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NASA NTRSDate not supplied

Creating the Test Environment for Exploration Habitats

The Crew and Thermal Systems Division’s (CTSD) 20 Foot Chamber has undergone significant modifications to support NASA’s future human exploration missions to the Moon, Mars, and beyond. Existing chamber systems such as Vacuum/Repress, Heating Ventilation and Air Conditioning (HVAC), and Fire Suppression System (FSS), were upgraded, while new systems such as Air Revitalization and Gas Distribution were added. In addition, the chamber was outfitted with crew quarters, hygiene areas, storage, and workspaces. Teams across the Johnson Space Center (JSC) worked together to overcome the logistical and operational challenges of the planned test parameters. The ultimate goal was to create an analog testbed in which a maximum of eight human test subjects could safely live and work for at least 11 days in the same conditions (e.g., reduced pressures, oxygen concentrations, and so on) as those expected inside a base spacecraft. This would allow researchers to study the effects of such environments on humans and to validate proposed Prebreathe Protocols to enable the safe performance of surface extravehicular activities (EVAs). Over the last 18 months, the CTSD Systems Test Branch has performed six tests of varying lengths and atmospheres for both NASA and commercial partners. To date, researchers now have collected data on 48 test subjects in the course of 42 days and over 130 simulated EVAs. This paper will discuss the history of the 20 Foot Chamber, its long road to becoming an analog testbed for human exploration, and the capabilities that make it a unique, world class facility for NASA and commercial human exploration missions. It will provide an overview of past and future testing and the lessons learned along the way.

Chris Briggs↗

NASA NTRSDate not supplied

NASA Extravehicular Activity Technology Roadmaps for Exploration

The National Aeronautics and Space Administration (NASA) has developed and matured many technologies over the decades to advance extravehicular activity (EVA) systems. Over the last 15 years, major steps were taken to advance the technology with the Exploration Extravehicular Mobility Unit (xEMU) government reference design at the Johnson Space Center (JSC) in Houston, Texas. The xEMU builds on the lessons learned of the Apollo, Space Shuttle, and International Space Station (ISS) EMUs, evolving the technology to increase performance for extreme environments. As NASA sets its goals toward Earth’s Moon and Mars, a spacesuit design tolerable of gravity and dust will be needed for these adverse environments. NASA has used roadmaps as the means of documenting actionable plans for strategizing technology developments needed to meet NASA’s mission and goals. To help reach and create a sustained presence on the Moon, NASA procured EVA services from industry through the Exploration EVA Services (xEVAS) contract. These services include certified contractor-provided spacesuits, tools, equipment, vehicle interfaces, and support to training and real-time operations. NASA will now focus on a mission to Mars. NASA leadership has set goals and objectives related to the Agency’s vision and a Moon to Mars (M2M) strategy. This paper presents an organizational framework to provide insight into how NASA’s vision is realized. Additionally, this paper covers the maturation and development of the spacesuit technology and reveals the EVA technology roadmaps for the M2M Program. These EVA roadmaps visualize an actionable path to EVA capabilities needed for Mars exploration.

Cinda Chullen↗

NASA NTRS2025

South Pole Lunar Lighting Studies for Driving Exploration on the Lunar Surface

NASA’s Artemis lunar missions will face new exploration challenges due to inherently low sun angles in the lunar south pole region. Whereas the Apollo missions were afforded sunlight for approximately 8 to 50-degrees above the horizon, sunlight for the next lunar missions will only be 1 to 2-degrees about the horizon. With this low angle, long shadows and high contrasts of light and dark areas will be faced by the crew and remote teleop operators while exploring the lunar surface. With this in mind, NASA developed an integrated virtual Lighting and Navigation Simulation to understand these challenges. Two studies have been conducted in this Lunar South Pole environment to evaluate the effects of natural and artificial lighting on driving and navigating a lunar rover across the surface. Early NASA studies, such as this, are used to aid in developing techniques and explore concepts of operations to promote mission success and crew safety. In the Phase One development evaluation, six astronauts were teleported to ten different lighting conditions. For each location, drivers were to drive to an imaginary target approximately 200-meters straight ahead and provide subjective feedback on their ability to drive under the given lighting conditions. Phase Two, a more operational study, four astronauts and a remote operator tested five different Artemis lunar mission scenarios. Results indicated sun direction at such low angles, especially when driving a high speed, can severely impact the crew’s ability to safely drive the rover. In the up-sun situation, with the sun directly in the driver’s eyes and compounded long shadows, the consistent preference among drivers was to initiate a tacking strategy of approximately +/- 20 to 30-degrees to improve visibility. This maneuver does require more time and rover energetics. Conversely, driving down-sun required the crew to tack as well to avoid the shadow of rover blocking the terrain. Less appreciated is how the surrounding landscape is lit. Traveling into shadowed areas, especially while facing a lit terrain beyond the shadowed area, drivers enter at their own risk due to pupil contraction making artificial lights useless. Additionally, the constant transitioning between dark and light areas are mentally taxing to the crew and natural navigational references such as the stars are invisible and therefore unusable. Slope and depth magnitude are very difficult hazards to judge when approaching a shadowed crater. This naturally leads to slower driving speeds than originally anticipated. As for observing scientifically interesting features, assessing them accurately varies greatly by the lighting condition. In most cases, the task can be completed, but the strategy is to use the sun to one’s advantage. Crew workload distribution in the cockpit for driving operations was split amongst crew. The driver primarily focused attention on visual terrain (80%) but referenced displays approximately 20% of the time, while the co-pilot/navigator generally provided directional cues to the driver. These natural lighting conditions present significant challenges for safe rover operations; however, early studies having given investigators a “first-look” into understanding of how operating on the South Lunar Pole can be accomplished.

LTv↗

NASA NTRSDate not supplied

NASA's Development of Advanced Space Suits for Lunar Exploration

Significant work has been completed recently at NASA’s Johnson Space Center in the Space Suit and Crew Survival Systems Branch to design and mature an advanced exploration EVA (Extra-Vehicular Activity) architecture. This effort culminated in the xEMU (eXploration Extra-vehicular Mobility Unit) space suit, which was completed in 2022 and tested extensively through 2023, including human performance, cycle life performance and thermal vacuum testing. An overview of the xEMU design and test campaign is provided, as well as its value as the government reference design supporting the transition to EVA commercial services for the Artemis program. Lastly, a development roadmap is discussed which outlines NASA’s identified strategy to enable the next generation of exploration EVA hardware for sustaining-class Lunar and Mars missions.

Shane M McFarland↗

NASA NTRSDate not supplied

Exploration Extravehicular Mobility Unit (xEMU) 11 Foot Vacuum Chamber Upgrades Test Results

The Exploration Extravehicular Mobility Unit (xEMU) uncrewed 11 foot vacuum chamber testing evaluated the capabilities of the 11 foot vacuum chamber facility to support advanced spacesuit testing. The government reference design xEMU spacesuit provided a high-fidelity test article to demonstrate 11 foot vacuum chamber capabilities which included: gas loading of the chamber at varying simulated metabolic rates and open loop suit abort operations, Intravehicular Activity (IVA) vacuum access, consumables recharge, IVA vehicle-provided thermal loop cooling, and IVA vehicle-provided power. To demonstrate the xEMU airlock operations transitioning from IVA to EVA conditions without a test subject in the suit, test support equipment was developed to remotely actuate both the Exploration, Servicing, and Cooling Umbilical (ESCU) and the vacuum access umbilical. This test also evaluated the performance of the Exploration Portable Life Support System (xPLSS) at vacuum conditions. Data was collected and analyzed for carbon dioxide (CO2) scrubbing performance of the Rapid Cycle Amine (RCA) swingbed, for thermal regulation performance of the Suit Water Membrane Evaporator (SWME), and for sensor performance across the xPLSS. This paper will detail the findings of the testing performed with these upgrades which discussed previously laid out in ICES-2025-342.

Robert F Marsch↗

NASA NTRSDate not supplied

Exploration Extravehicular Mobility Unit 11 Foot Vacuum Chamber Upgrades Test Results

Robert Marsch↗

DOE OSTI2017

Exploring Bioeconomy Growth through the Public Release of the Biomass Scenario Model

The Biomass Scenario Model (BSM) is an important tool for exploring vibrant future bioeconomy scenarios that leverage domestic resources. Developed by NREL and BETO, this model of the domestic biofuels supply chain has been used to explore success strategies for BETO's activities towards bioeconomy growth. The BSM offers a robust test bed for detailed exploration of effects of BETO activities within the complex context of resource availability; physical, technological, and economic constraints; behavior; and policy. The public release of the model in 2017 will allow broad engagement with the theme of the conference as model users can analyze bioeconomy growth, domestic biomass resource use, and associated effects. The BSM is a carefully validated, state-of-the-art, dynamic model of the biomass to biofuels supply chain. Using a system dynamics simulation modeling approach, the model tracks long-term deployment of biofuels given technology development and investment, considering land availability, the competing oil market, consumer demand, and government policies over time. Sample outputs include biofuels production, feedstock use, capital investment, incentives, and costs of feedstocks and fuels. BSM scenarios reveal technological, economic, and policy challenges, as well as opportunities for dynamic growth of the bioeconomy with strategic public and private investment at key points in the system. The model logic and results have been reviewed extensively, through collaborative analysis, expert reviews and external publications (https://www.zotero.org/groups/bsm_publications/).

09 BIOMASS FUELS↗

DOE OSTI2019

Poster and Slide: Energy Loss: Exploring Dissipation Pathways from Ions to In-reactor

Young Researcher Association is hosting an early career poster and networking session on July 25th, 2019. The poster session is designed to provide an overview of the researcher’s experience prior to employment at INL, and their current area of research and interests. The following is a brief description of the major sections of the poster. Before starting at INL in May of this year, my research focused on exploring the effects of electronic and nuclear energy loss on the damage evolution of Ti3SiC2 MAX phase during energetic irradiations. A series of ion irradiations revealed a threshold in electronic energy loss (Se), above which the formation of a FCC-MAX structure is enhanced, accommodating point defects and alleviating lattice strain. However, systematically separating the effects reveals that increasing Se/Sn suppresses this phase formation, thus allowing the strain from point defects to increase and saturate. Presently my research supports Post Irradiation Examination (PIE) of U-Mo fuel for the USHPRR and KJRR programs. The PIE evaluation currently consists of non-destructive examination (NDE) of fuel plates irradiated in the Advanced Test Reactor (ATR), focusing on plate profilometry and fuel swelling analysis. NDE will culminate in finalizing the destructive examination (DE) test plans and execution of said DE. Additionally, I am interested in exploring the roles that different energy dissipation pathways play in the damage evolution of the U-Mo fuel, exploring the mechanisms by which the fuel remains in its meta-stable phase under irradiation at high temperatures.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗

DOE OSTI2021

Design Space Exploration of Emerging Memory Technologies for Machine Learning Applications

Memory design space exploration methods study memory systems’ performances and limitations before implementation. The computer memory design space has grown exponentially because of the enormous growth of memory types, memory controllers, and application software. Computer simulators are commonly used for memory design space exploration. However, complex memory simulations take an enormous amount of time. Hence, in this paper, we proposed a machine learning-based design space exploration method for dynamic random-access memory and non-volatile memory systems. We applied our method to the CosmoGAN and LeNet applications to predict the following six memory response parameters: (i) bandwidth, (ii) power, (iii) average latency, (iv) average total latency, (v) memory reads, and (vi) memory writes. Our experimental results show that machine learning models can predict memory response parameter values faster than simulations. We used support vector machine, random forest, and gradient boosting machine learning models. We observed that the support vector machine provides better performance for bandwidth, average latency, and average total latency. The random forest model works better for memory reads and writes. The gradient boosting model provides superior prediction performance for power. We provide a detailed discussion on learning curve characteristics, error analysis, and memory type recommendation.

Hasan, S M Shamimul↗

DOE OSTI2022

The Geothermal Artificial Intelligence for geothermal exploration

Exploration of geothermal resources involves analysis and management of a large number of uncertainties, which makes investment and operations decisions challenging. Remote Sensing (RS), Machine Learning (ML) and Artificial Intelligence (AI) have potential in managing the challenges of geothermal exploration. In this paper, we present a methodology that integrates RS, ML and AI to create an initial assessment of geothermal potential, by resorting to known indicators of geothermal areas namely mineral markers, surface temperature, faults and deformation. We demonstrated the implementation of the method in two sites (Brady and Desert Peak geothermal sites) that are close to each other but have different characteristics (Brady having clear surface manifestations and Desert Peak being a blind site). Here, we processed various satellite images and geospatial data for mineral markers, temperature, faults and deformation and then implemented ML methods to obtain pattern of surface manifestation of geothermal sites. We developed an AI that uses patterns from surface manifestations to predict geothermal potential of each pixel. We tested the Geothermal AI using independent data sets obtaining accuracy of 92-95%; also tested the Geothermal AI trained on one site by executing it for the other site to predict the geothermal / non-geothermal delineation, the Geothermal AI performed quite well in prediction with 72-76% accuracy.

15 GEOTHERMAL ENERGY↗

DOE OSTI2021

Programs and Code for Geothermal Exploration Artificial Intelligence

The scripts below are used to run the Geothermal Exploration Artificial Intelligence developed within the "Detection of Potential Geothermal Exploration Sites from Hyperspectral Images via Deep Learning" project. It includes all scripts for pre-processing and processing, including: - Land Surface Temperature K-Means classifier - Labeling AI using Self Organizing Maps (SOM) - Post-processing for Permanent Scatterer InSAR (PSInSAR) analysis with SOM - Mineral marker summarizing - Artificial Intelligence (AI) Data splitting: creates data set from a single raster file - Artificial Intelligence Model: creates AI from a single data set, after splitting in Train, Validation and Test subsets - AI Mapper: creates a classification map based on a raster file

15 GEOTHERMAL ENERGY↗

NASA NTRS2006

The Application of Architecture Frameworks to Modelling Exploration Operations Costs

Developments in architectural frameworks and system-of-systems thinking have provided useful constructs for systems engineering. DoDAF concepts, language, and formalisms, in particular, provide a natural way of conceptualizing an operations cost model applicable to NASA's space exploration vision. Not all DoDAF products have meaning or apply to a DoDAF inspired operations cost model, but this paper describes how such DoDAF concepts as nodes, systems, and operational activities relate to the development of a model to estimate exploration operations costs. The paper discusses the specific implementation to the Mission Operations Directorate (MOD) operational functions/activities currently being developed and presents an overview of how this powerful representation can apply to robotic space missions as well.

Department of Defense Architecture Framework (DoDA↗

NASA NTRS2014

The Primordial Inflation Explorer (PIXIE)

The Primordial Inflation Explorer is an Explorer-class mission to measure the gravity-wave signature of primordial inflation through its distinctive imprint on the linear polarization of the cosmic microwave background. PIXIE uses an innovative optical design to achieve background-limited sensitivity in 400 spectral channels spanning 2.5 decades in frequency from 30 GHz to 6 THz (1 cm to 50 micron wavelength). Multi-moded non-imaging optics feed a polarizing Fourier Transform Spectrometer to produce a set of interference fringes, proportional to the difference spectrum between orthogonal linear polarizations from the two input beams. Multiple levels of symmetry and signal modulation combine to reduce the instrumental signature and confusion from unpolarized sources to negligible levels. PIXIE will map the full sky in Stokes I, Q, and U parameters with angular resolution 2.6 deg and sensitivity 0.2 μK per 1 deg square pixel. The principal science goal is the detection and characterization of linear polarization from an inflationary epoch in the early universe, with tensor-to-scalar ratio r less than 10(exp −3) at 5 standard deviations. In addition, PIXIE will measure the absolute frequency spectrum to constrain physical processes ranging from inflation to the nature of the first stars to the physical conditions within the interstellar medium of the Galaxy. We describe the PIXIE instrument and mission architecture with an emphasis on the expected level of systematic error suppression.

Primordial Inflation Explorer is an Explorer-class↗

NASA NTRSDate not supplied

TPS and Entry Systems Technologies for Future Mars and Titan Exploration

During the past twenty years, NASA effectively erased earlier Mars mishaps with six successful missions to the Red Planet. These missions delivered one orbiter and five payloads to the surface. Those payloads included three rovers, Spirit (2004) which roamed 11 years, Opportunity (2004) which roamed nearly 15 years, and Curiosity (2012) which is in its ninth year, along with two landers, Phoenix (2008) and InSIGHT (2018). In July, the Mars 2020 mission will send another large rover, Perseverance, which will land in 2021. The InSIGHT mission even demonstrated the capability to send CubeSats along to help with communication back to the surface. NASA has demonstrated the capability to land a metric ton of vehicles plus science instruments on Mars and expects that the same technologies will be equally successful landing Dragonfly on Titan in the 2030’s. The thermal protection systems (TPS) used on the Mars missions are sufficiently developed and matured to continue furthering science on both Mars and Titan, assuming that the TPS materials are sustained by industry. The purpose of this white paper is to encourage further exploration and science on both Mars and Titan because we have the technologies to support them. In addition, we will look forward to human exploration of Mars and identify the improvements in TPS materials required to facilitate landing the larger payloads.

Thermal Protection Systems↗

DOE OSTI2024

Characterizing gravitational wave detector networks: from A # to cosmic explorer

Gravitational-wave observations by the laser interferometer gravitational-wave observatory (LIGO) and Virgo have provided us a new tool to explore the Universe on all scales from nuclear physics to the cosmos and have the massive potential to further impact fundamental physics, astrophysics, and cosmology for decades to come. In this paper we have studied the science capabilities of a network of LIGO detectors when they reach their best possible sensitivity, called A # , given the infrastructure in which they exist and a new generation of observatories that are factor of 10 to 100 times more sensitive (depending on the frequency), in particular a pair of L-shaped cosmic explorer (CE) observatories (one 40 km and one 20 km arm length) in the US and the triangular Einstein telescope with 10 km arms in Europe. We use a set of science metrics derived from the top priorities of several funding agencies to characterize the science capabilities of different networks. The presence of one or two A # observatories in a network containing two or one next generation observatories, respectively, will provide good localization capabilities for facilitating multimessenger astronomy (MMA) and precision measurement of the Hubble parameter. Two CE observatories are indispensable for achieving precise localization of binary neutron star events, facilitating detection of electromagnetic counterparts and transforming MMA. Their combined operation is even more important in the detection and localization of high-redshift sources, such as binary neutron stars, beyond the star-formation peak, and primordial black hole mergers, which may occur roughly 100 million years after the Big Bang. The addition of the Einstein Telescope to a network of two CE observatories is critical for accomplishing all the identified science metrics including the nuclear equation of state, cosmological parameters, the growth of black holes through cosmic history, but also make new discoveries such as the presence of dark matter within or around neutron stars and black holes, continuous gravitational waves from rotating neutron stars, transient signals from supernovae, and the production of stellar-mass black holes in the early Universe. For most metrics the triple network of next generation terrestrial observatories are a factor 100 better than what can be accomplished by a network of three A # observatories.

Einstein telescope↗

DOE OSTI2020

eFESTA: Ensemble Feature Exploration with Surface Density Estimates

We propose surface density estimate (SDE) to model the spatial distribution of surface features-isosurfaces, ridge surfaces, and streamsurfaces-in 3D ensemble simulation data. The inputs of SDE computation are surface features represented as polygon meshes, and no field datasets are required (e.g., scalar fields or vector fields). The SDE is defined as the kernel density estimate of the infinite set of points on the input surfaces and is approximated by accumulating the surface densities of triangular patches. We also propose an algorithm to guide the selection of a proper kernel bandwidth for SDE computation. An ensemble Feature Exploration method based on Surface densiTy EstimAtes (eFESTA) is then proposed to extract and visualize the major trends of ensemble surface features. For an ensemble of surface features, each surface is first transformed into a density field based on its contribution to the SDE, and the resulting density fields are organized into a hierarchical representation based on the pairwise distances between them. The hierarchical representation is then used to guide visual exploration of the density fields as well as the underlying surface features. We demonstrate the application of our method using isosurface in ensemble scalar fields, Lagrangian coherent structures in uncertain unsteady flows, and streamsurfaces in ensemble fluid flows.

Computational modeling↗

DOE OSTI2023

ChemoGraph: Interactive Visual Exploration of the Chemical Space

Exploratory analysis of the chemical space is an important task in the field of cheminformatics. For example, in drug discovery research, chemists investigate sets of thousands of chemical compounds in order to identify novel yet structurally similar synthetic compounds to replace natural products. Manually exploring the chemical space inhabited by all possible molecules and chemical compounds is impractical, and therefore presents a challenge. To fill this gap, we present ChemoGraph, a novel visual analytics technique for interactively exploring related chemicals. In ChemoGraph, we formalize a chemical space as a hypergraph and apply novel machine learning models to compute related chemical compounds. It uses a database to find related compounds from a known space and a machine learning model to generate new ones, which helps enlarge the known space. Moreover, ChemoGraph highlights interactive features that support users in viewing, comparing, and organizing computationally identified related chemicals. With a drug discovery usage scenario and initial expert feedback from a case study, we demonstrate the usefulness of ChemoGraph.

chemical space exploration↗

DOE OSTI2013

Dixie Valley Engineered Geothermal System Exploration Methodology Project, Baseline Conceptual Model Report

The Engineered Geothermal System (EGS) Exploration Methodology Project is developing an exploration approach for EGS through the integration of geoscientific data. The Project chose the Dixie Valley Geothermal System in Nevada as a field laboratory site for methodlogy calibration purposes because, in the public domain, it is a highly characterized geothermal systems in the Basin and Range with a considerable amount of geoscience and most importantly, well data. This Baseline Conceptual Model report summarizes the results of the first three project tasks (1) collect and assess the existing public domain geoscience data, (2) design and populate a GIS database, and (3) develop a baseline (existing data) geothermal conceptual model, evaluate geostatistical relationships, and generate baseline, coupled EGS favorability/trust maps from +1km above sea level (asl) to -4km asl for the Calibration Area (Dixie Valley Geothermal Wellfield) to identify EGS drilling targets at a scale of 5km x 5km. It presents (1) an assessment of the readily available public domain data and some proprietary data provided by Terra-Gen Power, LLC, (2) a re-interpretation of these data as required, (3) an exploratory geostatistical data analysis, (4) the baseline geothermal conceptual model, and (5) the EGS favorability/trust mapping. The conceptual model presented applies to both the hydrothermal system and EGS in the Dixie Valley region.

15 GEOTHERMAL ENERGY↗