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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 343 records · Page 19
All sourcesNASA NTRSDOE OSTI
NASA NTRS1994

Enhancement of free tropospheric ozone production by deep convection

It is found from model simulations of trace gas and meteorological data from aircraft campaigns that deep convection may enhance the potential for photochemical ozone production in the middle and upper troposphere by up to a factor of 60. Examination of half a dozen individual convective episodes show that the degree of enhancement is highly variable. Factors affecting enhancement include boundary layer NO(x) mixing ratios, differences in the strength and structure of convective cells, as well as variation in the amount of background pollution already in the free troposphere.

Pickering, Kenneth E.↗
NASA NTRS2001

Options For Development of Space Fission Propulsion Systems

Fission technology can enable rapid, affordable access to any point in the solar system. Potential fission-based transportation options include high specific power continuous impulse propulsion systems and bimodal nuclear thermal rockets. Despite their tremendous potential for enhancing or enabling deep space and planetary missions, to date space fission system have only been used in Earth orbit. The first step towards utilizing advanced fission propulsion systems is development of a safe, near-term, affordable fission system that can enhance or enable near-term missions of interest. An evolutionary approach for developing space fission propulsion systems is proposed.

Houta, Mike↗
NASA NTRS2002

Optimization Shield Materials Trade Study for Lunar/Gateway Mission

The great cost of added radiation shielding is a potential limiting factor in many deep space missions. For this enabling technology, we are developing tools for optimized shield design over multi-segmented missions involving multiple work and living areas in the transport and duty phase of various space missions. The total shield mass over all pieces of equipment and habitats is optimized subject to career dose and dose rate constraints. Preliminary studies of deep space missions indicate that for long duration space missions, improved shield materials will be required. The details of this new method and its impact on space missions and other technologies will be discussed. This study will provide a vital tool for evaluating Gateway designs in their usage context. Providing protection against the hazards of space radiation is one of the challenges to the Gateway infrastructure designs. We will use the mission optimization software to scope the impact of Gateway operations on human exposures and the effectiveness of alternate shielding materials on Gateway infrastructure designs. It is being proposed to use Moon and the Lagrange points as the hub for deep space missions. This study will provide a guide to the effectiveness of multifunctional materials in preparation to more detailed geometry studies in progress.

Tripathi, R. K.↗
NASA NTRS2002

Shield Optimization in Simple Geometry for the Gateway Concept

The great cost of added radiation shielding is a potential limiting factor in many deep space missions. For this enabling technology, we are developing tools for optimized shield design over multi-segmented missions involving multiple work and living areas in the transport and duty phase of various space missions. The total shield mass over all pieces of equipment and habitats is optimized subject to career dose and dose rate constraints. Preliminary studies of deep space missions indicate that for long duration space missions, improved shield materials will be required. The details of this new method and its impact on space missions and other technologies will be discussed. This study will provide a vital tool for evaluating Gateway designs in their usage context. Providing protection against the hazards of space radiation is one of the challenges to the Gateway infrastructure designs. We will use the mission optimization software to scope the impact of Gateway operations on human exposures and the effectiveness of alternate shielding materials on Gateway infrastructure designs. This study will provide a guide to the effectiveness of multifunctional materials in preparation to more detailed geometry studies in progress.

Tripathi, R. K.↗
NASA NTRS2005

Reliability of the SRG110 Stirling Convertor Quantified

The NASA Glenn Research Center has been involved in the development of Stirling power-conversion technology and is currently providing technical support to the Department of Energy and Lockheed Martin, of Valley Forge, Pennsylvania, their system integration contractor, for the 110-W Stirling Radioisotope Generator (SRG110). Stirling radioisotope power-conversion technology is a potential candidate for future deep-space science and exploration missions. The SRG110 has a multifold increase in efficiency and would reduce the inventory of the radioisotope fuel by a factor of 4 in comparison to systems used to power past missions.

Shah, Ashwin R.↗
NASA NTRS2006

Uplink Array Calibration via Far-Field Power Maximization

Uplink antenna arrays have the potential to greatly increase the Deep Space Network s high-data-rate uplink capabilities as well as useful range, and to provide additional uplink signal power during critical spacecraft emergencies. While techniques for calibrating an array of receive antennas have been addressed previously, proven concepts for uplink array calibration have yet to be demonstrated. This article describes a method of utilizing the Moon as a natural far-field reflector for calibrating a phased array of uplink antennas. Using this calibration technique, the radio frequency carriers transmitted by each antenna of the array are optimally phased to ensure that the uplink power received by the spacecraft is maximized.

Vilnrotter, V.↗
NASA NTRS2018

Preliminary Test Results for Stability and Control Characteristics of a Generic T-Tail Transport Airplane at High Angle of Attack

Tests of a generic T-tail transport airplane, in flaps-up configuration, were conducted using two wind tunnels, a water tunnel, and computational fluid dynamics. Static force and moment testing, forced oscillation testing and dye flow visualization test techniques were used. The purpose of the testing was to obtain stability and control characteristics for development of a research flight simulator aerodynamic database. The purpose of that database was for assessment of aerodynamic model fidelity requirements to train airline pilots to recognize and recover from full stall conditions. Preliminary results, at initial stall conditions, include: an unstable stall pitch break, and near-neutral roll damping. Preliminary results, at deep stall conditions, include: a potential static longitudinal trim condition at approximately 35 degrees angle of attack, large aerodynamic asymmetries, and localized unstable dynamic stability.

Cunningham, Kevin↗
NASA NTRS2023

Damage Detection of a Pressure Vessel with Smart Sensing and Deep Learning

Structural Health Monitoring plays a crucial role in ensuring the safety and reliability of critical infrastructure, including pressure vessels involved in various applications. This research reports the damage detection of a pressure box employed in space habitat that operates in harsh environment where both structural failure and bolt joint loosening may occur. These failure modes are extremely hard to model based on first principles. We explore proper sensing mechanism and the associated inverse analysis algorithm that can elucidate the health condition of the pressure box. It is identified that piezoelectric impedance based active interrogation can provide necessary information for damage detection in such a system. Concurrently, deep learning technique leveraging spatial convolutional neural network is synthesized to analyze the raw data acquired and identify different types of damage. By training the deep learning model on a dataset of healthy and various damage scenarios, we can achieve high accuracy in identifying the presence of damage and its type. This research provides a data-driven methodology for structural damage detection using deep learning and has the potential to be extended to various systems with different failure modes.

Yang Zhang↗
NASA NTRS2018

CubeX: A Compact X-Ray Telescope Enables Both X-Ray Fluorescence Imaging Spectroscopy and Pulsar Timing Based Navigation

This paper describes the benefits of a miniaturized X-ray telescope payload in the context of a lunar mission. The first part describes the payload in detail, the second part summarizes a small satellite mission concept that utilizes its compact form factor and performance. The CubeX instrument can be used for both X-ray fluorescence (XRF) imaging spectroscopy and X-ray pulsar timing based navigation (XNAV). Using our recent technological advances in X-ray optics and sensors, CubeX combines high angular resolution (<1 arcminutes) Miniature Wolter-I X-ray optics (MiXO) with a common focal plane consisting of high spectral resolution (<150 eV at 1 keV) CMOS X-ray sensors and a high timing resolution (< 1 usec) SDD X-ray sensor. This novel combination of the instruments enables both XRF measurements and XNAV operations without moving parts. The high angular resolution of the MiXO opens a wide range of orbital configurations for observation. Given that performance, the instrument has unprecedented small volume (~1×1×6U), mass (<6 kg), and power (<9W) requirements and opens a wide range of applications for a variety of targets and missions including NEOs and Martian moons. In this paper we illustrate one potential application for a lunar mission concept: The elemental composition of the Moon holds keys to understanding the origin and evolution of both the Moon and the Earth. X-ray fluorescence (XRF), induced either by solar X-ray flux or energetic ions, carries decisive signatures of surface elemental composition. X-ray observations, therefore, give a unique, powerful diagnostic tool for remotely determining elemental abundances including major rock forming elements such as Mg, Al, Na, Si, Fe, and Ca. Through high-resolution XRF imaging spectroscopy, CubeX searches for small patches of elusive lower crust and mantle material excavated within and around impact craters. CubeX identifies regional compositional variations and allows straightforward comparison of elemental distributions with the surface topography from LRO and the gravity data from GRAIL. The elemental compositions of the lower crust and the mantle are sensitive to the conditions of the giant impact which led to the Moon's formation and the subsequent lunar magma ocean (LMO), and thus they are key missing pieces in understanding the formation and early evolution of the Moon. In between XRF observations, CubeX also leverages the technology of high resolution X-ray imaging and time series measurements to conduct XNAV operations and evaluate their performance. Deep space navigation is a critical issue for small planetary missions. XNAV can enable low-cost autonomous deep-space navigation, and has the potential to greatly assist, or even outperform, NASA's Deep Space Network (DSN) or ESA's European Space Tracking (ESTRACK). CubeX is designed to perform sequential observations of 3-4 millisecond pulsars (MSPs) to solve the spacecraft trajectory for absolute navigation, and explore the remote sensing capability of XNAV. In the presented mission concept, the Moon's relative proximity enables a straightforward evaluation of the XNAV performance through DSN.

x-ray spectroscopy↗
NASA NTRSDate not supplied

The first 300 sols of the SHERLOC investigation on the Mars 2020 rover

The Mars2020 mission is NASA’s latest flagship mission to Mars. The spacecraft launched in July 2020, and landed in Jezero crater on February 18, 2021 at the Octavia Butler Landing site. The rocks and sediments of Jezero crater have been argued to have preserved records of past, potentially habitable environments. The mission is characterizing the field site by analyzing chemistry, looking for organics and searching for potential biosignatures. The Scanning Habitable Environments with Ra-man and Luminescence for Organics and Chemicals (SHERLOC) is a robotic arm mounted instrument. SHERLOC combines imaging with UV resonance Raman and native deep UV fluorescence spectroscopy to identify potential biosignatures and understand the aqueous history of the Jezero Region. WATSON (Wide Angle Topographic Sensor for Operations and eNgineering), a refight of the Mars Hand Lens Imager (MAHLI) on the Curiosity rover is capable of color imaging over a wide range of resolutions (from infinity to 13.1 micron/pixel) and is used for both science and engineering. A second imager, the Autofocusing Con-textual Imager (ACI), produces gray scale images at 10.1 micron/pixel resolution at a 48 mm range.

A. E. Murphy↗
NASA NTRS2009

Dynamics and Predictability of Hurricane Humberto (2007) Revealed from Ensemble Analysis and Forecasting

This study uses short-range ensemble forecasts initialized with an Ensemble-Kalman filter to study the dynamics and predictability of Hurricane Humberto, which made landfall along the Texas coast in 2007. Statistical correlation is used to determine why some ensemble members strengthen the incipient low into a hurricane and others do not. It is found that deep moisture and high convective available potential energy (CAPE) are two of the most important factors for the genesis of Humberto. Variations in CAPE result in as much difference (ensemble spread) in the final hurricane intensity as do variations in deep moisture. CAPE differences here are related to the interaction between the cyclone and a nearby front, which tends to stabilize the lower troposphere in the vicinity of the circulation center. This subsequently weakens convection and slows genesis. Eventually the wind-induced surface heat exchange mechanism and differences in landfall time result in even larger ensemble spread. 1

Sippel, Jason A.↗
NASA NTRS1986

Communicating from the edge of the solar system

Communication technology for deep-space scientific missions is surveyed, with a focus on systems being developed for Galileo, a NASA orbiter/probe mission to Jupiter. The problems faced in designing the links from the probe (descending into the Jovian atmosphere) to the orbiter and from the orbiter to earth are examined; the technological solutions adopted for Galileo are discussed; the expansion of the Deep Space Network for the mission is described; the advantages and limitations of cm-band communication systems being considered to meet the data-rate requirements of future deep-space missions are outlined; and the potential of optical communication for the 21st century is indicated. Also included are tables listing past and planned deep-space missions and a brief description of the use of spacecraft radio signals by radio and planetary scientists to probe solar-system plasmas and gravitational fields.

Posner, E. C.↗
DOE OSTI2021

Molecular dynamics simulation of metallic Al–Ce liquids using a neural network machine learning interatomic potential

Al-rich Al-Ce alloys have the possibility of replacing heavier steel and cast-irons for use in high-temperature applications. Knowledge about the structures and properties of Al-Ce alloys at liquid state is vital for optimizing the manufacture process to produce desired allows. However, reliable molecular dynamics simulation of Al-Ce alloy systems remains a great challenge due to the lack of accurate Al-Ce interatomic potential. In this work, an artificial neural network (ANN) deep machine learning (ML) method is used to develop a reliable interatomic potential for Al-Ce alloy. Ab initio molecular dynamics (AIMD) simulation data on Al-Ce liquid with small unit cell (~200 atoms) and on the known Al-Ce crystalline compounds are collected to train the interatomic potential using ANN-ML. The obtained ANN-ML model reproduces well the energies, forces, and atomic structure of Al 90 Ce 10 liquid and crystalline phases of Al-Ce compounds in comparison with ab initio results. The developed ANN-ML potential is applied in molecular dynamics simulations to study the structures and properties of metallic Al 90 Ce 10 liquid, which would provide useful insight for guiding experimental process to produce desired Al-Ce allows.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRSDate not supplied

Applications of Nuclear Thermal Propulsion Systems for Deep Space Science Missions

Nuclear thermal propulsion (NTP) systems occupy a unique area in the space propulsion technology landscape due to their ability to combine moderate-to-high thrust systems normally seen in chemical propulsion systems with specific impulses that are closer to those observed in some electric propulsion systems. Consequently, NTP systems have the potential to greatly expand access to deep space and can enable a variety of missions that achieve the science goals outlined in NASA’s decadal surveys. This paper leverages previous analysis performed to show the applicability of NTP systems for notional science missions, expanding the analyzed portfolio to encompass additional science mission profiles and demonstrating how the use of NTP affects various mission parameters, such as trip time and delivered mass. This paper also outlines efforts to improve the fidelity of the existing NTP design concepts and vehicles that are utilized by leveraging previous work on nuclear propulsion systems for human-Mars missions. The fidelity of the analysis in this work is improved over previous studies, permitting commensurate improvements in the analyses of previous mission concept studies –the Triton lander, the solar polar orbiter, and the interstellar medium probe missions.

Nuclear thermal propulsion↗
DOE OSTI2020

Reynolds-Averaged Turbulence Modeling Using Deep Learning with Local Flow Features: An Empirical Approach

Reynolds-Averaged Navier-Stoke (RANS) models offer an alternative avenue in predicting flow characteristics when the corresponding experiments are difficult to achieve due to geometry complexity, limited budget, or knowledge. RANS models require the knowledge of subgrid scale physics to solve conservation equations for mass, energy, and momentum. Mechanistic turbulence models, such as k-epsilon, are generally evaluated and calibrated for specific flow conditions with various degrees of uncertainty. These models have limited capability to assimilate a substantial amount of data due to model form constraints. Meanwhile, deep learning (DL) has been proven to be universal approximators with the potential to assimilate available, relevant, and adequately evaluated data. Moreover, deep neural networks (DNNs) can create surrogate models without knowing function forms. Such a data-driven approach can be used in updating fluid models based on observations as opposed to hard-wiring models with precalibrated correlations. The paper presents progress in applying DNNs to model Reynolds stress using two machine learning (ML) frameworks. A novel flow feature coverage mapping is proposed to quantify the physics coverage of DL-based closures. It can be used to examine the sufficiency of training data and input flow features for data-driven turbulence models. The case of a backward-facing step is formulated to demonstrate that not only can DNNs discover underlying correlation behind fluid data but also they can be implemented in RANS to predict flow characteristics without numerical stability issues. Finally, the presented research is a crucial stepping-stone toward the data-driven turbulence modeling, which potentially benefits the design of data-driven experiments that can be used to validate fluid models with ML-based fluid closures.

42 ENGINEERING↗
DOE OSTI2022

Potential use of engineered nanoparticles in ocean fertilization for large-scale atmospheric carbon dioxide removal

Artificial ocean fertilization (AOF) aims to safely stimulate phytoplankton growth in the ocean and enhance carbon sequestration. AOF carbon sequestration efficiency appears lower than natural ocean fertilization processes due mainly to the low bioavailability of added nutrients, along with low export rates of AOF-produced biomass to the deep ocean. Here we explore the potential application of engineered nanoparticles (ENPs) to overcome these issues. Data from 123 studies show that some ENPs may enhance phytoplankton growth at concentrations below those likely to be toxic in marine ecosystems. ENPs may also increase bloom lifetime, boost phytoplankton aggregation and carbon export, and address secondary limiting factors in AOF. Life-cycle assessment and cost analyses suggest that net CO 2 capture is possible for iron, SiO 2 and Al 2 O 3 ENPs with costs of 2–5 times that of conventional AOF, whereas boosting AOF efficiency by ENPs should substantially enhance net CO 2 capture and reduce these costs. Therefore, ENP-based AOF can be an important component of the mitigation strategy to limit global warming.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRSDate not supplied

Toward The Development of Hailstorm Climatologies Derived From Reanalyses and Infared/Passive Microwave Satellite Imagers

Geostationary satellite imagers, such as those of the Geostationary Operational Environmental Satellite (GOES) and Meteosat series, provide both historical and near-real-time observations of cloud top patterns that are commonly associated with severe convection. Environmental conditions favorable for severe weather are thought to be represented well by reanalyses. Predicting exactly where convection and costly storm hazards like hail will occur using models or satellite imagery alone, however, is extremely challenging. The multivariate combination of satellite-observed cloud patterns with reanalysis environmental parameters, linked to United States Next Generation Weather Radar- (NEXRAD-) estimated Maximum Expected Size of Hail (MESH) using a deep neural network (DNN), enables estimation of potentially severe hail likelihood for any observed storm cell. These estimates are specifically designed to make hail likelihood distinctions based on satellite-indicated points of deep convection within environments favorable for storm development. We seek an approach that can be used to estimate climatological hailstorm frequency and risk throughout the historical satellite data record. This presentation demonstrates that statistical distributions of convective parameters from satellite and reanalysis show separation between non-severe/severe hailstorm classes for predictors including overshooting cloud top temperature and area characteristics, convective available potential energy, vertical wind shear, 500 hPa temperature, mid-level lapse rate, precipitable water, and convective inhibition. These complex, multivariate predictor relationships are exploited within a DNN to produce a hail likelihood metric with a critical success index of 0.504 and Heidke skill score of 0.403, which is exceptional among recent analogous hail studies. Furthermore, applications of the DNN to select case studies demonstrate good qualitative agreement between hail likelihood and MESH. These hail classifications are aggregated across an 11-year GOES-12/13 image database to derive a hail frequency and severity climatology, which denotes the Central Plains, the Midwest, and northwestern Mexico as being the most hail-prone regions within the domain studied. Opportunities for training and applying DNN-based hailstorm predictions to recently developed GOES-8/10/12/13/16 and Meteosat Second Generation convective storm detection and characterization climatologies over South America and South Africa, respectively, will also be presented.

Kristopher Bedka↗
DOE OSTI2025

Substantial and overlooked greenhouse gas emissions from deep Arctic lake sediment

Thermokarst lakes cause abrupt and sustained permafrost degradation and have the potential to release large quantities of ancient carbon to the atmosphere. Despite concerns about how lakes will affect the permafrost carbon feedback, the magnitude of carbon dioxide and methane emissions from deep permafrost soils remains poorly understood. Here we incubated a very deep sediment core (20 m) to constrain the potential productivity of thawed Yedoma and underlying Quaternary sand and gravel deposits. Through radiocarbon dating, sediment incubations and sediment facies classifications, we show that extensive permafrost thaw can occur beneath lakes on timescales of decades to centuries. Although it has been assumed that shallow, aerobic carbon dioxide production will dominate the climate impact of permafrost thaw, we found that anaerobic carbon dioxide and methane production from deep sediments was commensurate with aerobic production on a per gram carbon basis, and had double the global warming potential at warmer temperatures. Carbon release from deep Arctic sediments may thus have a more substantial impact on a changing climate than currently anticipated. These environments are presently overlooked in estimates of the permafrost carbon feedback.

54 ENVIRONMENTAL SCIENCES↗