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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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NASA NTRS2024

Exploration Systems Exterior Lighting Design Guidance

Humans are returning to the Moon — this time, to stay. NASA has selected a location that maximizes line-of-sight communication with Earth, solar visibility, and access to water ice: the Lunar South Pole (LSP). While the Sun is more consistently in the lunar sky at the poles, it never rises more than 7° above the horizon in the target landing regions. This harsh lighting environment--never experienced during the Apollo missions or in human spaceflight--presents an engineering challenge: Support and protect functional vision. The human vision system, which despite having a high-dynamic range, cannot see well into bright light and cannot adapt quickly from bright to dark or vice versa. The design of helmets, windows, and lighting solutions must work in a complementary fashion to achieve a system of lighting and functional vision support that enables crews to see into darkness while their eyes are light-adapted and see in bright light while still dark-adapted that protects their eyes from injury. This report brings together information and identifies gaps related to LSP Natural environment (topology, regolith, temperature, lighting, and so forth), NASA historical experience exploring the Moon, human vision capabilities and needs, exploration operations, simulation capabilities and needs (for design verification, training, mission planning, and so forth) and existing engineering, safety, medical and performance requirements related to support human exploration.

Simulation↗
NASA NTRS2024

Automatic Detection and Classification of Aurora in THEMIS All‐Sky Images

We report a novel machine-learning algorithm for automatically detecting and classifying aurora in all–sky images (ASI) that is largely trained without requiring ground–truth labels. By including a small number of labeled images, we are able to automatically label all of the approximately 700 million images in the Time History of Events and Macroscale Interactions during Substorms (THEMIS) ASI data set from 2008 to 2022. We use a two–stage approach. In the first stage, we adapt the Simple framework for Contrastive Learning of Representations (SimCLR) algorithm to learn latent representations of THEMIS all–sky images. We then finetune a classifier network on the latent representations our model learns of the manually labeled Oslo aurora THEMIS (OATH) data set. We demonstrate that this two–stage approach achieves excellent classification results on data for which there is no current ML classification benchmark. The outcome of this work will facilitate efficient information retrieval for researchers interested in specific categories of aurora and will enable large scale statistical studies and machine learning analyses of THEMIS all–sky images that have not previously been possible. To demonstrate possible ways to utilize this database, we performed a statistical analysis of the occurrence rates of auroral labels with respect to solar wind parameters, interplanetary magnetic field vector, and geomagnetic indices. We further investigate the occurrence rates of auroral phenomena in the annotated data set and their geoeffectiveness by utilizing the co–located THEMIS ground magnetometer data set.

Jeremiah W Johnson↗
NASA NTRS2001

Ar Atmosphere: Implications for Structure and Composition of Mercury's Crust

We examine the possibilities of sustaining an argon atmosphere by diffusion from the upper 10 km of crust, and alternatively by effusion from a molten or previously molten area at great depth . Ar-40 in the atmospheres of the planets is a measure of potassium abundance in the interiors since Ar-40 is a product of radiogenic decay of K-40 by electron capture with the subsequent emission of a 1.46 eV gamma-ray. Although the Ar-40 in the earth's atmosphere is expected to have accumulated since the late bombardment, Ar-40 in surface-bounded exospheres is eroded quickly by photoionization and electron impact ionization. Thus, the argon content in the exospheres of the Moon, Mercury and probably Europa is representative of current effusion rather than accumulation over the lifetime of the body. Argon content will be a function of K content, temperature, grain size distribution, connected pore volume and possible seismic activity. Although Mercury and the Moon differ in many details, we can train the solutions to diffusion equations to predict the average lunar atmosphere. Then these parameters can be varied for Hermean conditions. Assuming a lunar crustal potassium abundance of 300 ppm, the observed argon atmosphere requires equilibrium between the argon production in the upper 9 Km of the moon (1.135 x 10(exp -3) cm(exp -3) s(exp -1)) and its loss. Hodges et al. conclude that this loss rate and the observed time variability requires argon release through seismic activity, tapping a deep argon source. An important observation is that the extreme surface of the Moon is enhanced in argon rather than depleted, as one would expect from outgassing of radiogenic argon. Manka and Michel concluded that ion implantation explains the surface enhancement of Ar-40. About half of the argon ions produced in the lunar atmosphere would return to the surface, where they would become embedded in the rocks. Similarly, at Mercury we expect the surface rocks to be enhanced in Ar-40 wherever the magnetosphere has been open over time. Thus the measurement of surface composition will reveal the long-term effects of solar wind-magnetosphere interaction. Additional information is contained in the original extended abstract.

Killen, R. M.↗
NASA NTRS2015

NASAs Solar System Exploration Research Virtual Institute- Expanded Goals and More Partners

The NASA Solar System Exploration Research Virtual Institute (SSERVI) has been pursuing international partnerships since its inception as the NASA Lunar Science Institute (NLSI), in order to both leverage the science being done by its domestic member institutions as well as to help lunar science and exploration become a greater global endeavor. The international partners of the Institute have pursued a broad program of lunar science stimulated by scientific partnerships enabled by the SSERVI community. Furthermore, regional partnerships have been formed such as the new pan-European lunar science consortium, which promises both new scientific approaches and mission concepts. International partner membership requires long-term commitment from both the partner and SSERVI, together with tangible and specific plans for scientific interaction that will produce results of mutual benefit to both the institute's U.S. Teams and the international partner. International partners are invited to participate in all aspects of the Institute's activities and programs, on a basis of no exchange of funds. Through these activities, SSERVI researchers and international partners participate in sharing ideas, information, and data arising from their respective research efforts, and contribute to the training of young scientists. This talk will present an overview of the Institute and the international nodes. We will also discuss the various processes to become a SSERVI partner as well as the opportunities available for collaborations with the SSERVI national teams.

SSERVI↗
NASA NTRS2020

Enabling Dynamic Vehicle Analyses With Improved Atmospheric Attenuation Models in Glenn Research Center Communication Analysis Suite

To aid in meeting the NASA objective of returning humans to the Moon, the Glenn Research Center’s Communication Analysis Suite was augmented with two distinct capabilities. The first capability added was the vehicle propagator. This allows the addition of dynamic aircraft and ground vehicles around any celestial body within the solar system during an analysis. This functionality interpolates the position and velocity of the vehicle relative to a celestial body at the time steps analyzed using the type of path and either a series of waypoints or a direction and duration of travel. The implications of this new capability include lunar rovers and/or drones, such as Dragonfly, where the vehicle propagator will analyze the communications architecture. The newly created vehicle propagator is now in use in communications studies for the 2024 lunar missions, simulating the movement of lunar rovers across the Moon’s southern pole. The second capability added was the augmentation of the atmospheric attenuation model. The previous model did not have a uniform low-elevation attenuation model due to the trigonometric approximation for path length and the exponential nature of low-elevation scintillation. User-defined weather parameters were also added to the updated atmospheric attenuation model. The previous model solely used tabular data based upon the season and location of the transmitting antenna. Multiple simulations of the same configuration now return different results based on the differing weather parameters. Cognitive communications analysis efforts can use this second capability to generate neural network training data based on differing weather conditions at utilized ground stations, a critical step in allowing neural networks to learn how weather parameters impact communications performance.

vehicle propagation↗
NASA NTRS2013

NASA Tech Briefs, October 2013

Topics include: A Short-Range Distance Sensor with Exceptional Linearity; Miniature Trace Gas Detector Based on Microfabricated Optical Resonators; Commercial Non-Dispersive Infrared Spectroscopy Sensors for Sub-Ambient Carbon Dioxide Detection; Fast, Large-Area, Wide-Bandgap UV Photodetector for Cherenkov Light Detection; Mission Data System Java Edition Version 7; Adaptive Distributed Environment for Procedure Training (ADEPT); LEGEND, a LEO-to-GEO Environment Debris Model; Electronics/Computers; Millimeter-Wave Localizers for Aircraft-to-Aircraft Approach Navigation; Impedance Discontinuity Reduction Between High-Speed Differential Connectors and PCB Interfaces; SpaceCube Version 1.5; High-Pressure Lightweight Thrusters; Non-Magnetic, Tough, Corrosion- and Wear-Resistant Knives From Bulk Metallic Glasses and Composites; Ambient Dried Aerogels; Applications for Gradient Metal Alloys Fabricated Using Additive Manufacturing; Passivation of Flexible YBCO Superconducting Current Lead With Amorphous SiO2 Layer; Propellant-Flow-Actuated Rocket Engine Igniter; Lightweight Liquid Helium Dewar for High-Altitude Balloon Payloads; Method to Increase Performance of Foil Bearings Through Passive Thermal Management; Unibody Composite Pressurized Structure; JWST Integrated Science Instrument Module Alignment Optimization Tool; Radar Range Sidelobe Reduction Using Adaptive Pulse Compression Technique; Digitally Calibrated TR Modules Enabling Real-Time Beamforming SweepSAR Architectures; Electro-Optic Time-to-Space Converter for Optical Detector Jitter Mitigation; Partially Transparent Petaled Mask/Occulter for Visible-Range Spectrum; Educational NASA Computational and Scientific Studies (enCOMPASS); Coarse-Grain Bandwidth Estimation Scheme for Large-Scale Network; Detection of Moving Targets Using Soliton Resonance Effect; High-Efficiency Nested Hall Thrusters for Robotic Solar System Exploration; High-Voltage Clock Driver for Photon-Counting CCD Characterization; Development of the Code RITRACKS; and Enabling Microliquid Chromatography by Microbead Packing of Microchannels.

Source record↗
DOE OSTI2021

Controlling distributed energy resources via deep reinforcement learning for load flexibility and energy efficiency

Behind-the-meter distributed energy resources (DERs), including building solar photovoltaic (PV) technology and electric battery storage, are increasingly being considered as solutions to support carbon reduction goals and increase grid reliability and resiliency. However, dynamic control of these resources in concert with traditional building loads, to effect efficiency and demand flexibility, is not yet commonplace in commercial control products. Traditional rule-based control algorithms do not offer integrated closed-loop control to optimize across systems, and most often, PV and battery systems are operated for energy arbitrage and demand charge management, and not for the provision of grid services. More advanced control approaches, such as MPC control have not been widely adopted in industry because they require significant expertise to develop and deploy. Recent advances in deep reinforcement learning (DRL) offer a promising option to optimize the operation of DER systems and building loads with reduced setup effort. However, there are limited studies that evaluate the efficacy of these methods to control multiple building subsystems simultaneously. Additionally, most of the research has been conducted in simulated environments as opposed to real buildings. This paper proposes a DRL approach that uses a deep deterministic policy gradient algorithm for integrated control of HVAC and electric battery storage systems in the presence of on-site PV generation. The DRL algorithm, trained on synthetic data, was deployed in a physical test building and evaluated against a baseline that uses the current best-in-class rule-based control strategies. Performance in delivering energy efficiency, load shift, and load shed was tested using price-based signals. The results showed that the DRL-based controller can produce cost savings of up to 39.6% as compared to the baseline controller, while maintaining similar thermal comfort in the building. The project team has also integrated the simulation components developed during this work as an OpenAIGym environment and made it publicly available so that prospective DRL researchers can leverage this environment to evaluate alternate DRL algorithms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2022

Photovoltaic System Health-State Architecture for Data-Driven Failure Detection

The timely detection of photovoltaic (PV) system failures is important for maintaining optimal performance and lifetime reliability. A main challenge remains the lack of a unified health-state architecture for the uninterrupted monitoring and predictive performance of PV systems. To this end, existing failure detection models are strongly dependent on the availability and quality of site-specific historic data. The scope of this work is to address these fundamental challenges by presenting a health-state architecture for advanced PV system monitoring. The proposed architecture comprises of a machine learning model for PV performance modeling and accurate failure diagnosis. The predictive model is optimally trained on low amounts of on-site data using minimal features and coupled to functional routines for data quality verification, whereas the classifier is trained under an enhanced supervised learning regime. The results demonstrated high accuracies for the implemented predictive model, exhibiting normalized root mean square errors lower than 3.40% even when trained with low data shares. The classification results provided evidence that fault conditions can be detected with a sensitivity of 83.91% for synthetic power-loss events (power reduction of 5%) and of 97.99% for field-emulated failures in the test-bench PV system. Finally, this work provides insights on how to construct an accurate PV system with predictive and classification models for the timely detection of faults and uninterrupted monitoring of PV systems, regardless of historic data availability and quality. Such guidelines and insights on the development of accurate health-state architectures for PV plants can have positive implications in operation and maintenance and monitoring strategies, thus improving the system’s performance.

photovoltaics↗
NASA NTRS1991

Momentum harvesting techniques for solar system travel

Astronomers are lately estimating there are 400,000 earth visiting asteroids larger than 100 meters in diameter. These asteroids are uniquely accessible sources of building materials, propellants, oxygen, water, and minerals. They also constitute a huge momentum reserve, potentially usable for travel throughout the solar system. To use this momentum, these stealthy objects must be tracked and the ability to extract the desired momentum obtained. Momentum harvesting by momentum transfer from asteroid to spacecraft, and by using the momentum of the extraterrestrial material to help deliver itself to its destination is discussed. The purpose is neither to quantify nor justify the momentum exchange processes, but to stimulate collective imaginations with some intriguing possibilities which emerge when momentum as well as material is considered. A net and tether concept is the suggested means of asteroid capture, the basic momentum exchange process. The energy damping characteristics of the tether determines the velocity mismatch that can be tolerated, and hence the amount of momentum that can be harvested per capture. As the tether plays out of its reel, drag on the tether steadily accelerates the spacecraft and dilutes, in time, the would-be collision. A variety of concepts for riding and using asteroids after capture are introduced. The hitchhiker uses momentum transfer only. The beachcomber, the caveman, the swinger, the prospector, and the rock wrecker also take advantage of raw asteroid materials. The chemist and the hijacker go further, they process the asteroid into propellants. Or, an asteroid railway system could be constructed with each hijacked asteroid becoming a scheduled train. Travelers could board this space railway system assured that water, oxygen propellants, and shielding await them. Austere space travel could give way to comforts, with a speed and economy impossible without nature's gift of earth visiting asteroids.

Willoughby, Alan J.↗
NASA NTRS2007

Genesis Contingency Planning and Mishap Recovery: The Sample Curation View

Planning for sample preservation and curation was part of mission design from the beginning. One of the scientific objectives for Genesis included collecting samples of three regimes of the solar wind in addition to collecting bulk solar wind during the mission. Collectors were fabricated in different thicknesses for each regime of the solar wind and attached to separate frames exposed to the solar wind during specific periods of solar activity associated with each regime. The original plan to determine the solar regime sampled for specific collectors was to identify to which frame the collector was attached. However, the collectors were dislodged during the hard landing making identification by frame attachment impossible. Because regimes were also identified by thickness of the collector, the regime sampled is identified by measuring fragment thickness. A variety of collector materials and thin films applied to substrates were selected and qualified for flight. This diversity provided elemental measurement in more than one material, mitigating effects of diffusion rates and/or radiation damage. It also mitigated against different material and substrate strengths resulting in differing effects of the hard landing. For example, silicon crystal substrates broke into smaller fragments than sapphire-based substrates and diamond surfaces were more resilient to flying debris damage than gold. The primary responsibility of the curation team for recovery was process documentation. Contingency planning for the recovery phase expanded this responsibility to include not only equipment to document, but also gather, contain and identify samples from the landing area and the recovered spacecraft. The team developed contingency plans for various scenarios as part of mission planning that included topographic maps to aid in site recovery and identification of different modes of transport and purge capability depending on damage. A clean tent, set-up at Utah Test & Training Range to control the environment for processing the sample return capsule and cleanly installing a nitrogen purge to the canister, was used to control the environment for extracting collector fragments from the damaged canister and to document and package over 10,000 collector fragments.

Stansbery, E. K.↗
NASA NTRSDate not supplied

NASA’s Moon Trek Portal: New Capabilities Supporting Mission Planning and Engagement

Introduction: NASA’s Moon Trek (https://trek.nasa.gov/moon/) is one of a growing number of interactive, browser-based, online portals for planetary data visualization and analysis produced by NASA’s Solar System Treks Project (SSTP). Moon Trek continues to be enhanced with new data and new capabilities enabling it to facilitate the planning and conducting of upcoming lunar missions by NASA, its commercial partners, and its international partners, as well as advancing its role as a valuable outreach tool. A Comprehensive Online Web Portal: Developed at NASA’s Jet Propulsion Laboratory (JPL) and managed as a project of NASA’s Solar System Exploration Research Virtual Institute (SSERVI) at NASA Ames Research Center, Moon Trek is a browser-based web portal. The portal provides easy-to-use tools for browsing, data layering, data product blending, and feature search among thousands of data products covering topography, mineralogy, elemental abundance, geology, and much more. Visualizations are provided in var-ious map projections, interactive 3D viewing, and in virtual reality. Using an in-house stereo workflow, SSTP is able to produce new NAC-based high-resolution mosaics and DEMs. Diverse Applications for Lunar Exploration: Baseline analytic tools available to all users include dis-tance measurement, elevation profiling, sun angle calculation, and 3D print file generation. More advanced account-level tools allow users to perform more computationally intensive analyses. These include ray-traced lighting analysis for user-specified areas over user-specified time/date ranges and time intervals, electro-static surface potential analysis, subsetting of large data products, slope analysis, and Lunar Laser Ranging geometry calculation. Artificial intelligence (AI) and ma-chine learning (ML) based tools have been implemented for crater detection and hazard analysis, boulder detection and hazard analysis, and rockfall detection. New Tools Facilitating Exploration: Additional, new tools have recently been added and others are in development, offering even greater functionality in con-ducting analyses of potential landing sites and areas of surface operations. The new Line-of-Sight tool facilitates communications planning between locations on the lunar surface, between any given site on the lunar surface and a specified ground station on the Earth, and between a site on the lunar surface and a relay asset in lunar orbit, all taking into account local lunar topography. The new Data Plotter tool provides both tabular and graphical representations of pixel values along a user specified path for a growing number of data products. The new NAC Finder tool will identify and pro-vide access to NAC images that intersect a user-defined path or bounded area. The SSTP development team is looking to leverage the capabilities of its existing AI and ML crater, boulder, and rockfall detection and analysis tools, and extend that technology to a generalized feature detector that can be trained on instances of specific types of landforms and then search the lunar surface for more examples of such features. New traverse planning tools are being developed with use cases in generalized concept studies and specific mission planning in mind. These will facilitate finding optimal traverse paths based on constraints such as slope, lighting, hazard avoidance, and communications. These will be complemented by new traverse visualization capabilities. Users will be able to interactively ride along with a rover, examining 3D views of the terrain while adjusting camera height and viewing angle along with selecting different data layer overlays to drape across the terrain. Engaging the Public: The capabilities being developed for mission planning are being leveraged to further enhance Moon Trek’s proven utility as a valuable public outreach resource. This includes providing multiple lev-els of engagement with different points of entry. At its simplest level, promoting understanding through visualization, media and the public will be able to easily visualize and conduct their own exploration of lunar sites targeted by NASA and its partners. For a more in-depth experience, we are working with our stakeholders to promote understanding through interaction by extend-ing our current landing site and traverse analysis capabilities, making simplified access to these tools available to those who want to explore more deeply key factors in planning a mission through interactive and possibly even gamified experiences. The highest degree of public outreach, focusing on engagement through scientific participation, could be achieved through our work with missions and the NASA Office of the Chief Scientist on Moon Trek’s extension as a tool with specialized capabilities for facilitating citizen science. In such scenarios, participants become members of extended mis-sion science teams, using dedicated and integrated interfaces to analyze mission data to help answer questions key to lunar science and exploration. We are work-ing with NASA’s Office of Communications, museums, planetariums, and the media to help them easily integrate accurate, detailed visualizations of NASA’s lunar destinations and exploration into their content/productions and to engage diverse audiences in diverse venues.

Moon Trek↗
DOE OSTI2021

Model-Free Voltage Control of Active Distribution System with PVs Using Surrogate Model-Based Deep Reinforcement Learning

Accurate knowledge of the distribution system topology and parameters is required to achieve good voltage control performance, but this is difficult to obtain in practice. This paper proposes a physical-model-free voltage control method based on a surrogate-model-enabled deep reinforcement learning approach. Specifically, a surrogate model is trained in a supervised manner using the recorded limited number of historical data to learn the relationship between the power injections and voltage fluctuations of each node. Then, the deep reinforcement learning algorithm is applied to learn an optimal control strategy from the experiences obtained by continuous interactions with the surrogate model. The proposed method can achieve physical-model-free control of unbalanced distribution network and inform real-time decisions to deal with fast voltage fluctuations caused by the rapid variation of PV generation. Simulation results on an unbalance IEEE 123-bus system show that the proposed method can achieve similar performance as that of perfect physical-model-based approaches while being advantageous over other traditional methods.

active distribution network↗
DOE OSTI2021

Performance Comparison of Clipping Detection Techniques in AC Power Time Series

In this research, a variety of methods were developed to detect clipping periods in AC power time series. AC power data streams associated with 36 unique systems across the United States were collected, and data points representing clipping periods were manually labeled by experts. Using this data set for training and validation, novel logic-based and machine learning (ML) approaches were developed to classify time series values as clipping or non-clipping. These approaches were compared to the RdTools method for detecting clipping periods. The logic-based and ML XGBoost approaches achieved F-scores of 85.0 and 77.6, respectively, when cross-validated against the manually labeled data, as compared to the current RdTools approach (F-score of 56.4), indicating a significant improvement at detecting clipping periods. Additionally, the effects of each clipping filter when evaluating system degradation rates were assessed, using 31 unique systems across the United States. Results indicate that estimated system degradation rate can vary based on the type of clipping filter used, by up to 0.6% degradation rate for some cases.

clipping↗
NASA NTRS2006

Non Radiation Hardened Microprocessors in Spaced Based Remote Sensing Systems

The CALIPSO (Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observations) mission is a comprehensive suite of active and passive sensors including a 20Hz 230mj Nd:YAG lidar, a visible wavelength Earth-looking camera and an imaging infrared radiometer. CALIPSO flies in formation with the Earth Observing System Post-Meridian (EOS PM) train, provides continuous, near-simultaneous measurements and is a planned 3 year mission. CALIPSO was launched into a 98 degree sun synchronous Earth orbit in April of 2006 to study clouds and aerosols and acquires over 5 gigabytes of data every 24 hours. The ground track of one CALIPSO orbit as well as high and low intensity South Atlantic Anomaly outlines is shown. CALIPSO passes through the SAA several times each day. Spaced based remote sensing systems that include multiple instruments and/or instruments such as lidar generate large volumes of data and require robust real-time hardware and software mechanisms and high throughput processors. Due to onboard storage restrictions and telemetry downlink limitations these systems must pre-process and reduce the data before sending it to the ground. This onboard processing and realtime requirement load may mean that newer more powerful processors are needed even though acceptable radiation-hardened versions have not yet been released. CALIPSO's single board computer payload controller processor is actually a set of four (4) voting non-radiation hardened COTS Power PC 603r's built on a single width VME card by General Dynamics Advanced Information Systems (GDAIS). Significant radiation concerns for CALIPSO and other Low Earth Orbit (LEO) satellites include the South Atlantic Anomaly (SAA), the north and south poles and strong solar events. Over much of South America and extending into the South Atlantic Ocean the Van Allen radiation belts dip to just 200-800km and spacecraft entering this area are subjected to high energy protons and experience higher than normal Single Event Upset (SEU) and Single Event Latch-up (SEL) rates. Although less significant, spacecraft flying in the area around the poles experience similar upsets. Finally, powerful solar proton events in the range of 10MeV/10pfu to 100MeV/1pfu as are forecasted and tracked by NOAA's Space Environment Center in Colorado can result in Single Event Upset (SEU), Single Event Latch-up (SEL) and permanent failures such as Single Event Gate Rupture (SEGR) in some technologies. (Galactic Cosmic Rays (GCRs) are another source, especially for gate rupture) CALIPSO mitigates common radiation concerns in its data handling through the use of redundant processors, radiation-hardened Application Specific Integrated Circuits (ASIC), hardware-based Error Detection and Correction (EDAC), processor and memory scrubbing, redundant boot code and mirrored files. After presenting a system overview this paper will expand on each of these strategies. Where applicable, related on-orbit data collected since the CALIPSO initial boot on May 4, 2006 will be noted.

Decoursey, Robert J.↗
NASA NTRS2007

Human Space Exploration: The Moon, Mars, and Beyond

America is returning to the Moon in preparation for the first human footprint on Mars, guided by the U.S. Vision for Space Exploration. This presentation will discuss NASA's mission, the reasons for returning to the Moon and going to Mars, and how NASA will accomplish that mission in ways that promote leadership in space and economic expansion on the new frontier. The primary goals of the Vision for Space Exploration are to finish the International Space Station, retire the Space Shuttle, and build the new spacecraft needed, to return people to the Moon and go to Mars. The Vision commits NASA and the nation to an agenda of exploration that also includes robotic exploration and technology development, while building on lessons learned over 50 years of hard-won experience. Why the Moon? Many questions about the Moon's potential resources and how its history is linked to that of Earth were spurred by the brief Apollo explorations of the 1960s and 1970s. This new venture will carry more explorers to more diverse landing sites with more capable tools and equipment for extended expeditions. The Moon also will serve as a training ground before embarking on the longer, more difficult trip to Mars. NASA plans to build a lunar outpost at one of the lunar poles, learn to live off the land, and reduce dePendence on Earth for longer missions. America needs to extend its ability to survive in hostile environments close to our home planet before astronauts will reach Mars, a planet very much like Earth. NASA has worked with scientists to define lunar exploration goals and is addressing the opportunities for a range of scientific study on Mars. In order to reach the Moon and Mars within a lifetime and within budget, NASA is building on common hardware, shared knowledge, and unique experience derived from the Apollo Saturn, Space Shuttle and contemporary commercial launch vehicle programs. The journeys to the Moon and Mars will require a variety of vehicles, including the Ares I Crew Launch Vehicle, which transports the Orion Crew Exploration Vehicle, and the Ares V Cargo Launch Vehicle, which transports the Lunar Surface Access Module. The architecture for the lunar missions will use one launch to ferry the crew into orbit, where it will rendezvous with the Lunar Module in the Earth Departure Stage, which will then propel the combination into lunar orbit. The imperative to explore space with the combination of astronauts and robots will be the impetus for inventions such as solar power and water and waste recycling. This next chapter in NASA's history promises to write the next chapter in American history, as well. It will require this nation to provide the talent to develop tools, machines, materials, processes, technologies, and capabilities that can benefit nearly all aspects of life on Earth. Roles and responsibilities are shared between a nationwide Government and industry team. The Exploration Launch Projects Office at the Marshall Space Flight Center manages the design, development, testing, and evaluation of both vehicles and serves as lead systems integrator. A little over a year after it was chartered, the Exploration Launch Projects team is testing engine components, refining vehicle designs, performing wind tunnel tests, and building hardware for the first flight test of Ares I-l, scheduled for spring 2009. The U.S. Vision for Space Exploration lays out a roadmap for a long-term venture of discovery. This endeavor will inspire and attract the best and brightest students to power this nation successfully to the Moon, Mars, and beyond. If one equates the value proposition for space with simple dollars and cents, the potential of the new space economy is tremendous, from orbital space delivery services for the International Space Station to mining and solar energy collection on the Moon and asteroids. The Vision for Space Exploration is fundamentally about bringing the resources of the solar system within the economic sphere of humaind. Given the immense size of our solar system, the amount of available material and energy within it present an enormous economic opportunity.

Sexton, Jeffrey D.↗
NASA NTRS2010

Electrostatic Power Generation from Negatively Charged, Simulated Lunar Regolith

Research was conducted to develop an electrostatic power generator for future lunar missions that facilitate the utilization of lunar resources. The lunar surface is known to be negatively charged from the constant bombardment of electrons and protons from the solar wind. The resulting negative electrostatic charge on the dust particles, in the lunar vacuum, causes them to repel each other minimizing the potential. The result is a layer of suspended dust about one meter above the lunar surface. This phenomenon was observed by both Clementine and Surveyor spacecrafts. During the Apollo 17 lunar landing, the charged dust was a major hindrance, as it was attracted to the astronauts' spacesuits, equipment, and the lunar buggies. The dust accumulated on the spacesuits caused reduced visibility for the astronauts, and was unavoidably transported inside the spacecraft where it caused breathing irritation [1]. In the lunar vacuum, the maximum charge on the particles can be extremely high. An article in the journal "Nature", titled "Moon too static for astronauts?" (Feb 2, 2007) estimates that the lunar surface is charged with up to several thousand volts [2]. The electrostatic power generator was devised to alleviate the hazardous effects of negatively charged lunar soil by neutralizing the charged particles through capacitive coupling and thereby simultaneously harnessing power through electric charging [3]. The amount of power generated or collected is dependent on the areal coverage of the device and hovering speed over the lunar soil surface. A thin-film array of capacitors can be continuously charged and sequentially discharged using a time-differentiated trigger discharge process to produce a pulse train of discharge for DC mode output. By controlling the pulse interval, the DC mode power can be modulated for powering devices and equipment. In conjunction with a power storage system, the electrostatic power generator can be a power source for a lunar rover or other systems. The negatively charged lunar soil would also be neutralized mitigating some of the adverse effects resulting from lunar dust.

Choi, Sang H.↗
DOE OSTI2022

Spatiotemporal Modeling of Real World Backsheets Field Survey Data: Hierarchical (Multilevel) Generalized Additive Models

Assessing photovoltaic module backsheet durability is critical to increasing module lifetime. Laboratory-based accelerating testing has recently failed to predict large scale failures of widely adopted polymeric materials. Additionally, there is a growing concern on characterizing the non-uniformity of field exposure. Therefore, data from field surveys are critical to assess the performance of component lifetimes. Using a documented field survey protocol, 19 field surveys were conducted. The focus of this survey strategy is to investigate spatial continuity in degradation modes. By combining field survey data with real-time satellite weather data, stressor / response models have been trained. Generalized additive Models (GAM) model was created to predict the value of degradation based on measured predictors. Two different GAM constructions were testing using different implementations of basis splines. The model includes variables on the environmental stressors of the system and the location of each measurement in the PV mounting structure. The incorporation of hierarchical structure into the models allowed for material specific degradation rates, while maintaining the assumption of a global trend. The model performed well with an adjusted R2 of 0.975 for yellowness index prediction.

backsheet↗