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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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2013

A Particle-in-Cell Simulation for the Traveling Wave Direct Energy Converter (TWDEC) for Fusion Propulsion

A Particle-in-cell simulation model has been developed to study the physics of the Traveling Wave Direct Energy Converter (TWDEC) applied to the conversion of charged fusion products into electricity. In this model the availability of a beam of collimated fusion products is assumed; the simulation is focused on the conversion of the beam kinetic energy into alternating current (AC) electric power. The model is electrostatic, as the electro-dynamics of the relatively slow ions can be treated in the quasistatic approximation. A two-dimensional, axisymmetric (radial-axial coordinates) geometry is considered. Ion beam particles are injected on one end and travel along the axis through ring-shaped electrodes with externally applied time-varying voltages, thus modulating the beam by forming a sinusoidal pattern in the beam density. Further downstream, the modulated beam passes through another set of ring electrodes, now electrically oating. The modulated beam induces a time alternating potential di erence between adjacent electrodes. Power can be drawn from the electrodes by connecting a resistive load. As energy is dissipated in the load, a corresponding drop in beam energy is measured. The simulation encapsulates the TWDEC process by reproducing the time-dependent transfer of energy and the particle deceleration due to the electric eld phase time variations.

Chap, Andrew↗
NASA NTRS2015

Desensitized Optimal Filtering and Sensor Fusion Toolkit

Analytical Mechanics Associates, Inc., has developed a software toolkit that filters and processes navigational data from multiple sensor sources. A key component of the toolkit is a trajectory optimization technique that reduces the sensitivity of Kalman filters with respect to model parameter uncertainties. The sensor fusion toolkit also integrates recent advances in adaptive Kalman and sigma-point filters for non-Gaussian problems with error statistics. This Phase II effort provides new filtering and sensor fusion techniques in a convenient package that can be used as a stand-alone application for ground support and/or onboard use. Its modular architecture enables ready integration with existing tools. A suite of sensor models and noise distribution as well as Monte Carlo analysis capability are included to enable statistical performance evaluations.

Karlgaard, Christopher D.↗
NASA NTRS2019

Liftoff and Transition Database Generation for Launch Vehicles Using Data-Fusion-Based Modeling

A data fusion technique for merging multiple data sources with differing fidelity and resolution was developed to support the production of aerodynamic line load databases for the Liftoff and Transition (LOT) flight phase of the Space Launch System (SLS). The technique uses a reduced order model based on a high-fidelity line load data set from Computational Fluid Dynamics (CFD) to predict solutions for a much larger solution space. Even higher-fidelity force and moment information (from wind-tunnel tests) is then used to adjust the model. The adjustment uses constrained optimization through the method of Lagrange multipliers in order to minimize the deviation of the line load distribution from the spatially-dense CFD solution, while ensuring that the integrated force and moment values match those observed in physical wind tunnel measurements. Though the wind-tunnel data are operationally-dense (available at many flow conditions), they are spatially coarse (as only the overall forces and moments are available). Conversely, CFD for such complex configurations is expensive, and thus operationally sparse. Data fusion techniques are necessary to make the most efficient use of available information, delivering accurate results within time and resource constraints.

Wignall, T. J.↗
NASA NTRS2020

Nuclear Fusion Reactions in Deuterated Metals

Nuclear fusion reactions of D-D are examined in an environment comprised of high density cold fuel embedded in metal lattices in which a small fuel portion is activated by hot neutrons. Such an environment provides for enhanced screening of the Coulomb barrier due to conduction and shell electrons of the metal lattice, or by plasma induced by ionizing radiation (γ quanta). We show that neutrons are far more efficient than energetic charged particles, such as light particles (e−, e+) or heavy particles (p, d, α) in transferring kinetic energy to fuel nuclei (D) to initiate fusion processes. It is well-known that screening increases the probability of tunneling through the Coulomb barrier. Electron screening also significantly increases the probability of large- versus small-angle Coulomb scattering of the reacting nuclei to enable subsequent nuclear reactions via tunneling. This probability is incorporated into the astrophysical factor S(E). Aspects of screening effects to enable calculation of nuclear reaction rates are also evaluated, including Coulomb scattering and localized heating of the cold fuel, primary D-D reactions, and subsequent reactions with both the fuel and the lattice nuclei. The effect of screening for enhancement of the total nuclear reaction rate is a function of multiple parameters including fuel temperature and the relative scattering probability between the fuel and lattice metal nuclei. Screening also significantly increases the probability of interaction between hot fuel and lattice nuclei increasing the likelilhood of Oppenheimer-Phillips processes opening a potential route to reaction multiplication. We demonstrate that the screened Coulomb potential of the target ion is determined by the nonlinear Vlasov potential and not by the Debye potential. In general, the effect of screening becomes important at low kinetic energy of the projectile. We examine the range of applicability of both the analytical and asymptotic expressions for the well-known electron screening lattice potential energy Ue, which is valid only for E >> Ue (E is the energy in the center of mass reference frame). We demonstrate that for E ≤ Ue, a direct calculation of Gamow factor for screened Coulomb potential is required to avoid unreasonably high values of the enhancement factor f (E) by the analytical—and more so by the asymptotic—formulas.

Vladimir Pines↗
NASA NTRSDate not supplied

Fusion of Test and Analysis: Artemis I Booster to Mobile Launcher Interface Validation

NASA is in the midst of bold and exciting next steps in human exploration and spaceflight. The designs of the new Space Launch System (SLS), the Orion spacecraft and the Exploration Ground Systems (EGS) for vehicle processing and launch are essentially complete and there has been significant progress in manufacturing and assembly of specific hardware for the Artemis I and Artemis II missions. Equally as important, the program level and integrated system level testing and analyses are also well underway to support integrated verification, validation, and Certificate of Flight Readiness (CoFR) for Artemis I. Testing and analysis are key to addressing technical challenges faced by the Artemis missions. Building block approaches are required that provide the right balance between component, element, and/or system level testing that satisfies verification and validation objectives where uncertainties are quantified and minimized. Artemis I is a system of systems that requires a fusion of test and analysis that adeptly characterizes critical interfaces between major program elements. An example of this fusion involves characterizing the interface between the SLS booster and the Mobile Launcher (ML) Vertical Support Post (VSP) interfaces. Proper characterization of this interface represents a number of challenges beginning with the fact that it is a mating of ground support structure in the form of a civil structure to flight hardware. Both sides of the interface are built to different construction standards, but are governed by interface requirements to ensure compatibility when mated. From past program experience, the flexibility at the booster to ML interface is critical in developing accurate prelaunch stacking and cryogenic preloads, squat loads, and pad separation release of preloads and squat loads. This same premise holds for Artemis I. To characterize the asymmetric characteristics at this interface, careful consideration of static forces due to gravity loading with the commensurate effects due to leveling during booster stacking (i.e., spacing and shimming) and nonlinear geometric forces are necessary for inclusion in pre-test assessments. This paper will look at these issues for the upcoming Booster Pull Test in which two boosters will be installed on the ML and one of these boosters will undergo static lateral loading followed afterwards with dynamic excitation into resonance and free-decay. This paper evaluates the booster to ML interface characteristics by characterizing the interface flexibility between the booster aft skirt and the ML VSP interfaces. Furthermore, this paper methodically evaluates the effect of the following on the test outcome: gravitational effects on the booster and ML, the effects of VSP leveling, spacing, and shimming under gravitational loading during booster stacking, the effect of geometric nonlinear follower force due to cg offset as booster is laterally displaced, and the system coupling between the booster under test, ML, and the second booster. Simulated results for a static load pull and dynamic excitation provide insight into the differences in measurement responses when boundary conditions and geometric conditions are included and not included.

Joel W Sills Jr.↗
NASA NTRSDate not supplied

Accessing Icy Worlds using Lattice Confinement Fusion (LCF) Fast Fission

NASA proposed the Ocean Worlds Exploration Program to search for extraterrestrial life. The challenge is that up to 40 kilometer-thick ice must first be broken through to reach sub-surface oceans. These icy ocean worlds include Ceres, Europa, Enceladus, and Pluto. Each world may have a liquid water ocean beneath their ice crust. These oceans are likely heated by the parent planet’s tidal forces, or in the case of Pluto or Ceres, by residual radioactive decay. A robotic probe exploring the oceans beneath must either melt or bore through the ice crust first. Consequently, the proposed probe needs to contend with hydrostatic ice pressure, ice phase and density changes, then water pressure. Such a mission requires a small, but robust and long lived, electrical energy and heat source. Instead, we propose a novel, compact, scalable nuclear energy source using neither highly enriched uranium (HEU) nor 238Pu similar to the hybrid fusion-fission generator described by Forsley and Mosier-Boss. This nuclear energy source uses Lattice Confinement Fusion (LCF) neutrons to fast-fission thorium or depleted uranium where neither 232Th nor 238U isotopes are fissile

Theresa L Benyo↗
NASA NTRS2023

Flexible Data Fusion for Air Quality Estimation and Forecasting in Google Earth Engine to support Global Health Management Needs

The assessment and forecasting of air quality around the world at high spatial and temporal resolution can be enhanced by integrating data from multiple sources including models, satellites, regulatory monitors, and low-cost sensors. Such integration is subject to numerous technical challenges, however, including heterogeneous data resolution and formatting, different levels of data availability and reliability, and computational and capacity challenges to developing data fusion tools and platforms. This presentation will provide an overview of a NASA-funded effort to develop a data fusion system within the Google Earth Engine platform which integrates these air quality data sources to produce comprehensive assessments and forecasts of key air pollutants at sub-daily and sub-city scales. The system is being developed in collaboration with city- and regional-level air quality managers, and will provide them with information to the assess and anticipate the health impacts of poor air quality, track local changes in air quality due to ongoing transportation and land use changes, and identify potential gaps in their current air quality monitoring strategies. The presentation will report advances achieved through the project, including bringing local air quality monitoring data into Google Earth Engine, quantifying uncertainties in air quality estimates and forecasts, and tailored communications tools providing integration into end-user processes to meet their needs.

Carl Malings↗
NASA NTRSDate not supplied

Data Fusion for Urban Air Quality Assessment & Forecasting

This presentation provides an overview for our funded project with NASA's Health and Air Quality Applied Sciences Program. The project will expand an existing air quality data fusion tool implemented in Google Earth Engine (GEE) by our project team members at Sonoma Technology, Inc. (STI), a private air quality data company. We will expand the capabilities of this tool using new methods developed by the NASA GMAO which will give it the capability of providing sub-city scale resolution and hourly frequency estimates and forecasts of three key air quality indicators: surface-level particulate matter (PM2.5), nitrogen dioxide (NO2), and ozone (O3). We will combine a variety of Earth Observations including satellite data, global air quality forecasts, and local data from regulatory-grade monitors and/or low cost sensors. We will implement the new data fusion capabilities into the existing GEE tool in consultation with our end-users to best address their needs for sub-city scale air quality estimates and forecasts.

K Emma Knowland↗
NASA NTRSDate not supplied

Multiclass Flight Anomaly Detection Using Sensor Fusion Based on Dempster-Shafer Theory

As aviation systems in commercial operations continue to grow in complexity, the anomalies exhibited by these systems become more elaborate and difficult to detect. To address the challenge of detecting these complex anomalies, deep learning models have been used extensively in aviation anomaly detection studies, at the expense of end-user interpretability. Aiming to maintain the same level of interpretability as traditional threshold-exceedance methods, we continue our development of prediction models using ordinal patterns and their distributions throughout the flight. Specifically, this study extends our work into multiclass anomaly detection using sensor fusion based on Dempster-Shafer theory (DST), a second-order probability theory used to combine information from different sources of evidence. Our approach uses DST to reduce the uncertainty in the class predictions of an ensemble of classifiers. These classifiers rely on the similarity between flight data and class templates to make a prediction of the state of the aircraft. Our approach aims to take advantage of simple models trained on interpretable features (ordinal patterns) to correctly predict an anomaly and identify the flight dynamics linked to the anomaly. Our results show an improvement when using DST-based sensor fusion over simple majority voting. Additionally, our results provide insight into aircraft states linked to rare high-risk anomalies.

Risk detection↗
NASA NTRSDate not supplied

2024 IEEE GRSS Data Fusion Contest Flood Rapid Mapping

The Challenge Task As a result of climate change, extreme hydrometeorological events are becoming increasingly frequent. Flood rapid mapping products play an important role in informing flood emergency response and management. These maps are generated quickly from remote sensing data during or after an event to show the extent of flooding. They provide important information for emergency response, and damage assessment. The aim of this challenge is to develop data fusion algorithms that generate flood maps by processing spatial data from a variety of sources. The goal of this IEEE challenge (sponsored by NASA and CNES) is to design and develop an algorithm that will combine multi-source data to classify flood surface water extent–that is, water and non-water areas. Provided data sources include optical and SAR remote sensing images as well as a digital terrain model. The output is a gridded flood map where each grid cell is labeled water or non-water. The difficulty of detecting flooded areas can vary greatly depending on the conditions in the area of interest and the event. This data fusion challenge has two tracks representing this variance.

Jacqueline J Le Moigne-stewart↗
NASA NTRSDate not supplied

Air Quality Data Fusion with Sensors, Satellites, and Models

Global forecasting models, satellite remote sensing, and ground-based regulatory and low-cost monitors all have strengths and weaknesses with respect to providing locally relevant information about air quality. This presentation will give a brief overview of these data sources and then discuss a method for combining them via data fusion to support near-real-time air quality estimation and forecasting at sub-city scales. The basic idea behind the approach will be summarized, followed by an update on recent developments towards creating an operational system using Google Earth Engine and on quantifying uncertainties related to data fusion outputs.

Carl Malings↗
NASA NTRSDate not supplied

Flexible Data Fusion for Air Quality Estimation and Forecasting in Google Earth Engine to support Global Health Management Needs

The assessment and forecasting of air quality around the world at high spatial and temporal resolution can be enhanced by integrating data from multiple sources including models, satellites, regulatory monitors, and low-cost sensors. Such integration is subject to numerous technical challenges, however, including heterogeneous data resolution and formatting, different levels of data availability and reliability, and computational and capacity challenges to developing data fusion tools and platforms. This presentation will provide an overview of a NASA-funded effort to develop a data fusion system within the Google Earth Engine platform which integrates these air quality data sources to produce comprehensive assessments and forecasts of key air pollutants at sub-daily and sub-city scales. The system is being developed in collaboration with city- and regional-level air quality managers and will provide them with information to the assess and anticipate the health impacts of poor air quality, track local changes in air quality due to ongoing transportation and land use changes, and identify potential gaps in their current air quality monitoring strategies. The presentation will report advances achieved through the project, including bringing local air quality monitoring data into Google Earth Engine, quantifying uncertainties in air quality estimates and forecasts, and tailored communications tools providing integration into end-user processes to meet their needs.

Nathan R. Pavlovic↗
NASA NTRSDate not supplied

Bridging the Last Mile with Open-Source Advancements: Empowering Communities through Fusion of Aerosol Optical Depth (AOD) Products from Multi-Satellite Sensors

Aerosol Optical Depth (AOD) is a crucial parameter for understanding atmospheric aerosol distribution and their impact on climate and air quality. With the growing number of Earth observation satellites, there is an abundance of AOD products derived from various sensors onboard both geostationary and low-orbit satellites. The availability of multiple datasets provides an opportunity to harness the strengths of each sensor and create comprehensive and accurate AOD datasets for climate and air quality studies at different temporal and spatial scales. Our NASA aerosol MEaSURES project has made significant strides in recent years by undertaking the ambitious task of developing an open-source package tailored for fusing AOD products from different sources. The package is based on OOP (Object-Oriented Programming) design and is implemented in Python modules. Generic interfaces enable easy inclusion of large and heterogeneous data. The package may be utilized to produce harmonized AOD datasets with enhanced spatial and temporal coverage. The latest version of the package is able to process and integrate the dark-target AOD data from six different sensors: AHI Himawari-8, ABI GOES-West, ABI GOES-East, MODIS AQUA, MODIS TERRA, and VIIRS SNPP. Rigorous validation and intercomparison studies have been performed to assess the accuracy and reliability of the fused AOD product against ground-based measurements and reference datasets. The open-source nature of the developed package ensures transparency, reproducibility, and community engagement. The research community and stakeholders can access, contribute to, and further improve the fusion methodology, making it adaptable to other studies, or expanding it to include new satellite data as they become available. In this poster presentation, we will introduce the accomplishments and challenges faced during the development of the open-source package for AOD data fusion, and demonstrate the advantages of combining AOD products from the six aforementioned satellite sensors. The presentation aims to foster discussions, collaborations, and future directions in integrating Earth observation and remote sensing data, which may contribute to a better understanding of atmospheric aerosols and their impacts on our environment.

Zhaohui Zhang↗
NASA NTRS2024

Development of a Data Fusion Methodology for Lineload Aerodynamic Databases for a Launch Vehicle during Liftoff and Transition

The need for databases for the distributed loading on launch vehicles during the early portion of flight necessitates the use of expensive computational flows in regimes where wake effects dominate. While also being expensive, this is a regime that computational tools tend to historically have problems simulating accurately. To help tackle this problem, a method of data fusion to combine computational results to wind tunnel derived force and moment data is developed. Using this method, significant reduction in computational costs and increases in confidence of the final product is possible and has been used to generate several databases for the Space Launch System (SLS) at NASA. While the full details of database generation are not part of this work, the crucial method at its core is developed here. Two SLS geometries are used throughout the work to demonstrate the techniques. These are two of the larger geometries and represent both planned crewed missions to the Moon as well as potential cargo missions to deep space. The method uses principal component analysis (PCA) to generate a reduced ordered model (ROM) to help fill in the full parameter space. Other similar techniques are explored, but were not found to have a significant result on the predictions of the ROM. Because the full number of components are kept to generate the model, this lack of difference is expected. This method is then extended to ensure that predicted surfaces match trusted force and moment data derived from wind tunnel testing. This extension is done by setting up a constrained optimization problem in order to minimize the deviation from the surface resolved computational data while still integrating to the desired values. When generating the constrained optimization problem, a weighting factor to balance these competing needs is introduced. The work compares previously introduced weighting terms from similar work to the proposed terms and shows that the previously used terms do not have as desirable behavior in this flow regime. This method is then expanded by developing a technique to incorporate uncertainty quantification into the developed data fusion methodology. This expansion takes a two pronged approach. One examines transferring the uncertainties in the force and moment database and characterizes how those adjustments change the predicted lineloads. The second looks at model form error and looks how rebuilding the model using slightly different data changes the predictions. These two terms are then combined in order to create an uncertainty model that takes both effects into account. The limitations of the proposed methods is then discussed as well as possible techniques to address these shortcomings.

Launch Vehicles↗
NASA NTRS1999

Reconfigurable Sensor Fusion Processing Hardware

The paper presents the concept and initial test from the hardware implementation of a low-power, high-speed reconfigurable sensor fusion processor.

Hardware implementation multisensor fusion data fu↗