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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 1,297 records · Page 72
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NASA NTRS2021

Retrieving Aerosol Optical Depth and High Spatial Resolution Ocean Surface Wind Speed from CALIPSO: A Neural Network Approach

An innovative neural network retrieval algorithm is developed for retrieving ocean surface wind speed from the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) lidar measurements. The neural network is trained with CALIPSO ocean surface and atmospheric backscatter measurements together with collocated Advanced Microwave Scanning Radiometer for EOS(AMSR-E) ocean surface wind speed. Ocean surface wind speeds are derived by applying the neural network algorithm to CALIPSO measurements between 2008 and 2020. CALIPSO wind speed measurement of 2015 also compare with Advanced Microwave Scanning Radiometer 2 (AMSR-2) measurements on the Global Change Observation Mission –Water "Shizuku"(GCOM-W) satellite. Aerosol optical depths are then derived from CALIPSO’s ocean surface backscatter signal and theoretical ocean surface reflectance calculated from CALIPSO wind speed and Cox-Munk wind –surface slope variance relation. This CALIPSO wind speed retrieval technique is an improvement from Hu et al. (2008), as it can be applied to most clear skies with optical depths up to 1.5 without making assumptions about aerosol lidar ratio.

wind aerosols CALIPSO↗
NASA NTRSDate not supplied

Enhancing Neural Network Explainability with Variational Autoencoders

Machine intelligence has been used to tackle increasingly complex problems and deep learning solutions are at the forefront of tackling these problems. In general, these architectures have a great number of parameters that are methodically updated in training. The vast number and complexity of deep neural networks makes it very difficult to decipher the inner workings of the neurons and layers that make up the network. This paper posits that trustworthiness and trust in autonomous systems are increased through eXplainable Artificial Intelligence (XAI) and presents a method that enhances the explainability and understanding of a neural network decision. We leverage variational autoencoders to produce human interpretable features from complex data sets. We show that the explainable features can then be used for machine learning applications. Explainability inspires trust in autonomous systems that use deep learning, which is necessary for safety critical systems.

Loc Tran↗
NASA NTRS2004

Analysis of telescope site selection for optical Deep Space Network.

In this paper, therefore, we present an analysis of site selection for an optical deep space network as performed by the ODSN study group at JPL. Given a set of mission requirements, we illustrate how the high-level requirements, along with the properties of the atmospheric channel, can be used to determine the site selection and the architecture of an ODSN. Moreover, we characterize candidate sites for a global optical network and their possible suitability for global architectures such as the linear dispersed optical subnet (LDOS) and cluster optical subnet network (COS).

Cesarone, Robert↗
NASA NTRS2017

AI and Autonomy Initiatives for NASA’s Deep Space Network (DSN)

NASA’s Deep Space Network (DSN) consists of thirteen large (34- and 70-meter) antennas that are used to communicate with approximately 40 NASA and partner spacecraft, all at great distance from the earth (generally at Lunar distances and beyond). The DSN has a long history — over 50 years — and has evolved with cutting edge, often custom, telecommunications equipment and associated software systems. In recent years, and in preparation for an increasing future demand, there has been an effort to invest in initiatives that will result in significant cost savings in the future. These efforts are building on, or augmenting, the recent deployment of “Follow-the-Sun” operations (day shift remote operational control of the entire network from each of the three antenna complexes in turn) — which is being deployed in 2017. This paper focuses on Adaptive Demand Access: in a paradigm shift from completely pre-planned operations, this concept calls for spacecraft to signal their intent (or not) for near-future contacts, in case they have science results of interest, or have experienced an anomaly. This would take advantage of a beacon tone transmission, which can be detected using smaller antennas. When a connection request is received, the DSN ground systems would adaptively accommodate the request, inserting the contact into the plan as soon as possible, subject to constraints and priorities. The demand access concept incorporates onboard data analysis and science data processing, so that beacon tones can be generated with maximum information. This area is representative of several where infusing AI technologies can lead to improved effectiveness of the DSN as the network readies for support of expanded Mars exploration efforts in the 2020’s and beyond.

Wyatt, E. Jay↗
NASA NTRS2018

Next Generation Relay Services at Mars via an International Relay Network

Nearly all data acquired by vehicles on the surface of Mars are returned to Earth via Mars orbiters – more than 1.7 terabits so far. Successful communication between the various spacecraft is achieved via the careful implementation of internationally recognized CCSDS telecommunications protocols and the use of planning and coordination services provided by NASA’s Mars Program Office and the Multimission Ground Systems and Services (MGSS) Program at the Jet Propulsion Laboratory in Pasadena, CA. This modern Mars relay network has evolved since its inception in 2004 with the addition and loss of several missions, but it has fundamentally remained unchanged. Ground interfaces between the various spacecrafts’ mission operations centers on Earth remain largely unique for each participant, each mission maintains its own interfaces with deep space communications networks (e.g. DSN, ESTRACK), which are similar but still unique; and relay sessions at Mars require careful ground planning, coordination, and implementation. This paper will discuss the existing architecture and consider how several technologies may be applied to the next generation of relay services at Mars. Ultimately, these are expected to lead to the implementation of a delay and disruption tolerant network at Mars, a precursor to becoming a major element in an emerging Solar System Internet.

Edwards, Charles D. Jr↗
NASA NTRSDate not supplied

Development and Demonstration of a Wideband RF User Terminal for Roaming between Ka-band Relay Satellite Networks

The National Aeronautics and Space Administration (NASA) has provided low-latency communication relay services to science and exploration spacecraft for nearly four decades with the Tracking and Data Relay Satellite System (TDRSS). Through NASA’s Communications Services Project (CSP), the agency is pursuing a transition away from dedicated government-owned communication relay satellites and infrastructure in favour of commercially provided satellite communication (SATCOM) services. Many of these SATCOM services are offered in the K/Ka-band covering spectrum allocated to commercial, military, and civilian space operations. While these services were intended to provide broadband connectivity for terrestrial, maritime, and aviation customers, many can be tailored to support orbiting spacecraft. By introducing commercial SATCOM providers, NASA intends to create an interoperable network of networks which will enable missions to roam between multiple service providers. A key enabling technology needed for this vision is a multi-frequency (wideband), multi-waveform user terminal which can seamlessly roam between multiple providers. This paper discusses the development and ground demonstration of a new wideband RF user terminal prototype designed to roam across multiple networks over the Ka-band, allowing ubiquitous service, resiliency, and flexibility for the user.

satellite communication↗
NASA NTRSDate not supplied

Li-ion Battery Aging with Hybrid Physics-Informed Neural Networks and Fleet-wide Data

In this work, we propose a hybrid model for Li-ion battery discharge and aging prediction that leverages fleet-wide data to predict future capacity drops.The model is built upon an hybrid approach merging physics-based and empirical equations, as well as neural network models in a recurrent neural network cell. The hybrid physics-informed neural network can predict voltage discharge cycles given the loading profile, and estimate the used capacity of the battery under random-loading conditions by tracking aging parameters connected to the residual capacity of the battery. By merging information on the battery aging parameters with existing fleet-wide aging data, the model can predict the future residual capacity of the battery that is being monitored, and therefore enable predictions of voltage discharge curves far ahead in the battery life cycle. We validated the approach using the NASA Prognostics Data Repository Battery data-set, which contains experimental data on Li-ion batteries discharged at random loading conditions in a controlled environment. The approach also allows the identification of discrepancies between the battery aging trend and the trend observed at the fleet level, so that batteries behaving differently from the rest of the fleet can be subject to closer monitoring and further testing to refine predictions.

PINN↗
NASA NTRSDate not supplied

GeoNEX-SUBSETS: Geostationary Satellite Observations Over International Observing Network Sites

Satellite remote sensing is crucial for monitoring the Earth’s surface and for simulating global carbon and water cycles. Subsets of satellite data from sensors such as MODIS over established observing networks have been valuable for researchers for comparing the ground observed phenomena with satellite observations. Here, we introduce new subsets of the GeoNEX geostationary satellite datasets over locations of several ground observation networks. The NASA Earth Exchange group has been generating geostationary satellite products with our international partners and universities to cover the entire globe. In comparison to polar-orbiting satellite sensors such as MODIS, the new generation geostationary satellites observe target areas at a higher frequency (5-10 minutes), which also significantly increased the data volume. To reduce the burden of downloading and extracting geostationary time series data and provide easy access to the community, we provide subsets of GeoNEX products through our data portal [www.data.nas.nasa.gov]. The ready-to-use subset follows the same format as the MODIS fixed sites subset tools for users who are familiar with MODIS subset data and software. The selected networks include Fluxnet, AERONET, and Phenocam over the conterminous US. We demonstrate the usage of the GeoNEX fixed-site subset data and showcase thir advantages with three example studies. The first example is simulating the diurnal cycle of plant ecophysiology at Fluxnet sites. The high frequency of the GeoNEX time series allows us to run ecological models at sub-hour time steps and directly compare the simulated carbon and water fluxes with half-hourly Fluxnet data. The second example uses the geostationary data to track phenological changes. It highlights the high-frequent observations of geostationary satellites in helping mask cloud covers and capture the quick responses of vegetation to environmental changes. As such, these examples demonstrate the value of ready-to-use GeoNEX subsets data in terrestrial ecophysiology research.

GeoNEX↗
NASA NTRSDate not supplied

Learning Based Edge Computing in Air-to-Air Communication Network

This paper studies learning-based edge computing and communication in a dynamic Air-to-Air Ad-hoc Network (AAAN). Due to spectrum scarcity, we assume the number of Air-to-Air (A2A) communication links is greater than that of the available frequency channels, such that some communication links have to share the same channel, causing co-channel interference. We formulate the joint channel selection and power control optimization problem to maximize the aggregate spectrum utilization efficiency under resource and fairness constraints. A distributed deep Q learning-based edge computing and communication algorithm is proposed to find the optimal solution. In particular, we design two different neural network structures and each communication link can converge to the optimal operation by exploiting only the local information from its neighbors, making it scalable to large networks. Finally, experimental results demonstrate the effectiveness of the proposed solution in various AAAN scenarios.

Zhe Wang↗
NASA NTRSDate not supplied

Lessons Learned in the Implementation of a Training Knowledge Management System for the SERVIR Network

The SERVIR program is a unique partnership between NASA, the U.S. Agency for International Development (USAID), focusing on building capacity to use Earth observations for addressing development challenges. In that context, between 2004 and 2020, the program delivered approximately 365 trainings to almost 10,000 professionals. More recently, between November 2020 and August 2021, the SERVIR network executed some 55 training events addressing SERVIR’s 4 priority thematic areas, and roughly a quarter of SERVIR trainings overall have focused on themes related to Ecological Forecasting. Due to the ongoing COVID-19 pandemic, almost four-fifths of recent training events have been virtual, with the remainder being in-person under limited circumstances. The large number of training events delivered represents both an opportunity and a challenge in terms of knowledge management. While the training materials developed can later be reused in other parts of the SERVIR network, prior to recently, the lack of a central repository for those materials has prevented wider dissemination and use. The recently developed Training Knowledge Management System (TKMS) is now becoming an integral part of the SERVIR Capacity Building Framework, supporting the exchange of resources and methods for conducting training activities across the network. This presentation focuses on the structure of this system, as well as on the anticipated benefits for the User Communities for Earth Observations of Terrestrial Systems.

training↗
NASA NTRS2021

Advances in the Ocean Color Component of the Aerosol Robotic Network (AERONET-OC)

The Ocean Color Component of the Aerosol Robotic Network(AERONET-OC) supports activities related to ocean color such as validation of satellite data products, assessment of atmospheric correction schemes, and evaluation of bio-optical models through globally distributed standardized measurements of water-leaving radiance and aerosol optical depth. In view of duly assisting the AERONET-OC data user community, this work (i) summarizes the latest investigations on a number of scientific issues related to above-water radiometry, (ii) emphasizes the network expansion that from 2002 until the end of 2020 integrated 31 effective measurement sites, (iii) shows the equivalence of data product accuracy across sites and time for measurements performed with different instrument series, (iv) illustrates the variety of water types represented by the network sites ensuring validation activities across a diversity of observation conditions, and (v) documents the availability of water-leaving radiance data corrected for bidirectional effects by applying a method specifically developed for chlorophyll-a-dominated waters and an alternative one that is likely suitable for any water type.

In situ oceanic observations↗
NASA NTRS2020

CyberGAN: Generating High-fidelity Cybersecurity Data With Generative Adversarial Networks

Machine learning for cyber defense offers the promise of detecting adversarial activity against the ground data systems managing critical space assets. A fundamental challenge facing machine learning research in cybersecurity is the lack of high-fidelity, shareable datasets for robust evaluation and testing of machine learning-based solutions. High-fidelity, real-world datasets are necessary for reliable benchmarking of nominal system behavior and malicious activity. Unfortunately, such realistic datasets of both nominal and adversarial activity are rarely shared publicly by data owners due to security and privacy concerns. Besides, the available adversarial data is sparse, which makes training models on malicious activity much harder. This situation has impeded and continues to impede the research and successful adoption of machine learning methods for cyber defense. Researchers have dealt with this problem by generating data within a low-fidelity lab environment, using classified and thus unshareable datasets, or downloading low-fidelity public datasets made available by others. We propose an innovative solution to the problem by employing machine learning methods to generate high-fidelity data. Specifically, we propose the use of Generative Adversarial Networks (GANs) to generate high-fidelity data for cybersecurity purposes. GANs have found successful image processing and natural language applications, but have not yet been investigated for cyber data generation. Our proposed approach first involves training the `discriminator' network of the GAN with a sample of real-world data consisting of malicious and nominal samples. We then use the `generator' network to generate new high-fidelity data samples consisting of an appropriate mix of malicious and nominal activity. We demonstrate applications of our architecture by generating high-fidelity cybersecurity data containing both malicious and nominal samples. We thoroughly evaluate the fidelity of our generated data using heuristics and evaluate its usefulness for machine learning applications using three different datasets. Overall, our approach results in high-fidelity, shareable datasets.

Zhang, Yuening↗
NASA NTRSDate not supplied

The NASA Micro Pulse Lidar Network (MPLNET): Early Results from Development of Diurnal Climatologies

The NASA Micro Pulse Lidar Network (MPLNET) is a federated network of micro pulse lidar sites, mostly co-located with the NASA Aerosol Robotic Network (AERONET), providing information on the vertical properties of both aerosols and clouds. MPLNET began in 2000, and has grown to include ~80 sites worldwide. MPLNET data is collected continuously, offering the ability to examine diurnal changes in aerosol and cloud properties. To date, twelve sites have over 10 years of data and more will hit this milestone soon. Here we present early results from development of Level 3 monthly diurnal climatologies from MPLNET.

Ellsworth J Welton↗
NASA NTRS2022

Evaluating Network Performance of Containerized Test Framework for Distributed Space Systems

Distributed space systems are a mission architecture consisting of multiple spacecraft as a cohesive system which provide multipoint sampling, increased mission coverage, or improved sample resolution, while reducing mission risk through redundancy. To fully realize the potential of these systems, eventually scaling to hundreds or thousands of spacecraft, distributed space systems need to be operated as a single entity, which will enable a variety of novel scientific space missions. The Distributed Spacecraft Autonomy (DSA) project is a software project which aims to mature the technology needed for those systems, namely autonomous decision-making and swarm networking. The DSA project leverages a containerized swarm test framework to simulate spacecraft software, which can identify emergent behavior early in development. Container virtualization allows distributed spacecraft systems to be simulated entirely in software on a single computer, avoiding the overhead associated with conventional approaches like hardware facsimiles and virtual machines. For this approach to be effective, the simulated system behavior must not be artificially influenced by the swarm test framework itself. To address this, we present a series of benchmarks to quantify virtual network bandwidth available on a single-host computer and contextualize this against the network and application behavior of the DSA swarm test framework.

Distributed Spacecraft Autonomy↗
NASA NTRS2022

Identification of Flux Rope Orientation via Neural Networks

Geomagnetic disturbance forecasting is based on the identification of solar wind structures and accurate determination of their magnetic field orientation. For nowcasting activities, this is currently a tedious and manual process. Focusing on the main driver of geomagnetic disturbances, the twisted internal magnetic field of interplanetary coronal mass ejections (ICMEs), we explore a convolutional neural network’s (CNN) ability to predict the embedded magnetic flux rope’s orientation once it has been identified from in situ solar wind observations. Our work uses CNNs trained with magnetic field vectors from analytical flux rope data. The simulated flux ropes span many possible spacecraft trajectories and flux rope orientations. We train CNNs first with full duration flux ropes and then again with partial duration flux ropes. The former provides us with a baseline of how well CNNs can predict flux rope orientation while the latter provides insights into real-time forecasting by exploring how accuracy is affected by percentage of flux rope observed. The process of casting the physics problem as a machine learning problem is discussed as well as the impacts of different factors on prediction accuracy such as flux rope fluctuations and different neural network topologies. Finally, results from evaluating the trained network against observed ICMEs from Wind during 1995–2015 are presented.

Thomas Narock↗
NASA NTRS2022

The Space Superhighway: Systems Analysis of an In-Space Logistics Delivery Network

The number of assets in cislunar space is anticipated to dramatically increase in the coming decades. Many of these newer spacecraft are being designed to take advantage of capabilities currently in development like In-Space Servicing Assembly and Manufacturing (ISAM). Additionally, the National Aeronautics and Space Administration (NASA) intends to develop and maintain a human-lunar presence that will then serve as a steppingstone for human missions to Mars. With continued growth in space operations, the combined demand for payload and propellant delivery in cislunar space could exceed 1,000 t annually in the next decade. The Space Superhighway principally consists of a logistics network and is intended to both use and proliferate ISAM in order to meet projected payload demands. Several architecture and vehicle-level trade studies are explored in order to better understand what in-space logistics networks may be feasible. The network consists of commercial launch vehicles, resupply tankers, orbital depots, and in-space tugs. This work specifically analyzes high-level trades such as: launch and in-space vehicle propulsion systems, vehicle size, staging orbit, level of vehicle reuse, reuse method, and propellant management strategy. Each of the trades are analyzed across several destination orbits such as Low-Earth Orbit (LEO) and Near-Rectilinear Halo Orbit (NRHO). Several payload types are also considered such as storable and cryogenic chemical propellants, electric propellants, crew logistics, spares, smallsats, and satellite piece parts.

Daniel J Tiffin↗
NASA NTRS2022

An Examination of the Recent Stability of Ozonesonde Global Network Data

The recent Assessment of Standard Operating Procedures for OzoneSondes (ASOPOS 2.0; WMO/GAW Report #268) addressed questions of homogeneity and long-term stability in global electrochemical concentration cell (ECC) ozone sounding network time series. Among its recommendations was adoption of a standard for evaluating data quality in ozonesonde time series. Total column ozone (TCO) derived from the sondes compared to TCO from Aura’s Ozone Monitoring Instrument (OMI) is a primary quality indicator. Comparisons of sonde ozone with Aura’s Microwave Limb Sounder (MLS) are used to assess the stability of stratospheric ozone. This paper provides a comprehensive examination of global ozonesonde network data stability and accuracy since 2004 in light of the sudden post-2013 TCO “dropoff” of ~3-4% that was reported previously at select stations (Stauffer et al., 2020). Comparisons with Aura OMI TCO averaged across the network of 60 stations are stable within about ±2% over the past 18 years. Sonde TCO has similar stability compared to three other TCO satellite instruments, and the stratospheric ozone measurements average to within ±5% of MLS from 50 to 10 hPa. Thus, sonde data are reliable for trends, but with a caveat applied for a subset of dropoff stations in the tropics and subtropics. The dropoff is associated with only one of two major ECC instrument types. A detailed examination of ECC serial numbers pinpoints the timing of the dropoff. However, we find that overall, ozonesonde data are stable and accurate compared to independent measurements over the past two decades.

Ryan M. Stauffer↗
NASA NTRSDate not supplied

Launching Liquid Slugs Through Tube Networks: Investigating Multiphase Liquid Slug Acceleration Behaviors in Spacecraft Propulsion Systems using 3D Multiphase VOF CFD Simulations and High-Speed Flow-Visualization Validation Experiments

Propellant management in spacecraft propulsion systems is complex. In many current and future crewed and robotic spacecraft propulsion systems, a number of unintended or unavoidable scenarios can occasionally lead to liquid “slugs” of propellant accumulating in unwanted locations and thereafter being rapidly accelerated through complex and arbitrary tubing networks otherwise filled with vapor or gas. To develop better understanding of the multiphase fluid dynamics associated with these “slug launch” scenarios, NASA Marshal Space Flight Center’s (MSFC) ER42 branch – the propulsion fluid dynamics research branch – conducted 3D multiphase transient Volume of Fluid (VOF) CFD simulations using an in-house tool called LOCI-Stream VOF on semi-canonical tube networks with straight sections, bends, and bend complexes. In addition, high-speed flow visualization validation experiments using propellants were conducted on similar tube network geometries at the NASA White Sands Test Facility (WSTF). These simulations and flow-visualization experiments found these scenarios lead to a variety of unique multiphase fluid dynamic effects involving the leading and trailing edges of the slug in both straight sections and curved bends as well as the progressive erosion, acceleration, and eventual “punch-through” of the driving gas through the liquid slug. Understanding these multiphase fluid dynamic effects has led to unique and often counter-intuitive observations which have been of critical relevance to important engineering concerns associated with these “slug launch” scenarios.

Multiphase Fluid Dynamics↗