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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 361 records · Page 20

Delay-Throughput Performance the Deep-Space Ka-Band Link

In this paper, performance of a first-in, first-out (FIFO), selective retransmission scheme for the deep-space Ka-band link is presented and compared to the performance of a comparable X-band link. In this analysis, 16 months of water vapor radiometer (WVR) and advanced water vapor radiometer (AWVR) data from the three Deep Space Network (DSN) Communication Complexes (DSCC) were used to emulate weather effects on X-band and Ka-band links from Mars. Mars Reconnaissance Orbiter (MRO) X-band and Ka-band telecommunications parameters were used for spacecraft telecommunications capabilities. One pass per week per complex was selected from MRO's Deep Space Network (DSN) schedule from April 1, 2006 to August 31, 2007 for a total of 207 passes (69 passes per complex) for this analysis. For each pass both X-band and Ka-band links were designed using at most two data rates so that the expected pass capacity would be maximized subject to a minimum availability requirement (MAR). In conjunction with the WVR/AWVR data, elevation profiles of the selected passes and models for the performance of the antennas in the DSN were used to emulate the performance of both links. It was assumed that the retransmission of the data takes place not on the same pass as the original transmission but during subsequent passes. The data collected before a pass was assumed to be a fraction of the expected capacity of the pass as calculated through the link design process. Infinite spacecraft storage was assumed to obtain an upper bound on the spacecraft storage requirement. The independent parameters of this analysis were MAR and the ratio of data collected before a pass to the expected pass capacity. Since the selected passes did not occur at regular intervals, the delay in this analysis was measured in terms of number of passes. The throughput was measured in terms of number of bits received successfully on the ground. The results indicate that reasonable delay performance could be achieved with very high throughput for relatively low MAR values for data collection to expected pass capacity ratio of around 97% for Ka-band. The results indicate that, except for very low average delay requirements, the Ka-band link provides more than twice the throughput of the X-band link for the same amount of power consumed by the spacecraft. In addition, the results indicate that the required storage onboard the spacecraft is not prohibitive and good performance could be achieved by using a buffer size less than three times the maximum amount of data collected before a pass.

Shambayati, Shervin↗

Delay Tolerant Network Routing as a Machine Learning Classification Problem

This paper discusses a machine learning-based approach to routing for delay tolerant networks (DTNs) [1]. DTNs are networks which experience frequent disconnections between nodes, uncertainty of an end-to-end path, long one-way trip times, and may have high error rates and asymmetric links. Such networks exist in deep space satellite networks, very rural environments, disaster areas and underwater environments. In this work, we use machine learning classifiers to predict a set of neighboring nodes which are the most likely to deliver a message to a desired location based on message history delivery information.We use the Common Open Research Emulator (CORE) [2] to emulate the DTN environment based on real-world location traces and collect network traffic statistics from the Bundle Protocol implementation IBR-DTN [3]. The software architecture for classification-based routing, analysis and preparation of the network history data and prediction results are discussed.

Delay Tolerant Networks↗

A Testbed for Implementing Prognostic Methodologies on Cryogenic Propellant Loading Systems

Prognostics technologies determine the health state of a system and predict its remaining useful life. With this information, operators are able to make maintenance-related decisions, thus effectively streamlining operational and mission-level activities. Experimentation on testbeds representative of critical systems is very useful for the maturation of prognostics technology; precise emulation of actual fault conditions on such a testbed further validates these technologies. In this paper we present the development of a pneumatic valve testbed, initial experimental results and progress towards the maturation and validation of component-level prognostic methods in the context of cryogenic refueling operations. The pneumatic valve testbed allows for the injection of time-varying leaks with specified damage progression profiles in order to emulate common valve faults. The pneumatic valve testbed also contains a battery used to power some pneumatic components, enabling the study of the effects of battery degradation on the operation of the valves.

Prognositcs↗

High Performance Spacecraft Computing (HPSC) Middleware Update

High Performance Spacecraft Computing (HPSC) is a joint project between the National Aeronautics and Space Administration (NASA) and Air Force Research Lab (AFRL) to develop a high-performance multi-core radiation hardened flight processor. HPSC offers a new flight computing architecture to meet the needs of NASA missions through 2030 and beyond. Providing on the order of 100X the computational capacity of current flight processors for the same amount of power, the multicore architecture of the HPSC processor, or "Chiplet" provides unprecedented flexibility in a flight computing system by enabling the operating point to be set dynamically, trading among needs for computational performance, energy management and fault tolerance. The HPSC Chiplet is being developed by Boeing under contract to NASA, and is expected to provide prototypes, an evaluation board, system emulators, comprehensive system software, and a software development kit. In addition to the vendor deliverables, the AFRL is funding the development of a flexible Middleware to be developed by NASA Jet Propulsion Laboratory and NASA Goddard Space Flight Center. The HPSC Middleware provides a suite of thirteen high level services to manage the compute, memory and I/O resources of this complex device.This presentation will provide an HPSC project update, an overview of the latest HPSC System Software release, an overview of HPSC Middleware Release 2, and a preview of the third HPSC Middleware release. The presentation will begin with a project update that will provide a look at the high-level changes since the project was introduced at the Flight Software Workshop last year. Next, the presentation will provide an overview of the current suite of HPSC System Software which includes the vendor provided bootloaders, operating systems, emulator, and development tools. Next, the HPSC Middleware progress will be presented, which includes an overview of the features and capabilities of HPSC Middleware Release 2, followed by a look at the reference flight software applications which utilize the Middleware. Finally, the presentation will give a preview of the HPSC Middleware Release 3.

Cudmore, Alan↗

Leveraging Commercial Software Defined Radio for Low Cost Deep Space Testing

In a typical space mission development life cycle, there is a stage where the spacecraft needs to test against the ground station for interface compatibility to ensure that the spacecraft will be properly tracked after launch. This testing normally requires the spacecraft team to bring their flight equipment to the ground station facility. While recognizing that testing with actual flight or engineering module is the most preferred option because of maximum fidelity, there are occasion when the use of actual flight hardware is a logistically challenge because of spacecraft development. Having another test tool that can emulate the spacecraft signal – by recording the signal transmitted by the spacecraft and regenerate an RF signal for ground system testing - would be very useful. It is even more an attractive option if such spacecraft emulator is inexpensive and highly portable. In this paper, we describe a low-cost, light-weight recorder/playback assembly (RPA) that supports deep space missions testing. The equipment leverages on commercially available software defined radios (SDR) and public-domain software. The RPA has been used to support two missions. One effort is to validate that the Uchinoura 34-m tracking station of the Japanese Aerospace Exploration Agency (JAXA) would be able to track the upcoming NASA Exploration Mission 1 (EM-1) spacecraft, scheduled for launch in 2019. The second effort is to help with the testing and certification of the 21-m antenna ground station at the Morehead State University (MSU) in Kentucky, United States, prior to the time when the Lunar IceCube spacecraft is ready for actual compatibility testing. The RPA also enables students/staff training of the new ground station, using the RPA signal as test input into the system. This low-cost test signal allows the MSU team to save money on not having to develop a full-scale self-generated telemetry test signal source.

White, Leslie↗

Applying the Cognitive Space Gateway to Swarm Topologies

NASA's future vision for interplanetary networking includes a lunar network, Cube Satellite (CubeSat) constellations, and deep space robotic missions, comprising what could be viewed as a network of networks. Delay-tolerant networking (DTN) architecture and protocols provide a standard network layer among these varying scenarios and mitigate many challenges of the space environment, such as long delays, unplanned service interruptions, and asymmetric links. The Cognitive Space Gateway (CSG) is a routing method in a DTN architecture that uses spiking neural networks as the learning element to optimize routing decisions in a complex environment. This work aims to further develop cognitive networking technologies in several critical areas, including DTN, the CSG algorithm, CubeSat swarm topologies, and cloud services. To test the algorithm in a realistic scenario, the emulated network topology is based on a CubeSat swarm. The swarm may function as a mesh of nodes or as a hub-and-spoke network. An emulation environment will be built upon a commercial cloud service, such as Amazon Web Services (AWS) Elastic Compute Cloud. The cloud environment may enable a flexible, lower maintenance approach versus a multi-hop network based in a physical laboratory. The cloud platform will provide a secure environment allowing for collaboration among government and academic entities.

Ricardo Lent↗

Applying the Cognitive Space Gateway to Swarm Topologies

NASA’s future vision for interplanetary networking includes a lunar network, Cube Satellite (CubeSat) constellations, and deep space robotic missions, comprising what could be viewed as a network of networks. Delay-tolerant networking (DTN) architecture and protocols provide a standard network layer among these varying scenarios and mitigate many challenges of the space environment, such as long delays, unplanned service interruptions, and asymmetric links. The Cognitive Space Gateway (CSG) is a routing method in a DTN architecture that uses spiking neural networks as the learning element to optimize outing decisions in a complex environment. This work aims to further develop cognitive networking technologies in several critical areas, including DTN, the CSG algorithm, SmallSat swarm topologies, and cloud services. The CSG algorithm is tested in a realistic scenario in which the emulated network topology is based on a SmallSat swarm. The emulation environment will be built upon a commercial cloud service, such as Amazon Web Services (AWS) Elastic Compute Cloud. This work investigates the ability of such a platform to enable a flexible, lower maintenance approach to creating a multihop network outside of a physical laboratory. The cloud platform will provide a secure environment allowing for collaboration among government and academic entities.

Ricardo Lent↗

GeoNEX-ML: A Machine Learning System for Geostationary Satellite Imagery

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), Himawari-8/9 (JAXA), and GK-2A (Korea), we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Geostationary satellites↗

Deep Learning System for Efficient Processing of Geostationary Satellite Imagery

Improved capabilities of Earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. Further, a large majority of archived datasets generated by scientific processing is never used. This motivates the development of an efficient machine learning system for end-to-end processing of multi-level satellite datasets, from level 1 top of atmosphere observations to user friendly environmental variables of interest. Using current generation geostationary satellites GOES-16/17 (NOAA/NASA), and Himawari-8/9 (JAXA), we present an interchangeable set of machine models to perform spectral adjustment among sensors, physical model emulation, LEO-GEO emulation, and optical flow in a high performance computing environment. We use these tools on the NASA Earth eXchange (NEX) to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate surface reflectance, surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Thomas Vandal↗

The Earth Model Column Collaboratory (EMC2) v1.1: An Open-Source Ground-Based Lidar and Radar Instrument Simulator and Subcolumn Generator for Large-Scale Models

Climate models are essential for our comprehensive understanding of Earth's atmosphere and can provide critical insights on future changes decades ahead. Because of these critical roles, today's climate models are continuously being developed and evaluated using constraining observations and measurements obtained by satellites, airborne, and ground-based instruments. Instrument simulators can provide a bridge between the measured or retrieved quantities and their sampling in models and field observations while considering instrument sensitivity limitations. Here we present the Earth Model Column Collaboratory (EMC2), an open-source ground-based lidar and radar instrument simulator and subcolumn generator, specifically designed for large-scale models, in particular climate models, but also applicable to high-resolution model output. EMC2 provides a flexible framework enabling direct comparison of model output with ground-based observations, including generation of subcolumns that may statistically represent finer model spatial resolutions. In addition, EMC2 emulates ground-based (and air- or space-borne) measurements while remaining faithful to large-scale models' physical assumptions implemented in their cloud or radiation schemes. The simulator uses either single particle or bulk particle size distribution lookup tables, depending on the selected scheme approach, to perform the forward calculations. To facilitate model evaluation, EMC2 also includes three hydrometeor classification methods, namely, radar- and sounding-based cloud and precipitation detection and classification, lidar-based phase classification, and a Cloud Feedback Model Intercomparison Project Observational Simulator Package (COSP) lidar simulator emulator. The software is written in Python, is easy to use, and can be straightforwardly customized for different models, radars, and lidars. Following the description of the logic, functionality, features, and software structure of EMC2, we present a case study of highly supercooled mixed-phase cloud based on measurements from the U.S. Department of Energy Atmospheric Radiation Measurement (ARM) West Antarctic Radiation Experiment (AWARE). We compare observations with the application of EMC2 to outputs from four configurations of the NASA Goddard Institute for Space Studies (GISS) climate model (ModelE3) in single-column model (SCM) mode and from a large-eddy simulation (LES) model. We show that two of the four ModelE3 configurations can form and maintain highly supercooled precipitating cloud for several hours, consistent with observations and LES. While our focus is on one of these ModelE3 configurations, which performed slightly better in this case study, both of these configurations and the LES results post-processed with EMC2 generally provide reasonable agreement with observed lidar and radar variables. As briefly demonstrated here, EMC2 can provide a lightweight and flexible framework for comparing the results of both large-scale and high-resolution models directly with observations, with relatively little overhead and multiple options for achieving consistency with model microphysical or radiation scheme physics.

Earth Model Column Collaboratory↗

Atmospheric winds with deep optical flow

Improved capabilities of earth monitoring satellites are enabling a wide range of studies on the environmental effects of climate change, often leveraging the recent advancements in machine learning. At the same time, the new capabilities, including higher spatial resolution and temporal frequency, are expanding the amount of data generated at exponential rates. At the NASA Earth eXchange (NEX), we build deep learning methods to learn from cross sensor satellite-based Earth observations for generating new datasets with efficient processing techniques. Using current generation geostationary satellites on NEX, we present an interchangeable set of machine models to perform spectral adjustment, physical model emulation, LEO-GEO emulation, and optical flow. These tools are used to generate consistent virtual observations across sensors, perform atmospheric correction and cloud detection, and estimate land surface temperature and atmospheric winds. This approach aims to improve the robustness of remotely sensed data processing by learning from diverse sets of observations while enabling near real-time and on-demand capabilities.

Atmospheric winds↗

Laminar Non-Premixed Flames of Gaseous Fuel Aboard the International Space Station

From late 2017 to early 2022, six independent studies with flames of gaseous fuels were conducted on the International Space Station (ISS) in the U.S. combustion research facility. An exploration of flames at the extremes of high sooting and high dilution was conducted with a coaxial coflow burner, where the fuel and oxidizer velocities were typically matched. An investigation of electric-field effects also used the same coflow burner as well as a simple gas-jet burner, with a circular electrode mesh, downstream of the burner, at voltages of either polarity up to 10 kV. A study focused on material flammability in a quiescent atmosphere emulated the burning of condensed-phased fuels using cylindrical burners with a flat perforated outlet instrumented to measure the heat flux to the burner, i.e., emulated fuel. Three studies of soot processes, flame dynamics, and low-temperature combustion used porous spherical burners, yielding a nominally one-dimensional flame structure. The objectives and selected findings of each investigation will be briefly discussed after a short review of the advantages of studying combustion in microgravity, earlier ISS research, and the experimental hardware and its operation.

flames↗

Laminar Non-Premixed Flames of Gaseous Fuel Aboard the International Space Station

From late 2017 to early 2022, six independent studies with flames of gaseous fuels were conducted on the International Space Station (ISS) in the U.S. combustion research facility. An exploration of flames at the extremes of high sooting and high dilution was conducted with a coaxial coflow burner, where the fuel and oxidizer velocities were typically matched. An investigation of electric-field effects also used the same coflow burner as well as a simple gas-jet burner, with a circular electrode mesh, downstream of the burner, at voltages of either polarity up to 10 kV. A study focused on material flammability in a quiescent atmosphere emulated the burning of condensed-phased fuels using cylindrical burners with a flat perforated outlet instrumented to measure the heat flux to the burner, i.e., emulated fuel. Three studies of soot processes, flame dynamics, and low-temperature combustion used porous spherical burners, yielding a nominally one-dimensional flame structure. The objectives and selected findings of each investigation will be briefly discussed after a short review of the advantages of studying combustion in microgravity, earlier ISS research, and the experimental hardware and its operation.

flames↗

ACME: Advanced Combustion via Microgravity Experiments

From Nov. 2017 to Feb. 2022, the ACME project’s six independent investigations of laminar, non-premixed flames of gaseous fuels were conducted on the International Space Station (ISS) in the Combustion Integrated Rack (CIR). An exploration of flames at the extremes of high sooting and high dilution was conducted with a coaxial coflow burner, where the fuel and oxidizer velocities were typically matched. An investigation of electric-field effects also used the same coflow burner as well as a simple gas-jet burner, with a circular electrode mesh downstream of the burner, at voltages of either polarity up to 10 kV. A study focused on material flammability in a quiescent atmosphere emulated the burning of condensed-phased fuels using cylindrical burners with a flat perforated outlet instrumented to measure the heat flux to the burner, i.e., emulated fuel. Three studies of soot processes, flame dynamics, and low-temperature combustion used porous spherical burners yielding a nominally one-dimensional flame structure. The ACME research will be represented through images and text.

flames↗

Hybrid-Electric Aero-Propulsion Controls Laboratory: Overview and Capability

A hardware-in-the-loop (HIL) laboratory is developed to investigate control technologies for electrified aircraft propulsion (EAP). The laboratory emulates a propulsion system by reproducing the mechanical shaft interface to the electrical power system in hardware. The experimental electric power system includes supercapacitor energy storage and a dynamically variable electrical load. A novel method of scaling power and inertia is provided in software to accurately reproduce the transient, off-design turbomachinery performance dynamics without including actual turbomachinery. An overview of how the HIL system can accommodate a broad range of EAP architectures, including power extraction and insertion, with capability for transient energy management is described. The real-time system operates in the 100kW power class and is instrumented to emulate turbomachinery – power system interactions. The platform is an agile, flexible laboratory for low-cost, risk-reduction development and testing of propulsion control, operability, and energy management technologies.

Controls↗

Hybrid-Electric Aero-Propulsion Controls Laboratory: Overview and Capability

A hardware-in-the-loop (HIL) laboratory is developed to investigate control technologies for electrified aircraft propulsion (EAP). The laboratory emulates a propulsion system by reproducing the mechanical shaft interface to the electrical power system in hardware. The experimental electric power system includes supercapacitor energy storage and a dynamically variable electrical load. A novel method of scaling power and inertia is provided in software to accurately reproduce the transient, off-design turbomachinery performance dynamics without including actual turbomachinery. An overview of how the HIL system can accommodate a broad range of EAP architectures, including power extraction and insertion, with capability for transient energy management is described. The real-time system operates in the 100kW power class and is instrumented to emulate turbomachinery – power system interactions. The platform is an agile, flexible laboratory for low-cost, risk-reduction development and testing of propulsion control, operability, and energy management technologies.

controls↗

Ground-Based Capabilities for Lunar Infrastructure Testing

A focus of NASA’s Moon-to-Mars objectives is the development of the infrastructure on the lunar surface that will be needed to support broader lunar surface operations. This infrastructure is intended to support both United States and international partners to expand human presence on the lunar surface. As this lunar infrastructure is designed and established, it will be critical to ensure that the various hardware elements work together to both enable the capabilities and to avoid unintended actions. NASA’s Glenn Research Center (GRC) is establishing ground-based testing and emulation facilities to mimic the lunar environment that the surface power and communications infrastructure will operate in. These facilities are intended to represent power and communications providers, users, and interfaces to ensure that systems operate as intended to support the lunar economy. They will empower industry to rapidly evaluate new technologies under realistic conditions. In 2023 GRC is opening a new, state-of-the-art, Aerospace Communications Facility featuring hardware-in-the-loop and ground-to-orbit testbeds. The Multiple Asset Testbed for Research in Innovative Communications Systems (MATRICS) capability will emulate the lunar communications environment, enabling validation of mission concepts and technologies to reduce risk through performance and operations testing, training, and uncover potential issues in compatibility among communication systems providers and users. In addition to the communications testbed, GRC is also developing a full-scale power grid to reduce lunar mission risk. The Adaptable Surface Power Integration and Research (ASPIRE) project aims to reduce mission and hardware risk via high-fidelity integrated testing and pave the way for commercially supplied utility power on the lunar surface. The facility will be scalable and highly adaptable and will be available to NASA, Industry, Academia, and International Partners. ASPIRE will allow developers to integrate and demonstrate their power solutions in a relevant environment, and it will be able to characterize the lunar power performance in representative mission contexts.

ground-based testing↗

Machine Learning Global Simulation of Nonlocal Gravity Wave Propagation

Global climate models typically operate at a grid resolution of hundreds of kilometers and fail to resolve atmospheric mesoscale processes, e.g., clouds, precipitation, and gravity waves (GWs).Model representation of these processes and their sources is essential to the global circulation and planetary energy budget, but subgrid scale contributions from these processes are often only approximately represented in models using parameterizations. These parameterizations are subject to approximations and idealizations, which limit their capability and accuracy. The most drastic of these approximations is the “single-column approximation” which completely neglects the horizontal evolution of these processes, resulting in key biases in current climate models. With a focus on atmospheric GWs, we present the first-ever global simulation of atmospheric GW fluxes using machine learning (ML) models trained on the WINDSET dataset to emulate global GW emulation in the atmosphere, as an alternative to traditional single-column parameterizations. Using an Attention U-Net-based architecture trained on globally resolved GW momentum fluxes, we illustrate the importance and effectiveness of global nonlocality, when simulating GWs using data-driven schemes.

Aman Gupta↗