making quantum dot-based RNA sensors, which will be a useful tool for others studying small RNAs. These sensors were not used detect RNA in sorghum EVs because unlike EVs from Arabidopsis, sorghum EVs were not enriched for specific small RNAs. This interesting difference shifted the project in an exciting direction, which focused on comparing EV proteomes. Our study discovered that greater than 60% of Arabidopsis EV proteins were also found in the sorghum EV proteome, revealing a partial conservation between a eudicot and monocot plant. These studies have raised new, fundamental questions on the conserved function of EVs in plants.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1993

Limitations to the study of man in space in the U.S. space program

Research on humans conducted during spaceflight is fraught both with great opportunities and great obstacles. The purpose of this paper is to review some of the limitations to research in space in the United States with hope that an informed scientific community may lead to more rapid and efficient solution of these problems. Limitations arise because opportunities to study the same astronauts in well-controlled situations on repeated spaceflights are practically non-existent. Human research opportunities are further limited by the necessity of avoiding simultaneous mutually-interfering experiments. Environmental factors, including diet and other physiological perturbations concomitant with spaceflight, also complicate research design and interpretation. Technical limitations to research methods and opportunities further restrict the development of the knowledge base. Finally, Earth analogues of space travel all suffer from inadequacies. Though all of these obstacles will eventually be overcome, creativity, diligence, and persistence are required to further our knowledge of humans in space.

Bishop, Phillip A.↗
NASA NTRS1997

Star Identification Without Attitude Knowledge: Testing with X-Ray Timing Experiment Data

As the budget for the scientific exploration of space shrinks, the need for more autonomous spacecraft increases. For a spacecraft with a star tracker, the ability to determinate attitude from a lost in space state autonomously requires the capability to identify the stars in the field of view of the tracker. Although there have been efforts to produce autonomous star trackers which perform this function internally, many programs cannot afford these sensors. The author previously presented a method for identifying stars without a priori attitude knowledge specifically targeted for onboard computers as it minimizes the necessary computer storage. The method has previously been tested with simulated data. This paper provides results of star identification without a priori attitude knowledge using flight data from two 8 by 8 degree charge coupled device star trackers onboard the X-Ray Timing Experiment.

Ketchum, Eleanor↗